Atmospheric indicators

While the indices module stores the computing functions, this module defines Indicator classes and instances that include a number of functionalities, such as input validation, unit conversion, output meta-data handling, and missing value masking.

The concept followed here is to define Indicator subclasses for each input variable, then create instances for each indicator.

xclim.indicators.atmos.biologically_effective_degree_days(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', lat: Union[DataArray, str] = 'lat', *, thresh_tasmin: Quantified = '10 degC', method: str = 'gladstones', low_dtr: Quantified = '10 degC', high_dtr: Quantified = '13 degC', max_daily_degree_days: Quantified = '9 degC', start_date: DayOfYearStr = '04-01', end_date: DayOfYearStr = '11-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Biologically effective degree days (realm: atmos)

Considers daily minimum and maximum temperature with a given base threshold between 1 April and 31 October, with a maximum daily value for cumulative degree days (typically 9°C), and integrates modification coefficients for latitudes between 40°N and 50°N as well as for swings in daily temperature range. Metric originally published in Gladstones (1992).

This indicator will check for missing values according to the method “from_context”. Based on indice biologically_effective_degree_days().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• lat (str or DataArray) – Latitude coordinate. If None and method in [“gladstones”, “icclim”], a CF-conformant “latitude” field must be available within the passed DataArray. Default : ds.lat. [Required units : []]

• thresh_tasmin (quantity (string or DataArray, with units)) – The minimum temperature threshold. Default : 10 degC. [Required units : [temperature]]

• method ({‘icclim’, ‘gladstones’, ‘jones’}) – The formula to use for the calculation. The “gladstones” integrates a daily temperature range and latitude coefficient. End_date should be “11-01”. The “icclim” method ignores daily temperature range and latitude coefficient. End date should be “10-01”. The “jones” method integrates axial tilt, latitude, and day-of-year on coefficient. End_date should be “11-01”. Default : gladstones.

• low_dtr (quantity (string or DataArray, with units)) – The lower bound for daily temperature range adjustment (default: 10°C). Default : 10 degC. [Required units : [temperature]]

• high_dtr (quantity (string or DataArray, with units)) – The higher bound for daily temperature range adjustment (default: 13°C). Default : 13 degC. [Required units : [temperature]]

• max_daily_degree_days (quantity (string or DataArray, with units)) – The maximum amount of biologically effective degrees days that can be summed daily. Default : 9 degC. [Required units : [temperature]]

• start_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = April, south = October). Default : 04-01.

• end_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = October, south = April). This date is non-inclusive. Default : 11-01.

• freq (offset alias (string)) – Resampling frequency (default: “YS”; For Southern Hemisphere, should be “AS-JUL”). Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

bedd (DataArray) – Integral of mean daily temperature above {thresh_tasmin}, with maximum value of {max_daily_degree_days}, multiplied by day-length coefficient and temperature range modifier based on {method} method for days between {start_date} and {end_date} [K days], with additional attributes: description: Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture. Computed with {method} formula (Summation of min((max((Tn + Tx)/2 - {thresh_tasmin}, 0) * k) + TR_adj, Dmax), where coefficient k is a latitude-based day-length for days between {start_date} and {end_date}), coefficient TR_adj is a modifier accounting for large temperature swings, and Dmax is the maximum possibleamount of degree days that can be gained within a day ({max_daily_degree_days}).

Notes

The tasmax ceiling of 19°C is assumed to be the max temperature beyond which no further gains from daily temperature occur. Indice originally published in Gladstones [1992].

Let \(TX_{i}\) and \(TN_{i}\) be the daily maximum and minimum temperature at day \(i\), \(lat\) the latitude of the point of interest, \(degdays_{max}\) the maximum amount of degrees that can be summed per day (typically, 9). Then the sum of daily biologically effective growing degree day (BEDD) units between 1 April and 31 October is:

\[BEDD_i = \sum_{i=\text{April 1}}^{\text{October 31}} min\left( \left( max\left( \frac{TX_i + TN_i)}{2} - 10, 0 \right) * k \right) + TR_{adj}, degdays_{max}\right)\]

\[\begin{split}TR_{adj} = f(TX_{i}, TN_{i}) = \begin{cases} 0.25(TX_{i} - TN_{i} - 13), & \text{if } (TX_{i} - TN_{i}) > 13 \\ 0, & \text{if } 10 < (TX_{i} - TN_{i}) < 13\\ 0.25(TX_{i} - TN_{i} - 10), & \text{if } (TX_{i} - TN_{i}) < 10 \\ \end{cases}\end{split}\]

\[k = f(lat) = 1 + \left(\frac{\left| lat \right|}{50} * 0.06, \text{if }40 < |lat| <50, \text{else } 0\right)\]

A second version of the BEDD (method=”icclim”) does not consider \(TR_{adj}\) and \(k\) and employs a different end date (30 September) [Project team ECA&D and KNMI, 2013]. The simplified formula is as follows:

\[BEDD_i = \sum_{i=\text{April 1}}^{\text{September 30}} min\left( max\left(\frac{TX_i + TN_i)}{2} - 10, 0\right), degdays_{max}\right)\]

References

Gladstones [1992], Project team ECA&D and KNMI [2013]

xclim.indicators.atmos.calm_days(sfcWind: Union[DataArray, str] = 'sfcWind', *, thresh: Quantified = '2 m s-1', freq: str = 'MS', ds: Dataset = None, **indexer) → DataArray

Calm days (realm: atmos)

Number of days with surface wind speed below threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice calm_days().

Parameters

• sfcWind (str or DataArray) – Daily windspeed. Default : ds.sfcWind. [Required units : [speed]]

• thresh (quantity (string or DataArray, with units)) – Threshold average near-surface wind speed on which to base evaluation. Default : 2 m s-1. [Required units : [speed]]

• freq (offset alias (string)) – Resampling frequency. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

calm_days (DataArray) – Number of days with surface wind speed below {thresh} (number_of_days_with_sfcWind_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with surface wind speed below {thresh}.

Notes

Let \(WS_{ij}\) be the windspeed at day \(i\) of period \(j\). Then counted is the number of days where:

\[WS_{ij} < Threshold [m s-1]\]

xclim.indicators.atmos.cffwis_indices(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', sfcWind: Union[DataArray, str] = 'sfcWind', hurs: Union[DataArray, str] = 'hurs', lat: Union[DataArray, str] = 'lat', snd: Optional[Union[DataArray, str]] = None, ffmc0: Optional[Union[DataArray, str]] = None, dmc0: Optional[Union[DataArray, str]] = None, dc0: Optional[Union[DataArray, str]] = None, season_mask: Optional[Union[DataArray, str]] = None, *, season_method: str | None = None, overwintering: bool = False, dry_start: str | None = None, initial_start_up: bool = True, ds: Dataset = None, **params) → Tuple[DataArray, DataArray, DataArray, DataArray, DataArray, DataArray]

Canadian Fire Weather Index System indices. (realm: atmos)

Computes the 6 fire weather indexes as defined by the Canadian Forest Service: the Drought Code, the Duff-Moisture Code, the Fine Fuel Moisture Code, the Initial Spread Index, the Build Up Index and the Fire Weather Index.

This indicator will check for missing values according to the method “skip”. Based on indice cffwis_indices().

Parameters

• tas (str or DataArray) – Noon temperature. Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Rain fall in open over previous 24 hours, at noon. Default : ds.pr. [Required units : [precipitation]]

• sfcWind (str or DataArray) – Noon wind speed. Default : ds.sfcWind. [Required units : [speed]]

• hurs (str or DataArray) – Noon relative humidity. Default : ds.hurs. [Required units : []]

• lat (str or DataArray) – Latitude coordinate Default : ds.lat. [Required units : []]

• snd (str or DataArray, optional) – Noon snow depth, only used if season_method=’LA08’ is passed. [Required units : [length]]

• ffmc0 (str or DataArray, optional) – Initial values of the fine fuel moisture code. [Required units : []]

• dmc0 (str or DataArray, optional) – Initial values of the Duff moisture code. [Required units : []]

• dc0 (str or DataArray, optional) – Initial values of the drought code. [Required units : []]

• season_mask (str or DataArray, optional) – Boolean mask, True where/when the fire season is active. [Required units : []]

• season_method ({‘WF93’, None, ‘LA08’, ‘GFWED’}) – How to compute the start-up and shutdown of the fire season. If “None”, no start-ups or shutdowns are computed, similar to the R fire function. Ignored if season_mask is given. Default : None.

• overwintering (boolean) – Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given. Default : False.

• dry_start ({‘CFS’, None, ‘GFWED’}) – Whether to activate the DC and DMC “dry start” mechanism or not, see fire_weather_ufunc(). Default : None.

• initial_start_up (boolean) – If True (default), gridpoints where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. Any other keyword parameters as defined in fire_weather_ufunc() and in default_params. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• params – Default : None.

Returns

dc (DataArray) – Drought Code (drought_code), with additional attributes: description: Numeric rating of the average moisture content of deep, compact organic layers.dmc : DataArray Duff Moisture Code (duff_moisture_code), with additional attributes: description: Numeric rating of the average moisture content of loosely compacted organic layers of moderate depth.ffmc : DataArray Fine Fuel Moisture Code (fine_fuel_moisture_code), with additional attributes: description: Numeric rating of the average moisture content of litter and other cured fine fuels.isi : DataArray Initial Spread Index (initial_spread_index), with additional attributes: description: Numeric rating of the expected rate of fire spread.bui : DataArray Buildup Index (buildup_index), with additional attributes: description: Numeric rating of the total amount of fuel available for combustion.fwi : DataArray Fire Weather Index (fire_weather_index), with additional attributes: description: Numeric rating of fire intensity.

Notes

See Natural Resources Canada [n.d.], the xclim.indices.fire module documentation, and the docstring of fire_weather_ufunc() for more information.

References

Wang, Anderson, and Suddaby [2015]

xclim.indicators.atmos.cold_and_dry_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cold and dry days (realm: atmos)

Number of days with temperature below a given percentile and precipitation below a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_and_dry_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – First quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – First quartile of daily total precipitation computed by month. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

cold_and_dry_days (DataArray) – Number of days where temperature is below {tas_per_thresh}th percentile and precipitation is below {pr_per_thresh}th percentile [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is below {tas_per_thresh}th percentile and precipitation is below {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

Beniston [2009]

xclim.indicators.atmos.cold_and_wet_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cold and wet days (realm: atmos)

Number of days with temperature below a given percentile and precipitation above a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_and_wet_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – First quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – Third quartile of daily total precipitation computed by month. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

cold_and_wet_days (DataArray) – Number of days where temperature is below {tas_per_thresh}th percentile and precipitation is above {pr_per_thresh}th percentile [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is below {tas_per_thresh}th percentile and precipitation is above {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

Beniston [2009]

xclim.indicators.atmos.cold_spell_days(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '-10 degC', window: int = 5, freq: str = 'AS-JUL', op: str = '<', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Cold spell days (realm: atmos)

The number of days that are part of a cold spell. A cold spell is defined as a minimum number of consecutive days with mean daily temperature below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_days().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature below which a cold spell begins. Default : -10 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature below threshold to qualify as a cold spell. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cold_spell_days (DataArray) – Total number of days constituting events of at least {window} consecutive days where the mean daily temperature is below {thresh} (cold_spell_days) [days], with additional attributes: description: {freq} number of days that are part of a cold spell. A cold spell is defined as {window} or more consecutive days with mean daily temperature below {thresh}.

Notes

Let \(T_i\) be the mean daily temperature on day \(i\), the number of cold spell days during period \(\phi\) is given by:

\[\sum_{i \in \phi} \prod_{j=i}^{i+5} [T_j < thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.cold_spell_duration_index(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, window: int = 6, freq: str = 'YS', resample_before_rl: bool = True, bootstrap: bool = False, op: str = '<', ds: Dataset = None) → DataArray

Cold Spell Duration Index (CSDI) (realm: atmos)

Number of days part of a percentile-defined cold spell. A cold spell occurs when the daily minimum temperature is below a given percentile for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_duration_index().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmin_per (str or DataArray) – nth percentile of daily minimum temperature with dayofyear coordinate. Default : ds.tasmin_per. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature below threshold to qualify as a cold spell. Default : 6.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

csdi_{window} (DataArray) – Total number of days constituting events of at least {window} consecutive days where the daily minimum temperature is below the {tasmin_per_thresh}th percentile (cold_spell_duration_index) [days], with additional attributes: description: {freq} number of days with at least {window} consecutive days where the daily minimum temperature is below the {tasmin_per_thresh}th percentile. A {tasmin_per_window} day(s) window, centred on each calendar day in the {tasmin_per_period} period, is used to compute the {tasmin_per_thresh}th percentile(s).

Notes

Let \(TN_i\) be the minimum daily temperature for the day of the year \(i\) and \(TN10_i\) the 10th percentile of the minimum daily temperature over the 1961-1990 period for day of the year \(i\), the cold spell duration index over period \(\phi\) is defined as:

\[\sum_{i \in \phi} \prod_{j=i}^{i+6} \left[ TN_j < TN10_j \right]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false.

References

From the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI; [Zhang et al., 2011]).

xclim.indicators.atmos.cold_spell_frequency(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '-10 degC', window: int = 5, freq: str = 'AS-JUL', op: str = '<', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Cold spell frequency (realm: atmos)

The number of cold spell events. A cold spell is defined as a minimum number of consecutive days with mean daily temperature below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_frequency().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature below which a cold spell begins. Default : -10 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature below threshold to qualify as a cold spell. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cold_spell_frequency (DataArray) – Total number of series of at least {window} consecutive days where the mean daily temperature is below {thresh} (cold_spell_frequency), with additional attributes: description: {freq} number cold spell events. A cold spell is defined as a minimum number of consecutive days with mean daily temperature below {thresh}.

xclim.indicators.atmos.consecutive_frost_days(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Consecutive frost days (realm: atmos)

Maximum number of consecutive days where the daily minimum temperature is below 0°C

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_frost_days().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

consecutive_frost_days (DataArray) – Maximum number of consecutive days where minimum daily temperature is below {thresh} (spell_length_of_days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum number of consecutive days where minimum daily temperature is below {thresh}.

Notes

Let \(\mathbf{t}=t_0, t_1, \ldots, t_n\) be a minimum daily temperature series and \(thresh\) the threshold below which a day is considered a frost day. Let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([t_i < thresh] \neq [t_{i+1} < thresh]\), that is, the days where the temperature crosses the threshold. Then the maximum number of consecutive frost days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} < thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos.cool_night_index(tasmin: Union[DataArray, str] = 'tasmin', *, lat: xarray.DataArray | str | None = None, freq: str = 'YS', ds: Dataset = None) → DataArray

Cool night index (realm: atmos)

A night coolness variable which takes into account the mean minimum night temperatures during the month when ripening usually occurs beyond the ripening period.

This indicator will check for missing values according to the method “from_context”. Based on indice cool_night_index().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• lat ({‘south’, ‘north’}) – Latitude coordinate as an array, float or string. If None, a CF-conformant “latitude” field must be available within the passed DataArray. Default : None.

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cool_night_index (DataArray) – Cool night index [degC], with additional attributes: cell_methods: time: mean over days; description: Mean minimum temperature for September (northern hemisphere) or March (southern hemisphere).

Notes

Given that this indice only examines September and March months, it is possible to send in DataArrays containing only these timesteps. Users should be aware that due to the missing values checks in wrapped Indicators, datasets that are missing several months will be flagged as invalid. This check can be ignored by setting the following context:

References

Tonietto and Carbonneau [2004]

xclim.indicators.atmos.cooling_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '18.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cooling degree days (realm: atmos)

The cumulative degree days for days when the mean daily temperature is above a given threshold and buildings must be air conditioned.

This indicator will check for missing values according to the method “from_context”. Based on indice cooling_degree_days().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Temperature threshold above which air is cooled. Default : 18.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

cooling_degree_days (DataArray) – Cumulative sum of temperature degrees for mean daily temperature above {thresh} (integral_of_air_temperature_excess_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} cumulative cooling degree days (mean temperature above {thresh}).

Notes

Let \(x_i\) be the daily mean temperature at day \(i\). Then the cooling degree days above temperature threshold \(thresh\) over period \(\phi\) is given by:

\[\sum_{i \in \phi} (x_{i}-{thresh} [x_i > thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.corn_heat_units(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '4.44 degC', thresh_tasmax: Quantified = '10 degC', ds: Dataset = None) → DataArray

Corn heat units (realm: atmos)

A temperature-based index used to estimate the development of corn crops. Corn growth occurs when the daily minimum and maximum temperatures exceed given thresholds.

This indicator will check for missing values according to the method “skip”. Based on indice corn_heat_units().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The minimum temperature threshold needed for corn growth. Default : 4.44 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The maximum temperature threshold needed for corn growth. Default : 10 degC. [Required units : [temperature]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

chu (DataArray) – Corn heat units (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax}), with additional attributes: description: Temperature-based index used to estimate the development of corn crops. Corn growth occurs when the minimum and maximum daily temperatures both exceed {thresh_tasmin} and {thresh_tasmax}, respectively.

Notes

Formula used in calculating the Corn Heat Units for the Agroclimatic Atlas of Quebec [Audet et al., 2012].

The thresholds of 4.44°C for minimum temperatures and 10°C for maximum temperatures were selected following the assumption that no growth occurs below these values.

Let \(TX_{i}\) and \(TN_{i}\) be the daily maximum and minimum temperature at day \(i\). Then the daily corn heat unit is:

\[CHU_i = \frac{YX_{i} + YN_{i}}{2}\]

with

\[ \begin{align}\begin{aligned}YX_i & = 3.33(TX_i -10) - 0.084(TX_i -10)^2, &\text{if } TX_i > 10°C\\YN_i & = 1.8(TN_i -4.44), &\text{if } TN_i > 4.44°C\end{aligned}\end{align} \]

where \(YX_{i}\) and \(YN_{i}\) is 0 when \(TX_i \leq 10°C\) and \(TN_i \leq 4.44°C\), respectively.

References

Audet, Côté, Bachand, and Mailhot [2012], Bootsma, Tremblay, and Filion [1999]

xclim.indicators.atmos.daily_freezethaw_cycles(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '0 degC', thresh_tasmax: Quantified = '0 degC', op_tasmin: str = '<=', op_tasmax: str = '>', freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None, **indexer) → DataArray

Daily freeze-thaw cycles (realm: atmos)

The number of days with a freeze-thaw cycle. A freeze-thaw cycle is defined as a day where maximum daily temperature is above a given threshold and minimum daily temperature is at or below a given threshold, usually 0°C for both.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: window=1, op=sum.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]

• op_tasmin ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation for tasmin. Default: “<=”. Default : <=.

• op_tasmax ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation for tasmax. Default: “>”. Default : >.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dlyfrzthw (DataArray) – Number of days where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} [days], with additional attributes: description: {freq} number of days with a diurnal freeze-thaw cycle, where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin}.

Notes

Let \(TX_{i}\) be the maximum temperature at day \(i\) and \(TN_{i}\) be the daily minimum temperature at day \(i\). Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos.daily_pr_intensity(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Simple Daily Intensity Index (realm: atmos)

Average precipitation for days with daily precipitation above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_pr_intensity().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

sdii (DataArray) – Average precipitation during days with daily precipitation over {thresh} (Simple Daily Intensity Index: SDII) (lwe_thickness_of_precipitation_amount) [mm d-1], with additional attributes: description: {freq} Simple Daily Intensity Index (SDII) or {freq} average precipitation for days with daily precipitation over {thresh}.

Notes

Let \(\mathbf{p} = p_0, p_1, \ldots, p_n\) be the daily precipitation and \(thresh\) be the precipitation threshold defining wet days. Then the daily precipitation intensity is defined as:

\[\frac{\sum_{i=0}^n p_i [p_i \leq thresh]}{\sum_{i=0}^n [p_i \leq thresh]}\]

where \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.daily_temperature_range(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of daily temperature range (realm: atmos)

The average difference between the daily maximum and minimum temperatures.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range(). With injected parameters: op=mean.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dtr (DataArray) – Mean diurnal temperature range (air_temperature) [K], with additional attributes: cell_methods: time range within days time: mean over days; description: {freq} mean diurnal temperature range.

Notes

For a default calculation using op=’mean’ :

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then the mean diurnal temperature range in period \(j\) is:

\[DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}\]

xclim.indicators.atmos.daily_temperature_range_variability(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Variability of daily temperature range (realm: atmos)

The average day-to-day variation in daily temperature range.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range_variability().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dtrvar (DataArray) – Mean diurnal temperature range variability (air_temperature) [K], with additional attributes: cell_methods: time range within days time: difference over days time: mean over days; description: {freq} mean diurnal temperature range variability, defined as the average day-to-day variation in daily temperature range for the given time period.

Notes

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then calculated is the absolute day-to-day differences in period \(j\) is:

\[vDTR_j = \frac{ \sum_{i=2}^{I} |(TX_{ij}-TN_{ij})-(TX_{i-1,j}-TN_{i-1,j})| }{I}\]

xclim.indicators.atmos.days_over_precip_doy_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with precipitation above a given daily percentile (realm: atmos)

Number of days in a period where precipitation is above a given daily percentile and a fixed threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

days_over_precip_doy_thresh (DataArray) – Number of days with daily precipitation flux above the {pr_per_thresh}th percentile of {pr_per_period} (number_of_days_with_lwe_thickness_of_precipitation_amount_above_daily_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with precipitation above the {pr_per_thresh}th daily percentile. Only days with at least {thresh} are counted. A {pr_per_window} day(s) window, centered on each calendar day in the {pr_per_period} period, is used to compute the {pr_per_thresh}th percentile(s).

xclim.indicators.atmos.days_over_precip_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with precipitation above a given percentile (realm: atmos)

Number of days in a period where precipitation is above a given percentile, calculated over a given period and a fixed threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

days_over_precip_thresh (DataArray) – Number of days with precipitation flux above the {pr_per_thresh}th percentile of {pr_per_period} (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with precipitation above the {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are counted.

xclim.indicators.atmos.days_with_snow(prsn: Union[DataArray, str] = 'prsn', *, low: Quantified = '0 kg m-2 s-1', high: Quantified = '1E6 kg m-2 s-1', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Days with snowfall (realm: atmos)

Number of days with snow between a lower and upper limit.

This indicator will check for missing values according to the method “from_context”. Based on indice days_with_snow().

Parameters

• prsn (str or DataArray) – Solid precipitation flux. Default : ds.prsn. [Required units : [precipitation]]

• low (quantity (string or DataArray, with units)) – Minimum threshold solid precipitation flux. Default : 0 kg m-2 s-1. [Required units : [precipitation]]

• high (quantity (string or DataArray, with units)) – Maximum threshold solid precipitation flux. Default : 1E6 kg m-2 s-1. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

days_with_snow (DataArray) – Number of days with solid precipitation flux between {low} and {high} thresholds [days], with additional attributes: description: {freq} number of days with solid precipitation flux larger than {low} and smaller or equal to {high}.

References

Matthews, Andrey, and Picketts [2017]

xclim.indicators.atmos.degree_days_exceedance_date(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', sum_thresh: Quantified = '25 K days', op: str = '>', after_date: DayOfYearStr = None, freq: str = 'YS', ds: Dataset = None) → DataArray

Degree day exceedance date (realm: atmos)

The day of the year when the sum of degree days exceeds a threshold, occurring after a given date. Degree days are calculated above or below a given temperature threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice degree_days_exceedance_date().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base degree-days evaluation. Default : 0 degC. [Required units : [temperature]]

• sum_thresh (quantity (string or DataArray, with units)) – Threshold of the degree days sum. Default : 25 K days. [Required units : K days]

• op ({‘>’, ‘lt’, ‘<=’, ‘<’, ‘ge’, ‘>=’, ‘gt’, ‘le’}) – If equivalent to ‘>’, degree days are computed as tas - thresh and if equivalent to ‘<’, they are computed as thresh - tas. Default : >.

• after_date (date (string, MM-DD)) – Date at which to start the cumulative sum. In “mm-dd” format, defaults to the start of the sampling period. Default : None.

• freq (offset alias (string)) – Resampling frequency. If after_date is given, freq should be annual. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

degree_days_exceedance_date (DataArray) – Day of year when the integral of mean daily temperature {op} {thresh} exceeds {sum_thresh} (day_of_year), with additional attributes: description: <Dynamically generated string>

Notes

Let \(TG_{ij}\) be the daily mean temperature at day \(i\) of period \(j\), \(T\) is the reference threshold and \(ST\) is the sum threshold. Then, starting at day :math:i_0:, the degree days exceedance date is the first day \(k\) such that

\[\begin{split}\begin{cases} ST < \sum_{i=i_0}^{k} \max(TG_{ij} - T, 0) & \text{if $op$ is '>'} \\ ST < \sum_{i=i_0}^{k} \max(T - TG_{ij}, 0) & \text{if $op$ is '<'} \end{cases}\end{split}\]

The resulting \(k\) is expressed as a day of year.

Cumulated degree days have numerous applications including plant and insect phenology. See https://en.wikipedia.org/wiki/Growing_degree-day for examples (Wikipedia Contributors [2021]).

xclim.indicators.atmos.drought_code(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', lat: Union[DataArray, str] = 'lat', snd: Optional[Union[DataArray, str]] = None, dc0: Optional[Union[DataArray, str]] = None, season_mask: Optional[Union[DataArray, str]] = None, *, season_method: str | None = None, overwintering: bool = False, dry_start: str | None = None, initial_start_up: bool = True, ds: Dataset = None, **params) → DataArray

Daily drought code (realm: atmos)

The Drought Index is part of the Canadian Forest-Weather Index system. It is a numerical code that estimates the average moisture content of organic layers.

This indicator will check for missing values according to the method “skip”. Based on indice drought_code().

Parameters

• tas (str or DataArray) – Noon temperature. Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Rain fall in open over previous 24 hours, at noon. Default : ds.pr. [Required units : [precipitation]]

• lat (str or DataArray) – Latitude coordinate Default : ds.lat. [Required units : []]

• snd (str or DataArray, optional) – Noon snow depth. [Required units : [length]]

• dc0 (str or DataArray, optional) – Initial values of the drought code. [Required units : []]

• season_mask (str or DataArray, optional) – Boolean mask, True where/when the fire season is active. [Required units : []]

• season_method ({‘WF93’, None, ‘LA08’, ‘GFWED’}) – How to compute the start-up and shutdown of the fire season. If “None”, no start-ups or shutdowns are computed, similar to the R fire function. Ignored if season_mask is given. Default : None.

• overwintering (boolean) – Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given. Default : False.

• dry_start ({‘CFS’, None, ‘GFWED’}) – Whether to activate the DC and DMC “dry start” mechanism and which method to use. See fire_weather_ufunc(). Default : None.

• initial_start_up (boolean) – If True (default), grid points where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. Any other keyword parameters as defined in xclim.indices.fire.fire_weather_ufunc and in default_params. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• params – Default : None.

Returns

dc (DataArray) – Drought Code (drought_code), with additional attributes: description: Numerical code estimating the average moisture content of organic layers.

Notes

See Natural Resources Canada [n.d.], the xclim.indices.fire module documentation, and the docstring of fire_weather_ufunc() for more information.

References

Wang, Anderson, and Suddaby [2015]

xclim.indicators.atmos.dry_days(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '0.2 mm/d', freq: str = 'YS', op: str = '<', ds: Dataset = None, **indexer) → DataArray

Number of dry days (realm: atmos)

The number of days with daily precipitation under a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice dry_days().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0.2 mm/d. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dry_days (DataArray) – Number of dry days (number_of_days_with_lwe_thickness_of_precipitation_amount_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with daily precipitation under {thresh}.

Notes

Let \(PR_{ij}\) be the daily precipitation at day \(i\) of period \(j\). Then counted is the number of days where:

\[\sum PR_{ij} < Threshold [mm/day]\]

xclim.indicators.atmos.dry_spell_frequency(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1.0 mm', window: int = 3, freq: str = 'YS', resample_before_rl: bool = True, op: str = 'sum', ds: Dataset = None) → DataArray

Dry spell frequency (realm: atmos)

The frequency of dry periods of N days or more, during which the accumulated or maximum precipitation over a given time window of days is below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice dry_spell_frequency().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation amount under which a period is considered dry. The value against which the threshold is compared depends on op . Default : 1.0 mm. [Required units : [length]]

• window (number) – Minimum length of the spells. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• op ({‘sum’, ‘max’}) – Operation to perform on the window. Default is “sum”, which checks that the sum of accumulated precipitation over the whole window is less than the threshold. “max” checks that the maximal daily precipitation amount within the window is less than the threshold. This is the same as verifying that each individual day is below the threshold. Default : sum.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

dry_spell_frequency (DataArray) – Number of dry periods of {window} day(s) or more, during which the {op} precipitation on a window of {window} day(s) is below {thresh}., with additional attributes: description: The {freq} number of dry periods of {window} day(s) or more, during which the {op} precipitation on a window of {window} day(s) is below {thresh}.

xclim.indicators.atmos.dry_spell_total_length(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1.0 mm', window: int = 3, op: str = 'sum', freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None, **indexer) → DataArray

Dry spell total length (realm: atmos)

The total length of dry periods of N days or more, during which the accumulated or maximum precipitation over a given time window of days is below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice dry_spell_total_length().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Accumulated precipitation value under which a period is considered dry. Default : 1.0 mm. [Required units : [length]]

• window (number) – Number of days when the maximum or accumulated precipitation is under threshold. Default : 3.

• op ({‘sum’, ‘max’}) – Reduce operation. Default : sum.

• freq (offset alias (string)) – Resampling frequency. Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Indexing is done after finding the dry days, but before finding the spells. Default : YS.

• resample_before_rl (boolean) – Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Default : None.

Returns

dry_spell_total_length (DataArray) – Number of days in dry periods of {window} day(s) or more, during which the {op} precipitation within windows of {window} day(s) is under {thresh}. [days], with additional attributes: description: The {freq} number of days in dry periods of {window} day(s) or more, during which the {op} precipitation within windows of {window} day(s) is under {thresh}.

Notes

The algorithm assumes days before and after the timeseries are “wet”, meaning that the condition for being considered part of a dry spell is stricter on the edges. For example, with window=3 and op=’sum’, the first day of the series is considered part of a dry spell only if the accumulated precipitation within the first three days is under the threshold. In comparison, a day in the middle of the series is considered part of a dry spell if any of the three 3-day periods of which it is part are considered dry (so a total of five days are included in the computation, compared to only three).

xclim.indicators.atmos.extreme_temperature_range(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Extreme temperature range (realm: atmos)

The maximum of the maximum temperature minus the minimum of the minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice extreme_temperature_range().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

etr (DataArray) – Intra-period extreme temperature range (air_temperature) [K], with additional attributes: description: {freq} range between the maximum of daily maximum temperature and the minimum of dailyminimum temperature.

Notes

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then the extreme temperature range in period \(j\) is:

\[ETR_j = max(TX_{ij}) - min(TN_{ij})\]

xclim.indicators.atmos.fire_season(tas: Union[DataArray, str] = 'tas', snd: Optional[Union[DataArray, str]] = None, *, method: str = 'WF93', freq: str | None = None, temp_start_thresh: Quantified = '12 degC', temp_end_thresh: Quantified = '5 degC', temp_condition_days: int = 3, snow_condition_days: int = 3, snow_thresh: Quantified = '0.01 m', ds: Dataset = None) → DataArray

Fire season mask. (realm: atmos)

Binary mask of the active fire season, defined by conditions on consecutive daily temperatures and, optionally, snow depths.

Based on indice fire_season().

Parameters

• tas (str or DataArray) – Daily surface temperature, cffdrs recommends using maximum daily temperature. Default : ds.tas. [Required units : [temperature]]

• snd (str or DataArray, optional) – Snow depth, used with method == ‘LA08’. [Required units : [length]]

• method ({‘WF93’, ‘LA08’, ‘GFWED’}) – Which method to use. “LA08” and “GFWED” need the snow depth. Default : WF93.

• freq (offset alias (string)) – If given only the longest fire season for each period defined by this frequency, Every “seasons” are returned if None, including the short shoulder seasons. Default : None.

• temp_start_thresh (quantity (string or DataArray, with units)) – Minimal temperature needed to start the season. Must be scalar. Default : 12 degC. [Required units : [temperature]]

• temp_end_thresh (quantity (string or DataArray, with units)) – Maximal temperature needed to end the season. Must be scalar. Default : 5 degC. [Required units : [temperature]]

• temp_condition_days (number) – Number of days with temperature above or below the thresholds to trigger a start or an end of the fire season. Default : 3.

• snow_condition_days (number) – Parameters for the fire season determination. See fire_season(). Temperature is in degC, snow in m. The snow_thresh parameters is also used when dry_start is set to “GFWED”. Default : 3.

• snow_thresh (quantity (string or DataArray, with units)) – Minimal snow depth level to end a fire season, only used with method “LA08”. Must be scalar. Default : 0.01 m. [Required units : [length]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

fire_season (DataArray) – Fire season mask, with additional attributes: description: Fire season mask, computed with method {method}.

References

Lawson and Armitage [2008], Wotton and Flannigan [1993]

xclim.indicators.atmos.first_day_above(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', op: str = '>', after_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day above (realm: atmos)

Calculates the first day of a period when the temperature is higher than a certain threshold during a given number of days, after a given calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_above().

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_above (DataArray) – First day of year with temperature above threshold (day_of_year), with additional attributes: description: First day of year with temperature above {thresh} for at least {window} days after {after_date}.

Notes

Let \(x_i\) be the daily mean|max|min temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The first day above temperature threshold is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j > thresh]\]

where \(w\) is the number of days the temperature threshold should be exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.first_day_below(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', op: str = '<', after_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day below (realm: atmos)

Calculates the first day of a period when the temperature is lower than a certain threshold during a given number of days, after a given calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_below().

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “>”. Default : <.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_below (DataArray) – First day of year with temperature below threshold (day_of_year), with additional attributes: description: First day of year with temperature below {thresh} for at least {window} days after {after_date}.

xclim.indicators.atmos.first_day_tg_above(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', op: str = '>', after_date: DayOfYearStr = '01-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures superior to a given temperature threshold. (realm: atmos)

Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1).

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_above().

Parameters

• tas (str or DataArray) – Daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 01-01.

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_tg_above (DataArray) – First day of year with a period of at least {window} days of mean temperature above {thresh} (day_of_year), with additional attributes: description: First day of year with mean temperature above {thresh} for at least {window} days.

Notes

Let \(x_i\) be the daily mean|max|min temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The first day above temperature threshold is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j > thresh]\]

where \(w\) is the number of days the temperature threshold should be exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.first_day_tg_below(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', op: str = '<', after_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures inferior to a given temperature threshold. (realm: atmos)

Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1).

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_below().

Parameters

• tas (str or DataArray) – Daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “>”. Default : <.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_tg_below (DataArray) – First day of year with a period of at least {window} days of mean temperature below {thresh} (day_of_year), with additional attributes: description: First day of year with mean temperature below {thresh} for at least {window} days.

xclim.indicators.atmos.first_day_tn_above(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', op: str = '>', after_date: DayOfYearStr = '01-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures superior to a given temperature threshold. (realm: atmos)

Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1).

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_above().

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 01-01.

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_tn_above (DataArray) – First day of year with a period of at least {window} days of minimum temperature above {thresh} (day_of_year), with additional attributes: description: First day of year with minimum temperature above {thresh} for at least {window} days.

Notes

Let \(x_i\) be the daily mean|max|min temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The first day above temperature threshold is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j > thresh]\]

where \(w\) is the number of days the temperature threshold should be exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.first_day_tn_below(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', op: str = '<', after_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures inferior to a given temperature threshold. (realm: atmos)

Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1).

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_below().

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “>”. Default : <.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_tn_below (DataArray) – First day of year with a period of at least {window} days of minimum temperature below {thresh} (day_of_year), with additional attributes: description: First day of year with minimum temperature below {thresh} for at least {window} days.

xclim.indicators.atmos.first_day_tx_above(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '0 degC', op: str = '>', after_date: DayOfYearStr = '01-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures superior to a given temperature threshold. (realm: atmos)

Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1).

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_above().

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 01-01.

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_tx_above (DataArray) – First day of year with a period of at least {window} days of maximum temperature above {thresh} (day_of_year), with additional attributes: description: First day of year with maximum temperature above {thresh} for at least {window} days.

Notes

Let \(x_i\) be the daily mean|max|min temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The first day above temperature threshold is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j > thresh]\]

where \(w\) is the number of days the temperature threshold should be exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.first_day_tx_below(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '0 degC', op: str = '<', after_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures inferior to a given temperature threshold. (realm: atmos)

Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1).

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_below().

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “>”. Default : <.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_tx_below (DataArray) – First day of year with a period of at least {window} days of maximum temperature below {thresh} (day_of_year), with additional attributes: description: First day of year with maximum temperature below {thresh} for at least {window} days.

xclim.indicators.atmos.first_snowfall(prsn: Union[DataArray, str] = 'prsn', *, thresh: Quantified = '0.5 mm/day', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

First day where solid precipitation flux exceeded a given threshold (realm: atmos)

The first day where the solid precipitation flux exceeded a given threshold during a time period.

This indicator will check for missing values according to the method “from_context”. Based on indice first_snowfall().

Parameters

• prsn (str or DataArray) – Solid precipitation flux. Default : ds.prsn. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold precipitation flux on which to base evaluation. Default : 0.5 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

first_snowfall (DataArray) – Date of first day where the solid precipitation flux exceeded {thresh} (day_of_year), with additional attributes: description: {freq} first day where the solid precipitation flux exceeded {thresh}.

References

CBCL [2020].

xclim.indicators.atmos.fraction_over_precip_doy_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

(realm: atmos)

Percentage of the total precipitation over period occurring in days when the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

fraction_over_precip_doy_thresh (DataArray) – Fraction of precipitation due to days with daily precipitation above {pr_per_thresh}th daily percentile, with additional attributes: description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th daily percentile. Only days with at least {thresh} are included in the total. A {pr_per_window} day(s) window, centered on each calendar day in the {pr_per_period} period, is used to compute the {pr_per_thresh}th percentile(s).

xclim.indicators.atmos.fraction_over_precip_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Fraction of precipitation due to wet days with daily precipitation over a given percentile. (realm: atmos)

Percentage of the total precipitation over period occurring in days when the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

fraction_over_precip_thresh (DataArray) – Fraction of precipitation due to days with precipitation above {pr_per_thresh}th daily percentile, with additional attributes: description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are included in the total.

xclim.indicators.atmos.freezethaw_spell_frequency(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '0 degC', thresh_tasmax: Quantified = '0 degC', window: int = 1, op_tasmin: str = '<=', op_tasmax: str = '>', freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Freeze-thaw spell frequency (realm: atmos)

Frequency of daily freeze-thaw spells. A freeze-thaw spell is defined as a number of consecutive days where maximum daily temperatures are above a given threshold and minimum daily temperatures are at or below a given threshold, usually 0°C for both.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: op=count.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]

• window (number) – The minimal length of spells to be included in the statistics. Default : 1.

• op_tasmin ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation for tasmin. Default: “<=”. Default : <=.

• op_tasmax ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation for tasmax. Default: “>”. Default : >.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freezethaw_spell_frequency (DataArray) – Frequency of events where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} for at least {window} consecutive day(s). [days], with additional attributes: description: {freq} number of freeze-thaw spells, where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} for at least {window} consecutive day(s).

Notes

Let \(TX_{i}\) be the maximum temperature at day \(i\) and \(TN_{i}\) be the daily minimum temperature at day \(i\). Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos.freezethaw_spell_max_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '0 degC', thresh_tasmax: Quantified = '0 degC', window: int = 1, op_tasmin: str = '<=', op_tasmax: str = '>', freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Maximal length of freeze-thaw spells (realm: atmos)

Maximal length of daily freeze-thaw spells. A freeze-thaw spell is defined as a number of consecutive days where maximum daily temperatures are above a given threshold and minimum daily temperatures are at or below a threshold, usually 0°C for both.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: op=max.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]

• window (number) – The minimal length of spells to be included in the statistics. Default : 1.

• op_tasmin ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation for tasmin. Default: “<=”. Default : <=.

• op_tasmax ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation for tasmax. Default: “>”. Default : >.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freezethaw_spell_max_length (DataArray) – Maximal length of events where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} for at least {window} consecutive day(s). [days], with additional attributes: description: {freq} maximal length of freeze-thaw spells, where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} for at least {window} consecutive day(s).

Notes

Let \(TX_{i}\) be the maximum temperature at day \(i\) and \(TN_{i}\) be the daily minimum temperature at day \(i\). Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos.freezethaw_spell_mean_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '0 degC', thresh_tasmax: Quantified = '0 degC', window: int = 1, freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Freeze-thaw spell mean length (realm: atmos)

Average length of daily freeze-thaw spells. A freeze-thaw spell is defined as a number of consecutive days where maximum daily temperatures are above a given threshold and minimum daily temperatures are at or below a given threshold, usually 0°C for both.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: op=mean, op_tasmin=<=, op_tasmax=>.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]

• window (number) – The minimal length of spells to be included in the statistics. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freezethaw_spell_mean_length (DataArray) – Average length of events where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} for at least {window} consecutive day(s). [days], with additional attributes: description: {freq} average length of freeze-thaw spells, where maximum daily temperatures are above {thresh_tasmax} and minimum daily temperatures are at or below {thresh_tasmin} for at least {window} consecutive day(s).

Notes

Let \(TX_{i}\) be the maximum temperature at day \(i\) and \(TN_{i}\) be the daily minimum temperature at day \(i\). Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos.freezing_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Freezing degree days (realm: atmos)

The cumulative degree days for days when the average temperature is below a given threshold, typically 0°C.

This indicator will check for missing values according to the method “from_context”. Based on indice heating_degree_days().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

freezing_degree_days (DataArray) – Cumulative sum of temperature degrees for mean daily temperature below {thresh} (integral_of_air_temperature_deficit_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} freezing degree days (mean temperature below {thresh}).

Notes

This index intentionally differs from its ECA&D [Project team ECA&D and KNMI, 2013] equivalent: HD17. In HD17, values below zero are not clipped before the sum. The present definition should provide a better representation of the energy demand for heating buildings to the given threshold.

Let \(TG_{ij}\) be the daily mean temperature at day \(i\) of period \(j\). Then the heating degree days are:

\[HD17_j = \sum_{i=1}^{I} (17℃ - TG_{ij}) | TG_{ij} < 17℃)\]

xclim.indicators.atmos.freshet_start(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', op: str = '>', after_date: DayOfYearStr = '01-01', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Day of year of spring freshet start (realm: atmos)

Day of year of the spring freshet start, defined as the first day when the temperature exceeds a certain threshold for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_temperature_above().

Parameters

• tas (str or DataArray) – Daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 01-01.

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freshet_start (DataArray) – First day where temperature threshold of {thresh} is exceeded for at least {window} days (day_of_year), with additional attributes: description: Day of year of the spring freshet start, defined as the first day a temperature threshold of {thresh} is exceeded for at least {window} days.

Notes

Let \(x_i\) be the daily mean|max|min temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The first day above temperature threshold is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j > thresh]\]

where \(w\) is the number of days the temperature threshold should be exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.frost_days(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Frost days (realm: atmos)

Number of days where the daily minimum temperature is below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_days().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Freezing temperature. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

frost_days (DataArray) – Number of days where the daily minimum temperature is below {thresh} (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where the daily minimum temperature is below {thresh}.

Notes

Let \(TN_{ij}\) be the daily minimum temperature at day \(i\) of period \(j\) and :math`TT` the threshold. Then counted is the number of days where:

\[TN_{ij} < TT\]

xclim.indicators.atmos.frost_free_season_end(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', mid_date: DayOfYearStr = '07-01', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Frost free season end (realm: atmos)

First day when the temperature is below a given threshold for a given number of consecutive days after a median calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_free_season_end().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_free_season_end (DataArray) – First day, after {mid_date}, following a period of {window} days with minimum daily temperature below {thresh} (day_of_year), with additional attributes: description: Day of the year of the end of the frost-free season, defined as the interval between the first set of {window} days when the minimum daily temperature is at or above {thresh} and the first set (after {mid_date}) of {window} days when it is below {thresh}.

xclim.indicators.atmos.frost_free_season_length(tasmin: Union[DataArray, str] = 'tasmin', *, window: int = 5, mid_date: DayOfYearStr | None = '07-01', thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None) → DataArray

Frost free season length (realm: atmos)

Duration of the frost free season, defined as the period when the minimum daily temperature is above 0°C without a freezing window of N days, with freezing occurring after a median calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_free_season_length().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature above threshold to mark the beginning and end of frost free season. Default : 5.

• mid_date (date (string, MM-DD)) – Date the must be included in the season. It is the earliest the end of the season can be. If None, there is no limit. Default : 07-01.

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_free_season_length (DataArray) – Number of days between the first occurrence of at least {window} consecutive days with minimum daily temperature at or above {thresh} and the first occurrence of at least {window} consecutive days with minimum daily temperature below {thresh} after {mid_date} (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days between the first occurrence of at least {window} consecutive days with minimum daily temperature at or above {thresh} and the first occurrence of at least {window} consecutive days with minimum daily temperature below {thresh} after {mid_date}.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then counted is the number of days between the first occurrence of at least N consecutive days with:

\[TN_{ij} >= 0 ℃\]

and the first subsequent occurrence of at least N consecutive days with:

\[TN_{ij} < 0 ℃\]

xclim.indicators.atmos.frost_free_season_start(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Frost free season start (realm: atmos)

First day when minimum daily temperature exceeds a given threshold for a given number of consecutive days

This indicator will check for missing values according to the method “from_context”. Based on indice frost_free_season_start().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_free_season_start (DataArray) – First day following a period of {window} days with minimum daily temperature at or above {thresh} (day_of_year), with additional attributes: description: Day of the year of the beginning of the frost-free season, defined as the {window}th consecutive day when minimum daily temperature exceeds {thresh}.

Notes

Let \(x_i\) be the daily mean temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The start date of the start of growing season is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j >= thresh]\]

where \(w\) is the number of days the temperature threshold should be met or exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.frost_season_length(tasmin: Union[DataArray, str] = 'tasmin', *, window: int = 5, mid_date: DayOfYearStr | None = '01-01', thresh: Quantified = '0 degC', freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Frost season length (realm: atmos)

Duration of the freezing season, defined as the period when the daily minimum temperature is below 0°C without a thawing window of days, with the thaw occurring after a median calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_season_length().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature below threshold to mark the beginning and end of frost season. Default : 5.

• mid_date (date (string, MM-DD)) – Date the must be included in the season. It is the earliest the end of the season can be. If None, there is no limit. Default : 01-01.

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_season_length (DataArray) – Number of days between the first occurrence of at least {window} consecutive days with minimum daily temperature below {thresh} and the first occurrence of at least {window} consecutive days with minimum daily temperature at or above {thresh} after {mid_date} (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days between the first occurrence of at least {window} consecutive days with minimum daily temperature below {thresh} and the first occurrence of at least {window} consecutive days with minimum daily temperature at or above {thresh} after {mid_date}.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then counted is the number of days between the first occurrence of at least N consecutive days with:

\[TN_{ij} > 0 ℃\]

and the first subsequent occurrence of at least N consecutive days with:

\[TN_{ij} < 0 ℃\]

xclim.indicators.atmos.griffiths_drought_factor(pr: Union[DataArray, str] = 'pr', smd: Union[DataArray, str] = 'smd', *, limiting_func: str = 'xlim', ds: Dataset = None) → DataArray

Griffiths drought factor based on the soil moisture deficit. (realm: atmos)

The drought factor is a numeric indicator of the forest fire fuel availability in the deep litter bed. It is often used in the calculation of the McArthur Forest Fire Danger Index. The method implemented here follows Finkele et al. [2006].

This indicator will check for missing values according to the method “skip”. Based on indice griffiths_drought_factor().

Parameters

• pr (str or DataArray) – Total rainfall over previous 24 hours [mm/day]. Default : ds.pr. [Required units : [precipitation]]

• smd (str or DataArray) – Daily soil moisture deficit (often KBDI) [mm/day]. Default : ds.smd. [Required units : [precipitation]]

• limiting_func ({‘discrete’, ‘xlim’}) – How to limit the values of the drought factor. If “xlim” (default), use equation (14) in Finkele et al. [2006]. If “discrete”, use equation Eq (13) in Finkele et al. [2006], but with the lower limit of each category bound adjusted to match the upper limit of the previous bound. Default : xlim.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

df (DataArray) – Griffiths Drought Factor (griffiths_drought_factor), with additional attributes: description: Numeric indicator of the forest fire fuel availability in the deep litter bed

Notes

Calculation of the Griffiths drought factor depends on the rainfall over the previous 20 days. Thus, the first non-NaN time point in the drought factor returned by this function corresponds to the 20th day of the input data.

References

Finkele, Mills, Beard, and Jones [2006], Griffiths [1999], Holgate, Van DIjk, Cary, and Yebra [2017]

xclim.indicators.atmos.growing_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '4.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Growing degree days (realm: atmos)

The cumulative degree days for days when the average temperature is above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_degree_days().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 4.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

growing_degree_days (DataArray) – Cumulative sum of temperature degrees for mean daily temperature above {thresh} (integral_of_air_temperature_excess_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} growing degree days (mean temperature above {thresh}).

Notes

Let \(TG_{ij}\) be the mean daily temperature at day \(i\) of period \(j\). Then the growing degree days are:

\[GD4_j = \sum_{i=1}^I (TG_{ij}-{4} | TG_{ij} > {4}℃)\]

xclim.indicators.atmos.growing_season_end(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '5.0 degC', mid_date: DayOfYearStr = '07-01', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Growing season end (realm: atmos)

The first day when the temperature is below a certain threshold for a certain number of consecutive days after a given calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_end().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 5.0 degC. [Required units : [temperature]]

• mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

growing_season_end (DataArray) – First day of the first series of {window} days with mean daily temperature below {thresh}, occurring after {mid_date} (day_of_year), with additional attributes: description: Day of year of end of growing season, defined as the first day of consistent inferior threshold temperature of {thresh} after a run of {window} days superior to threshold temperature, occurring after {mid_date}.

Notes

Let \(x_i\) be the daily mean temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The start date of the end of growing season is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j < thresh]\]

where \(w\) is the number of days where temperature should be inferior to a given threshold after a given date, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.growing_season_length(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '5.0 degC', window: int = 6, mid_date: DayOfYearStr = '07-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Growing season length (realm: atmos)

Number of days between the first occurrence of a series of days with a daily average temperature above a threshold and the first occurrence of a series of days with a daily average temperature below that same threshold, occurring after a given calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_length().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 5.0 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature above threshold to mark the beginning and end of growing season. Default : 6.

• mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

growing_season_length (DataArray) – Number of days between the first occurrence of at least {window} consecutive days with mean daily temperature over {thresh} and the first occurrence of at least {window} consecutive days with mean daily temperature below {thresh}, occurring after {mid_date} (growing_season_length) [days], with additional attributes: description: {freq} number of days between the first occurrence of at least {window} consecutive days with mean daily temperature over {thresh} and the first occurrence of at least {window} consecutive days with mean daily temperature below {thresh}, occurring after {mid_date}.

Notes

Let \(TG_{ij}\) be the mean temperature at day \(i\) of period \(j\). Then counted is the number of days between the first occurrence of at least 6 consecutive days with:

\[TG_{ij} > 5 ℃\]

and the first occurrence after 1 July of at least 6 consecutive days with:

\[TG_{ij} < 5 ℃\]

References

Project team ECA&D and KNMI [2013]

xclim.indicators.atmos.growing_season_start(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '5.0 degC', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Growing season start (realm: atmos)

The first day when the temperature exceeds a certain threshold for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_start().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 5.0 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

growing_season_start (DataArray) – First day of the first series of {window} days with mean daily temperature above or equal to {thresh} (day_of_year), with additional attributes: description: Day of the year marking the beginning of the growing season, defined as the first day of the first series of {window} days with mean daily temperature above or equal to {thresh}.

Notes

Let \(x_i\) be the daily mean temperature at day of the year \(i\) for values of \(i\) going from 1 to 365 or 366. The start date of the start of growing season is given by the smallest index \(i\):

\[\prod_{j=i}^{i+w} [x_j >= thresh]\]

where \(w\) is the number of days the temperature threshold should be met or exceeded, and \([P]\) is 1 if \(P\) is true, and 0 if false.

xclim.indicators.atmos.heat_index(tas: Union[DataArray, str] = 'tas', hurs: Union[DataArray, str] = 'hurs', *, ds: Dataset = None) → DataArray

Heat index (realm: atmos)

The heat index is an estimate of the temperature felt by a person in the shade when relative humidity is taken into account.

Based on indice heat_index().

Parameters

• tas (str or DataArray) – Temperature. The equation assumes an instantaneous value. Default : ds.tas. [Required units : [temperature]]

• hurs (str or DataArray) – Relative humidity. The equation assumes an instantaneous value. Default : ds.hurs. [Required units : []]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_index (DataArray) – Heat index (air_temperature) [C], with additional attributes: description: Perceived temperature after relative humidity is taken into account.

Notes

While both the humidex and the heat index are calculated using dew point the humidex uses a dew point of 7 °C (45 °F) as a base, whereas the heat index uses a dew point base of 14 °C (57 °F). Further, the heat index uses heat balance equations which account for many variables other than vapour pressure, which is used exclusively in the humidex calculation.

References

Blazejczyk, Epstein, Jendritzky, Staiger, and Tinz [2012]

xclim.indicators.atmos.heat_wave_frequency(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '22.0 degC', thresh_tasmax: Quantified = '30 degC', window: int = 3, freq: str = 'YS', op: str = '>', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Heat wave frequency (realm: atmos)

Number of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_frequency(). Keywords : health,.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The minimum temperature threshold needed to trigger a heatwave event. Default : 22.0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_frequency (DataArray) – Total number of series of at least {window} consecutive days with daily minimum temperature above {thresh_tasmin} and daily maximum temperature above {thresh_tasmax} (heat_wave_events), with additional attributes: description: {freq} number of heat wave events within a given period. A heat wave occurs when daily minimum and maximum temperatures exceed {thresh_tasmin} and {thresh_tasmax}, respectively, over at least {window} days.

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities [Casati et al., 2013].

In Robinson [2001], the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

Casati, Yagouti, and Chaumont [2013], Robinson [2001]

xclim.indicators.atmos.heat_wave_index(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '25.0 degC', window: int = 5, freq: str = 'YS', op: str = '>', ds: Dataset = None) → DataArray

Heat wave index (realm: atmos)

Number of days that constitute heatwave events. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_index().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to designate a heatwave. Default : 25.0 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature above threshold to qualify as a heatwave. Default : 5.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_index (DataArray) – Total number of days constituting events of at least {window} consecutive days with daily maximum temperature above {thresh} (heat_wave_index) [days], with additional attributes: description: {freq} total number of days that are part of a heatwave within a given period. A heat wave occurs when daily maximum temperatures exceed {thresh} over at least {window} days.

xclim.indicators.atmos.heat_wave_max_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '22.0 degC', thresh_tasmax: Quantified = '30 degC', window: int = 3, freq: str = 'YS', op: str = '>', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Heat wave maximum length (realm: atmos)

Total duration of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_max_length(). Keywords : health,.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The minimum temperature threshold needed to trigger a heatwave event. Default : 22.0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_max_length (DataArray) – Longest series of at least {window} consecutive days with daily minimum temperature above {thresh_tasmin} and daily maximum temperature above {thresh_tasmax} (spell_length_of_days_with_air_temperature_above_threshold) [days], with additional attributes: description: {freq} maximum length of heat wave events occurring within a given period. A heat wave occurs when daily minimum and maximum temperatures exceed {thresh_tasmin} and {thresh_tasmax}, respectively, over at least {window} days.

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities [Casati et al., 2013].

In Robinson [2001], the parameters would be: thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

Casati, Yagouti, and Chaumont [2013], Robinson [2001]

xclim.indicators.atmos.heat_wave_total_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '22.0 degC', thresh_tasmax: Quantified = '30 degC', window: int = 3, freq: str = 'YS', op: str = '>', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Heat wave total length (realm: atmos)

Maximum length of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_total_length(). Keywords : health,.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – The minimum temperature threshold needed to trigger a heatwave event. Default : 22.0 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_total_length (DataArray) – Total length of events of at least {window} consecutive days with daily minimum temperature above {thresh_tasmin} and daily maximum temperature above {thresh_tasmax} (spell_length_of_days_with_air_temperature_above_threshold) [days], with additional attributes: description: {freq} total length of heat wave events occurring within a given period. A heat wave occurs when daily minimum and maximum temperatures exceed {thresh_tasmin} and {thresh_tasmax}, respectively, over at least {window} days.

Notes

See notes and references of heat_wave_max_length

xclim.indicators.atmos.heating_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '17.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Heating degree days (realm: atmos)

The cumulative degree days for days when the mean daily temperature is below a given threshold and buildings must be heated.

This indicator will check for missing values according to the method “from_context”. Based on indice heating_degree_days().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 17.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

heating_degree_days (DataArray) – Cumulative sum of temperature degrees for mean daily temperature below {thresh} (integral_of_air_temperature_deficit_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} cumulative heating degree days (mean temperature below {thresh}).

Notes

This index intentionally differs from its ECA&D [Project team ECA&D and KNMI, 2013] equivalent: HD17. In HD17, values below zero are not clipped before the sum. The present definition should provide a better representation of the energy demand for heating buildings to the given threshold.

Let \(TG_{ij}\) be the daily mean temperature at day \(i\) of period \(j\). Then the heating degree days are:

\[HD17_j = \sum_{i=1}^{I} (17℃ - TG_{ij}) | TG_{ij} < 17℃)\]

xclim.indicators.atmos.high_precip_low_temp(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, pr_thresh: Quantified = '0.4 mm/d', tas_thresh: Quantified = '-0.2 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Days with precipitation and cold temperature (realm: atmos)

Number of days with precipitation above a given threshold and temperature below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice high_precip_low_temp().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Daily mean, minimum or maximum temperature. Default : ds.tas. [Required units : [temperature]]

• pr_thresh (quantity (string or DataArray, with units)) – Precipitation threshold to exceed. Default : 0.4 mm/d. [Required units : [precipitation]]

• tas_thresh (quantity (string or DataArray, with units)) – Temperature threshold not to exceed. Default : -0.2 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

high_precip_low_temp (DataArray) – Days with precipitation at or above {pr_thresh} and temperature below {tas_thresh} [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with precipitation at or above {pr_thresh} and temperature below {tas_thresh}.

xclim.indicators.atmos.hot_spell_frequency(tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmax: Quantified = '30 degC', window: int = 3, freq: str = 'YS', op: str = '>', ds: Dataset = None) → DataArray

Hot spell frequency (realm: atmos)

Number of hot spells events within a given period. A hot spell occurs when the daily maximum temperatureexceeds a given threshold for a minimum number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice hot_spell_frequency(). Keywords : health,.

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hot_spell_frequency (DataArray) – Total number of series of at least {window} consecutive days with daily maximum temperature above {thresh_tasmax} (hot_spell_events), with additional attributes: description: {freq} number of hot spell events within a given period. A hot spell event occurs when the maximum daily temperature exceeds {thresh_tasmax} over at least {window} days.

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities [Casati et al., 2013].

In Robinson [2001], the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

Casati, Yagouti, and Chaumont [2013], Robinson [2001]

xclim.indicators.atmos.hot_spell_max_length(tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmax: Quantified = '30 degC', window: int = 1, freq: str = 'YS', op: str = '>', ds: Dataset = None) → DataArray

Hot spell maximum length (realm: atmos)

Maximum length of hot spells events within a given period. A hot spell occurs when the daily maximum temperature exceeds a given threshold for a minimum number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice hot_spell_max_length(). Keywords : health,.

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]

• window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hot_spell_max_length (DataArray) – Longest series of at least {window} consecutive days with daily maximum temperature above {thresh_tasmax} (spell_length_of_days_with_air_temperature_above_threshold) [days], with additional attributes: description: {freq} maximum length of hot spell events occurring within a given period. A hot spell event occurs when the maximum daily temperature exceeds {thresh_tasmax} over at least {window} days.

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities [Casati et al., 2013].

In Robinson [2001], the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

Casati, Yagouti, and Chaumont [2013], Robinson [2001]

xclim.indicators.atmos.huglin_index(tas: Union[DataArray, str] = 'tas', tasmax: Union[DataArray, str] = 'tasmax', lat: Union[DataArray, str] = 'lat', *, thresh: Quantified = '10 degC', method: str = 'jones', start_date: DayOfYearStr = '04-01', end_date: DayOfYearStr = '10-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Huglin heliothermal index (realm: atmos)

Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture. Considers daily minimum and maximum temperature with a given base threshold, typically between 1 April and 30September, and integrates a day-length coefficient calculation for higher latitudes. Metric originally published in Huglin (1978). Day-length coefficient based on Hall & Jones (2010).

This indicator will check for missing values according to the method “from_context”. Based on indice huglin_index().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• lat (str or DataArray) – Latitude coordinate. If None, a CF-conformant “latitude” field must be available within the passed DataArray. Default : ds.lat. [Required units : []]

• thresh (quantity (string or DataArray, with units)) – The temperature threshold. Default : 10 degC. [Required units : [temperature]]

• method ({‘jones’, ‘icclim’, ‘smoothed’}) – The formula to use for the latitude coefficient calculation. Default : jones.

• start_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = April, south = October). Default : 04-01.

• end_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = October, south = April). This date is non-inclusive. Default : 10-01.

• freq (offset alias (string)) – Resampling frequency (default: “YS”; For Southern Hemisphere, should be “AS-JUL”). Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hi (DataArray) – Integral of mean daily temperature above {thresh} multiplied by day-length coefficient with {method} method for days between {start_date} and {end_date}, with additional attributes: description: Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture, computed with {method} formula (Summation of ((Tn + Tx)/2 - {thresh}) * k), where coefficient k is a latitude-based day-length for days between {start_date} and {end_date}.

Notes

Let \(TX_{i}\) and \(TG_{i}\) be the daily maximum and mean temperature at day \(i\) and \(T_{thresh}\) the base threshold needed for heat summation (typically, 10 degC). A day-length multiplication, \(k\), based on latitude, \(lat\), is also considered. Then the Huglin heliothermal index for dates between 1 April and 30 September is:

\[HI = \sum_{i=\text{April 1}}^{\text{September 30}} \left( \frac{TX_i + TG_i)}{2} - T_{thresh} \right) * k\]

For the smoothed method, the day-length multiplication factor, \(k\), is calculated as follows:

\[\begin{split}k = f(lat) = \begin{cases} 1, & \text{if } |lat| <= 40 \\ 1 + ((abs(lat) - 40) / 10) * 0.06, & \text{if } 40 < |lat| <= 50 \\ NaN, & \text{if } |lat| > 50 \\ \end{cases}\end{split}\]

For compatibility with ICCLIM, end_date should be set to 11-01, method should be set to icclim. The day-length multiplication factor, \(k\), is calculated as follows:

\[\begin{split}k = f(lat) = \begin{cases} 1.0, & \text{if } |lat| <= 40 \\ 1.02, & \text{if } 40 < |lat| <= 42 \\ 1.03, & \text{if } 42 < |lat| <= 44 \\ 1.04, & \text{if } 44 < |lat| <= 46 \\ 1.05, & \text{if } 46 < |lat| <= 48 \\ 1.06, & \text{if } 48 < |lat| <= 50 \\ NaN, & \text{if } |lat| > 50 \\ \end{cases}\end{split}\]

A more robust day-length calculation based on latitude, calendar, day-of-year, and obliquity is available with method=”jones”. See: xclim.indices.generic.day_lengths() or Hall and Jones [2010] for more information.

References

Hall and Jones [2010], Huglin [1978]

xclim.indicators.atmos.humidex(tas: Union[DataArray, str] = 'tas', tdps: Optional[Union[DataArray, str]] = None, hurs: Optional[Union[DataArray, str]] = None, *, ds: Dataset = None) → DataArray

Humidex (realm: atmos)

The humidex describes the temperature felt by a person when relative humidity is taken into account. It can be interpreted as the equivalent temperature felt when the air is dry.

Based on indice humidex().

Parameters

• tas (str or DataArray) – Air temperature. Default : ds.tas. [Required units : [temperature]]

• tdps (str or DataArray, optional) – Dewpoint temperature. [Required units : [temperature]]

• hurs (str or DataArray, optional) – Relative humidity. [Required units : []]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

humidex (DataArray) – Humidex index (air_temperature) [C], with additional attributes: description: Humidex index describing the temperature felt by the average person in response to relative humidity.

Notes

The humidex is usually computed using hourly observations of dry bulb and dewpoint temperatures. It is computed using the formula based on Masterton and Richardson [1979]:

\[T + {\frac {5}{9}}\left[e - 10\right]\]

where \(T\) is the dry bulb air temperature (°C). The term \(e\) can be computed from the dewpoint temperature \(T_{dewpoint}\) in °K:

\[e = 6.112 \times \exp(5417.7530\left({\frac {1}{273.16}}-{\frac {1}{T_{\text{dewpoint}}}}\right)\]

where the constant 5417.753 reflects the molecular weight of water, latent heat of vaporization, and the universal gas constant [Mekis et al., 2015]. Alternatively, the term \(e\) can also be computed from the relative humidity h expressed in percent using Sirangelo et al. [2020]:

\[e = \frac{h}{100} \times 6.112 * 10^{7.5 T/(T + 237.7)}.\]

The humidex comfort scale [Canada, 2011] can be interpreted as follows:

• 20 to 29 : no discomfort;

• 30 to 39 : some discomfort;

• 40 to 45 : great discomfort, avoid exertion;

• 46 and over : dangerous, possible heat stroke;

Please note that while both the humidex and the heat index are calculated using dew point, the humidex uses a dew point of 7 °C (45 °F) as a base, whereas the heat index uses a dew point base of 14 °C (57 °F). Further, the heat index uses heat balance equations which account for many variables other than vapour pressure, which is used exclusively in the humidex calculation.

References

Canada [2011], Masterton and Richardson [1979], Mekis, Vincent, Shephard, and Zhang [2015], Sirangelo, Caloiero, Coscarelli, Ferrari, and Fusto [2020]

xclim.indicators.atmos.ice_days(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Ice days (realm: atmos)

Number of days where the daily maximum temperature is below 0°C

This indicator will check for missing values according to the method “from_context”. Based on indice ice_days().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Freezing temperature. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

ice_days (DataArray) – Number of days with maximum daily temperature below {thresh} (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where the maximum daily temperature is below {thresh}.

Notes

Let \(TX_{ij}\) be the daily maximum temperature at day \(i\) of period \(j\), and :math`TT` the threshold. Then counted is the number of days where:

\[TX_{ij} < TT\]

xclim.indicators.atmos.jetstream_metric_woollings(ua: Union[DataArray, str] = 'ua', *, ds: Dataset = None) → Tuple[DataArray, DataArray]

Strength and latitude of jetstream (realm: atmos)

Identify latitude and strength of maximum smoothed zonal wind speed in the region from 15 to 75°N and -60 to 0°E, using the formula outlined in [Woollings et al., 2010]. Wind is smoothened using a Lanczos filter approach.

Based on indice jetstream_metric_woollings().

Parameters

• ua (str or DataArray) – Eastward wind component (u) at between 750 and 950 hPa. Default : ds.ua. [Required units : [speed]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

jetlat (DataArray) – Latitude of maximum smoothed zonal wind speed [degrees_North], with additional attributes: description: Daily latitude of maximum Lanczos smoothed zonal wind speed.jetstr : DataArray Maximum strength of smoothed zonal wind speed [m s-1], with additional attributes: description: Daily maximum strength of Lanczos smoothed zonal wind speed.

References

Woollings, Hannachi, and Hoskins [2010]

xclim.indicators.atmos.keetch_byram_drought_index(pr: Union[DataArray, str] = 'pr', tasmax: Union[DataArray, str] = 'tasmax', pr_annual: Union[DataArray, str] = 'pr_annual', kbdi0: Optional[Union[DataArray, str]] = None, *, ds: Dataset = None) → DataArray

Keetch-Byram drought index (KBDI) for soil moisture deficit. (realm: atmos)

The KBDI indicates the amount of water necessary to bring the soil moisture content back to field capacity. It is often used in the calculation of the McArthur Forest Fire Danger Index. The method implemented here follows Finkele et al. [2006] but limits the maximum KBDI to 203.2 mm, rather than 200 mm, in order to align best with the majority of the literature.

This indicator will check for missing values according to the method “skip”. Based on indice keetch_byram_drought_index().

Parameters

• pr (str or DataArray) – Total rainfall over previous 24 hours [mm/day]. Default : ds.pr. [Required units : [precipitation]]

• tasmax (str or DataArray) – Maximum temperature near the surface over previous 24 hours [degC]. Default : ds.tasmax. [Required units : [temperature]]

• pr_annual (str or DataArray) – Mean (over years) annual accumulated rainfall [mm/year]. Default : ds.pr_annual. [Required units : [precipitation]]

• kbdi0 (str or DataArray, optional) – Previous KBDI values used to initialise the KBDI calculation [mm/day]. Defaults to 0. [Required units : [precipitation]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

kbdi (DataArray) – Keetch-Byran Drought Index (keetch_byram_drought_index) [mm/day], with additional attributes: description: Amount of water necessary to bring the soil moisture content back to field capacity

Notes

This method implements the method described in Finkele et al. [2006] (section 2.1.1) for calculating the KBDI with one small difference: in Finkele et al. [2006] the maximum KBDI is limited to 200 mm to represent the maximum field capacity of the soil (8 inches according to Keetch and Byram [1968]). However, it is more common in the literature to limit the KBDI to 203.2 mm which is a more accurate conversion from inches to mm. In this function, the KBDI is limited to 203.2 mm.

References

Dolling, Chu, and Fujioka [2005], Finkele, Mills, Beard, and Jones [2006], Holgate, Van DIjk, Cary, and Yebra [2017], Keetch and Byram [1968]

xclim.indicators.atmos.last_snowfall(prsn: Union[DataArray, str] = 'prsn', *, thresh: Quantified = '0.5 mm/day', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Last day where solid precipitation flux exceeded a given threshold (realm: atmos)

The last day where the solid precipitation flux exceeded a given threshold during a time period.

This indicator will check for missing values according to the method “from_context”. Based on indice last_snowfall().

Parameters

• prsn (str or DataArray) – Solid precipitation flux. Default : ds.prsn. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold precipitation flux on which to base evaluation. Default : 0.5 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

last_snowfall (DataArray) – Date of last day where the solid precipitation flux exceeded {thresh} (day_of_year), with additional attributes: description: {freq} last day where the solid precipitation flux exceeded {thresh}.

References

CBCL [2020].

xclim.indicators.atmos.last_spring_frost(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', before_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Last spring frost (realm: atmos)

The last day when temperature is below a given threshold for a certain number of days, limited by a final calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice last_spring_frost().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• before_date (date (string, MM-DD)) – Date of the year before which to look for the final frost event. Should have the format ‘%m-%d’. Default : 07-01.

• window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

last_spring_frost (DataArray) – Last day of minimum daily temperature below a threshold of {thresh} for at least {window} days before a given date ({before_date}) (day_of_year), with additional attributes: description: Day of year of last spring frost, defined as the last day a minimum temperature remains below a threshold of {thresh} for at least {window} days before a given date ({before_date}).

xclim.indicators.atmos.latitude_temperature_index(tas: Union[DataArray, str] = 'tas', lat: Union[DataArray, str] = 'lat', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Latitude temperature index (realm: atmos)

A climate indice based on mean temperature of the warmest month and a latitude-based coefficient to account for longer day-length favouring growing conditions. Developed specifically for viticulture. Mean temperature of warmest month multiplied by the difference of latitude factor coefficient minus latitude. Metric originally published in Jackson, D. I., & Cherry, N. J. (1988).

This indicator will check for missing values according to the method “from_context”. Based on indice latitude_temperature_index(). With injected parameters: lat_factor=60.

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• lat (str or DataArray) – Latitude coordinate. If None, a CF-conformant “latitude” field must be available within the passed DataArray. Default : ds.lat. [Required units : []]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

lti (DataArray) – Mean temperature of warmest month multiplied by the difference of {lat_factor} minus latitude, with additional attributes: description: A climate indice based on mean temperature of the warmest month and a latitude-based coefficient to account for longer day-length favouring growing conditions. Developed specifically for viticulture. Mean temperature of warmest month multiplied by the difference of {lat_factor} minus latitude.

Notes

The latitude factor of 75 is provided for examining the poleward expansion of wine-growing climates under scenarios of climate change (modified from Kenny and Shao [1992]). For comparing 20th century/observed historical records, the original scale factor of 60 is more appropriate.

Let \(Tn_{j}\) be the average temperature for a given month \(j\), \(lat_{f}\) be the latitude factor, and \(lat\) be the latitude of the area of interest. Then the Latitude-Temperature Index (\(LTI\)) is:

\[LTI = max(TN_{j}: j = 1..12)(lat_f - |lat|)\]

References

Jackson and Cherry [1988], Kenny and Shao [1992]

xclim.indicators.atmos.liquid_precip_accumulation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total accumulated liquid precipitation. (realm: atmos)

Total accumulated liquid precipitation. Precipitation is considered liquid when the average daily temperature is above 0°C.

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: phase=liquid.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean, maximum or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

liquidprcptot (DataArray) – Total accumulated precipitation when temperature is above {thresh} (lwe_thickness_of_liquid_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum over days; description: {freq} total {phase} precipitation, estimated as precipitation when temperature is above {thresh}.

Notes

Let \(PR_i\) be the mean daily precipitation of day \(i\), then for a period \(j\) starting at day \(a\) and finishing on day \(b\):

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

xclim.indicators.atmos.liquid_precip_ratio(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', freq: str = 'QS-DEC', ds: Dataset = None, **indexer) → DataArray

Fraction of liquid to total precipitation (realm: atmos)

The ratio of total liquid precipitation over the total precipitation. Liquid precipitation is approximated from total precipitation on days where temperature is above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice liquid_precip_ratio(). With injected parameters: prsn=None.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature under which precipitation is assumed to be solid. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : QS-DEC.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

liquid_precip_ratio (DataArray) – Fraction of liquid to total precipitation (temperature above {thresh}), with additional attributes: description: The {freq} ratio of rainfall to total precipitation. Rainfall is estimated as precipitation on days where temperature is above {thresh}.

Notes

Let \(PR_i\) be the mean daily precipitation of day \(i\), then for a period \(j\) starting at day \(a\) and finishing on day \(b\):

\[ \begin{align}\begin{aligned}PR_{ij} = \sum_{i=a}^{b} PR_i\\PRwet_{ij}\end{aligned}\end{align} \]

xclim.indicators.atmos.max_1day_precipitation_amount(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum 1-day total precipitation (realm: atmos)

Maximum total daily precipitation for a given period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_1day_precipitation_amount().

Parameters

• pr (str or DataArray) – Daily precipitation values. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

rx1day (DataArray) – Maximum 1-day total precipitation (lwe_thickness_of_precipitation_amount) [mm/day], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum 1-day total precipitation

Notes

Let \(PR_i\) be the mean daily precipitation of day i, then for a period j:

\[PRx_{ij} = max(PR_{ij})\]

xclim.indicators.atmos.max_daily_temperature_range(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum of daily temperature range (realm: atmos)

The maximum difference between the daily maximum and minimum temperatures.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range(). With injected parameters: op=max.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dtrmax (DataArray) – Maximum diurnal temperature range (air_temperature) [K], with additional attributes: cell_methods: time range within days time: max over days; description: {freq} maximum diurnal temperature range.

Notes

For a default calculation using op=’mean’ :

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then the mean diurnal temperature range in period \(j\) is:

\[DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}\]

xclim.indicators.atmos.max_n_day_precipitation_amount(pr: Union[DataArray, str] = 'pr', *, window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

maximum n-day total precipitation (realm: atmos)

Maximum of the moving sum of daily precipitation for a given period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_n_day_precipitation_amount().

Parameters

• pr (str or DataArray) – Daily precipitation values. Default : ds.pr. [Required units : [precipitation]]

• window (number) – Window size in days. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

rx{window}day (DataArray) – maximum {window}-day total precipitation amount (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum {window}-day total precipitation amount.

xclim.indicators.atmos.max_pr_intensity(pr: Union[DataArray, str] = 'pr', *, window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum precipitation intensity over time window (realm: atmos)

Maximum precipitation intensity over a given rolling time window.

This indicator will check for missing values according to the method “from_context”. Based on indice max_pr_intensity(). Keywords : IDF curves.

Parameters

• pr (str or DataArray) – Hourly precipitation values. Default : ds.pr. [Required units : [precipitation]]

• window (number) – Window size in hours. Default : 1.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

max_pr_intensity (DataArray) – Maximum precipitation intensity over rolling {window}h time window (precipitation) [mm h-1], with additional attributes: cell_methods: time: max; description: {freq} maximum precipitation intensity over rolling {window}h time window.

xclim.indicators.atmos.maximum_consecutive_dry_days(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum consecutive dry days (realm: atmos)

The longest number of consecutive days where daily precipitation below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_dry_days().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold precipitation on which to base evaluation. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cdd (DataArray) – Maximum consecutive days with daily precipitation below {thresh} (number_of_days_with_lwe_thickness_of_precipitation_amount_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} maximum number of consecutive days with daily precipitation below {thresh}.

Notes

Let \(\mathbf{p}=p_0, p_1, \ldots, p_n\) be a daily precipitation series and \(thresh\) the threshold under which a day is considered dry. Then let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([p_i < thresh] \neq [p_{i+1} < thresh]\), that is, the days where the precipitation crosses the threshold. Then the maximum number of consecutive dry days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [p_{s_j} < thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos.maximum_consecutive_frost_free_days(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum consecutive frost free days (realm: atmos)

Maximum number of consecutive frost-free days: where the daily minimum temperature is above or equal to 0°C

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_frost_free_days().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

consecutive_frost_free_days (DataArray) – Maximum number of consecutive days with minimum temperature at or above {thresh} (spell_length_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum number of consecutive days with minimum daily temperature at or above {thresh}.

Notes

Let \(\mathbf{t}=t_0, t_1, \ldots, t_n\) be a daily minimum temperature series and \(thresh\) the threshold above or equal to which a day is considered a frost free day. Let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([t_i <= thresh] \neq [t_{i+1} <= thresh]\), that is, the days where the temperature crosses the threshold. Then the maximum number of consecutive frost free days is given by:

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} >= thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos.maximum_consecutive_warm_days(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '25 degC', freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum consecutive warm days (realm: atmos)

Maximum number of consecutive days where the maximum daily temperature exceeds a certain threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_tx_days().

Parameters

• tasmax (str or DataArray) – Max daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature. Default : 25 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

maximum_consecutive_warm_days (DataArray) – Maximum number of consecutive days with maximum daily temperature above {thresh} (spell_length_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: maximum over days; description: {freq} longest spell of consecutive days with maximum daily temperature above {thresh}.

Notes

Let \(\mathbf{t}=t_0, t_1, \ldots, t_n\) be a daily maximum temperature series and \(thresh\) the threshold above which a day is considered a summer day. Let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([t_i < thresh] \neq [t_{i+1} < thresh]\), that is, the days where the temperature crosses the threshold. Then the maximum number of consecutive tx_days (summer days) is given by:

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} > thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos.maximum_consecutive_wet_days(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Maximum consecutive wet days (realm: atmos)

The longest number of consecutive days where daily precipitation is at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_wet_days().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold precipitation on which to base evaluation. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cwd (DataArray) – Maximum consecutive days with daily precipitation at or above {thresh} (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} maximum number of consecutive days with daily precipitation at or above {thresh}.

Notes

Let \(\mathbf{x}=x_0, x_1, \ldots, x_n\) be a daily precipitation series and \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([p_i > thresh] \neq [p_{i+1} > thresh]\), that is, the days where the precipitation crosses the wet day threshold. Then the maximum number of consecutive wet days is given by:

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [x_{s_j} > 0^\circ C]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos.mcarthur_forest_fire_danger_index(drought_factor: Union[DataArray, str] = 'drought_factor', tasmax: Union[DataArray, str] = 'tasmax', hurs: Union[DataArray, str] = 'hurs', sfcWind: Union[DataArray, str] = 'sfcWind', *, ds: Dataset = None) → DataArray

McArthur forest fire danger index (FFDI) Mark 5. (realm: atmos)

The FFDI is a numeric indicator of the potential danger of a forest fire.

This indicator will check for missing values according to the method “skip”. Based on indice mcarthur_forest_fire_danger_index().

Parameters

• drought_factor (str or DataArray) – The drought factor, often the daily Griffiths drought factor (see griffiths_drought_factor()). Default : ds.drought_factor. [Required units : []]

• tasmax (str or DataArray) – The daily maximum temperature near the surface, or similar. Different applications have used different inputs here, including the previous/current day’s maximum daily temperature at a height of 2m, and the daily mean temperature at a height of 2m. Default : ds.tasmax. [Required units : [temperature]]

• hurs (str or DataArray) – The relative humidity near the surface and near the time of the maximum daily temperature, or similar. Different applications have used different inputs here, including the mid-afternoon relative humidity at a height of 2m, and the daily mean relative humidity at a height of 2m. Default : ds.hurs. [Required units : []]

• sfcWind (str or DataArray) – The wind speed near the surface and near the time of the maximum daily temperature, or similar. Different applications have used different inputs here, including the mid-afternoon wind speed at a height of 10m, and the daily mean wind speed at a height of 10m. Default : ds.sfcWind. [Required units : [speed]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ffdi (DataArray) – McArthur Forest Fire Danger Index (mcarthur_forest_fire_danger_index), with additional attributes: description: Numeric rating of the potential danger of a forest fire

References

Dowdy [2018], Holgate, Van DIjk, Cary, and Yebra [2017], Noble, Gill, and Bary [1980]

xclim.indicators.atmos.mean_radiant_temperature(rsds: Union[DataArray, str] = 'rsds', rsus: Union[DataArray, str] = 'rsus', rlds: Union[DataArray, str] = 'rlds', rlus: Union[DataArray, str] = 'rlus', *, stat: str = 'average', ds: Dataset = None) → DataArray

Mean radiant temperature (realm: atmos)

The average temperature of solar and thermal radiation incident on the body’s exterior.

Based on indice mean_radiant_temperature().

Parameters

• rsds (str or DataArray) – Surface Downwelling Shortwave Radiation Default : ds.rsds. [Required units : [radiation]]

• rsus (str or DataArray) – Surface Upwelling Shortwave Radiation Default : ds.rsus. [Required units : [radiation]]

• rlds (str or DataArray) – Surface Downwelling Longwave Radiation Default : ds.rlds. [Required units : [radiation]]

• rlus (str or DataArray) – Surface Upwelling Longwave Radiation Default : ds.rlus. [Required units : [radiation]]

• stat ({‘average’, ‘sunlit’, ‘instant’}) – Which statistic to apply. If “average”, the average of the cosine of the solar zenith angle is calculated. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. If “sunlit”, the cosine of the solar zenith angle is calculated during the sunlit period of each interval. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. This is necessary if mrt is not None. Default : average.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

mrt (DataArray) – Mean radiant temperature [K], with additional attributes: description: The incidence of radiation on the body from all directions.

Notes

This code was inspired by the thermofeel package [Brimicombe et al., 2021].

References

Di Napoli, Hogan, and Pappenberger [2020]

xclim.indicators.atmos.potential_evapotranspiration(tasmin: Optional[Union[DataArray, str]] = None, tasmax: Optional[Union[DataArray, str]] = None, tas: Optional[Union[DataArray, str]] = None, lat: Optional[Union[DataArray, str]] = None, hurs: Optional[Union[DataArray, str]] = None, rsds: Optional[Union[DataArray, str]] = None, rsus: Optional[Union[DataArray, str]] = None, rlds: Optional[Union[DataArray, str]] = None, rlus: Optional[Union[DataArray, str]] = None, sfcWind: Optional[Union[DataArray, str]] = None, *, method: str = 'BR65', peta: float = 0.00516409319477, petb: float = 0.0874972822289, ds: Dataset = None) → DataArray

Potential evapotranspiration (realm: atmos)

The potential for water evaporation from soil and transpiration by plants if the water supply is sufficient, calculated with a given method.

Based on indice potential_evapotranspiration().

Parameters

• tasmin (str or DataArray, optional) – Minimum daily temperature. [Required units : [temperature]]

• tasmax (str or DataArray, optional) – Maximum daily temperature. [Required units : [temperature]]

• tas (str or DataArray, optional) – Mean daily temperature. [Required units : [temperature]]

• lat (str or DataArray, optional) – Latitude. If not given, it is sought on tasmin or tas using cf-xarray accessors. [Required units : []]

• hurs (str or DataArray, optional) – Relative humidity. [Required units : []]

• rsds (str or DataArray, optional) – Surface Downwelling Shortwave Radiation [Required units : [radiation]]

• rsus (str or DataArray, optional) – Surface Upwelling Shortwave Radiation [Required units : [radiation]]

• rlds (str or DataArray, optional) – Surface Downwelling Longwave Radiation [Required units : [radiation]]

• rlus (str or DataArray, optional) – Surface Upwelling Longwave Radiation [Required units : [radiation]]

• sfcWind (str or DataArray, optional) – Surface wind velocity (at 10 m) [Required units : [speed]]

• method ({‘baierrobertson65’, ‘TW48’, ‘FAO_PM98’, ‘hargreaves85’, ‘HG85’, ‘mcguinnessbordne05’, ‘MB05’, ‘thornthwaite48’, ‘allen98’, ‘BR65’}) – Which method to use, see notes. Default : BR65.

• peta (number) – Used only with method MB05 as \(a\) for calculation of PET, see Notes section. Default value resulted from calibration of PET over the UK. Default : 0.00516409319477.

• petb (number) – Used only with method MB05 as \(b\) for calculation of PET, see Notes section. Default value resulted from calibration of PET over the UK. Default : 0.0874972822289.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

evspsblpot (DataArray) – Potential evapotranspiration (“{method}” method) (water_potential_evapotranspiration_flux) [kg m-2 s-1], with additional attributes: description: The potential for water evaporation from soil and transpiration by plants if the water supply is sufficient, calculated with the {method} method.

Notes

Available methods are:

• “baierrobertson65” or “BR65”, based on Baier and Robertson [1965]. Requires tasmin and tasmax, daily [D] freq.

• “hargreaves85” or “HG85”, based on George H. Hargreaves and Zohrab A. Samani [1985]. Requires tasmin and tasmax, daily [D] freq. (optional: tas can be given in addition of tasmin and tasmax).

• “mcguinnessbordne05” or “MB05”, based on Tanguy et al. [2018]. Requires tas, daily [D] freq, with latitudes ‘lat’.

• “thornthwaite48” or “TW48”, based on Thornthwaite [1948]. Requires tasmin and tasmax, monthly [MS] or daily [D] freq. (optional: tas can be given instead of tasmin and tasmax).

• “allen98” or “FAO_PM98”, based on Allen et al. [1998]. Modification of Penman-Monteith method. Requires tasmin and tasmax, relative humidity, radiation flux and wind speed (10 m wind will be converted to 2 m).

The McGuinness-Bordne [McGuinness and Borone, 1972] equation is:

\[PET[mm day^{-1}] = a * \frac{S_0}{\lambda}T_a + b *\frsc{S_0}{\lambda}\]

where \(a\) and \(b\) are empirical parameters; \(S_0\) is the extraterrestrial radiation [MJ m-2 day-1], assuming a solar constant of 1367 W m-2; \(\\lambda\) is the latent heat of vaporisation [MJ kg-1] and \(T_a\) is the air temperature [°C]. The equation was originally derived for the USA, with \(a=0.0147\) and \(b=0.07353\). The default parameters used here are calibrated for the UK, using the method described in Tanguy et al. [2018].

Methods “BR65”, “HG85” and “MB05” use an approximation of the extraterrestrial radiation. See extraterrestrial_solar_radiation().

References

Allen, Pereira, Raes, and Smith [1998], Baier and Robertson [1965], McGuinness and Borone [1972], Tanguy, Prudhomme, Smith, and Hannaford [2018], Thornthwaite [1948], George H. Hargreaves and Zohrab A. Samani [1985]

xclim.indicators.atmos.precip_accumulation(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total accumulated precipitation (solid and liquid) (realm: atmos)

Total accumulated precipitation. If the average daily temperature is given, the phase parameter can be used to restrict the calculation to precipitation of only one phase (liquid or solid). Precipitation is considered solid if the average daily temperature is below 0°C (and vice versa).

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: tas=None, phase=None.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

prcptot (DataArray) – Total accumulated precipitation (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum over days; description: {freq} total precipitation.

Notes

Let \(PR_i\) be the mean daily precipitation of day \(i\), then for a period \(j\) starting at day \(a\) and finishing on day \(b\):

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

xclim.indicators.atmos.rain_approximation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', method: str = 'binary', ds: Dataset = None) → DataArray

Rainfall approximation (realm: atmos)

Liquid precipitation estimated from total precipitation and temperature with a given method and temperature threshold.

Based on indice rain_approximation().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean, maximum, or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Freezing point temperature. Non-scalar values are not allowed with method ‘brown’. Default : 0 degC. [Required units : [temperature]]

• method ({‘binary’, ‘brown’, ‘auer’}) – Which method to use when approximating snowfall from total precipitation. See notes. Default : binary.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

prlp (DataArray) – Liquid precipitation (“{method}” method with temperature at or above {thresh}) (precipitation_flux) [kg m-2 s-1], with additional attributes: description: Liquid precipitation estimated from total precipitation and temperature with method {method} and threshold temperature {thresh}.

Notes

This method computes the snowfall approximation and subtracts it from the total precipitation to estimate the liquid rain precipitation.

xclim.indicators.atmos.rain_on_frozen_ground_days(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '1 mm/d', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of rain on frozen ground days (realm: atmos)

The number of days with rain above a given threshold after a series of seven days with average daily temperature below 0°C. Precipitation is assumed to be rain when the daily average temperature is above 0°C.

This indicator will check for missing values according to the method “from_context”. Based on indice rain_on_frozen_ground_days().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Precipitation threshold to consider a day as a rain event. Default : 1 mm/d. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

rain_frzgr (DataArray) – Number of rain on frozen ground days (mean daily temperature > 0℃ and precipitation > {thresh}) (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days], with additional attributes: description: {freq} number of days with rain above {thresh} after a series of seven days with average daily temperature below 0℃. Precipitation is assumed to be rain when the daily average temperature is above 0℃.

Notes

Let \(PR_i\) be the mean daily precipitation and \(TG_i\) be the mean daily temperature of day \(i\). Then for a period \(j\), rain on frozen grounds days are counted where:

\[PR_{i} > Threshold [mm]\]

and where

\[TG_{i} ≤ 0℃\]

is true for continuous periods where \(i ≥ 7\)

xclim.indicators.atmos.relative_humidity(tas: Union[DataArray, str] = 'tas', huss: Union[DataArray, str] = 'huss', ps: Union[DataArray, str] = 'ps', *, ice_thresh: Quantified | None = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Relative humidity from temperature, specific humidity, and pressure (realm: atmos)

Calculation of relative humidity from temperature, specific humidity, and pressure using the saturation vapour pressure.

Based on indice relative_humidity(). With injected parameters: tdps=None, invalid_values=mask.

Parameters

• tas (str or DataArray) – Temperature array Default : ds.tas. [Required units : [temperature]]

• huss (str or DataArray) – Specific humidity. Must be given if tdps is not given. Default : ds.huss. [Required units : []]

• ps (str or DataArray) – Air Pressure. Must be given if tdps is not given. Default : ds.ps. [Required units : [pressure]]

• ice_thresh (quantity (string or DataArray, with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Does nothing if ‘method’ is “bohren98”. Default : None. [Required units : [temperature]]

• method ({‘goffgratch46’, ‘sonntag90’, ‘tetens30’, ‘wmo08’, ‘bohren98’}) – Which method to use, see notes of this function and of saturation_vapor_pressure(). Default : sonntag90.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hurs (DataArray) – Relative Humidity (“{method}” method) (relative_humidity) [%], with additional attributes: description: <Dynamically generated string>

Notes

In the following, let \(T\), \(T_d\), \(q\) and \(p\) be the temperature, the dew point temperature, the specific humidity and the air pressure.

For the “bohren98” method : This method does not use the saturation vapour pressure directly, but rather uses an approximation of the ratio of \(\frac{e_{sat}(T_d)}{e_{sat}(T)}\). With \(L\) the enthalpy of vaporization of water and \(R_w\) the gas constant for water vapour, the relative humidity is computed as:

\[RH = e^{\frac{-L (T - T_d)}{R_wTT_d}}\]

From Bohren and Albrecht [1998], formula taken from Lawrence [2005]. \(L = 2.5\times 10^{-6}\) J kg-1, exact for \(T = 273.15\) K, is used.

Other methods: With \(w\), \(w_{sat}\), \(e_{sat}\) the mixing ratio, the saturation mixing ratio and the saturation vapour pressure. If the dewpoint temperature is given, relative humidity is computed as:

\[RH = 100\frac{e_{sat}(T_d)}{e_{sat}(T)}\]

Otherwise, the specific humidity and the air pressure must be given so relative humidity can be computed as:

\[RH = 100\frac{w}{w_{sat}} w = \frac{q}{1-q} w_{sat} = 0.622\frac{e_{sat}}{P - e_{sat}}\]

The methods differ by how \(e_{sat}\) is computed. See the doc of xclim.core.utils.saturation_vapor_pressure().

References

Bohren and Albrecht [1998], Lawrence [2005]

xclim.indicators.atmos.relative_humidity_from_dewpoint(tas: Union[DataArray, str] = 'tas', tdps: Union[DataArray, str] = 'tdps', *, ice_thresh: Quantified | None = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Relative humidity from temperature and dewpoint temperature (realm: atmos)

Calculation of relative humidity from temperature and dew point using the saturation vapour pressure.

Based on indice relative_humidity(). With injected parameters: huss=None, ps=None, invalid_values=mask.

Parameters

• tas (str or DataArray) – Temperature array Default : ds.tas. [Required units : [temperature]]

• tdps (str or DataArray) – Dewpoint temperature, if specified, overrides huss and ps. Default : ds.tdps. [Required units : [temperature]]

• ice_thresh (quantity (string or DataArray, with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Does nothing if ‘method’ is “bohren98”. Default : None. [Required units : [temperature]]

• method ({‘goffgratch46’, ‘sonntag90’, ‘tetens30’, ‘wmo08’, ‘bohren98’}) – Which method to use, see notes of this function and of saturation_vapor_pressure(). Default : sonntag90.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hurs (DataArray) – Relative humidity (“{method}” method) (relative_humidity) [%], with additional attributes: description: <Dynamically generated string>

Notes

In the following, let \(T\), \(T_d\), \(q\) and \(p\) be the temperature, the dew point temperature, the specific humidity and the air pressure.

For the “bohren98” method : This method does not use the saturation vapour pressure directly, but rather uses an approximation of the ratio of \(\frac{e_{sat}(T_d)}{e_{sat}(T)}\). With \(L\) the enthalpy of vaporization of water and \(R_w\) the gas constant for water vapour, the relative humidity is computed as:

\[RH = e^{\frac{-L (T - T_d)}{R_wTT_d}}\]

From Bohren and Albrecht [1998], formula taken from Lawrence [2005]. \(L = 2.5\times 10^{-6}\) J kg-1, exact for \(T = 273.15\) K, is used.

Other methods: With \(w\), \(w_{sat}\), \(e_{sat}\) the mixing ratio, the saturation mixing ratio and the saturation vapour pressure. If the dewpoint temperature is given, relative humidity is computed as:

\[RH = 100\frac{e_{sat}(T_d)}{e_{sat}(T)}\]

Otherwise, the specific humidity and the air pressure must be given so relative humidity can be computed as:

\[RH = 100\frac{w}{w_{sat}} w = \frac{q}{1-q} w_{sat} = 0.622\frac{e_{sat}}{P - e_{sat}}\]

The methods differ by how \(e_{sat}\) is computed. See the doc of xclim.core.utils.saturation_vapor_pressure().

References

Bohren and Albrecht [1998], Lawrence [2005]

xclim.indicators.atmos.rprctot(pr: Union[DataArray, str] = 'pr', prc: Union[DataArray, str] = 'prc', *, thresh: Quantified = '1.0 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Proportion of accumulated precipitation arising from convective processes (realm: atmos)

The proportion of total precipitation due to convective processes. Only days with surpassing a minimum precipitation flux are considered.

This indicator will check for missing values according to the method “from_context”. Based on indice rprctot().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• prc (str or DataArray) – Daily convective precipitation. Default : ds.prc. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1.0 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

rprctot (DataArray) – Proportion of accumulated precipitation arising from convective processeswith precipitation of at least {thresh}, with additional attributes: cell_methods: time: sum; description: {freq} proportion of accumulated precipitation arising from convective processes with precipitation of at least {thresh}.

xclim.indicators.atmos.saturation_vapor_pressure(tas: Union[DataArray, str] = 'tas', *, ice_thresh: Quantified | None = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Saturation vapour pressure (e_sat) (realm: atmos)

Calculation of the saturation vapour pressure from the temperature, according to a given method. If ice_thresh is given, the calculation is done with reference to ice for temperatures below this threshold.

Based on indice saturation_vapor_pressure().

Parameters

• tas (str or DataArray) – Temperature array. Default : ds.tas. [Required units : [temperature]]

• ice_thresh (quantity (string or DataArray, with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Default : None. [Required units : [temperature]]

• method ({‘goffgratch46’, ‘sonntag90’, ‘tetens30’, ‘its90’, ‘wmo08’}) – Which method to use, see notes. Default : sonntag90.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

e_sat (DataArray) – Saturation vapour pressure (“{method}” method) [Pa], with additional attributes: description: <Dynamically generated string>

Notes

In all cases implemented here \(log(e_{sat})\) is an empirically fitted function (usually a polynomial) where coefficients can be different when ice is taken as reference instead of water. Available methods are:

• “goffgratch46” or “GG46”, based on Goff and Gratch [1946], values and equation taken from Vömel [2016].

• “sonntag90” or “SO90”, taken from SONNTAG [1990].

• “tetens30” or “TE30”, based on Tetens [1930], values and equation taken from Vömel [2016].

• “wmo08” or “WMO08”, taken from World Meteorological Organization [2008].

• “its90” or “ITS90”, taken from Hardy [1998].

References

Goff and Gratch [1946], Hardy [1998], SONNTAG [1990], Tetens [1930], Vömel [2016], World Meteorological Organization [2008]

xclim.indicators.atmos.snowfall_approximation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', method: str = 'binary', ds: Dataset = None) → DataArray

Snowfall approximation (realm: atmos)

Solid precipitation estimated from total precipitation and temperature with a given method and temperature threshold.

Based on indice snowfall_approximation().

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean, maximum, or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Freezing point temperature. Non scalar values are not allowed with method “brown”. Default : 0 degC. [Required units : [temperature]]

• method ({‘binary’, ‘brown’, ‘auer’}) – Which method to use when approximating snowfall from total precipitation. See notes. Default : binary.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

prsn (DataArray) – Solid precipitation (“{method}” method with temperature at or below {thresh}) (solid_precipitation_flux) [kg m-2 s-1], with additional attributes: description: Solid precipitation estimated from total precipitation and temperature with method {method} and threshold temperature {thresh}.

Notes

The following methods are available to approximate snowfall and are drawn from the Canadian Land Surface Scheme [Melton, 2019, Verseghy, 2009].

• 'binary' : When the temperature is under the freezing threshold, precipitation is assumed to be solid. The method is agnostic to the type of temperature used (mean, maximum or minimum).

• 'brown' : The phase between the freezing threshold goes from solid to liquid linearly over a range of 2°C over the freezing point.

• 'auer' : The phase between the freezing threshold goes from solid to liquid as a degree six polynomial over a range of 6°C over the freezing point.

References

Melton [2019], Verseghy [2009]

xclim.indicators.atmos.solid_precip_accumulation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total accumulated solid precipitation. (realm: atmos)

Total accumulated solid precipitation. Precipitation is considered solid when the average daily temperature is at or below 0°C.

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: phase=solid.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean, maximum or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

solidprcptot (DataArray) – Total accumulated solid precipitation (lwe_thickness_of_snowfall_amount) [mm], with additional attributes: cell_methods: time: sum over days; description: {freq} total solid precipitation, estimated as precipitation when temperature at or below {thresh}.

Notes

Let \(PR_i\) be the mean daily precipitation of day \(i\), then for a period \(j\) starting at day \(a\) and finishing on day \(b\):

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

xclim.indicators.atmos.specific_humidity(tas: Union[DataArray, str] = 'tas', hurs: Union[DataArray, str] = 'hurs', ps: Union[DataArray, str] = 'ps', *, ice_thresh: Quantified | None = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Specific humidity from temperature, relative humidity, and pressure (realm: atmos)

Calculation of specific humidity from temperature, relative humidity, and pressure using the saturation vapour pressure.

Based on indice specific_humidity(). With injected parameters: invalid_values=mask.

Parameters

• tas (str or DataArray) – Temperature array Default : ds.tas. [Required units : [temperature]]

• hurs (str or DataArray) – Relative Humidity. Default : ds.hurs. [Required units : []]

• ps (str or DataArray) – Air Pressure. Default : ds.ps. [Required units : [pressure]]

• ice_thresh (quantity (string or DataArray, with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Default : None. [Required units : [temperature]]

• method ({‘tetens30’, ‘wmo08’, ‘goffgratch46’, ‘sonntag90’}) – Which method to use, see notes of this function and of saturation_vapor_pressure(). Default : sonntag90.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

huss (DataArray) – Specific Humidity (“{method}” method) (specific_humidity), with additional attributes: description: <Dynamically generated string>

Notes

In the following, let \(T\), \(hurs\) (in %) and \(p\) be the temperature, the relative humidity and the air pressure. With \(w\), \(w_{sat}\), \(e_{sat}\) the mixing ratio, the saturation mixing ratio and the saturation vapour pressure, specific humidity \(q\) is computed as:

\[w_{sat} = 0.622\frac{e_{sat}}{P - e_{sat}} w = w_{sat} * hurs / 100 q = w / (1 + w)\]

The methods differ by how \(e_{sat}\) is computed. See xclim.core.utils.saturation_vapor_pressure().

If invalid_values is not None, the saturation specific humidity \(q_{sat}\) is computed as:

\[q_{sat} = w_{sat} / (1 + w_{sat})\]

References

World Meteorological Organization [2008]

xclim.indicators.atmos.specific_humidity_from_dewpoint(tdps: Union[DataArray, str] = 'tdps', ps: Union[DataArray, str] = 'ps', *, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Specific humidity from dew point temperature and pressure (realm: atmos)

Calculation of the specific humidity from dew point temperature and pressure using the saturation vapour pressure.

Based on indice specific_humidity_from_dewpoint().

Parameters

• tdps (str or DataArray) – Dewpoint temperature array. Default : ds.tdps. [Required units : [temperature]]

• ps (str or DataArray) – Air pressure array. Default : ds.ps. [Required units : [pressure]]

• method ({‘tetens30’, ‘wmo08’, ‘goffgratch46’, ‘sonntag90’}) – Method to compute the saturation vapour pressure. Default : sonntag90.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

huss_fromdewpoint (DataArray) – Specific humidity (“{method}” method) (specific_humidity), with additional attributes: description: Computed from dewpoint temperature and pressure through the saturation vapor pressure, which was calculated according to the {method} method.

Notes

If \(e\) is the water vapour pressure, and \(p\) the total air pressure, then specific humidity is given by

\[q = m_w e / ( m_a (p - e) + m_w e )\]

where \(m_w\) and \(m_a\) are the molecular weights of water and dry air respectively. This formula is often written with \(ε = m_w / m_a\), which simplifies to \(q = ε e / (p - e (1 - ε))\).

References

World Meteorological Organization [2008]

xclim.indicators.atmos.standardized_precipitation_evapotranspiration_index(wb: Union[DataArray, str] = 'wb', *, wb_cal: Quantified, freq: str = 'MS', window: int = 1, dist: str = 'gamma', method: str = 'APP', ds: Dataset = None) → DataArray

Standardized Precipitation Evapotranspiration Index (SPEI) (realm: atmos)

Water budget (precipitation - evapotranspiration) over a moving window, normalized such that the SPEI averages to 0 for the calibration data. The window unit X is the minimal time period defined by the resampling frequency.

This indicator will check for missing values according to the method “from_context”. Based on indice standardized_precipitation_evapotranspiration_index().

Parameters

• wb (str or DataArray) – Daily water budget (pr - pet). Default : ds.wb. [Required units : [precipitation]]

• wb_cal (quantity (string or DataArray, with units)) – Daily water budget used for calibration. Default : ds.wb. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. A monthly or daily frequency is expected. Default : MS.

• window (number) – Averaging window length relative to the resampling frequency. For example, if freq=”MS”, i.e. a monthly resampling, the window is an integer number of months. Default : 1.

• dist ({‘fisk’, ‘gamma’}) – Name of the univariate distribution. (see scipy.stats). Default : gamma.

• method ({‘APP’, ‘ML’}) – Name of the fitting method, such as ML (maximum likelihood), APP (approximate). The approximate method uses a deterministic function that doesn’t involve any optimization. Available methods vary with the distribution: ‘gamma’:{‘APP’, ‘ML’}, ‘fisk’:{‘ML’} Default : APP.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

spei (DataArray) – Standardized precipitation evapotranspiration index (SPEI) (spei), with additional attributes: description: Water budget (precipitation minus evapotranspiration) over a moving {window}-X window, normalized such that SPEI averages to 0 for calibration data. The window unit X is the minimal time period defined by the resampling frequency {freq}.

Notes

See Standardized Precipitation Index (SPI) for more details on usage.

xclim.indicators.atmos.standardized_precipitation_index(pr: Union[DataArray, str] = 'pr', *, pr_cal: Quantified, freq: str = 'MS', window: int = 1, dist: str = 'gamma', method: str = 'APP', ds: Dataset = None) → DataArray

Standardized Precipitation Index (SPI) (realm: atmos)

Precipitation over a moving window, normalized such that SPI averages to 0 for the calibration data. The window unit X is the minimal time period defined by the resampling frequency.

This indicator will check for missing values according to the method “from_context”. Based on indice standardized_precipitation_index().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• pr_cal (quantity (string or DataArray, with units)) – Daily precipitation used for calibration. Usually this is a temporal subset of pr over some reference period. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. A monthly or daily frequency is expected. Default : MS.

• window (number) – Averaging window length relative to the resampling frequency. For example, if freq=”MS”, i.e. a monthly resampling, the window is an integer number of months. Default : 1.

• dist ({‘fisk’, ‘gamma’}) – Name of the univariate distribution. (see scipy.stats). Default : gamma.

• method ({‘APP’, ‘ML’}) – Name of the fitting method, such as ML (maximum likelihood), APP (approximate). The approximate method uses a deterministic function that doesn’t involve any optimization. Default : APP.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

spi (DataArray) – Standardized Precipitation Index (SPI) (spi), with additional attributes: description: Precipitations over a moving {window}-X window, normalized such that SPI averages to 0 for calibration data. The window unit X is the minimal time period defined by resampling frequency {freq}.

Notes

The length N of the N-month SPI is determined by choosing the window = N. Supported statistical distributions are: [“gamma”]

References

McKee, Doesken, and Kleist [1993]

xclim.indicators.atmos.tg(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, ds: Dataset = None) → DataArray

Mean temperature (realm: atmos)

The average daily temperature assuming a symmetrical temperature distribution (Tg = (Tx + Tn) / 2).

Based on indice tas().

Parameters

• tasmin (str or DataArray) – Minimum (daily) temperature Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum (daily) temperature Default : ds.tasmax. [Required units : [temperature]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tg (DataArray) – Daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean within days; description: Estimated mean temperature from maximum and minimum temperatures.

xclim.indicators.atmos.tg10p(tas: Union[DataArray, str] = 'tas', tas_per: Union[DataArray, str] = 'tas_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '<', ds: Dataset = None, **indexer) → DataArray

Days with mean temperature below the 10th percentile (realm: atmos)

Number of days with mean temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tg10p().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• tas_per (str or DataArray) – 10th percentile of daily mean temperature. Default : ds.tas_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg10p (DataArray) – Number of days with mean temperature below the 10th percentile (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with mean temperature below the 10th percentile. A {tas_per_window} day(s) window, centered on each calendar day in the {tas_per_period} period, is used to compute the 10th percentile.

Notes

The 10th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos.tg90p(tas: Union[DataArray, str] = 'tas', tas_per: Union[DataArray, str] = 'tas_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Days with mean temperature above the 90th percentile (realm: atmos)

Number of days with mean temperature above the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tg90p().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• tas_per (str or DataArray) – 90th percentile of daily mean temperature. Default : ds.tas_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg90p (DataArray) – Number of days with mean temperature above the 90th percentile (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with mean temperature above the 90th percentile. A {tas_per_window} day(s) window, centered on each calendar day in the {tas_per_period} period, is used to compute the 90th percentile.

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos.tg_days_above(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '10.0 degC', freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with mean temperature above a given threshold (realm: atmos)

The number of days with mean temperature above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_days_above().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 10.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_days_above (DataArray) – The number of days with mean temperature above {thresh} (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily mean temperature exceeds {thresh}.

Notes

Let \(TG_{ij}\) be the mean daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TG_{ij} > Threshold [℃]\]

xclim.indicators.atmos.tg_days_below(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '10.0 degC', freq: str = 'YS', op: str = '<', ds: Dataset = None, **indexer) → DataArray

Number of days with mean temperature below a given threshold (realm: atmos)

The number of days with mean temperature below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_days_below().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 10.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_days_below (DataArray) – The number of days with mean temperature below {thresh} (number_of_days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily mean temperature is below {thresh}.

Notes

Let \(TG_{ij}\) be the mean daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TG_{ij} < Threshold [℃]\]

xclim.indicators.atmos.tg_max(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum of mean temperature (realm: atmos)

Maximum of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_max().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_max (DataArray) – Maximum daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum of daily mean temperature.

Notes

Let \(TN_{ij}\) be the mean temperature at day \(i\) of period \(j\). Then the maximum daily mean temperature for period \(j\) is:

\[TNx_j = max(TN_{ij})\]

xclim.indicators.atmos.tg_mean(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean temperature (realm: atmos)

Mean of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_mean (DataArray) – Mean daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean over days; description: {freq} mean of daily mean temperature.

Notes

Let \(TN_i\) be the mean daily temperature of day \(i\), then for a period \(p\) starting at day \(a\) and finishing on day \(b\):

\[TG_p = \frac{\sum_{i=a}^{b} TN_i}{b - a + 1}\]

xclim.indicators.atmos.tg_min(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Minimum of mean temperature (realm: atmos)

Minimum of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_min().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_min (DataArray) – Minimum daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: minimum over days; description: {freq} minimum of daily mean temperature.

Notes

Let \(TG_{ij}\) be the mean temperature at day \(i\) of period \(j\). Then the minimum daily mean temperature for period \(j\) is:

\[TGn_j = min(TG_{ij})\]

xclim.indicators.atmos.thawing_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Thawing degree days (realm: atmos)

The cumulative degree days for days when the average temperature is above a given threshold, typically 0°C.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_degree_days().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

thawing_degree_days (DataArray) – Cumulative sum of temperature degrees for mean daily temperature above {thresh} (integral_of_air_temperature_excess_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} thawing degree days (mean temperature above {thresh}).

Notes

Let \(TG_{ij}\) be the mean daily temperature at day \(i\) of period \(j\). Then the growing degree days are:

\[GD4_j = \sum_{i=1}^I (TG_{ij}-{4} | TG_{ij} > {4}℃)\]

xclim.indicators.atmos.tn10p(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '<', ds: Dataset = None, **indexer) → DataArray

Days with minimum temperature below the 10th percentile (realm: atmos)

Number of days with minimum temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tn10p().

Parameters

• tasmin (str or DataArray) – Mean daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmin_per (str or DataArray) – 10th percentile of daily minimum temperature. Default : ds.tasmin_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn10p (DataArray) – Number of days with minimum temperature below the 10th percentile (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with minimum temperature below the 10th percentile. A {tasmin_per_window} day(s) window, centered on each calendar day in the {tasmin_per_period} period, is used to compute the 10th percentile.

Notes

The 10th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos.tn90p(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Days with minimum temperature above the 90th percentile (realm: atmos)

Number of days with minimum temperature above the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tn90p().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmin_per (str or DataArray) – 90th percentile of daily minimum temperature. Default : ds.tasmin_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn90p (DataArray) – Number of days with minimum temperature above the 90th percentile (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with minimum temperature above the 90th percentile. A {tasmin_per_window} day(s) window, centered on each calendar day in the {tasmin_per_period} period, is used to compute the 90th percentile.

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos.tn_days_above(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '20.0 degC', freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with minimum temperature above a given threshold (realm: atmos)

The number of days with minimum temperature above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_above().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 20.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_days_above (DataArray) – The number of days with minimum temperature above {thresh} (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily minimum temperature exceeds {thresh}.

Notes

Let \(TN_{ij}\) be the minimum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TN_{ij} > Threshold [℃]\]

xclim.indicators.atmos.tn_days_below(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '-10.0 degC', freq: str = 'YS', op: str = '<', ds: Dataset = None, **indexer) → DataArray

Number of days with minimum temperature below a given threshold (realm: atmos)

The number of days with minimum temperature below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_below().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : -10.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_days_below (DataArray) – The number of days with minimum temperature below {thresh} (number_of_days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily minimum temperature is below {thresh}.

Notes

Let \(TN_{ij}\) be the minimum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TN_{ij} < Threshold [℃]\]

xclim.indicators.atmos.tn_max(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum of minimum temperature (realm: atmos)

Maximum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_max().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_max (DataArray) – Maximum daily minimum temperature (air_temperature) [K], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum of daily minimum temperature.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then the maximum daily minimum temperature for period \(j\) is:

\[TNx_j = max(TN_{ij})\]

xclim.indicators.atmos.tn_mean(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of minimum temperature (realm: atmos)

Mean of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_mean().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_mean (DataArray) – Mean daily minimum temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean over days; description: {freq} mean of daily minimum temperature.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then mean values in period \(j\) are given by:

\[TN_{ij} = \frac{ \sum_{i=1}^{I} TN_{ij} }{I}\]

xclim.indicators.atmos.tn_min(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Minimum temperature (realm: atmos)

Minimum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_min().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_min (DataArray) – Minimum daily minimum temperature (air_temperature) [K], with additional attributes: cell_methods: time: minimum over days; description: {freq} minimum of daily minimum temperature.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then the minimum daily minimum temperature for period \(j\) is:

\[TNn_j = min(TN_{ij})\]

xclim.indicators.atmos.tropical_nights(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: Quantified = '20.0 degC', freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Tropical nights (realm: atmos)

Number of days where minimum temperature is above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_above().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 20.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tropical_nights (DataArray) – Number of days with minimum daily temperature above {thresh} (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of Tropical Nights, defined as days with minimum daily temperature above {thresh}.

Notes

Let \(TN_{ij}\) be the minimum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TN_{ij} > Threshold [℃]\]

xclim.indicators.atmos.tx10p(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '<', ds: Dataset = None, **indexer) → DataArray

Days with maximum temperature below the 10th percentile (realm: atmos)

Number of days with maximum temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tx10p().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• tasmax_per (str or DataArray) – 10th percentile of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx10p (DataArray) – Number of days with maximum temperature below the 10th percentile (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with maximum temperature below the 10th percentile. A {tasmax_per_window} day(s) window, centered on each calendar day in the {tasmax_per_period} period, is used to compute the 10th percentile.

Notes

The 10th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos.tx90p(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Days with maximum temperature above the 90th percentile (realm: atmos)

Number of days with maximum temperature above the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tx90p().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• tasmax_per (str or DataArray) – 90th percentile of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx90p (DataArray) – Number of days with maximum temperature above the 90th percentile (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with maximum temperature above the 90th percentile. A {tasmax_per_window} day(s) window, centered on each calendar day in the {tasmax_per_period} period, is used to compute the 90th percentile.

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos.tx_days_above(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '25.0 degC', freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with maximum temperature above a given threshold (realm: atmos)

The number of days with maximum temperature above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_days_above().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 25.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_days_above (DataArray) – The number of days with maximum temperature above {thresh} (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily maximum temperature exceeds {thresh}.

Notes

Let \(TX_{ij}\) be the maximum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TX_{ij} > Threshold [℃]\]

xclim.indicators.atmos.tx_days_below(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: Quantified = '25.0 degC', freq: str = 'YS', op: str = '<', ds: Dataset = None, **indexer) → DataArray

Number of days with maximum temperature below a given threshold (realm: atmos)

The number of days with maximum temperature below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_days_below().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh (quantity (string or DataArray, with units)) – Threshold temperature on which to base evaluation. Default : 25.0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘lt’, ‘le’, ‘<’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_days_below (DataArray) – The number of days with maximum temperature below {thresh} (number_of_days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily max temperature is below {thresh}.

Notes

Let \(TX_{ij}\) be the maximum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TX_{ij} < Threshold [℃]\]

xclim.indicators.atmos.tx_max(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum temperature (realm: atmos)

Maximum of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_max().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_max (DataArray) – Maximum daily maximum temperature (air_temperature) [K], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum of daily maximum temperature.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then the maximum daily maximum temperature for period \(j\) is:

\[TXx_j = max(TX_{ij})\]

xclim.indicators.atmos.tx_mean(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of maximum temperature (realm: atmos)

Mean of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_mean().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_mean (DataArray) – Mean daily maximum temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean over days; description: {freq} mean of daily maximum temperature.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then mean values in period \(j\) are given by:

\[TX_{ij} = \frac{ \sum_{i=1}^{I} TX_{ij} }{I}\]

xclim.indicators.atmos.tx_min(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Minimum of maximum temperature (realm: atmos)

Minimum of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_min().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_min (DataArray) – Minimum daily maximum temperature (air_temperature) [K], with additional attributes: cell_methods: time: minimum over days; description: {freq} minimum of daily maximum temperature.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then the minimum daily maximum temperature for period \(j\) is:

\[TXn_j = min(TX_{ij})\]

xclim.indicators.atmos.tx_tn_days_above(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: Quantified = '22 degC', thresh_tasmax: Quantified = '30 degC', freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with daily minimum and maximum temperatures exceeding thresholds (realm: atmos)

Number of days with daily maximum and minimum temperatures above given thresholds.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_tn_days_above().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• thresh_tasmin (quantity (string or DataArray, with units)) – Threshold temperature for tasmin on which to base evaluation. Default : 22 degC. [Required units : [temperature]]

• thresh_tasmax (quantity (string or DataArray, with units)) – Threshold temperature for tasmax on which to base evaluation. Default : 30 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_tn_days_above (DataArray) – Number of days with daily minimum above {thresh_tasmin} and daily maximum temperatures above {thresh_tasmax} (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: description: {freq} number of days where daily maximum temperature exceeds {thresh_tasmax} and minimum temperature exceeds {thresh_tasmin}.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\), \(TN_{ij}\) the daily minimum temperature at day \(i\) of period \(j\), \(TX_{thresh}\) the threshold for maximum daily temperature, and \(TN_{thresh}\) the threshold for minimum daily temperature. Then counted is the number of days where:

\[TX_{ij} > TX_{thresh} [℃]\]

and where:

\[TN_{ij} > TN_{thresh} [℃]\]

xclim.indicators.atmos.universal_thermal_climate_index(tas: Union[DataArray, str] = 'tas', hurs: Union[DataArray, str] = 'hurs', sfcWind: Union[DataArray, str] = 'sfcWind', mrt: Optional[Union[DataArray, str]] = None, rsds: Optional[Union[DataArray, str]] = None, rsus: Optional[Union[DataArray, str]] = None, rlds: Optional[Union[DataArray, str]] = None, rlus: Optional[Union[DataArray, str]] = None, *, stat: str = 'average', mask_invalid: bool = True, ds: Dataset = None) → DataArray

Universal Thermal Climate Index (UTCI) (realm: atmos)

UTCI is the equivalent temperature for the environment derived from a reference environment and is used to evaluate heat stress in outdoor spaces.

Based on indice universal_thermal_climate_index().

Parameters

• tas (str or DataArray) – Mean temperature Default : ds.tas. [Required units : [temperature]]

• hurs (str or DataArray) – Relative Humidity Default : ds.hurs. [Required units : []]

• sfcWind (str or DataArray) – Wind velocity Default : ds.sfcWind. [Required units : [speed]]

• mrt (str or DataArray, optional) – Mean radiant temperature [Required units : [temperature]]

• rsds (str or DataArray, optional) – Surface Downwelling Shortwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]

• rsus (str or DataArray, optional) – Surface Upwelling Shortwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]

• rlds (str or DataArray, optional) – Surface Downwelling Longwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]

• rlus (str or DataArray, optional) – Surface Upwelling Longwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]

• stat ({‘average’, ‘sunlit’, ‘instant’}) – Which statistic to apply. If “average”, the average of the cosine of the solar zenith angle is calculated. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. If “sunlit”, the cosine of the solar zenith angle is calculated during the sunlit period of each interval. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. This is necessary if mrt is not None. Default : average.

• mask_invalid (boolean) – If True (default), UTCI values are NaN where any of the inputs are outside their validity ranges : -50°C < tas < 50°C, -30°C < tas - mrt < 30°C and 0.5 m/s < sfcWind < 17.0 m/s. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

utci (DataArray) – Universal Thermal Climate Index (UTCI) [K], with additional attributes: description: UTCI is the equivalent temperature for the environment derived from a reference environment and is used to evaluate heat stress in outdoor spaces.

Notes

See: http://www.utci.org/utcineu/utcineu.php

References

Bröde [2009], Błażejczyk, Jendritzky, Bröde, Fiala, Havenith, Epstein, Psikuta, and Kampmann [2013]

xclim.indicators.atmos.warm_and_dry_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Warm and dry days (realm: atmos)

Number of days with temperature above a given percentile and precipitation below a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_and_dry_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – Third quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – First quartile of daily total precipitation computed by month. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

warm_and_dry_days (DataArray) – Number of days where temperature is above {tas_per_thresh}th percentile and precipitation is below {pr_per_thresh}th percentile [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is above {tas_per_thresh}th percentile and precipitation is below {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

Beniston [2009]

xclim.indicators.atmos.warm_and_wet_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Warm and wet days (realm: atmos)

Number of days with temperature above a given percentile and precipitation above a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_and_wet_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – Third quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – Third quartile of daily total precipitation computed by month. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

warm_and_wet_days (DataArray) – Number of days where temperature above {tas_per_thresh}th percentile and precipitation above {pr_per_thresh}th percentile [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is above {tas_per_thresh}th percentile and precipitation is above {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

Beniston [2009]

xclim.indicators.atmos.warm_spell_duration_index(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, window: int = 6, freq: str = 'YS', resample_before_rl: bool = True, bootstrap: bool = False, op: str = '>', ds: Dataset = None) → DataArray

Warm spell duration index (realm: atmos)

Number of days part of a percentile-defined warm spell. A warm spell occurs when the maximum daily temperature is above a given percentile for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_spell_duration_index().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• tasmax_per (str or DataArray) – percentile(s) of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]

• window (number) – Minimum number of days with temperature above threshold to qualify as a warm spell. Default : 6.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

warm_spell_duration_index (DataArray) – Number of days with at least {window} consecutive days where the maximum daily temperature is above the {tasmax_per_thresh}th percentile(s) (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with at least {window} consecutive days where the maximum daily temperature is above the {tasmax_per_thresh}th percentile(s). A {tasmax_per_window} day(s) window, centred on each calendar day in the {tasmax_per_period} period, is used to compute the {tasmax_per_thresh}th percentile(s).

References

From the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI; [Zhang et al., 2011]). Used in Alexander, Zhang, Peterson, Caesar, Gleason, Klein Tank, Haylock, Collins, Trewin, Rahimzadeh, Tagipour, Rupa Kumar, Revadekar, Griffiths, Vincent, Stephenson, Burn, Aguilar, Brunet, Taylor, New, Zhai, Rusticucci, and Vazquez-Aguirre [2006]

xclim.indicators.atmos.water_budget(pr: Union[DataArray, str] = 'pr', evspsblpot: Optional[Union[DataArray, str]] = None, tasmin: Optional[Union[DataArray, str]] = None, tasmax: Optional[Union[DataArray, str]] = None, tas: Optional[Union[DataArray, str]] = None, lat: Optional[Union[DataArray, str]] = None, hurs: Optional[Union[DataArray, str]] = None, rsds: Optional[Union[DataArray, str]] = None, rsus: Optional[Union[DataArray, str]] = None, rlds: Optional[Union[DataArray, str]] = None, rlus: Optional[Union[DataArray, str]] = None, sfcWind: Optional[Union[DataArray, str]] = None, *, ds: Dataset = None) → DataArray

Water budget (realm: atmos)

Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget.

Based on indice water_budget(). With injected parameters: method=dummy.

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• evspsblpot (str or DataArray, optional) – Potential evapotranspiration [Required units : [precipitation]]

• tasmin (str or DataArray, optional) – Minimum daily temperature. [Required units : [temperature]]

• tasmax (str or DataArray, optional) – Maximum daily temperature. [Required units : [temperature]]

• tas (str or DataArray, optional) – Mean daily temperature. [Required units : [temperature]]

• lat (str or DataArray, optional) – Latitude coordinate, needed if evspsblpot is not given. If None, a CF-conformant “latitude” field must be available within the pr DataArray. [Required units : []]

• hurs (str or DataArray, optional) – Relative humidity. [Required units : []]

• rsds (str or DataArray, optional) – Surface Downwelling Shortwave Radiation [Required units : [radiation]]

• rsus (str or DataArray, optional) – Surface Upwelling Shortwave Radiation [Required units : [radiation]]

• rlds (str or DataArray, optional) – Surface Downwelling Longwave Radiation [Required units : [radiation]]

• rlus (str or DataArray, optional) – Surface Upwelling Longwave Radiation [Required units : [radiation]]

• sfcWind (str or DataArray, optional) – Surface wind velocity (at 10 m) [Required units : [speed]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

water_budget (DataArray) – Water budget [kg m-2 s-1], with additional attributes: description: Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget.

xclim.indicators.atmos.water_budget_from_tas(pr: Union[DataArray, str] = 'pr', evspsblpot: Optional[Union[DataArray, str]] = None, tasmin: Optional[Union[DataArray, str]] = None, tasmax: Optional[Union[DataArray, str]] = None, tas: Optional[Union[DataArray, str]] = None, lat: Optional[Union[DataArray, str]] = None, hurs: Optional[Union[DataArray, str]] = None, rsds: Optional[Union[DataArray, str]] = None, rsus: Optional[Union[DataArray, str]] = None, rlds: Optional[Union[DataArray, str]] = None, rlus: Optional[Union[DataArray, str]] = None, sfcWind: Optional[Union[DataArray, str]] = None, *, method: str = 'BR65', ds: Dataset = None) → DataArray

Water budget (realm: atmos)

Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget, where the potential evapotranspiration is calculated with a given method.

Based on indice water_budget().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• evspsblpot (str or DataArray, optional) – Potential evapotranspiration [Required units : [precipitation]]

• tasmin (str or DataArray, optional) – Minimum daily temperature. [Required units : [temperature]]

• tasmax (str or DataArray, optional) – Maximum daily temperature. [Required units : [temperature]]

• tas (str or DataArray, optional) – Mean daily temperature. [Required units : [temperature]]

• lat (str or DataArray, optional) – Latitude coordinate, needed if evspsblpot is not given. If None, a CF-conformant “latitude” field must be available within the pr DataArray. [Required units : []]

• hurs (str or DataArray, optional) – Relative humidity. [Required units : []]

• rsds (str or DataArray, optional) – Surface Downwelling Shortwave Radiation [Required units : [radiation]]

• rsus (str or DataArray, optional) – Surface Upwelling Shortwave Radiation [Required units : [radiation]]

• rlds (str or DataArray, optional) – Surface Downwelling Longwave Radiation [Required units : [radiation]]

• rlus (str or DataArray, optional) – Surface Upwelling Longwave Radiation [Required units : [radiation]]

• sfcWind (str or DataArray, optional) – Surface wind velocity (at 10 m) [Required units : [speed]]

• method (str) – Method to use to calculate the potential evapotranspiration. Default : BR65.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

water_budget_from_tas (DataArray) – Water budget (“{method}” method) [kg m-2 s-1], with additional attributes: description: Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget, where the potential evapotranspiration is calculated with the {method} method.

xclim.indicators.atmos.wet_precip_accumulation(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total accumulated precipitation (solid and liquid) during wet days (realm: atmos)

Total accumulated precipitation on days with precipitation. A day is considered to have precipitation if the precipitation is greater than or equal to a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot().

Parameters

• pr (str or DataArray) – Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold over which precipitation starts being cumulated. Default : 1 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

wet_prcptot (DataArray) – Total accumulated precipitation over days where precipitation exceeds {thresh} (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum over days; description: {freq} total precipitation over wet days, defined as days where precipitation exceeds {thresh}.

xclim.indicators.atmos.wetdays(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1.0 mm/day', freq: str = 'YS', op: str = '>=', ds: Dataset = None, **indexer) → DataArray

Number of wet days (realm: atmos)

The number of days with daily precipitation at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1.0 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>=”. Default : >=.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

wetdays (DataArray) – Number of days with daily precipitation at or above {thresh} (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with daily precipitation at or above {thresh}.

xclim.indicators.atmos.wetdays_prop(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '1.0 mm/day', freq: str = 'YS', op: str = '>=', ds: Dataset = None, **indexer) → DataArray

Proportion of wet days (realm: atmos)

The proportion of days with daily precipitation at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays_prop().

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Precipitation value over which a day is considered wet. Default : 1.0 mm/day. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘gt’, ‘ge’, ‘>’, ‘>=’}) – Comparison operation. Default: “>=”. Default : >=.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

wetdays_prop (DataArray) – Proportion of days with precipitation at or above {thresh} [1], with additional attributes: cell_methods: time: sum over days; description: {freq} proportion of days with precipitation at or above {thresh}.

xclim.indicators.atmos.wind_chill_index(tas: Union[DataArray, str] = 'tas', sfcWind: Union[DataArray, str] = 'sfcWind', *, method: str = 'CAN', ds: Dataset = None) → DataArray

Wind chill (realm: atmos)

Wind chill factor is an index that equates to how cold an average person feels. It is calculated from the temperature and the wind speed at 10 m. As defined by Environment and Climate Change Canada, a second formula is used for light winds. The standard formula is otherwise the same as used in the United States.

Based on indice wind_chill_index(). With injected parameters: mask_invalid=True.

Parameters

• tas (str or DataArray) – Surface air temperature. Default : ds.tas. [Required units : [temperature]]

• sfcWind (str or DataArray) – Surface wind speed (10 m). Default : ds.sfcWind. [Required units : [speed]]

• method ({‘US’, ‘CAN’}) – If “CAN” (default), a “slow wind” equation is used where winds are slower than 5 km/h, see Notes. Default : CAN.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

wind_chill (DataArray) – Wind chill factor [degC], with additional attributes: description: <Dynamically generated string>

Notes

Following the calculations of Environment and Climate Change Canada, this function switches from the standardized index to another one for slow winds. The standard index is the same as used by the National Weather Service of the USA [US Department of Commerce, n.d.]. Given a temperature at surface \(T\) (in °C) and 10-m wind speed \(V\) (in km/h), the Wind Chill Index \(W\) (dimensionless) is computed as:

\[W = 13.12 + 0.6125*T - 11.37*V^0.16 + 0.3965*T*V^0.16\]

Under slow winds (\(V < 5\) km/h), and using the canadian method, it becomes:

\[W = T + \frac{-1.59 + 0.1345 * T}{5} * V\]

Both equations are invalid for temperature over 0°C in the canadian method.

The american Wind Chill Temperature index (WCT), as defined by USA’s National Weather Service, is computed when method=’US’. In that case, the maximal valid temperature is 50°F (10 °C) and minimal wind speed is 3 mph (4.8 km/h).

For more information, see:

• National Weather Service FAQ: [US Department of Commerce, n.d.].

• The New Wind Chill Equivalent Temperature Chart: [Osczevski and Bluestein, 2005].

References

Mekis, Vincent, Shephard, and Zhang [2015], US Department of Commerce [n.d.]

xclim.indicators.atmos.wind_speed_from_vector(uas: Union[DataArray, str] = 'uas', vas: Union[DataArray, str] = 'vas', *, calm_wind_thresh: Quantified = '0.5 m/s', ds: Dataset = None) → Tuple[DataArray, DataArray]

Wind speed and direction from vector (realm: atmos)

Calculation of the magnitude and direction of the wind speed from the two components west-east and south-north.

Based on indice uas_vas_2_sfcwind().

Parameters

• uas (str or DataArray) – Eastward wind velocity Default : ds.uas. [Required units : [speed]]

• vas (str or DataArray) – Northward wind velocity Default : ds.vas. [Required units : [speed]]

• calm_wind_thresh (quantity (string or DataArray, with units)) – The threshold under which winds are considered “calm” and for which the direction is set to 0. On the Beaufort scale, calm winds are defined as < 0.5 m/s. Default : 0.5 m/s. [Required units : [speed]]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

sfcWind (DataArray) – Near-surface wind speed (wind_speed) [m s-1], with additional attributes: description: Wind speed computed as the magnitude of the (uas, vas) vector.sfcWindfromdir : DataArray Near-surface wind from direction (wind_from_direction) [degree], with additional attributes: description: Wind direction computed as the angle of the (uas, vas) vector. A direction of 0° is attributed to winds with a speed under {calm_wind_thresh}.

Notes

Winds with a velocity less than calm_wind_thresh are given a wind direction of 0°, while stronger northerly winds are set to 360°.

xclim.indicators.atmos.wind_vector_from_speed(sfcWind: Union[DataArray, str] = 'sfcWind', sfcWindfromdir: Union[DataArray, str] = 'sfcWindfromdir', *, ds: Dataset = None) → Tuple[DataArray, DataArray]

Wind vector from speed and direction (realm: atmos)

Calculation of the two components (west-east and north-south) of the wind from the magnitude of its speed and direction of origin.

Based on indice sfcwind_2_uas_vas().

Parameters

• sfcWind (str or DataArray) – Wind velocity Default : ds.sfcWind. [Required units : [speed]]

• sfcWindfromdir (str or DataArray) – Direction from which the wind blows, following the meteorological convention where 360 stands for North. Default : ds.sfcWindfromdir. [Required units : []]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

uas (DataArray) – Near-surface eastward wind (eastward_wind) [m s-1], with additional attributes: description: Eastward wind speed computed from the magnitude of its speed and direction of origin.vas : DataArray Near-surface northward wind (northward_wind) [m s-1], with additional attributes: description: Northward wind speed computed from magnitude of its speed and direction of origin.

xclim.indicators.atmos.windy_days(sfcWind: Union[DataArray, str] = 'sfcWind', *, thresh: Quantified = '10.8 m s-1', freq: str = 'MS', ds: Dataset = None, **indexer) → DataArray

Windy days (realm: atmos)

Number of days with surface wind speed at or above threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice windy_days().

Parameters

• sfcWind (str or DataArray) – Daily average near-surface wind speed. Default : ds.sfcWind. [Required units : [speed]]

• thresh (quantity (string or DataArray, with units)) – Threshold average near-surface wind speed on which to base evaluation. Default : 10.8 m s-1. [Required units : [speed]]

• freq (offset alias (string)) – Resampling frequency. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

windy_days (DataArray) – Number of days with surface wind speed at or above {thresh} (number_of_days_with_sfcWind_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with surface wind speed at or above {thresh}.

Notes

Let \(WS_{ij}\) be the windspeed at day \(i\) of period \(j\). Then counted is the number of days where:

\[WS_{ij} >= Threshold [m s-1]\]

Land indicators

xclim.indicators.land.base_flow_index(q: Union[DataArray, str] = 'q', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Base flow index (realm: land)

Return the base flow index, defined as the minimum 7-day average flow divided by the mean flow.

This indicator will check for missing values according to the method “from_context”. Based on indice base_flow_index().

Parameters

• q (str or DataArray) – Rate of river discharge. Default : ds.q. [Required units : [discharge]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

base_flow_index (DataArray) – Base flow index, with additional attributes: description: Minimum of the 7-day moving average flow divided by the mean flow.

Notes

Let \(\mathbf{q}=q_0, q_1, \ldots, q_n\) be the sequence of daily discharge and \(\overline{\mathbf{q}}\) the mean flow over the period. The base flow index is given by:

\[\frac{\min(\mathrm{CMA}_7(\mathbf{q}))}{\overline{\mathbf{q}}}\]

where \(\mathrm{CMA}_7\) is the seven days moving average of the daily flow:

\[\mathrm{CMA}_7(q_i) = \frac{\sum_{j=i-3}^{i+3} q_j}{7}\]

xclim.indicators.land.blowing_snow(snd: Union[DataArray, str] = 'snd', sfcWind: Union[DataArray, str] = 'sfcWind', *, snd_thresh: Quantified = '5 cm', sfcWind_thresh: Quantified = '15 km/h', window: int = 3, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Blowing snow days (realm: land)

The number of days with snowfall, snow depth, and windspeed over given thresholds for a period of days.

This indicator will check for missing values according to the method “from_context”. Based on indice blowing_snow().

Parameters

• snd (str or DataArray) – Surface snow depth. Default : ds.snd. [Required units : [length]]

• sfcWind (str or DataArray) – Wind velocity Default : ds.sfcWind. [Required units : [speed]]

• snd_thresh (quantity (string or DataArray, with units)) – Threshold on net snowfall accumulation over the last window days. Default : 5 cm. [Required units : [length]]

• sfcWind_thresh (quantity (string or DataArray, with units)) – Wind speed threshold. Default : 15 km/h. [Required units : [speed]]

• window (number) – Period over which snow is accumulated before comparing against threshold. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

{freq}_blowing_snow (DataArray) – Days with snowfall and wind speed at or above given thresholds [days], with additional attributes: description: The {freq} number of days with snowfall over last {window} days above {snd_thresh} and wind speed above {sfcWind_thresh}.

xclim.indicators.land.continuous_snow_cover_end(snd: Union[DataArray, str] = 'snd', *, thresh: Quantified = '2 cm', window: int = 14, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

End date of continuous snow cover (realm: land)

The first date on which snow depth is below a given threshold for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice continuous_snow_cover_end().

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : [length]]

• thresh (quantity (string or DataArray, with units)) – Threshold snow thickness. Default : 2 cm. [Required units : [length]]

• window (number) – Minimum number of days with snow depth below threshold. Default : 14.

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

continuous_snow_cover_end (DataArray) – End date of continuous snow cover (day_of_year), with additional attributes: description: Day of year when snow depth is below {thresh} for {window} consecutive days.

References

Chaumont, Mailhot, Diaconescu, Fournier, and Logan [2017]

xclim.indicators.land.continuous_snow_cover_start(snd: Union[DataArray, str] = 'snd', *, thresh: Quantified = '2 cm', window: int = 14, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Start date of continuous snow cover (realm: land)

The first date on which snow depth is greater than or equal to a given threshold for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice continuous_snow_cover_start().

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : [length]]

• thresh (quantity (string or DataArray, with units)) – Threshold snow thickness. Default : 2 cm. [Required units : [length]]

• window (number) – Minimum number of days with snow depth above or equal to threshold. Default : 14.

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

continuous_snow_cover_start (DataArray) – Start date of continuous snow cover (day_of_year), with additional attributes: description: Day of year when snow depth is above or equal to {thresh} for {window} consecutive days.

References

Chaumont, Mailhot, Diaconescu, Fournier, and Logan [2017]

xclim.indicators.land.doy_qmax(da: Union[DataArray, str] = 'da', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Day of year of the maximum streamflow (realm: land)

This indicator will check for missing values according to the method “from_context”. Based on indice select_resample_op(). With injected parameters: op=<function doymax at 0x7fbe732e3520>.

Parameters

• da (str or DataArray) – Input data. Default : ds.da. [Required units : [discharge]]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. Default : None.

Returns

q{indexer}_doy_qmax (DataArray) – Day of the year of the maximum streamflow over {indexer}, with additional attributes: description: Day of the year of the maximum streamflow over {indexer}.

xclim.indicators.land.doy_qmin(da: Union[DataArray, str] = 'da', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Day of year of the minimum streamflow (realm: land)

This indicator will check for missing values according to the method “from_context”. Based on indice select_resample_op(). With injected parameters: op=<function doymin at 0x7fbe732e35b0>.

Parameters

• da (str or DataArray) – Input data. Default : ds.da. [Required units : [discharge]]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. Default : None.

Returns

q{indexer}_doy_qmin (DataArray) – Day of the year of the minimum streamflow over {indexer}, with additional attributes: description: Day of the year of the minimum streamflow over {indexer}.

xclim.indicators.land.fit(discharge: Union[DataArray, str] = 'discharge', *, dist: str = 'norm', method: str = 'ML', dim: str = 'time', ds: Dataset = None, **fitkwargs) → DataArray

Distribution parameters fitted over the time dimension. (realm: land)

Based on indice fit().

Parameters

• discharge (str or DataArray) – The amount of water, in all phases, flowing in the river channel and flood plain. Default : ds.discharge. [Required units : m3 s-1]

• dist (str) – Name of the univariate distribution, such as beta, expon, genextreme, gamma, gumbel_r, lognorm, norm (see :py:mod:scipy.stats for full list). If the PWM method is used, only the following distributions are currently supported: ‘expon’, ‘gamma’, ‘genextreme’, ‘genpareto’, ‘gumbel_r’, ‘pearson3’, ‘weibull_min’. Default : norm.

• method ({‘APP’, ‘ML’, ‘PWM’}) – Fitting method, either maximum likelihood (ML), probability weighted moments (PWM), also called L-Moments, or approximate method (APP). The PWM method is usually more robust to outliers. Default : ML.

• dim (str) – The dimension upon which to perform the indexing (default: “time”). Other arguments passed directly to _fitstart() and to the distribution’s fit. Default : time.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• fitkwargs – Default : None.

Returns

params (DataArray) – {dist} distribution parameters ({dist} parameters), with additional attributes: cell_methods: time: fit; description: Parameters of the {dist} distribution.

Notes

Coordinates for which all values are NaNs will be dropped before fitting the distribution. If the array still contains NaNs, the distribution parameters will be returned as NaNs.

xclim.indicators.land.freq_analysis(discharge: Union[DataArray, str] = 'discharge', *, mode: str, t: int | Sequence[int], dist: str, window: int = 1, freq: str | None = None, ds: Dataset = None, **indexer) → DataArray

Return level (realm: land)

Streamflow frequency analysis on the basis of a given mode and distribution.

This indicator will check for missing values according to the method “skip”. Based on indice frequency_analysis().

Parameters

• discharge (str or DataArray) – The amount of water, in all phases, flowing in the river channel and flood plain. Default : ds.discharge. [Required units : m3 s-1]

• mode ({‘max’, ‘min’}) – Whether we are looking for a probability of exceedance (high) or a probability of non-exceedance (low). Default : ds.discharge.

• t (number or sequence of numbers) – Return period. The period depends on the resolution of the input data. If the input array’s resolution is yearly, then the return period is in years. Default : ds.discharge.

• dist (str) – Name of the univariate distribution, e.g. beta, expon, genextreme, gamma, gumbel_r, lognorm, norm. Default : ds.discharge.

• window (number) – Averaging window length (days). Default : 1.

• freq (offset alias (string)) – Resampling frequency. If None, the frequency is assumed to be ‘YS’ unless the indexer is season=’DJF’, in which case freq would be set to AS-DEC. Default : None.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. Default : None.

Returns

q{window}{mode (r}{indexer} : DataArray) – N-year return level discharge [m^3 s-1], with additional attributes: description: Streamflow frequency analysis for the {mode} {indexer} {window}-day flow estimated using the {dist} distribution.

xclim.indicators.land.rb_flashiness_index(q: Union[DataArray, str] = 'q', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Richards-Baker Flashiness Index (realm: land)

Measurement of flow oscillations relative to average flow, quantifying the frequency and speed of flow changes.

This indicator will check for missing values according to the method “from_context”. Based on indice rb_flashiness_index().

Parameters

• q (str or DataArray) – Rate of river discharge. Default : ds.q. [Required units : [discharge]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

rbi (DataArray) – Richards-Baker Flashiness Index, with additional attributes: description: {freq} of Richards-Baker Index, an index measuring the flashiness of flow.

Notes

Let \(\mathbf{q}=q_0, q_1, \ldots, q_n\) be the sequence of daily discharge, the R-B Index is given by:

\[\frac{\sum_{i=1}^n |q_i - q_{i-1}|}{\sum_{i=1}^n q_i}\]

References

Baker, Richards, Loftus, and Kramer [2004]

xclim.indicators.land.snd_max_doy(snd: Union[DataArray, str] = 'snd', *, freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Day of year of maximum snow depth (realm: land)

Day of the year when snow depth reaches its maximum value.

This indicator will check for missing values according to the method “from_context”. Based on indice snd_max_doy().

Parameters

• snd (str or DataArray) – Surface snow depth. Default : ds.snd. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

{freq}_snd_max_doy (DataArray) – Day of the year when snow depth reaches its maximum value (day_of_year), with additional attributes: description: The {freq} day of the year when snow depth reaches its maximum value.

xclim.indicators.land.snow_cover_duration(snd: Union[DataArray, str] = 'snd', *, thresh: Quantified = '2 cm', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Snow cover duration (realm: land)

Number of days when the snow depth is greater than or equal to a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_cover_duration().

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : [length]]

• thresh (quantity (string or DataArray, with units)) – Threshold snow thickness. Default : 2 cm. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

snow_cover_duration (DataArray) – Number of days with snow depth at or above threshold [days], with additional attributes: description: The {freq} number of days with snow depth greater than or equal to {thresh}.

xclim.indicators.land.snow_depth(snd: Union[DataArray, str] = 'snd', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean snow depth (realm: land)

Mean of daily snow depth.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_depth().

Parameters

• snd (str or DataArray) – Mean daily snow depth. Default : ds.snd. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

snow_depth (DataArray) – Mean of daily snow depth (surface_snow_thickness) [cm], with additional attributes: cell_methods: time: mean over days; description: The {freq} mean of daily mean snow depth.

xclim.indicators.land.snow_melt_we_max(snw: Union[DataArray, str] = 'snw', *, window: int = 3, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Maximum snow melt (realm: land)

The water equivalent of the maximum snow melt.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_melt_we_max().

Parameters

• snw (str or DataArray) – Snow amount (mass per area). Default : ds.snw. [Required units : [mass]/[area]]

• window (number) – Number of days during which the melt is accumulated. Default : 3.

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

{freq}_snow_melt_we_max (DataArray) – Maximum snow melt (change_over_time_in_surface_snow_amount) [kg m-2], with additional attributes: description: The {freq} maximum negative change in melt amount over {window} days.

xclim.indicators.land.snw_max(snw: Union[DataArray, str] = 'snw', *, freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Maximum snow amount (realm: land)

The maximum snow water equivalent amount on the surface.

This indicator will check for missing values according to the method “from_context”. Based on indice snw_max().

Parameters

• snw (str or DataArray) – Snow amount (mass per area). Default : ds.snw. [Required units : [mass]/[area]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

{freq}_snw_max (DataArray) – Maximum snow water equivalent amount (surface_snow_amount) [kg m-2], with additional attributes: description: The {freq} maximum snow water equivalent amount on the surface.

xclim.indicators.land.snw_max_doy(snw: Union[DataArray, str] = 'snw', *, freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Day of year of maximum snow amount (realm: land)

The day of year when snow water equivalent amount on the surface reaches its maximum.

This indicator will check for missing values according to the method “from_context”. Based on indice snw_max_doy().

Parameters

• snw (str or DataArray) – Surface snow amount. Default : ds.snw. [Required units : [mass]/[area]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

{freq}_snw_max_doy (DataArray) – Day of year of maximum daily snow water equivalent amount (day_of_year), with additional attributes: description: The {freq} day of year when snow water equivalent amount on the surface reaches its maximum.

xclim.indicators.land.stats(discharge: Union[DataArray, str] = 'discharge', *, op: str, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Statistic of the daily flow for a given period. (realm: land)

This indicator will check for missing values according to the method “any”. Based on indice select_resample_op().

Parameters

• discharge (str or DataArray) – The amount of water, in all phases, flowing in the river channel and flood plain. Default : ds.discharge. [Required units : m3 s-1]

• op ({‘max’, ‘min’, ‘mean’, ‘sum’, ‘var’, ‘argmin’, ‘count’, ‘std’, ‘argmax’}) – Reduce operation. Can either be a DataArray method or a function that can be applied to a DataArray. Default : ds.discharge.

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Time attribute and values over which to subset the array. For example, use season=’DJF’ to select winter values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are considered. Default : None.

Returns

q{indexer}{op (r} : DataArray) – Daily flow statistics [m^3 s-1], with additional attributes: description: {freq} {op} of daily flow ({indexer}).

xclim.indicators.land.winter_storm(snd: Union[DataArray, str] = 'snd', *, thresh: Quantified = '25 cm', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Winter storm days (realm: land)

Number of days with snowfall accumulation greater or equal to threshold (default: 25 cm).

This indicator will check for missing values according to the method “from_context”. Based on indice winter_storm().

Parameters

• snd (str or DataArray) – Surface snow depth. Default : ds.snd. [Required units : [length]]

• thresh (quantity (string or DataArray, with units)) – Threshold on snowfall accumulation require to label an event a winter storm. Default : 25 cm. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

{freq}_winter_storm (DataArray) – Days with snowfall at or above a given threshold [days], with additional attributes: description: The {freq} number of days with snowfall accumulation above {thresh}.

Notes

Snowfall accumulation is estimated by the change in snow depth.

Ice-related indicators

xclim.indicators.seaIce.sea_ice_area(siconc: Union[DataArray, str] = 'siconc', areacello: Union[DataArray, str] = 'areacello', *, thresh: Quantified = '15 pct', ds: Dataset = None) → DataArray

Sea ice area (realm: seaIce)

A measure of total ocean surface covered by sea ice.

This indicator will check for missing values according to the method “skip”. Based on indice sea_ice_area().

Parameters

• siconc (str or DataArray) – Sea ice concentration (area fraction). Default : ds.siconc. [Required units : []]

• areacello (str or DataArray) – Grid cell area (usually over the ocean). Default : ds.areacello. [Required units : [area]]

• thresh (quantity (string or DataArray, with units)) – Minimum sea ice concentration for a grid cell to contribute to the sea ice extent. Default : 15 pct. [Required units : []]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

sea_ice_area (DataArray) – Sum of ice-covered areas where sea ice concentration exceeds {thresh} (sea_ice_area) [m2], with additional attributes: cell_methods: lon: sum lat: sum; description: The sum of ice-covered areas where sea ice concentration exceeds {thresh}.

Notes

To compute sea ice area over a subregion, first mask or subset the input sea ice concentration data.

References

“What is the difference between sea ice area and extent?” - NSIDC [2008]

xclim.indicators.seaIce.sea_ice_extent(siconc: Union[DataArray, str] = 'siconc', areacello: Union[DataArray, str] = 'areacello', *, thresh: Quantified = '15 pct', ds: Dataset = None) → DataArray

Sea ice extent (realm: seaIce)

A measure of the extent of all areas where sea ice concentration exceeds a threshold.

This indicator will check for missing values according to the method “skip”. Based on indice sea_ice_extent().

Parameters

• siconc (str or DataArray) – Sea ice concentration (area fraction). Default : ds.siconc. [Required units : []]

• areacello (str or DataArray) – Grid cell area. Default : ds.areacello. [Required units : [area]]

• thresh (quantity (string or DataArray, with units)) – Minimum sea ice concentration for a grid cell to contribute to the sea ice extent. Default : 15 pct. [Required units : []]

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

sea_ice_extent (DataArray) – Sum of ocean areas where sea ice concentration exceeds {thresh} (sea_ice_extent) [m2], with additional attributes: cell_methods: lon: sum lat: sum; description: The sum of ocean areas where sea ice concentration exceeds {thresh}.

Notes

To compute sea ice area over a subregion, first mask or subset the input sea ice concentration data.

References

“What is the difference between sea ice area and extent?” - NSIDC [2008]

Virtual indicator submodules

CF Standard indices

Indicators found here are defined by the team at clix-meta. Adapted documentation from that repository follows:

The repository aims to provide a platform for thinking about, and developing, a unified view of metadata elements required to describe climate indices (aka climate indicators).

To facilitate data exchange and dissemination the metadata should, as far as possible, follow the Climate and Forecasting (CF) Conventions. Considering the very rich and diverse flora of climate indices, this is however not always possible. By collecting a wide range of different indices it is easier to discover any common patterns and features that are currently not well covered by the CF Conventions. Currently identified issues frequently relate to standard_name and/or cell_methods which both are controlled vocabularies of the CF Conventions.

xclim.indicators.cf.cdd(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: threshold=1 mm day-1, reducer=max, op=<.

Parameters

• pr (str or DataArray) – Surface precipitation flux (all phases). Default : ds.pr. [Required units : kg m-2 s-1]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cdd (DataArray) – Maximum consecutive dry days (Precip < 1mm) (spell_length_of_days_with_lwe_thickness_of_precipitation_amount_below_threshold) [day], with additional attributes: cell_methods: time: sum over days; proposed_standard_name: spell_length_with_lwe_thickness_of_precipitation_amount_below_threshold

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.cddcoldTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=>.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cddcold{threshold} (DataArray) – Cooling Degree Days (Tmean > {threshold}C) (integral_wrt_time_of_air_temperature_excess) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

ET-SCI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.cfd(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', constrain: Sequence[str] | None = None, ds: Dataset = None) → DataArray

Calculate the number of times some condition is met. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, then this counts the number of times data < threshold. Finally, count the number of occurrences when condition is met.

This indicator will check for missing values according to the method “from_context”. Based on indice count_occurrences(). With injected parameters: threshold=0 degree_Celsius, op=<.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• constrain (Any) – Optionally allowed conditions. Default : None.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cfd (DataArray) – Maximum number of consecutive frost days (Tmin < 0 C) (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_below_threshold

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.csu(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', constrain: Sequence[str] | None = None, ds: Dataset = None) → DataArray

Calculate the number of times some condition is met. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, then this counts the number of times data < threshold. Finally, count the number of occurrences when condition is met.

This indicator will check for missing values according to the method “from_context”. Based on indice count_occurrences(). With injected parameters: threshold=25 degree_Celsius, op=>.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• constrain (Any) – Optionally allowed conditions. Default : None.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

csu (DataArray) – Maximum number of consecutive summer days (Tmax >25 C) (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_above_threshold

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctmgeTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=>.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctmge{threshold} (DataArray) – Maximum number of consecutive days with Tmean >= {threshold}C (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_at_or_above_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctmgtTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=>.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctmgt{threshold} (DataArray) – Maximum number of consecutive days with Tmean > {threshold}C (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_above_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctmleTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=<.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctmle{threshold} (DataArray) – Maximum number of consecutive days with Tmean <= {threshold}C (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_at_or_below_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctmltTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=<.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctmlt{threshold} (DataArray) – Maximum number of consecutive days with Tmean < {threshold}C (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_below_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctngeTT(tasmin: Union[DataArray, str] = 'tasmin', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=>.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmin. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctnge{threshold} (DataArray) – Maximum number of consecutive days with Tmin >= {threshold}C (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_at_or_above_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctngtTT(tasmin: Union[DataArray, str] = 'tasmin', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=>.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmin. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctngt{threshold} (DataArray) – Maximum number of consecutive days with Tmin > {threshold}C (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_above_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctnleTT(tasmin: Union[DataArray, str] = 'tasmin', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=<.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmin. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctnle{threshold} (DataArray) – Maximum number of consecutive days with Tmin <= {threshold}C (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_at_or_below_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctnltTT(tasmin: Union[DataArray, str] = 'tasmin', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=<.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmin. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctnlt{threshold} (DataArray) – Maximum number of consecutive days with Tmin < {threshold}C (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_below_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctxgeTT(tasmax: Union[DataArray, str] = 'tasmax', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=>.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmax. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctxge{threshold} (DataArray) – Maximum number of consecutive days with Tmax >= {threshold}C (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_at_or_above_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctxgtTT(tasmax: Union[DataArray, str] = 'tasmax', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=>.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmax. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctxgt{threshold} (DataArray) – Maximum number of consecutive days with Tmax > {threshold}C (spell_length_of_days_with_air_temperature_above_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_above_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctxleTT(tasmax: Union[DataArray, str] = 'tasmax', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=<.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmax. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctxle{threshold} (DataArray) – Maximum number of consecutive days with Tmax <= {threshold}C (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_at_or_below_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ctxltTT(tasmax: Union[DataArray, str] = 'tasmax', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: reducer=max, op=<.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tasmax. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ctxlt{threshold} (DataArray) – Maximum number of consecutive days with Tmax < {threshold}C (spell_length_of_days_with_air_temperature_below_threshold) [day], with additional attributes: cell_methods: time: maximum over days; proposed_standard_name: spell_length_with_air_temperature_below_threshold

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.cwd(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate statistics on lengths of spells. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Then the spells are determined, and finally the statistics according to the specified reducer are calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice spell_length(). With injected parameters: threshold=1 mm day-1, reducer=max, op=>.

Parameters

• pr (str or DataArray) – Surface precipitation flux (all phases). Default : ds.pr. [Required units : kg m-2 s-1]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cwd (DataArray) – Maximum consecutive wet days (Precip >= 1mm) (spell_length_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [day], with additional attributes: cell_methods: time: sum over days; proposed_standard_name: spell_length_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ddgtTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=>.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ddgt{threshold} (DataArray) – Degree Days (Tmean > {threshold}C) (integral_wrt_time_of_air_temperature_excess) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ddltTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=<.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ddlt{threshold} (DataArray) – Degree Days (Tmean < {threshold}C) (integral_wrt_time_of_air_temperature_deficit) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.dtr(tasmax: Union[DataArray, str] = 'tasmax', tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate the diurnal temperature range and reduce according to a statistic. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice diurnal_temperature_range(). With injected parameters: reducer=mean.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

dtr (DataArray) – Mean Diurnal Temperature Range [degree_Celsius], with additional attributes: cell_methods: time: range within days time: mean over days; proposed_standard_name: air_temperature_range

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.etr(tasmax: Union[DataArray, str] = 'tasmax', tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate the extreme temperature range as the maximum of daily maximum temperature minus the minimum of daily minimum temperature. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice extreme_temperature_range().

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

etr (DataArray) – Intra-period extreme temperature range [degree_Celsius], with additional attributes: cell_methods: time: range; proposed_standard_name: air_temperature_range

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.fg(sfcWind: Union[DataArray, str] = 'sfcWind', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• sfcWind (str or DataArray) – Surface wind speed. Default : ds.sfcWind. [Required units : m s-1]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

fg (DataArray) – Mean of daily mean wind strength (wind_speed) [meter second-1], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.fxx(wsgsmax: Union[DataArray, str] = 'wsgsmax', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• wsgsmax (str or DataArray) – Maximum surface wind speed. Default : ds.wsgsmax. [Required units : m s-1]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

fxx (DataArray) – Maximum value of daily maximum wind gust strength (wind_speed_of_gust) [meter second-1], with additional attributes: cell_methods: time: maximum

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.gd4(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=>, threshold=4 degree_Celsius.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

gd4 (DataArray) – Growing degree days (sum of Tmean > 4 C) (integral_wrt_time_of_air_temperature_excess) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.gddgrowTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=>.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

gddgrow{threshold} (DataArray) – Annual Growing Degree Days (Tmean > {threshold}C) (integral_wrt_time_of_air_temperature_excess) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

ET-SCI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.hd17(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=<, threshold=17 degree_Celsius.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hd17 (DataArray) – Heating degree days (sum of Tmean < 17 C) (integral_wrt_time_of_air_temperature_excess) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.hddheatTT(tas: Union[DataArray, str] = 'tas', *, threshold: Quantified, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the temperature sum above/below a threshold. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the sum is calculated for those data values that fulfill the condition after subtraction of the threshold value. If the sum is for values below the threshold the result is multiplied by -1.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_sum(). With injected parameters: op=<.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• threshold (quantity (string or DataArray, with units)) – air temperature Default : ds.tas. [Required units : degree_Celsius]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hddheat{threshold} (DataArray) – Heating Degree Days (Tmean < {threshold}C) (integral_wrt_time_of_air_temperature_deficit) [degree_Celsius day], with additional attributes: cell_methods: time: sum over days

References

ET-SCI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.iter_indicators()

Iterate over the (name, indicator) pairs in the cf indicator module.

xclim.indicators.cf.maxdtr(tasmax: Union[DataArray, str] = 'tasmax', tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate the diurnal temperature range and reduce according to a statistic. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice diurnal_temperature_range(). With injected parameters: reducer=max.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

maxdtr (DataArray) – Maximum Diurnal Temperature Range [degree_Celsius], with additional attributes: cell_methods: time: range within days time: maximum over days; proposed_standard_name: air_temperature_range

References

SMHI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.pp(psl: Union[DataArray, str] = 'psl', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• psl (str or DataArray) – Air pressure at sea level. Default : ds.psl. [Required units : Pa]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

pp (DataArray) – Mean of daily sea level pressure (air_pressure_at_sea_level) [hPa], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.rh(hurs: Union[DataArray, str] = 'hurs', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• hurs (str or DataArray) – Relative humidity. Default : ds.hurs. [Required units : %]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

rh (DataArray) – Mean of daily relative humidity (relative_humidity) [%], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.sd(snd: Union[DataArray, str] = 'snd', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : m]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

sd (DataArray) – Mean of daily snow depth (surface_snow_thickness) [cm], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.sdii(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', constrain: Sequence[str] | None = None, ds: Dataset = None) → DataArray

Calculate a simple statistic of the data for which some condition is met. (realm: atmos)

First, the threshold is transformed to the same standard_name and units as the input data. Then the thresholding is performed as condition(data, threshold), i.e. if condition is <, data < threshold. Finally, the statistic is calculated for those data values that fulfill the condition.

This indicator will check for missing values according to the method “from_context”. Based on indice thresholded_statistics(). With injected parameters: op=>, threshold=1 mm day-1, reducer=mean.

Parameters

• pr (str or DataArray) – Surface precipitation flux (all phases). Default : ds.pr. [Required units : kg m-2 s-1]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• constrain (Any) – Optionally allowed conditions. Default: None. Default : None.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

sdii (DataArray) – Average precipitation during Wet Days (SDII) (lwe_precipitation_rate) [mm day-1], with additional attributes: cell_methods: time: mean over days

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.ss(sund: Union[DataArray, str] = 'sund', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=sum.

Parameters

• sund (str or DataArray) – Duration of sunshine. Default : ds.sund. [Required units : s]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

ss (DataArray) – Sunshine duration, sum (duration_of_sunshine) [hour]

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tg(tas: Union[DataArray, str] = 'tas', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tg (DataArray) – Mean of daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tmm(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tmm (DataArray) – Mean daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean over days

References

clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tmmax(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tmmax (DataArray) – Maximum daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tmmean(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tmmean (DataArray) – Mean daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tmmin(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=min.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tmmin (DataArray) – Minimum daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tmn(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=min.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tmn (DataArray) – Minimum daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: minimum over days

References

clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tmx(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• tas (str or DataArray) – Mean surface temperature. Default : ds.tas. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tmx (DataArray) – Maximum daily mean temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tn(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tn (DataArray) – Mean of daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tnm(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tnm (DataArray) – Mean daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean over days

References

clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tnmax(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tnmax (DataArray) – Maximum daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tnmean(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tnmean (DataArray) – Mean daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tnmin(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=min.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tnmin (DataArray) – Minimum daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: minimum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tnn(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=min.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tnn (DataArray) – Minimum daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: minimum over days

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tnx(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tnx (DataArray) – Maximum daily minimum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.tx(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tx (DataArray) – Mean of daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.txm(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

txm (DataArray) – Mean daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean over days

References

clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.txmax(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

txmax (DataArray) – Maximum daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.txmean(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=mean.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

txmean (DataArray) – Mean daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: mean over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.txmin(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=min.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

txmin (DataArray) – Minimum daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: minimum over days

References

CLIPC clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.txn(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=min.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

txn (DataArray) – Minimum daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: minimum over days

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.txx(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate a simple statistic of the data. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice statistics(). With injected parameters: reducer=max.

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

txx (DataArray) – Maximum daily maximum temperature (air_temperature) [degree_Celsius], with additional attributes: cell_methods: time: maximum over days

References

ETCCDI clix-meta https://github.com/clix-meta/clix-meta

xclim.indicators.cf.vdtr(tasmax: Union[DataArray, str] = 'tasmax', tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'MS', ds: Dataset = None) → DataArray

Calculate the average absolute day-to-day difference in diurnal temperature range. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice interday_diurnal_temperature_range().

Parameters

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : MS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

vdtr (DataArray) – Mean day-to-day variation in Diurnal Temperature Range [degree_Celsius], with additional attributes: proposed_standard_name: air_temperature_difference

References

ECA&D clix-meta https://github.com/clix-meta/clix-meta

ICCLIM indices

The European Climate Assessment & Dataset project (ECAD) defines a set of 26 core climate indices. Those have been made accessible directly in xclim through their ECAD name for compatibility. However, the methods in this module are only wrappers around the corresponding methods of xclim.indices. Note that none of the checks performed by the xclim.utils.Indicator class (like with xclim.atmos indicators)are performed in this module.

xclim.indicators.icclim.BEDD(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Biologically effective growing degree days. (realm: atmos)

Growing-degree days with a base of 10°C and an upper limit of 19°C and adjusted for latitudes between 40°N and 50°N for April to October (Northern Hemisphere; October to April in Southern Hemisphere). A temperature range adjustment also promotes small and large swings in daily temperature range. Used as a heat-summation metric in viticulture agroclimatology.

This indicator will check for missing values according to the method “from_context”. Based on indice biologically_effective_degree_days(). With injected parameters: lat=None, thresh_tasmin=10 degC, method=icclim, low_dtr=None, high_dtr=None, max_daily_degree_days=9 degC, start_date=04-01, end_date=10-01.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency (default: “YS”; For Southern Hemisphere, should be “AS-JUL”). Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

BEDD (DataArray) – Biologically effective growing degree days (Summation of min(max((Tmin + Tmax)/2 - 10°C, 0), 9°C), for days between 1 April and 30 September) [K days], with additional attributes: description: Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture. Computed with {method} formula (Summation of min((max((Tn + Tx)/2 - {thresh_tasmin}, 0) * k) + TR_adj, Dmax), where coefficient k is a latitude-based day-length for days between {start_date} and {end_date}), coefficient TR_adj is a modifier accounting for large temperature swings, and Dmax is the maximum possibleamount of degree days that can be gained within a day ({max_daily_degree_days}).

Notes

The tasmax ceiling of 19°C is assumed to be the max temperature beyond which no further gains from daily temperature occur. Indice originally published in Gladstones [1992].

Let \(TX_{i}\) and \(TN_{i}\) be the daily maximum and minimum temperature at day \(i\), \(lat\) the latitude of the point of interest, \(degdays_{max}\) the maximum amount of degrees that can be summed per day (typically, 9). Then the sum of daily biologically effective growing degree day (BEDD) units between 1 April and 31 October is:

\[BEDD_i = \sum_{i=\text{April 1}}^{\text{October 31}} min\left( \left( max\left( \frac{TX_i + TN_i)}{2} - 10, 0 \right) * k \right) + TR_{adj}, degdays_{max}\right)\]

\[\begin{split}TR_{adj} = f(TX_{i}, TN_{i}) = \begin{cases} 0.25(TX_{i} - TN_{i} - 13), & \text{if } (TX_{i} - TN_{i}) > 13 \\ 0, & \text{if } 10 < (TX_{i} - TN_{i}) < 13\\ 0.25(TX_{i} - TN_{i} - 10), & \text{if } (TX_{i} - TN_{i}) < 10 \\ \end{cases}\end{split}\]

\[k = f(lat) = 1 + \left(\frac{\left| lat \right|}{50} * 0.06, \text{if }40 < |lat| <50, \text{else } 0\right)\]

A second version of the BEDD (method=”icclim”) does not consider \(TR_{adj}\) and \(k\) and employs a different end date (30 September) [Project team ECA&D and KNMI, 2013]. The simplified formula is as follows:

\[BEDD_i = \sum_{i=\text{April 1}}^{\text{September 30}} min\left( max\left(\frac{TX_i + TN_i)}{2} - 10, 0\right), degdays_{max}\right)\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CD(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cold and dry days (realm: atmos)

Number of days with temperature below a given percentile and precipitation below a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_and_dry_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – Daily 25th percentile of temperature. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – Daily 25th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

CD (DataArray) – Cold and dry days [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is below {tas_per_thresh}th percentile and precipitation is below {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CDD(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum consecutive dry days (realm: atmos)

The longest number of consecutive days where daily precipitation below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_dry_days(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

CDD (DataArray) – Maximum number of consecutive dry days (RR<1 mm) (number_of_days_with_lwe_thickness_of_precipitation_amount_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} maximum number of consecutive days with daily precipitation below {thresh}.

Notes

Let \(\mathbf{p}=p_0, p_1, \ldots, p_n\) be a daily precipitation series and \(thresh\) the threshold under which a day is considered dry. Then let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([p_i < thresh] \neq [p_{i+1} < thresh]\), that is, the days where the precipitation crosses the threshold. Then the maximum number of consecutive dry days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [p_{s_j} < thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CFD(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Consecutive frost days (realm: atmos)

Maximum number of consecutive days where the daily minimum temperature is below 0°C

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_frost_days(). With injected parameters: thresh=0 degC.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

CFD (DataArray) – Maximum number of consecutive frost days (TN<0°C) (spell_length_of_days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum number of consecutive days where minimum daily temperature is below {thresh}.

Notes

Let \(\mathbf{t}=t_0, t_1, \ldots, t_n\) be a minimum daily temperature series and \(thresh\) the threshold below which a day is considered a frost day. Let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([t_i < thresh] \neq [t_{i+1} < thresh]\), that is, the days where the temperature crosses the threshold. Then the maximum number of consecutive frost days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} < thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CSDI(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', resample_before_rl: bool = True, bootstrap: bool = False, op: str = '<', ds: Dataset = None) → DataArray

Cold Spell Duration Index (CSDI) (realm: atmos)

Number of days part of a percentile-defined cold spell. A cold spell occurs when the daily minimum temperature is below a given percentile for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_duration_index(). With injected parameters: window=6.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmin_per (str or DataArray) – nth percentile of daily minimum temperature with dayofyear coordinate. Default : ds.tasmin_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘<’, ‘le’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

CSDI (DataArray) – Cold-spell duration index (cold_spell_duration_index) [days], with additional attributes: description: {freq} number of days with at least {window} consecutive days where the daily minimum temperature is below the {tasmin_per_thresh}th percentile. A {tasmin_per_window} day(s) window, centred on each calendar day in the {tasmin_per_period} period, is used to compute the {tasmin_per_thresh}th percentile(s).

Notes

Let \(TN_i\) be the minimum daily temperature for the day of the year \(i\) and \(TN10_i\) the 10th percentile of the minimum daily temperature over the 1961-1990 period for day of the year \(i\), the cold spell duration index over period \(\phi\) is defined as:

\[\sum_{i \in \phi} \prod_{j=i}^{i+6} \left[ TN_j < TN10_j \right]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CSU(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum consecutive warm days (realm: atmos)

Maximum number of consecutive days where the maximum daily temperature exceeds a certain threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_tx_days(). With injected parameters: thresh=25 degC.

Parameters

• tasmax (str or DataArray) – Max daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

CSU (DataArray) – Maximum number of consecutive summer day (spell_length_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: maximum over days; description: {freq} longest spell of consecutive days with maximum daily temperature above {thresh}.

Notes

Let \(\mathbf{t}=t_0, t_1, \ldots, t_n\) be a daily maximum temperature series and \(thresh\) the threshold above which a day is considered a summer day. Let \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([t_i < thresh] \neq [t_{i+1} < thresh]\), that is, the days where the temperature crosses the threshold. Then the maximum number of consecutive tx_days (summer days) is given by:

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} > thresh]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CW(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cold and wet days (realm: atmos)

Number of days with temperature below a given percentile and precipitation above a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_and_wet_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – Daily 25th percentile of temperature. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – Daily 75th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

CW (DataArray) – cold and wet days [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is below {tas_per_thresh}th percentile and precipitation is above {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.CWD(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', resample_before_rl: bool = True, ds: Dataset = None) → DataArray

Maximum consecutive wet days (realm: atmos)

The longest number of consecutive days where daily precipitation is at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_wet_days(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

CWD (DataArray) – Maximum number of consecutive wet days (RR≥1 mm) (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} maximum number of consecutive days with daily precipitation at or above {thresh}.

Notes

Let \(\mathbf{x}=x_0, x_1, \ldots, x_n\) be a daily precipitation series and \(\mathbf{s}\) be the sorted vector of indices \(i\) where \([p_i > thresh] \neq [p_{i+1} > thresh]\), that is, the days where the precipitation crosses the wet day threshold. Then the maximum number of consecutive wet days is given by:

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [x_{s_j} > 0^\circ C]\]

where \([P]\) is 1 if \(P\) is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.DTR(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of daily temperature range (realm: atmos)

The average difference between the daily maximum and minimum temperatures.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range(). With injected parameters: op=mean.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

DTR (DataArray) – Mean of diurnal temperature range (air_temperature) [K], with additional attributes: cell_methods: time range within days time: mean over days; description: {freq} mean diurnal temperature range.

Notes

For a default calculation using op=’mean’ :

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then the mean diurnal temperature range in period \(j\) is:

\[DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.ETR(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Extreme temperature range (realm: atmos)

The maximum of the maximum temperature minus the minimum of the minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice extreme_temperature_range().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

ETR (DataArray) – Intra-period extreme temperature range (air_temperature) [K], with additional attributes: description: {freq} range between the maximum of daily maximum temperature and the minimum of dailyminimum temperature.

Notes

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then the extreme temperature range in period \(j\) is:

\[ETR_j = max(TX_{ij}) - min(TN_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.FD(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Frost days (realm: atmos)

Number of days where the daily minimum temperature is below a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_days(). With injected parameters: thresh=0 degC.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

FD (DataArray) – Frost days (TN<0°C) (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where the daily minimum temperature is below {thresh}.

Notes

Let \(TN_{ij}\) be the daily minimum temperature at day \(i\) of period \(j\) and :math`TT` the threshold. Then counted is the number of days where:

\[TN_{ij} < TT\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.GD4(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Growing degree days (realm: atmos)

The cumulative degree days for days when the average temperature is above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_degree_days(). With injected parameters: thresh=4 degC.

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

GD4 (DataArray) – Growing degree days (sum of TG>4°C) (integral_of_air_temperature_excess_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} growing degree days (mean temperature above {thresh}).

Notes

Let \(TG_{ij}\) be the mean daily temperature at day \(i\) of period \(j\). Then the growing degree days are:

\[GD4_j = \sum_{i=1}^I (TG_{ij}-{4} | TG_{ij} > {4}℃)\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.GSL(tas: Union[DataArray, str] = 'tas', *, mid_date: DayOfYearStr = '07-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Growing season length (realm: atmos)

Number of days between the first occurrence of a series of days with a daily average temperature above a threshold and the first occurrence of a series of days with a daily average temperature below that same threshold, occurring after a given calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_length(). With injected parameters: thresh=5 degC, window=6.

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

GSL (DataArray) – Growing season length (growing_season_length) [days], with additional attributes: description: {freq} number of days between the first occurrence of at least {window} consecutive days with mean daily temperature over {thresh} and the first occurrence of at least {window} consecutive days with mean daily temperature below {thresh}, occurring after {mid_date}.

Notes

Let \(TG_{ij}\) be the mean temperature at day \(i\) of period \(j\). Then counted is the number of days between the first occurrence of at least 6 consecutive days with:

\[TG_{ij} > 5 ℃\]

and the first occurrence after 1 July of at least 6 consecutive days with:

\[TG_{ij} < 5 ℃\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.HD17(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Heating degree days (realm: atmos)

The cumulative degree days for days when the mean daily temperature is below a given threshold and buildings must be heated.

This indicator will check for missing values according to the method “from_context”. Based on indice heating_degree_days(). With injected parameters: thresh=17 degC.

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

HD17 (DataArray) – Heating degree days (sum of17°C - TG) (integral_of_air_temperature_deficit_wrt_time) [K days], with additional attributes: cell_methods: time: sum over days; description: {freq} cumulative heating degree days (mean temperature below {thresh}).

Notes

This index intentionally differs from its ECA&D [Project team ECA&D and KNMI, 2013] equivalent: HD17. In HD17, values below zero are not clipped before the sum. The present definition should provide a better representation of the energy demand for heating buildings to the given threshold.

Let \(TG_{ij}\) be the daily mean temperature at day \(i\) of period \(j\). Then the heating degree days are:

\[HD17_j = \sum_{i=1}^{I} (17℃ - TG_{ij}) | TG_{ij} < 17℃)\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.HI(tas: Union[DataArray, str] = 'tas', tasmax: Union[DataArray, str] = 'tasmax', lat: Optional[Union[DataArray, str]] = None, *, freq: str = 'YS', ds: Dataset = None) → DataArray

Huglin Heliothermal Index. (realm: atmos)

Growing-degree days with a base of 10°C and adjusted for latitudes between 40°N and 50°N for April-September (Northern Hemisphere; October-March in Southern Hemisphere). Originally proposed in Huglin [1978]. Used as a heat-summation metric in viticulture agroclimatology.

This indicator will check for missing values according to the method “from_context”. Based on indice huglin_index(). With injected parameters: thresh=10 degC, method=icclim, start_date=04-01, end_date=11-01.

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• lat (str or DataArray, optional) – Latitude coordinate. If None, a CF-conformant “latitude” field must be available within the passed DataArray. [Required units : []]

• freq (offset alias (string)) – Resampling frequency (default: “YS”; For Southern Hemisphere, should be “AS-JUL”). Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

• HI (DataArray) – Huglin heliothermal index (Summation of ((Tmean + Tmax)/2 - 10°C) * Latitude-based day-length coefficient (k), for days between 1 April and 31 October)

• , with additional attributes (description: Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture, computed with {method} formula (Summation of ((Tn + Tx)/2 - {thresh}) * k), where coefficient k is a latitude-based day-length for days between {start_date} and {end_date}.)

Notes

Let \(TX_{i}\) and \(TG_{i}\) be the daily maximum and mean temperature at day \(i\) and \(T_{thresh}\) the base threshold needed for heat summation (typically, 10 degC). A day-length multiplication, \(k\), based on latitude, \(lat\), is also considered. Then the Huglin heliothermal index for dates between 1 April and 30 September is:

\[HI = \sum_{i=\text{April 1}}^{\text{September 30}} \left( \frac{TX_i + TG_i)}{2} - T_{thresh} \right) * k\]

For the smoothed method, the day-length multiplication factor, \(k\), is calculated as follows:

\[\begin{split}k = f(lat) = \begin{cases} 1, & \text{if } |lat| <= 40 \\ 1 + ((abs(lat) - 40) / 10) * 0.06, & \text{if } 40 < |lat| <= 50 \\ NaN, & \text{if } |lat| > 50 \\ \end{cases}\end{split}\]

For compatibility with ICCLIM, end_date should be set to 11-01, method should be set to icclim. The day-length multiplication factor, \(k\), is calculated as follows:

\[\begin{split}k = f(lat) = \begin{cases} 1.0, & \text{if } |lat| <= 40 \\ 1.02, & \text{if } 40 < |lat| <= 42 \\ 1.03, & \text{if } 42 < |lat| <= 44 \\ 1.04, & \text{if } 44 < |lat| <= 46 \\ 1.05, & \text{if } 46 < |lat| <= 48 \\ 1.06, & \text{if } 48 < |lat| <= 50 \\ NaN, & \text{if } |lat| > 50 \\ \end{cases}\end{split}\]

A more robust day-length calculation based on latitude, calendar, day-of-year, and obliquity is available with method=”jones”. See: xclim.indices.generic.day_lengths() or Hall and Jones [2010] for more information.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.ID(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Ice days (realm: atmos)

Number of days where the daily maximum temperature is below 0°C

This indicator will check for missing values according to the method “from_context”. Based on indice ice_days(). With injected parameters: thresh=0 degC.

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

ID (DataArray) – Ice days (TX<0°C) (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where the maximum daily temperature is below {thresh}.

Notes

Let \(TX_{ij}\) be the daily maximum temperature at day \(i\) of period \(j\), and :math`TT` the threshold. Then counted is the number of days where:

\[TX_{ij} < TT\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.PRCPTOT(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total accumulated precipitation (solid and liquid) during wet days (realm: atmos)

Total accumulated precipitation on days with precipitation. A day is considered to have precipitation if the precipitation is greater than or equal to a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

PRCPTOT (DataArray) – Precipitation sum over wet days (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum over days; description: {freq} total precipitation over wet days, defined as days where precipitation exceeds {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R10mm(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', op: str = '>=', ds: Dataset = None, **indexer) → DataArray

Number of wet days (realm: atmos)

The number of days with daily precipitation at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays(). With injected parameters: thresh=10 mm/day.

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>=”. Default : >=.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R10mm (DataArray) – Heavy precipitation days (precipitation≥10 mm) (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with daily precipitation at or above {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R20mm(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', op: str = '>=', ds: Dataset = None, **indexer) → DataArray

Number of wet days (realm: atmos)

The number of days with daily precipitation at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays(). With injected parameters: thresh=20 mm/day.

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>=”. Default : >=.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R20mm (DataArray) – Very heavy precipitation days (precipitation≥20 mm) (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with daily precipitation at or above {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R75p(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with precipitation above a given percentile (realm: atmos)

Number of days in a period where precipitation is above a given percentile, calculated over a given period and a fixed threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – 75th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R75p (DataArray) – Number of days with precipitation flux above the {pr_per_thresh}th percentile of {pr_per_period} (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with precipitation above the {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are counted.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R75pTOT(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Fraction of precipitation due to wet days with daily precipitation over a given percentile. (realm: atmos)

Percentage of the total precipitation over period occurring in days when the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – 75th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R75pTOT (DataArray) – Precipitation fraction due to moderate wet days (>75th percentile), with additional attributes: description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are included in the total.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R95p(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with precipitation above a given percentile (realm: atmos)

Number of days in a period where precipitation is above a given percentile, calculated over a given period and a fixed threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – 95th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R95p (DataArray) – Number of days with precipitation flux above the {pr_per_thresh}th percentile of {pr_per_period} (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with precipitation above the {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are counted.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R95pTOT(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Fraction of precipitation due to wet days with daily precipitation over a given percentile. (realm: atmos)

Percentage of the total precipitation over period occurring in days when the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – 95th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R95pTOT (DataArray) – Precipitation fraction due to very wet days (>95th percentile), with additional attributes: description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are included in the total.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R99p(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with precipitation above a given percentile (realm: atmos)

Number of days in a period where precipitation is above a given percentile, calculated over a given period and a fixed threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – 99th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R99p (DataArray) – Number of days with precipitation flux above the {pr_per_thresh}th percentile of {pr_per_period} (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with precipitation above the {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are counted.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.R99pTOT(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Fraction of precipitation due to wet days with daily precipitation over a given percentile. (realm: atmos)

Percentage of the total precipitation over period occurring in days when the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• pr_per (str or DataArray) – 99th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

R99pTOT (DataArray) – Precipitation fraction due to extremely wet days (>99th percentile), with additional attributes: description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are included in the total.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.RR(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total accumulated precipitation (solid and liquid) (realm: atmos)

Total accumulated precipitation. If the average daily temperature is given, the phase parameter can be used to restrict the calculation to precipitation of only one phase (liquid or solid). Precipitation is considered solid if the average daily temperature is below 0°C (and vice versa).

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: tas=None, phase=None.

Parameters

• pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

RR (DataArray) – Precipitation sum (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum over days; description: {freq} total precipitation.

Notes

Let \(PR_i\) be the mean daily precipitation of day \(i\), then for a period \(j\) starting at day \(a\) and finishing on day \(b\):

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.RR1(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', op: str = '>=', ds: Dataset = None, **indexer) → DataArray

Number of wet days (realm: atmos)

The number of days with daily precipitation at or above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>=”. Default : >=.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

RR1 (DataArray) – Wet days (RR≥1 mm) (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with daily precipitation at or above {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.RX1day(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum 1-day total precipitation (realm: atmos)

Maximum total daily precipitation for a given period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_1day_precipitation_amount().

Parameters

• pr (str or DataArray) – Daily precipitation values. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

RX1day (DataArray) – Highest 1-day precipitation amount (lwe_thickness_of_precipitation_amount) [mm/day], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum 1-day total precipitation

Notes

Let \(PR_i\) be the mean daily precipitation of day i, then for a period j:

\[PRx_{ij} = max(PR_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.RX5day(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

maximum n-day total precipitation (realm: atmos)

Maximum of the moving sum of daily precipitation for a given period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_n_day_precipitation_amount(). With injected parameters: window=5.

Parameters

• pr (str or DataArray) – Daily precipitation values. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

RX5day (DataArray) – Highest 5-day precipitation amount (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum {window}-day total precipitation amount.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.SD(snd: Union[DataArray, str] = 'snd', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean snow depth (realm: atmos)

Mean of daily snow depth.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_depth().

Parameters

• snd (str or DataArray) – Mean daily snow depth. Default : ds.snd. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

SD (DataArray) – Mean of daily snow depth (surface_snow_thickness) [cm], with additional attributes: cell_methods: time: mean over days; description: The {freq} mean of daily mean snow depth.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.SD1(snd: Union[DataArray, str] = 'snd', *, freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Snow cover duration (realm: atmos)

Number of days when the snow depth is greater than or equal to a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_cover_duration(). With injected parameters: thresh=1 cm.

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

SD1 (DataArray) – Snow days (SD≥1 cm) [days], with additional attributes: description: The {freq} number of days with snow depth greater than or equal to {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.SD50cm(snd: Union[DataArray, str] = 'snd', *, freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Snow cover duration (realm: atmos)

Number of days when the snow depth is greater than or equal to a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_cover_duration(). With injected parameters: thresh=50 cm.

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

SD50cm (DataArray) – Snow days (SD≥50 cm) [days], with additional attributes: description: The {freq} number of days with snow depth greater than or equal to {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.SD5cm(snd: Union[DataArray, str] = 'snd', *, freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Snow cover duration (realm: atmos)

Number of days when the snow depth is greater than or equal to a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice snow_cover_duration(). With injected parameters: thresh=5 cm.

Parameters

• snd (str or DataArray) – Surface snow thickness. Default : ds.snd. [Required units : [length]]

• freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

SD5cm (DataArray) – Snow days (SD≥5 cm) [days], with additional attributes: description: The {freq} number of days with snow depth greater than or equal to {thresh}.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.SDII(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Simple Daily Intensity Index (realm: atmos)

Average precipitation for days with daily precipitation above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_pr_intensity(). With injected parameters: thresh=1 mm/day.

Parameters

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

SDII (DataArray) – Average precipitation during days with daily precipitation over {thresh} (Simple Daily Intensity Index: SDII) (lwe_thickness_of_precipitation_amount) [mm d-1], with additional attributes: description: {freq} Simple Daily Intensity Index (SDII) or {freq} average precipitation for days with daily precipitation over {thresh}.

Notes

Let \(\mathbf{p} = p_0, p_1, \ldots, p_n\) be the daily precipitation and \(thresh\) be the precipitation threshold defining wet days. Then the daily precipitation intensity is defined as:

\[\frac{\sum_{i=0}^n p_i [p_i \leq thresh]}{\sum_{i=0}^n [p_i \leq thresh]}\]

where \([P]\) is 1 if \(P\) is true, and 0 if false.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.SU(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Number of days with maximum temperature above a given threshold (realm: atmos)

The number of days with maximum temperature above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_days_above(). With injected parameters: thresh=25 degC.

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

SU (DataArray) – Summer days (TX>25°C) (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where daily maximum temperature exceeds {thresh}.

Notes

Let \(TX_{ij}\) be the maximum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TX_{ij} > Threshold [℃]\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TG(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean temperature (realm: atmos)

Mean of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TG (DataArray) – Mean daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean over days; description: {freq} mean of daily mean temperature.

Notes

Let \(TN_i\) be the mean daily temperature of day \(i\), then for a period \(p\) starting at day \(a\) and finishing on day \(b\):

\[TG_p = \frac{\sum_{i=a}^{b} TN_i}{b - a + 1}\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TG10p(tas: Union[DataArray, str] = 'tas', tas_per: Union[DataArray, str] = 'tas_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '<', ds: Dataset = None, **indexer) → DataArray

Days with mean temperature below the 10th percentile (realm: atmos)

Number of days with mean temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tg10p().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• tas_per (str or DataArray) – 10th percentile of daily mean temperature. Default : ds.tas_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘<’, ‘le’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TG10p (DataArray) – Days with TG<10th percentile of daily mean temperature (cold days) (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with mean temperature below the 10th percentile. A {tas_per_window} day(s) window, centered on each calendar day in the {tas_per_period} period, is used to compute the 10th percentile.

Notes

The 10th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TG90p(tas: Union[DataArray, str] = 'tas', tas_per: Union[DataArray, str] = 'tas_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Days with mean temperature above the 90th percentile (realm: atmos)

Number of days with mean temperature above the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tg90p().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• tas_per (str or DataArray) – 90th percentile of daily mean temperature. Default : ds.tas_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TG90p (DataArray) – Days with TG>90th percentile of daily mean temperature (warm days) (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with mean temperature above the 90th percentile. A {tas_per_window} day(s) window, centered on each calendar day in the {tas_per_period} period, is used to compute the 90th percentile.

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TGn(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Minimum of mean temperature (realm: atmos)

Minimum of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_min().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TGn (DataArray) – Minimum daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: minimum over days; description: {freq} minimum of daily mean temperature.

Notes

Let \(TG_{ij}\) be the mean temperature at day \(i\) of period \(j\). Then the minimum daily mean temperature for period \(j\) is:

\[TGn_j = min(TG_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TGx(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum of mean temperature (realm: atmos)

Maximum of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_max().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TGx (DataArray) – Maximum daily mean temperature (air_temperature) [K], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum of daily mean temperature.

Notes

Let \(TN_{ij}\) be the mean temperature at day \(i\) of period \(j\). Then the maximum daily mean temperature for period \(j\) is:

\[TNx_j = max(TN_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TN(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of minimum temperature (realm: atmos)

Mean of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_mean().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TN (DataArray) – Mean daily minimum temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean over days; description: {freq} mean of daily minimum temperature.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then mean values in period \(j\) are given by:

\[TN_{ij} = \frac{ \sum_{i=1}^{I} TN_{ij} }{I}\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TN10p(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '<', ds: Dataset = None, **indexer) → DataArray

Days with minimum temperature below the 10th percentile (realm: atmos)

Number of days with minimum temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tn10p().

Parameters

• tasmin (str or DataArray) – Mean daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmin_per (str or DataArray) – 10th percentile of daily minimum temperature. Default : ds.tasmin_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘<’, ‘le’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TN10p (DataArray) – Days with TN<10th percentile of daily minimum temperature (cold nights) (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with minimum temperature below the 10th percentile. A {tasmin_per_window} day(s) window, centered on each calendar day in the {tasmin_per_period} period, is used to compute the 10th percentile.

Notes

The 10th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TN90p(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Days with minimum temperature above the 90th percentile (realm: atmos)

Number of days with minimum temperature above the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tn90p().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmin_per (str or DataArray) – 90th percentile of daily minimum temperature. Default : ds.tasmin_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TN90p (DataArray) – Days with TN>90th percentile of daily minimum temperature (warm nights) (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with minimum temperature above the 90th percentile. A {tasmin_per_window} day(s) window, centered on each calendar day in the {tasmin_per_period} period, is used to compute the 90th percentile.

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TNn(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Minimum temperature (realm: atmos)

Minimum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_min().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TNn (DataArray) – Minimum daily minimum temperature (air_temperature) [K], with additional attributes: cell_methods: time: minimum over days; description: {freq} minimum of daily minimum temperature.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then the minimum daily minimum temperature for period \(j\) is:

\[TNn_j = min(TN_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TNx(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum of minimum temperature (realm: atmos)

Maximum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_max().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TNx (DataArray) – Maximum daily minimum temperature (air_temperature) [K], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum of daily minimum temperature.

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then the maximum daily minimum temperature for period \(j\) is:

\[TNx_j = max(TN_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TR(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', op: str = '>', ds: Dataset = None, **indexer) → DataArray

Tropical nights (realm: atmos)

Number of days where minimum temperature is above a given threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_above(). With injected parameters: thresh=20 degC.

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TR (DataArray) – Tropical nights (TN>20°C) (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of Tropical Nights, defined as days with minimum daily temperature above {thresh}.

Notes

Let \(TN_{ij}\) be the minimum daily temperature at day \(i\) of period \(j\). Then counted is the number of days where:

\[TN_{ij} > Threshold [℃]\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TX(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of maximum temperature (realm: atmos)

Mean of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_mean().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TX (DataArray) – Mean daily maximum temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean over days; description: {freq} mean of daily maximum temperature.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then mean values in period \(j\) are given by:

\[TX_{ij} = \frac{ \sum_{i=1}^{I} TX_{ij} }{I}\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TX10p(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '<', ds: Dataset = None, **indexer) → DataArray

Days with maximum temperature below the 10th percentile (realm: atmos)

Number of days with maximum temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tx10p().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• tasmax_per (str or DataArray) – 10th percentile of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘lt’, ‘<’, ‘le’, ‘<=’}) – Comparison operation. Default: “<”. Default : <.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TX10p (DataArray) – Days with TX<10th percentile of daily maximum temperature (cold day-times) (days_with_air_temperature_below_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with maximum temperature below the 10th percentile. A {tasmax_per_window} day(s) window, centered on each calendar day in the {tasmax_per_period} period, is used to compute the 10th percentile.

Notes

The 10th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TX90p(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', bootstrap: bool = False, op: str = '>', ds: Dataset = None, **indexer) → DataArray

Days with maximum temperature above the 90th percentile (realm: atmos)

Number of days with maximum temperature above the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tx90p().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• tasmax_per (str or DataArray) – 90th percentile of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TX90p (DataArray) – Days with TX>90th percentile of daily maximum temperature (warm day-times) (days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with maximum temperature above the 90th percentile. A {tasmax_per_window} day(s) window, centered on each calendar day in the {tasmax_per_period} period, is used to compute the 90th percentile.

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TXn(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Minimum of maximum temperature (realm: atmos)

Minimum of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_min().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TXn (DataArray) – Minimum daily maximum temperature (air_temperature) [K], with additional attributes: cell_methods: time: minimum over days; description: {freq} minimum of daily maximum temperature.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then the minimum daily maximum temperature for period \(j\) is:

\[TXn_j = min(TX_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.TXx(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum temperature (realm: atmos)

Maximum of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_max().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

TXx (DataArray) – Maximum daily maximum temperature (air_temperature) [K], with additional attributes: cell_methods: time: maximum over days; description: {freq} maximum of daily maximum temperature.

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then the maximum daily maximum temperature for period \(j\) is:

\[TXx_j = max(TX_{ij})\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.WD(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Warm and dry days (realm: atmos)

Number of days with temperature above a given percentile and precipitation below a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_and_dry_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – Daily 75th percentile of temperature. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – Daily 25th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

WD (DataArray) – Warm and dry days [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is above {tas_per_thresh}th percentile and precipitation is below {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.WSDI(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', resample_before_rl: bool = True, bootstrap: bool = False, op: str = '>', ds: Dataset = None) → DataArray

Warm spell duration index (realm: atmos)

Number of days part of a percentile-defined warm spell. A warm spell occurs when the maximum daily temperature is above a given percentile for a given number of consecutive days.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_spell_duration_index(). With injected parameters: window=6.

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• tasmax_per (str or DataArray) – percentile(s) of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• resample_before_rl (boolean) – Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. Default : True.

• bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.

• op ({‘>’, ‘gt’, ‘ge’, ‘>=’}) – Comparison operation. Default: “>”. Default : >.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

WSDI (DataArray) – Warm-spell duration index (number_of_days_with_air_temperature_above_threshold) [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days with at least {window} consecutive days where the maximum daily temperature is above the {tasmax_per_thresh}th percentile(s). A {tasmax_per_window} day(s) window, centred on each calendar day in the {tasmax_per_period} period, is used to compute the {tasmax_per_thresh}th percentile(s).

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.WW(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Warm and wet days (realm: atmos)

Number of days with temperature above a given percentile and precipitation above a given percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_and_wet_days().

Parameters

• tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]

• tas_per (str or DataArray) – Daily 75th percentile of temperature. Default : ds.tas_per. [Required units : [temperature]]

• pr_per (str or DataArray) – Daily 75th percentile of wet day precipitation flux. Default : ds.pr_per. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

WW (DataArray) – Warm and wet days [days], with additional attributes: cell_methods: time: sum over days; description: {freq} number of days where temperature is above {tas_per_thresh}th percentile and precipitation is above {pr_per_thresh}th percentile.

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written (Beniston [2009])

References

European Climate Assessment & Dataset https://www.ecad.eu/

xclim.indicators.icclim.iter_indicators()

Iterate over the (name, indicator) pairs in the icclim indicator module.

xclim.indicators.icclim.vDTR(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Variability of daily temperature range (realm: atmos)

The average day-to-day variation in daily temperature range.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range_variability().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

• indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

vDTR (DataArray) – Mean absolute day-to-day difference in DTR (air_temperature) [K], with additional attributes: cell_methods: time range within days time: difference over days time: mean over days; description: {freq} mean diurnal temperature range variability, defined as the average day-to-day variation in daily temperature range for the given time period.

Notes

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then calculated is the absolute day-to-day differences in period \(j\) is:

\[vDTR_j = \frac{ \sum_{i=2}^{I} |(TX_{ij}-TN_{ij})-(TX_{i-1,j}-TN_{i-1,j})| }{I}\]

References

European Climate Assessment & Dataset https://www.ecad.eu/

ANUCLIM indices

The ANUCLIM (v6.1) software package BIOCLIM sub-module produces a set of bioclimatic parameters derived values of temperature and precipitation. The methods in this module are wrappers around a subset of corresponding methods of xclim.indices.

Furthermore, according to the ANUCLIM user-guide [Xu and Hutchinson, 2010], input values should be at a weekly or monthly frequency. However, the implementation here expands these definitions and can calculate the result with daily input data.

xclim.indicators.anuclim.P10_MeanTempWarmestQuarter(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Mean temperature of warmest/coldest quarter. (realm: atmos)

The warmest (or coldest) quarter of the year is determined, and the mean temperature of this period is calculated. If the input data frequency is daily (“D”) or weekly (“W”), quarters are defined as 13-week periods, otherwise as three (3) months.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean_warmcold_quarter(). With injected parameters: op=warmest.

Parameters

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P10_MeanTempWarmestQuarter (DataArray) – (air_temperature) [K], with additional attributes: cell_methods: time: mean

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P11_MeanTempColdestQuarter(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Mean temperature of warmest/coldest quarter. (realm: atmos)

The warmest (or coldest) quarter of the year is determined, and the mean temperature of this period is calculated. If the input data frequency is daily (“D”) or weekly (“W”), quarters are defined as 13-week periods, otherwise as three (3) months.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean_warmcold_quarter(). With injected parameters: op=coldest.

Parameters

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P11_MeanTempColdestQuarter (DataArray) – (air_temperature) [K], with additional attributes: cell_methods: time: mean

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P12_AnnualPrecip(pr: Union[DataArray, str] = 'pr', *, thresh: Quantified = '0 mm/d', freq: str = 'YS', ds: Dataset = None) → DataArray

Accumulated total precipitation. (realm: atmos)

The total accumulated precipitation from days where precipitation exceeds a given amount. A threshold is provided in order to allow the option of reducing the impact of days with trace precipitation amounts on period totals.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot().

Parameters

• pr (str or DataArray) – Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. Default : ds.pr. [Required units : [precipitation]]

• thresh (quantity (string or DataArray, with units)) – Threshold over which precipitation starts being cumulated. Default : 0 mm/d. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P12_AnnualPrecip (DataArray) – Annual Precipitation (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P13_PrecipWettestPeriod(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Precipitation of the wettest/driest day, week, or month, depending on the time step. (realm: atmos)

The wettest (or driest) period is determined, and the total precipitation of this period is calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot_wetdry_period(). With injected parameters: op=wettest.

Parameters

• pr (str or DataArray) – Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P13_PrecipWettestPeriod (DataArray) – (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P14_PrecipDriestPeriod(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Precipitation of the wettest/driest day, week, or month, depending on the time step. (realm: atmos)

The wettest (or driest) period is determined, and the total precipitation of this period is calculated.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot_wetdry_period(). With injected parameters: op=driest.

Parameters

• pr (str or DataArray) – Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P14_PrecipDriestPeriod (DataArray) – (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P15_PrecipSeasonality(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Precipitation Seasonality (C of V). (realm: atmos)

The annual precipitation Coefficient of Variation (C of V) expressed in percent. Calculated as the standard deviation of precipitation values for a given year expressed as a percentage of the mean of those values.

This indicator will check for missing values according to the method “from_context”. Based on indice precip_seasonality().

Parameters

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Units need to be defined as a rate (e.g. mm d-1, mm week-1). Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P15_PrecipSeasonality (DataArray) – , with additional attributes: cell_methods: time: standard_deviation; description: “The standard deviation of the precipitation estimates expressed as a percentage of the mean of those estimates.”

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

If input units are in mm s-1 (or equivalent), values are converted to mm/day to avoid potentially small denominator values.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P16_PrecipWettestQuarter(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Total precipitation of wettest/driest quarter. (realm: atmos)

The wettest (or driest) quarter of the year is determined, and the total precipitation of this period is calculated. If the input data frequency is daily (“D”) or weekly (“W”) quarters are defined as 13-week periods, otherwise are three (3) months.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot_wetdry_quarter(). With injected parameters: op=wettest.

Parameters

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P16_PrecipWettestQuarter (DataArray) – (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P17_PrecipDriestQuarter(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Total precipitation of wettest/driest quarter. (realm: atmos)

The wettest (or driest) quarter of the year is determined, and the total precipitation of this period is calculated. If the input data frequency is daily (“D”) or weekly (“W”) quarters are defined as 13-week periods, otherwise are three (3) months.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot_wetdry_quarter(). With injected parameters: op=driest.

Parameters

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P17_PrecipDriestQuarter (DataArray) – (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P18_PrecipWarmestQuarter(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Total precipitation of warmest/coldest quarter. (realm: atmos)

The warmest (or coldest) quarter of the year is determined, and the total precipitation of this period is calculated. If the input data frequency is daily (“D) or weekly (“W”), quarters are defined as 13-week periods, otherwise are 3 months.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot_warmcold_quarter(). With injected parameters: op=warmest.

Parameters

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P18_PrecipWarmestQuarter (DataArray) – (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P19_PrecipColdestQuarter(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Total precipitation of warmest/coldest quarter. (realm: atmos)

The warmest (or coldest) quarter of the year is determined, and the total precipitation of this period is calculated. If the input data frequency is daily (“D) or weekly (“W”), quarters are defined as 13-week periods, otherwise are 3 months.

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot_warmcold_quarter(). With injected parameters: op=coldest.

Parameters

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Default : ds.pr. [Required units : [precipitation]]

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P19_PrecipColdestQuarter (DataArray) – (lwe_thickness_of_precipitation_amount) [mm], with additional attributes: cell_methods: time: sum

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P1_AnnMeanTemp(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Mean of daily average temperature. (realm: atmos)

Resample the original daily mean temperature series by taking the mean over each period.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean().

Parameters

• tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P1_AnnMeanTemp (DataArray) – Annual Mean Temperature (air_temperature) [K], with additional attributes: cell_methods: time: mean

Notes

Let \(TN_i\) be the mean daily temperature of day \(i\), then for a period \(p\) starting at day \(a\) and finishing on day \(b\):

\[TG_p = \frac{\sum_{i=a}^{b} TN_i}{b - a + 1}\]

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P2_MeanDiurnalRange(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', op: str | Callable = 'mean', ds: Dataset = None) → DataArray

Statistics of daily temperature range. (realm: atmos)

The mean difference between the daily maximum temperature and the daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• op ({‘mean’, ‘max’, ‘min’, ‘std’}) – Reduce operation. Can either be a DataArray method or a function that can be applied to a DataArray. Default : mean.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P2_MeanDiurnalRange (DataArray) – Mean Diurnal Range [K], with additional attributes: cell_methods: time: range

Notes

For a default calculation using op=’mean’ :

Let \(TX_{ij}\) and \(TN_{ij}\) be the daily maximum and minimum temperature at day \(i\) of period \(j\). Then the mean diurnal temperature range in period \(j\) is:

\[DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}\]

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P3_Isothermality(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Isothermality. (realm: atmos)

The mean diurnal temperature range divided by the annual temperature range.

This indicator will check for missing values according to the method “from_context”. Based on indice isothermality().

Parameters

• tasmin (str or DataArray) – Average daily minimum temperature at daily, weekly, or monthly frequency. Default : ds.tasmin. [Required units : [temperature]]

• tasmax (str or DataArray) – Average daily maximum temperature at daily, weekly, or monthly frequency. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P3_Isothermality (DataArray) – , with additional attributes: cell_methods: time: range; description: The mean diurnal range (P2) divided by the Annual Temperature Range (P7).

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the output with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P4_TempSeasonality(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Temperature seasonality (coefficient of variation). (realm: atmos)

The annual temperature coefficient of variation expressed in percent. Calculated as the standard deviation of temperature values for a given year expressed as a percentage of the mean of those temperatures.

This indicator will check for missing values according to the method “from_context”. Based on indice temperature_seasonality().

Parameters

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P4_TempSeasonality (DataArray) – , with additional attributes: cell_methods: time: standard_deviation; description: “The standard deviation of the mean temperatures expressed as a percentage of the mean of those temperatures. For this calculation, the mean in degrees Kelvin is used. This avoids the possibility of having to divide by zero, but it does mean that the values are usually quite small.”

Notes

For this calculation, the mean in degrees Kelvin is used. This avoids the possibility of having to divide by zero, but it does mean that the values are usually quite small.

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P5_MaxTempWarmestPeriod(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Highest max temperature. (realm: atmos)

The maximum value of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_max().

Parameters

• tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P5_MaxTempWarmestPeriod (DataArray) – Max Temperature of Warmest Period (air_temperature) [K], with additional attributes: description: The highest maximum temperature in all periods of the year.; cell_methods: time: maximum

Notes

Let \(TX_{ij}\) be the maximum temperature at day \(i\) of period \(j\). Then the maximum daily maximum temperature for period \(j\) is:

\[TXx_j = max(TX_{ij})\]

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P6_MinTempColdestPeriod(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Lowest minimum temperature. (realm: atmos)

Minimum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_min().

Parameters

• tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P6_MinTempColdestPeriod (DataArray) – Min Temperature of Coldest Period (air_temperature) [K], with additional attributes: description: The lowest minimum temperature in all periods of the year.; cell_methods: time: minimum

Notes

Let \(TN_{ij}\) be the minimum temperature at day \(i\) of period \(j\). Then the minimum daily minimum temperature for period \(j\) is:

\[TNn_j = min(TN_{ij})\]

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P7_TempAnnualRange(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Calculate the extreme temperature range as the maximum of daily maximum temperature minus the minimum of daily minimum temperature. (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice extreme_temperature_range().

Parameters

• tasmin (str or DataArray) – Minimum surface temperature. Default : ds.tasmin. [Required units : K]

• tasmax (str or DataArray) – Maximum surface temperature. Default : ds.tasmax. [Required units : K]

• freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P7_TempAnnualRange (DataArray) – Temperature Annual Range [K], with additional attributes: cell_methods: time: range

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P8_MeanTempWettestQuarter(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Mean temperature of wettest/driest quarter. (realm: atmos)

The wettest (or driest) quarter of the year is determined, and the mean temperature of this period is calculated. If the input data frequency is daily (“D”) or weekly (“W”), quarters are defined as 13-week periods, otherwise are 3 months.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean_wetdry_quarter(). With injected parameters: op=wettest.

Parameters

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P8_MeanTempWettestQuarter (DataArray) – (air_temperature) [K], with additional attributes: cell_methods: time: mean

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.P9_MeanTempDriestQuarter(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Mean temperature of wettest/driest quarter. (realm: atmos)

The wettest (or driest) quarter of the year is determined, and the mean temperature of this period is calculated. If the input data frequency is daily (“D”) or weekly (“W”), quarters are defined as 13-week periods, otherwise are 3 months.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean_wetdry_quarter(). With injected parameters: op=driest.

Parameters

• tas (str or DataArray) – Mean temperature at daily, weekly, or monthly frequency. Default : ds.tas. [Required units : [temperature]]

• pr (str or DataArray) – Total precipitation rate at daily, weekly, or monthly frequency. Default : ds.pr. [Required units : [precipitation]]

• freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.

• ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

P9_MeanTempDriestQuarter (DataArray) – (air_temperature) [K], with additional attributes: cell_methods: time: mean

Notes

According to the ANUCLIM user-guide (Xu and Hutchinson [2010], ch. 6), input values should be at a weekly (or monthly) frequency. However, the xclim.indices implementation here will calculate the result with input data with daily frequency as well. As such weekly or monthly input values, if desired, should be calculated prior to calling the function.

References

ANUCLIM https://fennerschool.anu.edu.au/files/anuclim61.pdf (ch. 6)

xclim.indicators.anuclim.iter_indicators()

Iterate over the (name, indicator) pairs in the anuclim indicator module.