Generic indices recipes

You will find below a few example of icclim v6 Generic indices/indicators.

import icclim
from icclim import build_threshold

# Change `data` to your own netcdf file path.
data = "netcdf_files/gridded.1991-2010.nc"

Count occurrences

Occurrences of events where tas is between 20 and 30 degree Celsius and precipitation are above 3 mm/day.

# Equivalent to using `okay_temp = "<= 30 deg_C AND >= 20 deg_C"`
okay_temp = build_threshold("<= 30 deg_C") & build_threshold(">= 20 deg_C")
some_rain = icclim.build_threshold("> 3 mm/day")

dataset = icclim.count_occurrences(
    in_files={
        "tmax": {"study": data, "thresholds": okay_temp},
        "precip": {"study": data, "thresholds": some_rain},
    }
)
# .compute must be called to ask dask to run the actual computation
conputed_data = dataset.count_occurrences.compute()

tx99p_dataset = icclim.count_occurrences(
    in_files=data, var_name="tasmax", threshold=">= 99 doy_per"
)
# .compute must be called to ask dask to run the actual computation
tx99p = dataset.count_occurrences.compute()

Sum

Sum of precipitation that are above 4 mm/day.

rain_sum_above_4mm = icclim.sum(
    in_files=data, var_name="precip", threshold="> 4 mm/day"
).sum.compute()

Standard Deviation

Standard deviation of tas variable.

tas_std = icclim.std(in_files=data, var_name="tas").std.compute()

Average

Average of the tas variable, per year by default.

tas_average = icclim.average(in_files=data, var_name="tas").average.compute()

Average of the tas values that are above the 87th period percentile (computed on the whole period here), per year by default.

tas_average_above_percentile_of_period = icclim.average(
    in_files=data, var_name="tas", threshold="> 87 period_per"
).average.compute()

Maximum Consecutive Occurrences

Almost equivalent to ECAD’s index CDD (Consecutive Dry Days, days when pr is below 1 mm/day).

CDD = icclim.max_consecutive_occurrence(
    in_files=data, var_name="precip", threshold="< 1.3 mm/day"
).max_consecutive_occurrence.compute()

Sum of Spell Lengths

Almost equivalent to ECAD’s index WSDI (Warm Spell Duration Index, maximum consecutive occurrence of tasmax > 90th doy percentile)

custom_wsdi = icclim.sum_of_spell_lengths(
    in_files=data, var_name="precip", threshold="> 90 doy_per AND > 28 degC"
).sum_of_spell_lengths.compute()

Excess

Excess of minimal daily temperature above the 22 daily percentile threshold computed overs the 1991-1995 reference period, with a focus on the June to August periods.

jja_tmin_excess = (
    icclim.excess(
        climp_file,
        var_name=["tmin"],
        threshold=icclim.build_threshold(
            "22 doy_per", base_period_time_range=["1991-01-01", "1995-12-31"]
        ),
        slice_mode="jja",
    )
    .compute()
    .excess
)

Deficit

Deficit of minimal daily temperature below 17 degree Celsius.

result13 = icclim.index(
    climp_file,
    var_name=["tmin"],
    index_name="deficit",
    threshold=build_threshold("17 degC"),
).compute()

Fraction of Total

Fraction of precipitations above the 75th period percentile, where percentiles are computed only on values above 1 mm/day This is equivalent to the ECAD’s index R75pTOT.

result14 = (
    icclim.fraction_of_total(
        climp_file,
        var_name=["precip"],
        threshold=build_threshold("> 75 period_per", threshold_min_value="1 mm/day"),
    )
    .compute()
    .fraction_of_total
)

Maximum

Maximum of tas temperature per month.

max_of_tas = (
    icclim.maximum(
        climp_file,
        var_name=["tas"],
        slice_mode="month",
    )
    .compute()
    .maximum
)

Minimum

Minimum of tas temperature per month.

min_of_tas = (
    icclim.minimum(
        climp_file,
        var_name=["tas"],
        slice_mode="month",
    )
    .compute()
    .minimum
)

Max of Rolling Sum

Maximum of rolling sum of precipitation that are above the period median, where the median is computed for the whole period (default behavior when there is no base_period_time_range) only on values above 1mm/day.

max_of_rolling_sum = (
    icclim.index(
        climp_file,
        index_name="max_of_rolling_sum",
        var_name=["precip"],
        threshold=build_threshold(">= 50 period_per", threshold_min_value="1 mmday"),
    )
    .compute()
    .max_of_rolling_sum
)

Min of Rolling Sum

Minimum of rolling sum of precipitation that are above the period median, where the median is computed for the whole period (default behavior when there is no base_period_time_range) only on values above 1mm/day.

min_of_rolling_sum = (
    icclim.min_of_rolling_sum(
        climp_file,
        var_name=["precip"],
        threshold=build_threshold(">= 50 period_per", threshold_min_value="1 mmday"),
    )
    .compute()
    .min_of_rolling_sum
)

Max of Rolling Average

Maximum of rolling average of precipitation that are above the period median, where the median is computed for the whole period (default behavior when there is no base_period_time_range) only on values above 1mm/day.

max_of_rolling_average = (
    icclim.index(
        climp_file,
        index_name="max_of_rolling_average",
        var_name=["precip"],
        threshold=build_threshold(">= 50 period_per", threshold_min_value="1 mmday"),
    )
    .compute()
    .max_of_rolling_average
)

Min of Rolling Average

Minimum of rolling average of precipitation that are above the period median, where the median is computed for the whole period (default behavior when there is no base_period_time_range) only on values above 1mm/day.

min_of_rolling_average = (
    icclim.min_of_rolling_average(
        climp_file,
        var_name=["precip"],
        threshold=build_threshold(">= 50 period_per", threshold_min_value="1 mmday"),
    )
    .compute()
    .min_of_rolling_average
)

Mean of difference

Mean of the difference between tasmax in tasmin. It’s a generification of ECAD’s index DTR.

dtr = (
    icclim.index(
        climp_file,
        index_name="mean_of_difference",
        var_name=["tmax", "tmin"],
    )
    .compute()
    .mean_of_difference
)

Difference of extremes

Difference of the maximum of tasmax and the minimum of tasmin. It’s a generification of ECAD’s index ETR.

dtr = (
    icclim.index(
        climp_file,
        index_name="difference_of_extremes",
        var_name=["tmax", "tmin"],
    )
    .compute()
    .difference_of_extremes
)

Difference of means

Difference between averaged tas and the averaged tas values of the reference period. Also known as the anomaly.

anomaly = (
    icclim.difference_of_means(
        climp_file,
        var_name=["tas"],
        base_period_time_range=["1991-01-01", "1995-12-31"],
    )
    .compute()
    .difference_of_means
)

Mean Of Absolute One Time Step Difference

Mean of absolute difference between tasmax and tasmin with a one time step lag (usually 1 day). This is equivalent to the pseudo-code:

a = tasmax[T + 1] - tasmin[T + 1]
b = tasmax[T] - tasmin[T]
average(a - b)

It’s a generification of ECAD’s index vDTR.

result = (
    icclim.mean_of_absolute_one_time_step_difference(
        climp_file,
        var_name=["tmax", "tmin"],
    )
    .compute()
    .mean_of_absolute_one_time_step_difference
)