Calculate custom index: number of days with freezing mean temperature

Example notebook that runs icclim.

The example calculates the number of days when the minimum temperature is freezing or below for the dataset chosen by the user on C4I. It uses the custom user index functionality of icclim.

We assume to have the tas variable in netCDF files in a ./data folder. The data can be dowloaded using the metalink provided with this notebook. The data described in a .metalink file can be dowloaded with tools such as aria2 or a browser plugin such as DownThemAll! If you wish to use a different dataset, you can use the climate 4 impact portal to search and select the data you wish to use and a metalink file to the ESGF data will be provided.

The data is read using xarray and a plot of the time series over a specific region is generated, as well as an average spatial map. Several output types examples are shown.

The dataset that is expected for this notebook are tas parameter for one specific climate model and experiment as well as one member. The time period should be continuous.

To keep this example fast to run, the following period is considered: 2015-01-01 to 2019-12-31, and plots are shown over European region.

Packages Installation

%pip install icclim matplotlib nc_time_axis

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Note: you may need to restart the kernel to use updated packages.

import datetime
import sys
from pathlib import Path

import cartopy.crs as ccrs
import cftime
import icclim
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr

print("python: ", sys.version)
print("numpy: ", np.__version__)
print("xarray: ", xr.__version__)
print("pandas: ", pd.__version__)
print("icclim: ", icclim.__version__)
print("cftime: ", cftime.__version__)

python:  3.11.7 | packaged by conda-forge | (main, Dec 15 2023, 08:38:37) [GCC 12.3.0]
numpy:  1.26.2
xarray:  2023.10.1
pandas:  2.1.4
icclim:  6.6.0
cftime:  1.6.3

Specification of parameters

# studied period
dt1 = datetime.datetime(2015, 1, 1, tzinfo=datetime.timezone.utc)
dt2 = datetime.datetime(2019, 12, 31, tzinfo=datetime.timezone.utc)
out_f = "ndays_tas_below_freezing_icclim.nc"
data_dir = Path("data")
filenames = [str(f) for f in data_dir.glob("tas_day_GFDL-ESM4*.nc")]
filenames

['data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_19300101-19491231.nc',
 'data/tas_day_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20550101-20741231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_19700101-19891231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_18700101-18891231.nc',
 'data/tas_day_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20350101-20541231.nc',
 'data/tas_day_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20150101-20341231.nc',
 'data/tas_day_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20750101-20941231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_19100101-19291231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_18500101-18691231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_18900101-19091231.nc',
 'data/tas_day_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20950101-21001231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_19900101-20091231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_20100101-20141231.nc',
 'data/tas_day_GFDL-ESM4_historical_r1i1p1f1_gr1_19500101-19691231.nc']

from icclim.generic_indices.registry import GenericIndicatorRegistry

icclim.index(
    index_name=GenericIndicatorRegistry.CountOccurrences,
    in_files=filenames,
    threshold="< 0 deg_C",
    var_name="tas",
    slice_mode="year",
    time_range=[dt1, dt2],
    out_file=out_f,
    logs_verbosity="HIGH",
)

2024-01-19 15:52:48,544    ********************************************************************************************
2024-01-19 15:52:48,546    *                                                                                          *
2024-01-19 15:52:48,547    *          icclim                6.6.0   *
2024-01-19 15:52:48,549    *                                                                                          *
2024-01-19 15:52:48,550    *                                                                                          *
2024-01-19 15:52:48,550    *          Fri Jan 19 14:52:48 2024                                                    *
2024-01-19 15:52:48,551    *                                                                                          *
2024-01-19 15:52:48,551    *          BEGIN EXECUTION                                                                 *
2024-01-19 15:52:48,552    *                                                                                          *
2024-01-19 15:52:48,553    ********************************************************************************************
2024-01-19 15:52:48,553 Processing: 0%
/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable does not have a `cell_methods` attribute.
  _check_cell_methods(
/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:45: UserWarning: Variable does not have a `standard_name` attribute.
  check_valid(vardata, "standard_name", data["standard_name"])
2024-01-19 15:52:54,024 Processing: 100%
2024-01-19 15:52:54,025    ********************************************************************************************
2024-01-19 15:52:54,026    *                                                                                          *
2024-01-19 15:52:54,027    *          icclim                6.6.0   *
2024-01-19 15:52:54,027    *                                                                                          *
2024-01-19 15:52:54,028    *                                                                                          *
2024-01-19 15:52:54,029    *          Fri Jan 19 14:52:54 2024                                                    *
2024-01-19 15:52:54,029    *                                                                                          *
2024-01-19 15:52:54,030    *          END EXECUTION                                                                   *
2024-01-19 15:52:54,031    *                                                                                          *
2024-01-19 15:52:54,031    *          CP SECS = 12.588339775                                                            *
2024-01-19 15:52:54,032    *                                                                                          *
2024-01-19 15:52:54,032    ********************************************************************************************

<xarray.Dataset>
Dimensions:            (lat: 180, lon: 288, time: 5, bounds: 2)
Coordinates:
    height             float64 2.0
  * lat                (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
  * lon                (lon) float64 0.625 1.875 3.125 ... 356.9 358.1 359.4
  * time               (time) object 2015-07-02 00:00:00 ... 2019-07-02 00:00:00
  * bounds             (bounds) int64 0 1
Data variables:
    count_occurrences  (time, lat, lon) int64 dask.array<chunksize=(1, 180, 288), meta=np.ndarray>
    time_bounds        (time, bounds) object 2015-01-01 00:00:00 ... 2019-12-...
Attributes:
    title:        number_of_days_when_average_air_temperature_is_lower_than_t...
    references:   icclim
    institution:  Climate impact portal (https://climate4impact.eu)
    history:      \n [2024-01-19 14:52:50] Calculation of count_occurrences i...
    source:
    Conventions:  CF-1.6

xarray.Dataset

• Dimensions:
  • lat: 180
  • lon: 288
  • time: 5
  • bounds: 2
• Coordinates: (5)
  • height
    ()
    float64
    2.0

    long_name :

    height

    units :

    m

    cell_methods :

    time: point

    axis :

    Z

    positive :

    up

    standard_name :

    height

    description :

    ~2 m standard surface air temperature and surface humidity height

    array(2.)

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    cell_methods :

    time: point

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    0.625 1.875 3.125 ... 358.1 359.4

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    cell_methods :

    time: point

    array([  0.625,   1.875,   3.125, ..., 356.875, 358.125, 359.375])

  • time
    (time)
    object
    2015-07-02 00:00:00 ... 2019-07-...

    bounds :

    time_bounds

    array([cftime.DatetimeNoLeap(2015, 7, 2, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2016, 7, 2, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2017, 7, 2, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2018, 7, 2, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2019, 7, 2, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • bounds
    (bounds)
    int64
    0 1

    array([0, 1])

• Data variables: (2)
  • count_occurrences
    (time, lat, lon)
    int64
    dask.array<chunksize=(1, 180, 288), meta=np.ndarray>

    long_name :

    Number of days when average air temperature is lower than 273.15 K for each year.

    units :

    d

    cell_methods :

    time: sum over days

    cell_measures :

    area: areacella

    standard_name :

    number_of_days_when_average_air_temperature_is_lower_than_threshold

    interp_method :

    conserve_order2

    original_name :

    tas

    history :

    Array Chunk Bytes 1.98 MiB 405.00 kiB Shape (5, 180, 288) (1, 180, 288) Dask graph 5 chunks in 50 graph layers Data type int64 numpy.ndarray

  • time_bounds
    (time, bounds)
    object
    2015-01-01 00:00:00 ... 2019-12-...

    array([[cftime.datetime(2015, 1, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True),
            cftime.DatetimeNoLeap(2015, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.datetime(2016, 1, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True),
            cftime.DatetimeNoLeap(2016, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.datetime(2017, 1, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True),
            cftime.DatetimeNoLeap(2017, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.datetime(2018, 1, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True),
            cftime.DatetimeNoLeap(2018, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.datetime(2019, 1, 1, 0, 0, 0, 0, calendar='noleap', has_year_zero=True),
            cftime.DatetimeNoLeap(2019, 12, 31, 0, 0, 0, 0, has_year_zero=True)]],
          dtype=object)

• Indexes: (4)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([             0.625, 1.8749999999999998,              3.125,
                        4.375,              5.625,              6.875,
                        8.125,              9.375,             10.625,
                       11.875,
           ...
                      348.125,            349.375,            350.625,
                      351.875,            353.125,            354.375,
                      355.625,            356.875,            358.125,
                      359.375],
          dtype='float64', name='lon', length=288))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2015-07-02 00:00:00, 2016-07-02 00:00:00, 2017-07-02 00:00:00,
                 2018-07-02 00:00:00, 2019-07-02 00:00:00],
                dtype='object', length=5, calendar='noleap', freq='365D'))

  • bounds
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int64', name='bounds'))

• Attributes: (6)

  title :

  number_of_days_when_average_air_temperature_is_lower_than_threshold

  references :

  icclim

  institution :

  Climate impact portal (https://climate4impact.eu)

  history :

  [2024-01-19 14:52:50] Calculation of count_occurrences index (annual) - icclim version: 6.6.0

  source :

  Conventions :

  CF-1.6

Plot setup

with xr.open_dataset(out_f, decode_times=False) as ds:
    nf_xr = ds
    ds["time"] = xr.decode_cf(ds).time

print(nf_xr)

# Select a single x,y combination from the data
longitude = nf_xr["count_occurrences"]["lon"].sel(lon=3.5, method="nearest")
latitude = nf_xr["count_occurrences"]["lat"].sel(lat=44.2, method="nearest")

print("Long, Lat values:", longitude, latitude)

<xarray.Dataset>
Dimensions:            (lat: 180, lon: 288, time: 5, bounds: 2)
Coordinates:
    height             float64 ...
  * lat                (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
  * lon                (lon) float64 0.625 1.875 3.125 ... 356.9 358.1 359.4
  * time               (time) object 2015-07-02 00:00:00 ... 2019-07-02 00:00:00
  * bounds             (bounds) int64 0 1
Data variables:
    count_occurrences  (time, lat, lon) int64 ...
    time_bounds        (time, bounds) int64 ...
Attributes:
    title:        number_of_days_when_average_air_temperature_is_lower_than_t...
    references:   icclim
    institution:  Climate impact portal (https://climate4impact.eu)
    history:      \n [2024-01-19 14:52:50] Calculation of count_occurrences i...
    source:
    Conventions:  CF-1.6
Long, Lat values: <xarray.DataArray 'lon' ()>
array(3.125)
Coordinates:
    height   float64 ...
    lon      float64 3.125
Attributes:
    long_name:      longitude
    units:          degrees_east
    axis:           X
    bounds:         lon_bnds
    standard_name:  longitude
    cell_methods:   time: point <xarray.DataArray 'lat' ()>
array(44.5)
Coordinates:
    height   float64 ...
    lat      float64 44.5
Attributes:
    long_name:      latitude
    units:          degrees_north
    axis:           Y
    bounds:         lat_bnds
    standard_name:  latitude
    cell_methods:   time: point

Subset and plot count_occurrences

# Slice the data spatially using a single lat/lon point
one_point = nf_xr["count_occurrences"].sel(lat=latitude, lon=longitude)

# Use xarray to create a quick time series plot
one_point.plot.line()
plt.show()

# You can clean up your plot as you wish using standard matplotlib approaches
f, ax = plt.subplots(figsize=(12, 6))
one_point.plot.line(
    hue="lat",
    marker="o",
    ax=ax,
    color="grey",
    markerfacecolor="purple",
    markeredgecolor="purple",
)
ax.set(title="Time Series For a Single Lat / Lon Location")

# Uncomment the line below if you wish to export the figure as a .png file
# plt.savefig("single_point_timeseries.png")
plt.show()

# Convert to dataframe -- then this can easily be exported to a csv
one_point_df = one_point.to_dataframe()
# View just the first 5 rows of the data
one_point_df.head()

# Export data to .csv file
# one_point_df.to_csv("one-location.csv")

	height	lat	lon	count_occurrences
time
2015-07-02 00:00:00	2.0	44.5	3.125	51
2016-07-02 00:00:00	2.0	44.5	3.125	41
2017-07-02 00:00:00	2.0	44.5	3.125	29
2018-07-02 00:00:00	2.0	44.5	3.125	39
2019-07-02 00:00:00	2.0	44.5	3.125	40

# Time subsetting: this is just an example on how to do it
start_date = "2018-01-01"
end_date = "2019-12-31"

nf = nf_xr["count_occurrences"].sel(time=slice(start_date, end_date))

# Quickly plot the data using xarray.plot()
nf.plot(x="lon", y="lat", col="time", col_wrap=1)

plt.suptitle("Two Time Steps of Number of freezing days", y=1.03)
plt.show()

# Set spatial extent and centre
central_lat = 47.0
central_lon = 1.0
extent = [-30, 30, 30, 56]  # Western Europe

# Calculate time average
nf_avg = nf.mean(dim="time", keep_attrs=True)

# Set plot projection
map_proj = ccrs.AlbersEqualArea(
    central_longitude=central_lon, central_latitude=central_lat
)

# Define plot
f, ax = plt.subplots(figsize=(14, 6), subplot_kw={"projection": map_proj})

# Plot data with proper colormap scale range
levels = np.arange(0, 90, 5)
p = nf_avg.plot(levels=levels, cmap="RdBu_r", transform=ccrs.PlateCarree())

# Plot information
plt.suptitle("Two Time Steps of Europe number of freezing days", y=1)

# Add the coastlines to axis and set extent
ax.coastlines()
ax.gridlines()
ax.set_extent(extent)

# Save plot as png
plt.savefig("c4i_nf_icclim.png")

# Re-order longitude so that there is no blank line at 0 deg because 0 deg is within our spatial selection
nf_avg.coords["lon"] = (nf_avg.coords["lon"] + 180) % 360 - 180
nf_avg = nf_avg.sortby(nf_avg.lon)

# Define plot
f, ax = plt.subplots(figsize=(14, 6), subplot_kw={"projection": map_proj})

# Define colorscale
levels = np.arange(0, 90, 15)

# Contours lines
p = nf_avg.plot.contour(
    levels=levels, colors="k", linewidths=0.5, transform=ccrs.PlateCarree()
)

# Contour filled colors
p = nf_avg.plot.contourf(
    levels=levels, cmap="RdBu_r", extend="both", transform=ccrs.PlateCarree()
)

# Plot information
plt.suptitle("Two Time Steps of Europe number of freezing days", y=1)

# Add the coastlines to axis and set extent
ax.coastlines()
ax.gridlines()
ax.set_extent(extent)

# Save plot as png
plt.savefig("c4i_nf_contours_icclim.png")