Compatible with icclim 7.1.0+

Tutorial on calculating a climate index for the Mean Diurnal Temperature Range

About

This tutorial will demonstrate how to calculate a climate index using a specific climate indices package. The example provided is of the Mean Diurnal Temperature Range index, but similar principles can be applied to many other types of single or multi-variable indices available in the icclim package.

The data is provided by Copernicus Climate Change Service (C3S) and includes daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations (E-OBS) of maximum temperature, minimum temperature, and precipitation.

The tutorial will first show how to download the necessary data from the C3S Climate Data Store (CDS). It will then describe how to calculate the Mean Diurnal Temperature Range index, and finally plot a map of the Mean Diurnal Temperature Range index climatology over Europe.

The steps shown in this tutorial can be applied to other climate datasets with the proper variable to calculate specific climate indices. For the current index, Mean Diurnal Temperature Range, the Daily Maximum and Minimum Temperature are needed.

How to access the notebook

This tutorial is in the form of a Jupyter notebook.

NBViewer
(this will not run the notebook, only render it)

To run this notebook in your own environment, we suggest you install Anaconda, which contains most of the libraries you will need. You also need at least python version 3.8 for this notebook to work because of some requirements from some packages.

You will need to install icclim (%pip install icclim) for calculating the climate indices, and the CDS API (%pip install cdsapi) for downloading data programatically from the CDS. You will also need to install matplotlib (%pip install matplotlib) and cartopy (%conda install cartopy) to enable plotting of the results. The % character is to ensure installation occurs in your environment. The installation of cartopy requires a conda environment, if not already installed.

Search, download and view data

Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need.

Install CDS API

To install the CDS API, run the following command if not already installed in your environment.

from zipfile import ZipFile

import cartopy.crs as ccrs
import cdsapi
import matplotlib.pyplot as plt
import urllib3

import icclim

%pip install cdsapi

Install icclim

To install icclim, run the following command if not already installed in your environment.

%pip install icclim

Install matplotlib and cartopy

To install matplotlib and cartopy to enable plotting, run the following command if not already installed in your environment. A conda environment is expected as cartopy cannot be correctly installed with pip.

%pip install matplotlib

%conda install -y cartopy

Import libraries

We will be working with data in NetCDF format and calculating climate indices. We will use the icclim package and its dependencies for working with multidimensional arrays, in particular Xarray. We will also need libraries for plotting and viewing data, in this case we will use Matplotlib and Cartopy.

# CDS API
# icclim package for calculating climate indices
# To unzip dataset files from the CDS
# Libraries for working with multidimensional arrays
# To add specific units spelling
# Libraries for plotting and visualising data
# Disable warnings for data download via API
urllib3.disable_warnings()

Enter your CDS API key

We will request data from the Climate Data Store (CDS) programmatically with the help of the CDS API. Let us make use of the option to manually set the CDS API credentials. First, you have to define two variables: URL and KEY which build together your CDS API key. The string of characters that make up your KEY include your personal User ID and CDS API key. To obtain these, first register or login to the CDS (http://cds.climate.copernicus.eu), then visit https://cds.climate.copernicus.eu/api-how-to and copy the string of characters listed after “key:”. Replace the ######### below with this string.

URL = "https://cds.climate.copernicus.eu/api/v2"
KEY = "#########"

Here we specify a data directory in which we will download our data and all output files that we will generate:

DATADIR = "./"

Search for climate data to calculate the Mean Diurnal Temperature Range index

The Mean Diurnal Temperature Range index we will calculate takes two parameters as input, the daily maximum and minimum temperature. Data for those parameters are available as part of the E-OBS daily gridded meteorological data for Europe from 1950 to present, but we will select a shorter period so that the download is faster: we will select the period from 2011 to 2021. We will search for this data on the CDS website: http://cds.climate.copernicus.eu. The specific dataset we will use is the E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations.

Having selected the dataset, we now need to specify what product type, variables, temporal and geographic coverage we are interested in. These can all be selected in the “Download data” tab. In this tab a form appears in which we will select the following parameters to download:

• Product type: Ensemble mean

• Variable: Maximum temperature and Minimum temperature

• Grid resolution: 0.1deg

• Period: 2011_2021

• Version: 25.0e (Latest version)

• Format: Zip file (.zip)

At the end of the download form, select “Show API request”. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cells below). You will do this once for the Maximum and Minimum temperature.

Download data

… having copied the API request into the cell below, running this will retrieve and download the data you requested into your local directory. However, before you run the cell below, the terms and conditions of this particular dataset need to have been accepted in the CDS. The option to view and accept these conditions is given at the end of the download form, just above the “Show API request” option.

c = cdsapi.Client(url=URL, key=KEY)

# For the full period, use period: 'full_period' but it takes a long time to download data
# To download last decade use 'period': '2011_2021',

c.retrieve(
    "insitu-gridded-observations-europe",
    {
        "format": "zip",
        "product_type": "ensemble_mean",
        "variable": [
            "maximum_temperature",
            "minimum_temperature",
        ],
        "grid_resolution": "0.1deg",
        "period": "2011_2021",
        "version": "25.0e",
    },
    "eobs_tasminmax.zip",
)

Inspect Data

We have requested the data in a zip archive. This zip archive contains a file in the NetCDF format. This is a commonly used format for array-oriented scientific data. To read and process this data we will make use of the underlying Xarray library that is used by the software to calculate the climate index. Xarray is an open source project and Python package that makes working with labelled multi-dimensional arrays simple and efficient. We will uncompress the archive and retrieve the filename(s). The archive contain several files, because we have two parameters, both the minimum and the maximum temperatures (tn and tx respectively), but since we requested only one time period, we have a list of one file per parameter.

# Create a ZipFile Object and load eobs_pr.zip in it
with ZipFile("eobs_tasminmax.zip", "r") as zip_obj:
    # Get a list of all archived file names from the zip
    list_of_file_names = zip_obj.namelist()
    # Extract all the contents of zip file in current directory
    zip_obj.extractall()

# List the NetCDF filenames of the dataset

['tx_ens_mean_0.1deg_reg_2011-2021_v25.0e.nc', 'tn_ens_mean_0.1deg_reg_2011-2021_v25.0e.nc']

Calculate Mean Diurnal Temperature Range index using icclim

Let’s calculate the Mean Diurnal Temperature Range index (DTR) function using the icclim software. We retrieve the calculated values in an Xarray dataset, but alternatively we could also write the values automatically in an output NetCDF file using the keyword out_file when calling the icclim function.

# Add out_file='out_icclim.nc' to also output data into a NetCDF file
dtr = icclim.index(
    index_name="DTR", in_files=list_of_file_names, slice_mode="year", out_file="out.nc"
)

2022-08-10 14:33:46,361 --- icclim 5.4.0
2022-08-10 14:33:46,362 --- BEGIN EXECUTION
2022-08-10 14:33:46,362 Processing: 0%
2022-08-10 14:33:46,416 Calculating climate index: DTR
/Users/page/miniconda3/envs/icclimv5/lib/python3.8/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable has a non-conforming cell_methods: Got `time: mean`, which do not include the expected `time: minimum`
  _check_cell_methods(
/Users/page/miniconda3/envs/icclimv5/lib/python3.8/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable has a non-conforming cell_methods: Got `time: mean`, which do not include the expected `time: maximum`
  _check_cell_methods(
/Users/page/miniconda3/envs/icclimv5/lib/python3.8/site-packages/icclim/main.py:322: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs
  result_ds.to_netcdf(
2022-08-10 14:34:45,509 Processing: 100%
2022-08-10 14:34:45,526 --- icclim 5.4.0
2022-08-10 14:34:45,527 --- CPU SECS = 173.529
2022-08-10 14:34:45,529 --- END EXECUTION

Before we plot the results, we can query our newly created Xarray dataset …

dtr

<xarray.Dataset>
Dimensions:      (time: 11, latitude: 465, longitude: 705, bounds: 2)
Coordinates:
  * time         (time) datetime64[ns] 2011-07-02 ... 2021-07-02
  * latitude     (latitude) float64 25.05 25.15 25.25 ... 71.25 71.35 71.45
  * longitude    (longitude) float64 -24.95 -24.85 -24.75 ... 45.25 45.35 45.45
  * bounds       (bounds) int64 0 1
Data variables:
    DTR          (time, latitude, longitude) float32 dask.array<chunksize=(1, 93, 141), meta=np.ndarray>
    time_bounds  (time, bounds) datetime64[ns] 2011-01-01 ... 2021-12-31
Attributes:
    title:        temperature index DTR
    references:   ATBD of the ECA&D indices calculation (https://knmi-ecad-as...
    institution:  Climate impact portal (https://climate4impact.eu)
    history:      Mon Mar 28 11:53:26 2022: ncks --no-abc -d time,22280,26297...
    source:
    Conventions:  CF-1.6

xarray.Dataset

• Dimensions:
  • time: 11
  • latitude: 465
  • longitude: 705
  • bounds: 2
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2011-07-02 ... 2021-07-02

    bounds :

    time_bounds

    array(['2011-07-02T00:00:00.000000000', '2012-07-01T12:00:00.000000000',
           '2013-07-02T00:00:00.000000000', '2014-07-02T00:00:00.000000000',
           '2015-07-02T00:00:00.000000000', '2016-07-01T12:00:00.000000000',
           '2017-07-02T00:00:00.000000000', '2018-07-02T00:00:00.000000000',
           '2019-07-02T00:00:00.000000000', '2020-07-01T12:00:00.000000000',
           '2021-07-02T00:00:00.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float64
    25.05 25.15 25.25 ... 71.35 71.45

    units :

    degrees_north

    long_name :

    Latitude values

    axis :

    Y

    standard_name :

    latitude

    array([25.049861, 25.149861, 25.249861, ..., 71.24986 , 71.34986 , 71.44986 ])

  • longitude
    (longitude)
    float64
    -24.95 -24.85 ... 45.35 45.45

    units :

    degrees_east

    long_name :

    Longitude values

    axis :

    X

    standard_name :

    longitude

    array([-24.95014, -24.85014, -24.75014, ...,  45.24986,  45.34986,  45.44986])

  • bounds
    (bounds)
    int64
    0 1

    array([0, 1])

• Data variables: (2)
  • DTR
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 93, 141), meta=np.ndarray>

    units :

    degree_Celsius

    standard_name :

    air_temperature

    cell_methods :

    tasmin: time: mean tasmax: time: mean time range within days time: mean over days

    history :

    tasmin: tasmax: [2022-08-10 14:33:46] dtr: DTR(tasmin=tn, tasmax=tx, freq='YS') with options check_missing=any - xclim version: 0.37.0

    long_name :

    Mean diurnal temperature range

    description :

    Annual mean diurnal temperature range.

    Array Chunk Bytes 13.76 MiB 51.22 kiB Shape (11, 465, 705) (1, 93, 141) Count 8152 Tasks 275 Chunks Type float32 numpy.ndarray

  • time_bounds
    (time, bounds)
    datetime64[ns]
    2011-01-01 ... 2021-12-31

    array([['2011-01-01T00:00:00.000000000', '2011-12-31T00:00:00.000000000'],
           ['2012-01-01T00:00:00.000000000', '2012-12-31T00:00:00.000000000'],
           ['2013-01-01T00:00:00.000000000', '2013-12-31T00:00:00.000000000'],
           ['2014-01-01T00:00:00.000000000', '2014-12-31T00:00:00.000000000'],
           ['2015-01-01T00:00:00.000000000', '2015-12-31T00:00:00.000000000'],
           ['2016-01-01T00:00:00.000000000', '2016-12-31T00:00:00.000000000'],
           ['2017-01-01T00:00:00.000000000', '2017-12-31T00:00:00.000000000'],
           ['2018-01-01T00:00:00.000000000', '2018-12-31T00:00:00.000000000'],
           ['2019-01-01T00:00:00.000000000', '2019-12-31T00:00:00.000000000'],
           ['2020-01-01T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],
           ['2021-01-01T00:00:00.000000000', '2021-12-31T00:00:00.000000000']],
          dtype='datetime64[ns]')

• Attributes: (6)

  title :

  temperature index DTR

  references :

  ATBD of the ECA&D indices calculation (https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf)

  institution :

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

  history :

  Mon Mar 28 11:53:26 2022: ncks --no-abc -d time,22280,26297 /data3/Else/EOBSv25.0e/Grid_0.1deg/tn//tn_ensmean_master_rectime.nc /data3/Else/EOBSv25.0e/Grid_0.1deg/tn//tn_ens_mean_0.1deg_reg_2011-2021_v25.0e.nc Mon Mar 28 11:44:47 2022: ncks --no-abc --mk_rec_dmn time /data3/Else/EOBSv25.0e/Grid_0.1deg/tn//tn_ensmean_master.nc /data3/Else/EOBSv25.0e/Grid_0.1deg/tn//tn_ensmean_master_rectime.nc tasmin: tasmax: [2022-08-10 14:33:46] dtr: DTR(tasmin=tn, tasmax=tx, freq='YS') with options check_missing=any - xclim version: 0.37.0 [2022-08-10 14:33:46] Calculation of DTR index(annual time series) from 07-02-2011 to 07-02-2021 - icclim version: 5.4.0

  source :

  Conventions :

  CF-1.6

We see that dtr (dataset for the Mean Diurnal Temperature Range index) has one variable called “DTR”. If you view the documentation of icclim you will see that this is the Mean Diurnal Temperature Range index valid for a grid cell at the surface. The units are days.

While an Xarray dataset may contain multiple variables, an Xarray data array holds a single multi-dimensional variable and its coordinates.

Now let’s plot the average Mean Diurnal Temperature Range index for this time over Europe, and we take the same European subset as that used for the C3S Climate Bulletins.

# Set spatial extent and centre
central_lat = 53.0
central_lon = 7.5
extent = [-25, 40, 34, 72]  # Western Europe

# Calculate time average
dtr_avg = dtr["DTR"].mean(dim="time", keep_attrs=True)

# Select European subset
dtr_sub = dtr_avg.where(
    (dtr_avg.latitude < 72)
    & (dtr_avg.latitude > 34)
    & (dtr_avg.longitude < 40)
    & (dtr_avg.longitude > -25),
    drop=True,
)

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

# Define plot
f, ax = plt.subplots(figsize=(18, 9), subplot_kw={"projection": map_proj})

# Plot data with proper colormap scale range
p = dtr_sub.plot(cmap="RdBu_r", transform=ccrs.PlateCarree())

# Plot information
plt.suptitle(
    "Mean Diurnal Temperature Range index, Average over entire time period", y=1
)

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

# Save plot as png
plt.savefig(f"{DATADIR}E-OBS_dtr_index.png")

We can notice a stronger diurnal temperature range in warm and dry climates such as central Spain, some south central regions in France, and large regions north of Greece. Since the dataset only has data over the continent, the values over the sea are not to take into account.

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