Calculate SU: the number of Summer Days#
Example notebook that runs icclim.
The example calculates the number of summer days (SU indicator) for the dataset chosen by the user on C4I.
We assume to have the tas variable in netCDF files in a
./data folder for model CMCC and for one member r1i1p1f1.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.
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.
Installation and preparation of the needed modules#
[26]:
%pip install icclim matplotlib nc_time_axis
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Note: you may need to restart the kernel to use updated packages.
[5]:
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 the parameters#
[12]:
# studied period
dt1 = datetime.datetime(2015, 1, 1, tzinfo=datetime.timezone.utc)
dt2 = datetime.datetime(2019, 12, 31, tzinfo=datetime.timezone.utc)
DATA_DIR = Path("./data")
out_f = "su_icclim.nc"
filenames = [str(f) for f in DATA_DIR.glob("tas_day_CMCC*.nc")]
filenames
[12]:
['data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_19250101-19491231.nc',
'data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_19500101-19741231.nc',
'data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_19750101-19991231.nc',
'data/tas_day_CMCC-ESM2_ssp585_r1i1p1f1_gn_20900101-21001231.nc',
'data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_20000101-20141231.nc',
'data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_19000101-19241231.nc',
'data/tas_day_CMCC-ESM2_ssp585_r1i1p1f1_gn_20400101-20641231.nc',
'data/tas_day_CMCC-ESM2_ssp585_r1i1p1f1_gn_20650101-20891231.nc',
'data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_18500101-18741231.nc',
'data/tas_day_CMCC-ESM2_ssp585_r1i1p1f1_gn_20150101-20391231.nc',
'data/tas_day_CMCC-ESM2_historical_r1i1p1f1_gn_18750101-18991231.nc']
Compute Summer Days index (SU)#
Usually SU is computed on the maximum daily temperature (tasmax), but here we show that using var_name we can force icclim to use a different variable to compute indices, as long as its units is compatible.
[13]:
icclim.index(
index_name="SU",
in_files=filenames,
var_name="tas",
slice_mode="JJA",
time_range=[dt1, dt2],
out_file=out_f,
logs_verbosity="HIGH",
)
2024-01-24 09:19:55,887 ********************************************************************************************
2024-01-24 09:19:55,888 * *
2024-01-24 09:19:55,889 * icclim 6.6.0 *
2024-01-24 09:19:55,890 * *
2024-01-24 09:19:55,891 * *
2024-01-24 09:19:55,892 * Wed Jan 24 08:19:55 2024 *
2024-01-24 09:19:55,892 * *
2024-01-24 09:19:55,893 * BEGIN EXECUTION *
2024-01-24 09:19:55,893 * *
2024-01-24 09:19:55,894 ********************************************************************************************
2024-01-24 09:19:55,894 Processing: 0%
/home/bzah/workspace/cerfacs/icclim/src/icclim/generic_indices/generic_indicators.py:1319: UserWarning: Unable to infer the frequency of the time series. To mute this, set xclim's option data_validation='log'.
check_freq(da, src_freq, strict=True)
/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"])
2024-01-24 09:19:59,681 Processing: 100%
2024-01-24 09:19:59,682 ********************************************************************************************
2024-01-24 09:19:59,683 * *
2024-01-24 09:19:59,683 * icclim 6.6.0 *
2024-01-24 09:19:59,683 * *
2024-01-24 09:19:59,684 * *
2024-01-24 09:19:59,684 * Wed Jan 24 08:19:59 2024 *
2024-01-24 09:19:59,684 * *
2024-01-24 09:19:59,685 * END EXECUTION *
2024-01-24 09:19:59,685 * *
2024-01-24 09:19:59,685 * CP SECS = 16.977143654 *
2024-01-24 09:19:59,686 * *
2024-01-24 09:19:59,686 ********************************************************************************************
[13]:
<xarray.Dataset>
Dimensions: (lat: 192, lon: 288, time: 5, bounds: 2)
Coordinates:
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
height float64 2.0
* time (time) object 2015-07-16 12:00:00 ... 2019-07-16 12:00:00
* bounds (bounds) int64 0 1
Data variables:
SU (time, lat, lon) float64 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
time_bounds (time, bounds) object 2015-06-01 00:00:00 ... 2019-08-31 00:...
Attributes:
title: number_of_days_when_maximum_air_temperature_is_greater_than...
references: ATBD of the ECA&D indices calculation (https://knmi-ecad-as...
institution: Climate impact portal (https://climate4impact.eu)
history: 2020-12-21T16:22:42Z altered by CMOR: Treated scalar dimens...
source:
Conventions: CF-1.6Plot settings#
[14]:
with xr.open_dataset(out_f, decode_times=False) as ds:
su_xr = ds
ds["time"] = xr.decode_cf(ds).time
print(su_xr)
# Select a single x,y combination from the data
longitude = su_xr["SU"]["lon"].sel(lon=3.5, method="nearest")
latitude = su_xr["SU"]["lat"].sel(lat=44.2, method="nearest")
print("Long, Lat values:", longitude, latitude)
<xarray.Dataset>
Dimensions: (lat: 192, lon: 288, time: 5, bounds: 2)
Coordinates:
* lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
* lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
height float64 ...
* time (time) object 2015-07-16 12:00:00 ... 2019-07-16 12:00:00
* bounds (bounds) int64 0 1
Data variables:
SU (time, lat, lon) float64 ...
time_bounds (time, bounds) int64 ...
Attributes:
title: number_of_days_when_maximum_air_temperature_is_greater_than...
references: ATBD of the ECA&D indices calculation (https://knmi-ecad-as...
institution: Climate impact portal (https://climate4impact.eu)
history: 2020-12-21T16:22:42Z altered by CMOR: Treated scalar dimens...
source:
Conventions: CF-1.6
Long, Lat values: <xarray.DataArray 'lon' ()>
array(3.75)
Coordinates:
lon float64 3.75
height float64 ...
Attributes:
bounds: lon_bnds
units: degrees_east
axis: X
long_name: Longitude
standard_name: longitude <xarray.DataArray 'lat' ()>
array(43.82198953)
Coordinates:
lat float64 43.82
height float64 ...
Attributes:
bounds: lat_bnds
units: degrees_north
axis: Y
long_name: Latitude
standard_name: latitude
[25]:
su_xr.attrs["title"]
[25]:
'number_of_days_when_maximum_air_temperature_is_greater_than_threshold'
Note#
Notice that the title is not quite right in the resulting dataset.
SU assumes to be computed on tasmax, so its output title includes
maximum_air_temperature but here we used a air_temperature variable.Subset and Plot SU#
[15]:
# Slice the data spatially using a single lat/lon point
one_point = su_xr["SU"].sel(lat=latitude, lon=longitude)
# Use xarray to create a quick time series plot
one_point.plot.line()
plt.show()
[16]:
# 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()
[17]:
# 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")
[17]:
| lat | lon | height | SU | |
|---|---|---|---|---|
| time | ||||
| 2015-07-16 12:00:00 | 43.82199 | 3.75 | 2.0 | 37.0 |
| 2016-07-16 12:00:00 | 43.82199 | 3.75 | 2.0 | 9.0 |
| 2017-07-16 12:00:00 | 43.82199 | 3.75 | 2.0 | 19.0 |
| 2018-07-16 12:00:00 | 43.82199 | 3.75 | 2.0 | 30.0 |
| 2019-07-16 12:00:00 | 43.82199 | 3.75 | 2.0 | 25.0 |
[18]:
# Time subsetting: this is just an example on how to do it
start_date = "2018-01-01"
end_date = "2019-12-31"
su = su_xr["SU"].sel(time=slice(start_date, end_date))
[19]:
# Quickly plot the data using xarray.plot()
su.plot(x="lon", y="lat", col="time", col_wrap=1)
plt.suptitle("Two Time Steps of Summer Days", y=1.03)
plt.show()
[20]:
# Set spatial extent and centre
central_lat = 47.0
central_lon = 1.0
extent = [-30, 30, 30, 56] # Western Europe
# Calculate time average
su_avg = su.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 = su_avg.plot(levels=levels, cmap="RdBu_r", transform=ccrs.PlateCarree())
# Plot information
plt.suptitle("Two Time Steps of Europe Summer 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_su_icclim.png")
[21]:
# Re-order longitude so that there is no blank line at 0 deg because 0 deg is within our spatial selection
su_avg.coords["lon"] = (su_avg.coords["lon"] + 180) % 360 - 180
su_avg = su_avg.sortby(su_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 = su_avg.plot.contour(
levels=levels, colors="k", linewidths=0.5, transform=ccrs.PlateCarree()
)
# Contour filled colors
p = su_avg.plot.contourf(
levels=levels, cmap="RdBu_r", extend="both", transform=ccrs.PlateCarree()
)
# Plot information
plt.suptitle("Two Time Steps of Europe Summer 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_su_contours_icclim.png")
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