Advanced Summer Days calculation with xarray using CMIP6 data

We will show here how to count the annual summer days for a particular geolocation of your choice using the results of a climate model, in particular, we can chose one of the historical or one of the shared socioeconomic pathway (ssp) experiments of the Coupled Model Intercomparison Project CMIP6.

Thanks to the data and computer scientists Marco Kulüke, Fabian Wachsmann, Regina Kwee-Hinzmann, Caroline Arnold, Felix Stiehler, Maria Moreno, and Stephan Kindermann at DKRZ for their contribution to this notebook.

In this use case you will learn the following:

• How to access a dataset from the DKRZ CMIP6 model data archive

• How to count the annual number of summer days for a particular geolocation using this model dataset

• How to visualize the results

You will use:

• Intake for finding the data in the catalog of the DKRZ archive

• Xarray for loading and processing the data

• hvPlot for visualizing the data in the Jupyter notebook and save the plots in your local computer

0. Load Packages

import numpy as np                    # fundamental package for scientific computing
import pandas as pd                   # data analysis and manipulation tool
import xarray as xr                   # handling labelled multi-dimensional arrays
import intake                         # to find data in a catalog, this notebook explains how it works
from ipywidgets import widgets        # to use widgets in the Jupyer Notebook
from geopy.geocoders import Nominatim # Python client for several popular geocoding web services
import folium                         # visualization tool for maps
import hvplot.pandas                  # visualization tool for interactive plots

1. Which dataset do we need? -> Choose Shared Socioeconomic Pathway, Place, and Year

# Produce the widget where we can select what experiment we are interested on 

#experiments = {'historical':range(1850, 2015), 'ssp126':range(2015, 2101), 
#               'ssp245':range(2015, 2101), 'ssp370':range(2015, 2101), 'ssp585':range(2015, 2101)}
#experiment_box = widgets.Dropdown(options=experiments, description="Select  experiment: ", disabled=False,)
#display(experiment_box)

# Produce the widget where we can select what geolocation and year are interested on 

#print("Feel free to change the default values.")

#place_box = widgets.Text(description="Enter place:", value="Hamburg")
#display(place_box)

#x = experiment_box.value
#year_box = widgets.Dropdown(options=x, description="Select year: ", disabled=False,)
#display(year_box)

pb="Hamburg"
yb=2021
eb="ssp370"
%store -r

1.1 Find Coordinates of chosen Place

If ambiguous, the most likely coordinates will be chosen, e.g. “Hamburg” results in “Hamburg, 20095, Deutschland”, (53.55 North, 10.00 East)

# We use the module Nominatim gives us the geographical coordinates of the place we selected above

geolocator = Nominatim(user_agent="any_agent")
location = geolocator.geocode(pb)

print(location.address)
print((location.latitude, location.longitude))

Hamburg, Deutschland
(53.550341, 10.000654)

1.2 Show Place on a Map

# We use the folium package to plot our selected geolocation in a map

m = folium.Map(location=[location.latitude, location.longitude])
tooltip = location.latitude, location.longitude
folium.Marker([location.latitude, location.longitude], tooltip=tooltip).add_to(m)
display(m)

Make this Notebook Trusted to load map: File -> Trust Notebook

We have defined the place and time. Now, we can search for the climate model dataset.

2. Intake Catalog

2.1 Load the Intake Catalog

# Path to master catalog on the DKRZ server
#dkrz_catalog=intake.open_catalog(["https://dkrz.de/s/intake"])
#
#only for the web page we need to take the original link:
parent_col=intake.open_catalog(["https://gitlab.dkrz.de/data-infrastructure-services/intake-esm/-/raw/master/esm-collections/cloud-access/dkrz_catalog.yaml"])
list(parent_col)

# Open the catalog with the intake package and name it "col" as short for "collection"
col=parent_col["dkrz_cmip6_disk"]

2.2 Browse the Intake Catalog

In this example we chose the Max-Planck Earth System Model in High Resolution Mode (“MPI-ESM1-2-HR”) and the maximum temperature near surface (“tasmax”) as variable. We also choose an experiment. CMIP6 comprises several kind of experiments. Each experiment has various simulation members. you can find more information in the CMIP6 Model and Experiment Documentation.

# Store the name of the model we chose in a variable named "climate_model"

climate_model = "MPI-ESM1-2-HR" # here we choose Max-Plack Institute's Earth Sytem Model in high resolution

# This is how we tell intake what data we want

query = dict(
    source_id      = climate_model, # the model 
    variable_id    = "tasmax", # temperature at surface, maximum
    table_id       = "day", # daily maximum
    experiment_id  = eb, # what we selected in the drop down menu,e.g. SSP2.4-5 2015-2100
    member_id      = "r10i1p1f1", # "r" realization, "i" initialization, "p" physics, "f" forcing
)

# Intake looks for the query we just defined in the catalog of the CMIP6 data pool at DKRZ
col.df["uri"]=col.df["uri"].str.replace("lustre/","lustre02/") 
cat = col.search(**query)

del col

# Show query results
cat.df

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	dcpp_init_year	version	time_range	project	format	uri
0	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20150101-20191231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
1	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20200101-20241231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
2	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20250101-20291231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
3	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20300101-20341231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
4	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20350101-20391231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
5	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20400101-20441231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
6	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20450101-20491231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
7	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20500101-20541231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
8	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20550101-20591231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
9	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20600101-20641231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
10	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20650101-20691231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
11	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20700101-20741231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
12	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20750101-20791231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
13	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20800101-20841231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
14	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20850101-20891231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
15	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20900101-20941231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
16	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	20950101-20991231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...
17	ScenarioMIP	DKRZ	MPI-ESM1-2-HR	ssp370	r10i1p1f1	day	tasmax	gn	NaN	v20190710	21000101-21001231	CMIP6	netcdf	/work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ...

2.3 Create Dictionary and get Data into one Object

# cdf_kwargs are given to xarray.open_dataset
# cftime is like datetime but extends to all four digit years and many calendar types

dset_dict = cat.to_dataset_dict(cdf_kwargs={"chunks": {"time": -1}, "use_cftime": True})

--> The keys in the returned dictionary of datasets are constructed as follows:
	'activity_id.source_id.experiment_id.table_id.grid_label'

/tmp/ipykernel_883/1061271266.py:4: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.
  dset_dict = cat.to_dataset_dict(cdf_kwargs={"chunks": {"time": -1}, "use_cftime": True})

100.00% [1/1 00:07<00:00]

# get all data into one object

for key, value in dset_dict.items():
    model = key.split(".")[2]  # extract model name from key
    tasmax_xr = value["tasmax"].squeeze()  # extract variable from dataset

tasmax_xr

<xarray.DataArray 'tasmax' (time: 31411, lat: 192, lon: 384)>
dask.array<getitem, shape=(31411, 192, 384), dtype=float32, chunksize=(1827, 192, 384), chunktype=numpy.ndarray>
Coordinates:
  * time            (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00
  * lat             (lat) float64 -89.28 -88.36 -87.42 ... 87.42 88.36 89.28
  * lon             (lon) float64 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1
    height          float64 2.0
    member_id       <U9 'r10i1p1f1'
    dcpp_init_year  float64 nan
Attributes:
    standard_name:  air_temperature
    long_name:      Daily Maximum Near-Surface Air Temperature
    comment:        maximum near-surface (usually, 2 meter) air temperature (...
    units:          K
    cell_methods:   area: mean time: maximum
    cell_measures:  area: areacella

xarray.DataArray

'tasmax'

• time: 31411
• lat: 192
• lon: 384

• dask.array<chunksize=(1826, 192, 384), meta=np.ndarray>

  Array Chunk Bytes 8.63 GiB 513.84 MiB Shape (31411, 192, 384) (1827, 192, 384) Dask graph 18 chunks in 56 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • time
    (time)
    object
    2015-01-01 12:00:00 ... 2100-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeProlepticGregorian(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeProlepticGregorian(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • lat
    (lat)
    float64
    -89.28 -88.36 ... 88.36 89.28

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,
           -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,
           -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,
           -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,
           -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,
           -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,
           -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,
           -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,
           -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,
           -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,
           -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,
           -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,
           -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,
           -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,
           -10.75321 ,  -9.818148,  -8.883087,  -7.948025,  -7.012963,  -6.077901,
            -5.14284 ,  -4.207778,  -3.272716,  -2.337654,  -1.402593,  -0.467531,
             0.467531,   1.402593,   2.337654,   3.272716,   4.207778,   5.14284 ,
             6.077901,   7.012963,   7.948025,   8.883087,   9.818148,  10.75321 ,
            11.688272,  12.623333,  13.558395,  14.493457,  15.428518,  16.36358 ,
            17.298641,  18.233703,  19.168764,  20.103825,  21.038887,  21.973948,
            22.909009,  23.84407 ,  24.779132,  25.714193,  26.649254,  27.584315,
            28.519375,  29.454436,  30.389497,  31.324558,  32.259618,  33.194679,
            34.129739,  35.064799,  35.99986 ,  36.93492 ,  37.86998 ,  38.805039,
            39.740099,  40.675159,  41.610218,  42.545277,  43.480336,  44.415395,
            45.350454,  46.285512,  47.22057 ,  48.155628,  49.090686,  50.025743,
            50.960801,  51.895857,  52.830914,  53.76597 ,  54.701026,  55.636081,
            56.571136,  57.50619 ,  58.441244,  59.376297,  60.311349,  61.246401,
            62.181452,  63.116502,  64.051551,  64.986599,  65.921645,  66.856691,
            67.791734,  68.726776,  69.661816,  70.596854,  71.531889,  72.466921,
            73.401949,  74.336973,  75.271992,  76.207005,  77.142011,  78.077007,
            79.011992,  79.946962,  80.881913,  81.816839,  82.751728,  83.686567,
            84.621327,  85.55596 ,  86.490367,  87.424304,  88.357004,  89.284228])

  • lon
    (lon)
    float64
    0.0 0.9375 1.875 ... 358.1 359.1

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.    ,   0.9375,   1.875 , ..., 357.1875, 358.125 , 359.0625])

  • height
    ()
    float64
    2.0

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

  • member_id
    ()
    <U9
    'r10i1p1f1'

    array('r10i1p1f1', dtype='<U9')

  • dcpp_init_year
    ()
    float64
    nan

    array(nan)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2015-01-01 12:00:00, 2015-01-02 12:00:00, 2015-01-03 12:00:00,
                 2015-01-04 12:00:00, 2015-01-05 12:00:00, 2015-01-06 12:00:00,
                 2015-01-07 12:00:00, 2015-01-08 12:00:00, 2015-01-09 12:00:00,
                 2015-01-10 12:00:00,
                 ...
                 2100-12-22 12:00:00, 2100-12-23 12:00:00, 2100-12-24 12:00:00,
                 2100-12-25 12:00:00, 2100-12-26 12:00:00, 2100-12-27 12:00:00,
                 2100-12-28 12:00:00, 2100-12-29 12:00:00, 2100-12-30 12:00:00,
                 2100-12-31 12:00:00],
                dtype='object',
                length=31411,
                calendar='proleptic_gregorian',
                freq='D'))

  • lat
    PandasIndex

    PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,
           -86.49036676628116, -85.55596048489265,  -84.6213271076488,
           -83.68656681656385, -82.75172847343066, -81.81683872860319,
            -80.8819133467975,
           ...
             80.8819133467975,  81.81683872860319,  82.75172847343066,
            83.68656681656385,   84.6213271076488,  85.55596048489265,
            86.49036676628116,  87.42430374606988,  88.35700351866494,
            89.28422753251364],
          dtype='float64', name='lat', length=192))

  • lon
    PandasIndex

    PandasIndex(Index([     0.0,   0.9375,    1.875,   2.8125,     3.75,   4.6875,    5.625,
             6.5625,      7.5,   8.4375,
           ...
            350.625, 351.5625,    352.5, 353.4375,  354.375, 355.3125,   356.25,
           357.1875,  358.125, 359.0625],
          dtype='float64', name='lon', length=384))

• Attributes: (6)

  standard_name :

  air_temperature

  long_name :

  Daily Maximum Near-Surface Air Temperature

  comment :

  maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')

  units :

  K

  cell_methods :

  area: mean time: maximum

  cell_measures :

  area: areacella

3. Select Year and Look at (Meta) Data

tasmax_year_xr = tasmax_xr.sel(time=str(yb))

# Let's have a look at the xarray data array

tasmax_year_xr

<xarray.DataArray 'tasmax' (time: 365, lat: 192, lon: 384)>
dask.array<getitem, shape=(365, 192, 384), dtype=float32, chunksize=(365, 192, 384), chunktype=numpy.ndarray>
Coordinates:
  * time            (time) object 2021-01-01 12:00:00 ... 2021-12-31 12:00:00
  * lat             (lat) float64 -89.28 -88.36 -87.42 ... 87.42 88.36 89.28
  * lon             (lon) float64 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1
    height          float64 2.0
    member_id       <U9 'r10i1p1f1'
    dcpp_init_year  float64 nan
Attributes:
    standard_name:  air_temperature
    long_name:      Daily Maximum Near-Surface Air Temperature
    comment:        maximum near-surface (usually, 2 meter) air temperature (...
    units:          K
    cell_methods:   area: mean time: maximum
    cell_measures:  area: areacella

xarray.DataArray

'tasmax'

• time: 365
• lat: 192
• lon: 384

• dask.array<chunksize=(365, 192, 384), meta=np.ndarray>

  Array Chunk Bytes 102.66 MiB 102.66 MiB Shape (365, 192, 384) (365, 192, 384) Dask graph 1 chunks in 57 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • time
    (time)
    object
    2021-01-01 12:00:00 ... 2021-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeProlepticGregorian(2021, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2021, 1, 2, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2021, 1, 3, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeProlepticGregorian(2021, 12, 29, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2021, 12, 30, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeProlepticGregorian(2021, 12, 31, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • lat
    (lat)
    float64
    -89.28 -88.36 ... 88.36 89.28

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,
           -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,
           -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,
           -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,
           -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,
           -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,
           -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,
           -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,
           -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,
           -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,
           -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,
           -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,
           -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,
           -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,
           -10.75321 ,  -9.818148,  -8.883087,  -7.948025,  -7.012963,  -6.077901,
            -5.14284 ,  -4.207778,  -3.272716,  -2.337654,  -1.402593,  -0.467531,
             0.467531,   1.402593,   2.337654,   3.272716,   4.207778,   5.14284 ,
             6.077901,   7.012963,   7.948025,   8.883087,   9.818148,  10.75321 ,
            11.688272,  12.623333,  13.558395,  14.493457,  15.428518,  16.36358 ,
            17.298641,  18.233703,  19.168764,  20.103825,  21.038887,  21.973948,
            22.909009,  23.84407 ,  24.779132,  25.714193,  26.649254,  27.584315,
            28.519375,  29.454436,  30.389497,  31.324558,  32.259618,  33.194679,
            34.129739,  35.064799,  35.99986 ,  36.93492 ,  37.86998 ,  38.805039,
            39.740099,  40.675159,  41.610218,  42.545277,  43.480336,  44.415395,
            45.350454,  46.285512,  47.22057 ,  48.155628,  49.090686,  50.025743,
            50.960801,  51.895857,  52.830914,  53.76597 ,  54.701026,  55.636081,
            56.571136,  57.50619 ,  58.441244,  59.376297,  60.311349,  61.246401,
            62.181452,  63.116502,  64.051551,  64.986599,  65.921645,  66.856691,
            67.791734,  68.726776,  69.661816,  70.596854,  71.531889,  72.466921,
            73.401949,  74.336973,  75.271992,  76.207005,  77.142011,  78.077007,
            79.011992,  79.946962,  80.881913,  81.816839,  82.751728,  83.686567,
            84.621327,  85.55596 ,  86.490367,  87.424304,  88.357004,  89.284228])

  • lon
    (lon)
    float64
    0.0 0.9375 1.875 ... 358.1 359.1

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.    ,   0.9375,   1.875 , ..., 357.1875, 358.125 , 359.0625])

  • height
    ()
    float64
    2.0

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

  • member_id
    ()
    <U9
    'r10i1p1f1'

    array('r10i1p1f1', dtype='<U9')

  • dcpp_init_year
    ()
    float64
    nan

    array(nan)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2021-01-01 12:00:00, 2021-01-02 12:00:00, 2021-01-03 12:00:00,
                 2021-01-04 12:00:00, 2021-01-05 12:00:00, 2021-01-06 12:00:00,
                 2021-01-07 12:00:00, 2021-01-08 12:00:00, 2021-01-09 12:00:00,
                 2021-01-10 12:00:00,
                 ...
                 2021-12-22 12:00:00, 2021-12-23 12:00:00, 2021-12-24 12:00:00,
                 2021-12-25 12:00:00, 2021-12-26 12:00:00, 2021-12-27 12:00:00,
                 2021-12-28 12:00:00, 2021-12-29 12:00:00, 2021-12-30 12:00:00,
                 2021-12-31 12:00:00],
                dtype='object',
                length=365,
                calendar='proleptic_gregorian',
                freq='D'))

  • lat
    PandasIndex

    PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,
           -86.49036676628116, -85.55596048489265,  -84.6213271076488,
           -83.68656681656385, -82.75172847343066, -81.81683872860319,
            -80.8819133467975,
           ...
             80.8819133467975,  81.81683872860319,  82.75172847343066,
            83.68656681656385,   84.6213271076488,  85.55596048489265,
            86.49036676628116,  87.42430374606988,  88.35700351866494,
            89.28422753251364],
          dtype='float64', name='lat', length=192))

  • lon
    PandasIndex

    PandasIndex(Index([     0.0,   0.9375,    1.875,   2.8125,     3.75,   4.6875,    5.625,
             6.5625,      7.5,   8.4375,
           ...
            350.625, 351.5625,    352.5, 353.4375,  354.375, 355.3125,   356.25,
           357.1875,  358.125, 359.0625],
          dtype='float64', name='lon', length=384))

• Attributes: (6)

  standard_name :

  air_temperature

  long_name :

  Daily Maximum Near-Surface Air Temperature

  comment :

  maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')

  units :

  K

  cell_methods :

  area: mean time: maximum

  cell_measures :

  area: areacella

We see not only the numbers, but also information about it, such as long name, units, and the data history. This information is called metadata.

4. Compare Model Grid Cell with chosen Location

# Find nearest model coordinate by finding the index of the nearest grid point

abslat = np.abs(tasmax_year_xr["lat"] - location.latitude)
abslon = np.abs(tasmax_year_xr["lon"] - location.longitude)
c = np.maximum(abslon, abslat)

([xloc], [yloc]) = np.where(c == np.min(c)) # xloc and yloc are the indices of the neares model grid point

# Draw map again

m = folium.Map(location=[location.latitude, location.longitude], zoom_start=8)


tooltip = location.latitude, location.longitude
folium.Marker(
    [location.latitude, location.longitude],
    tooltip=tooltip,
    popup="Location selected by You",
).add_to(m)

#
tooltip = float(tasmax_year_xr["lat"][yloc].values), float(tasmax_year_xr["lon"][xloc].values)
folium.Marker(
    [tasmax_year_xr["lat"][yloc], tasmax_year_xr["lon"][xloc]],
    tooltip=tooltip,
    popup="Model Grid Cell Center",
).add_to(m)

# Define coordinates of model grid cell (just for visualization)
rect_lat1_model = (tasmax_year_xr["lat"][yloc - 1] + tasmax_year_xr["lat"][yloc]) / 2
rect_lon1_model = (tasmax_year_xr["lon"][xloc - 1] + tasmax_year_xr["lon"][xloc]) / 2
rect_lat2_model = (tasmax_year_xr["lat"][yloc + 1] + tasmax_year_xr["lat"][yloc]) / 2
rect_lon2_model = (tasmax_year_xr["lon"][xloc + 1] + tasmax_year_xr["lon"][xloc]) / 2

# Draw model grid cell
folium.Rectangle(
    bounds=[[rect_lat1_model, rect_lon1_model], [rect_lat2_model, rect_lon2_model]],
    color="#ff7800",
    fill=True,
    fill_color="#ffff00",
    fill_opacity=0.2,
).add_to(m)

m

Make this Notebook Trusted to load map: File -> Trust Notebook

Climate models have a finite resolution. Hence, models do not provide the data of a particular point, but the mean over a model grid cell. Take this in mind when comparing model data with observed data (e.g. weather stations).

Now, we will visualize the daily maximum temperature time series of the model grid cell.

5. Draw Temperature Time Series and Count Summer days

The definition of a summer day varies from region to region. According to the German Weather Service, “a summer day is a day on which the maximum air temperature is at least 25.0 °C”. Depending on the place you selected, you might want to apply a different threshold to calculate the summer days index.

tasmax_year_place_xr = tasmax_year_xr[:, yloc, xloc] - 273.15 # Convert Kelvin to °C
tasmax_year_place_df = pd.DataFrame(index = tasmax_year_place_xr['time'].values, 
                                    columns = ['Temperature', 'Summer Day Threshold']) # create the dataframe

tasmax_year_place_df.loc[:, 'Model Temperature'] = tasmax_year_place_xr.values # insert model data into the dataframe
tasmax_year_place_df.loc[:, 'Summer Day Threshold'] = 25 # insert the threshold into the dataframe

# Plot data and define title and legend
tasmax_year_place_df.hvplot.line(y=['Model Temperature', 'Summer Day Threshold'], 
                                 value_label='Temperature in °C', legend='bottom', 
                                 title='Daily maximum Temperature near Surface for '+pb, 
                                 height=500, width=620)

As we can see, the maximum daily temperature is highly variable over the year. As we are using the mean temperature in a model grid cell, the amount of summer days might we different that what you would expect at a single location.

# Summer days index calculation
no_summer_days_model = tasmax_year_place_xr[tasmax_year_place_xr.load() > 25].size # count the number of summer days

# Print results in a sentence
print("According to the German Weather Service definition, in the " +eb +" experiment the " 
      +climate_model +" model shows " +str(no_summer_days_model) +" summer days for " + pb 
      + " in " + str(yb) +".")

According to the German Weather Service definition, in the ssp370 experiment the MPI-ESM1-2-HR model shows 2 summer days for Hamburg in 2021.

Try another location and year