Google Cloud CMIP6 Public Data: Basic Python Example

---

Overview

This notebooks shows how to query the Google Cloud CMIP6 catalog and load the data using Python.

Prerequisites

Concepts	Importance	Notes
Intro to Xarray	Necessary
Understanding of NetCDF	Helpful	Familiarity with metadata structure

• Time to learn: 10 minutes

---

Imports

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import zarr
import fsspec
import nc_time_axis

%matplotlib inline
plt.rcParams['figure.figsize'] = 12, 6

Browse Catalog

The data catatalog is stored as a CSV file. Here we read it with Pandas.

df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')
df.head()

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	HighResMIP	CMCC	CMCC-CM2-HR4	highresSST-present	r1i1p1f1	Amon	ps	gn	gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...	NaN	20170706
1	HighResMIP	CMCC	CMCC-CM2-HR4	highresSST-present	r1i1p1f1	Amon	rsds	gn	gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...	NaN	20170706
2	HighResMIP	CMCC	CMCC-CM2-HR4	highresSST-present	r1i1p1f1	Amon	rlus	gn	gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...	NaN	20170706
3	HighResMIP	CMCC	CMCC-CM2-HR4	highresSST-present	r1i1p1f1	Amon	rlds	gn	gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...	NaN	20170706
4	HighResMIP	CMCC	CMCC-CM2-HR4	highresSST-present	r1i1p1f1	Amon	psl	gn	gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...	NaN	20170706

The columns of the dataframe correspond to the CMI6 controlled vocabulary.

Here we filter the data to find monthly surface air temperature for historical experiments.

df_ta = df.query("activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'")
df_ta

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
973	CMIP	NOAA-GFDL	GFDL-ESM4	historical	r3i1p1f1	Amon	tas	gr1	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...	NaN	20180701
1766	CMIP	NOAA-GFDL	GFDL-ESM4	historical	r2i1p1f1	Amon	tas	gr1	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...	NaN	20180701
8074	CMIP	NOAA-GFDL	GFDL-CM4	historical	r1i1p1f1	Amon	tas	gr1	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/histo...	NaN	20180701
22185	CMIP	IPSL	IPSL-CM6A-LR	historical	r8i1p1f1	Amon	tas	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
22298	CMIP	IPSL	IPSL-CM6A-LR	historical	r2i1p1f1	Amon	tas	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
...	...	...	...	...	...	...	...	...	...	...	...
522952	CMIP	MRI	MRI-ESM2-0	historical	r7i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...	NaN	20210813
523274	CMIP	MRI	MRI-ESM2-0	historical	r6i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...	NaN	20210907
523712	CMIP	CMCC	CMCC-CM2-SR5	historical	r3i1p2f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...	NaN	20211108
523721	CMIP	CMCC	CMCC-CM2-SR5	historical	r2i1p2f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/histor...	NaN	20211109
523769	CMIP	EC-Earth-Consortium	EC-Earth3-Veg	historical	r1i1p1f1	Amon	tas	gr	gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...	NaN	20211207

635 rows × 11 columns

Now we do further filtering to find just the models from NCAR.

df_ta_ncar = df_ta.query('institution_id == "NCAR"')
df_ta_ncar

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
56049	CMIP	NCAR	CESM2-WACCM	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...	NaN	20190227
56143	CMIP	NCAR	CESM2-WACCM	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...	NaN	20190227
56326	CMIP	NCAR	CESM2-WACCM	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...	NaN	20190227
59875	CMIP	NCAR	CESM2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190308
61655	CMIP	NCAR	CESM2	historical	r4i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r4...	NaN	20190308
61862	CMIP	NCAR	CESM2	historical	r5i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r5...	NaN	20190308
62691	CMIP	NCAR	CESM2	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r2...	NaN	20190308
63131	CMIP	NCAR	CESM2	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r3...	NaN	20190308
63266	CMIP	NCAR	CESM2	historical	r6i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r6...	NaN	20190308
64615	CMIP	NCAR	CESM2	historical	r8i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r8...	NaN	20190311
64914	CMIP	NCAR	CESM2	historical	r7i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r7...	NaN	20190311
64983	CMIP	NCAR	CESM2	historical	r9i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r9...	NaN	20190311
66341	CMIP	NCAR	CESM2	historical	r10i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190313
200772	CMIP	NCAR	CESM2	historical	r11i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190514
385224	CMIP	NCAR	CESM2-FV2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...	NaN	20191120
386297	CMIP	NCAR	CESM2-WACCM-FV2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...	NaN	20191120
420771	CMIP	NCAR	CESM2-WACCM-FV2	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...	NaN	20200226
421251	CMIP	NCAR	CESM2-WACCM-FV2	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...	NaN	20200226
422013	CMIP	NCAR	CESM2-FV2	historical	r3i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...	NaN	20200226
422459	CMIP	NCAR	CESM2-FV2	historical	r2i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...	NaN	20200226

Load Data

Now we will load a single store using fsspec, zarr, and xarray.

# get the path to a specific zarr store (the first one from the dataframe above)
zstore = df_ta_ncar.zstore.values[-1]
print(zstore)

# create a mutable-mapping-style interface to the store
mapper = fsspec.get_mapper(zstore)

# open it using xarray and zarr
ds = xr.open_zarr(mapper, consolidated=True)
ds

gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historical/r2i1p1f1/Amon/tas/gn/v20200226/

<xarray.Dataset> Size: 110MB
Dimensions:    (lat: 96, nbnd: 2, lon: 144, time: 1980)
Coordinates:
  * lat        (lat) float64 768B -90.0 -88.11 -86.21 ... 86.21 88.11 90.0
    lat_bnds   (lat, nbnd) float64 2kB dask.array<chunksize=(96, 2), meta=np.ndarray>
  * lon        (lon) float64 1kB 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5
    lon_bnds   (lon, nbnd) float64 2kB dask.array<chunksize=(144, 2), meta=np.ndarray>
  * time       (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    time_bnds  (time, nbnd) object 32kB dask.array<chunksize=(1980, 2), meta=np.ndarray>
Dimensions without coordinates: nbnd
Data variables:
    tas        (time, lat, lon) float32 109MB dask.array<chunksize=(990, 96, 144), meta=np.ndarray>
Attributes: (12/48)
    Conventions:                     CF-1.7 CMIP-6.2
    DODS_EXTRA.Unlimited_Dimension:  time
    activity_id:                     CMIP
    branch_method:                   standard
    branch_time_in_child:            674885.0
    branch_time_in_parent:           10950.0
    ...                              ...
    tracking_id:                     hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...
    variable_id:                     tas
    variant_info:                    CMIP6 CESM2-FV2 historical experiment (1...
    variant_label:                   r2i1p1f1
    netcdf_tracking_ids:             hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a...
    version_id:                      v20200226

xarray.Dataset

• Dimensions:
  • lat: 96
  • nbnd: 2
  • lon: 144
  • time: 1980
• Coordinates: (6)
  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    _ChunkSizes :

    96

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    title :

    Latitude

    type :

    double

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

  • lat_bnds
    (lat, nbnd)
    float64
    dask.array<chunksize=(96, 2), meta=np.ndarray>

    _ChunkSizes :

    [96, 2]

    units :

    degrees_north

    Array Chunk Bytes 1.50 kiB 1.50 kiB Shape (96, 2) (96, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    _ChunkSizes :

    144

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    title :

    Longitude

    type :

    double

    units :

    degrees_east

    valid_max :

    360.0

    valid_min :

    0.0

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

  • lon_bnds
    (lon, nbnd)
    float64
    dask.array<chunksize=(144, 2), meta=np.ndarray>

    _ChunkSizes :

    [144, 2]

    units :

    degrees_east

    Array Chunk Bytes 2.25 kiB 2.25 kiB Shape (144, 2) (144, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    object
    1850-01-15 12:00:00 ... 2014-12-...

    _ChunkSizes :

    512

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1980, 2), meta=np.ndarray>

    _ChunkSizes :

    [1, 2]

    Array Chunk Bytes 30.94 kiB 30.94 kiB Shape (1980, 2) (1980, 2) Dask graph 1 chunks in 2 graph layers Data type object numpy.ndarray

• Data variables: (1)
  • tas
    (time, lat, lon)
    float32
    dask.array<chunksize=(990, 96, 144), meta=np.ndarray>

    _ChunkSizes :

    [1, 96, 144]

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    TREFHT

    description :

    near-surface (usually, 2 meter) air temperature

    frequency :

    mon

    id :

    tas

    long_name :

    Near-Surface Air Temperature

    mipTable :

    Amon

    out_name :

    tas

    prov :

    Amon ((isd.003))

    realm :

    atmos

    standard_name :

    air_temperature

    time :

    time

    time_label :

    time-mean

    time_title :

    Temporal mean

    title :

    Near-Surface Air Temperature

    type :

    real

    units :

    K

    variable_id :

    tas

    Array Chunk Bytes 104.41 MiB 52.21 MiB Shape (1980, 96, 144) (990, 96, 144) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,
                 1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,
                 1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,
                 1850-10-15 12:00:00,
                 ...
                 2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,
                 2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,
                 2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,
                 2014-12-15 12:00:00],
                dtype='object', length=1980, calendar='noleap', freq=None))

• Attributes: (48)

  Conventions :

  CF-1.7 CMIP-6.2

  DODS_EXTRA.Unlimited_Dimension :

  time

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  674885.0

  branch_time_in_parent :

  10950.0

  case_id :

  1560

  cesm_casename :

  b.e21.BHIST.f19_g17.CMIP6-historical-2deg.002

  contact :

  cesm_cmip6@ucar.edu

  creation_date :

  2019-11-05T02:05:46Z

  data_specs_version :

  01.00.31

  experiment :

  Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation.

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-FV2.historical.none.r2i1p1f1

  grid :

  native 1.9x2.5 finite volume grid (96x144 latxlon)

  grid_label :

  gn

  initialization_index :

  1

  institution :

  National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA

  institution_id :

  NCAR

  license :

  CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  model_doi_url :

  https://doi.org/10.5065/D67H1H0V

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  CESM2-FV2

  parent_time_units :

  days since 0001-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  2

  realm :

  atmos

  source :

  CESM2 (2017): atmosphere: CAM6 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)

  source_id :

  CESM2-FV2

  source_type :

  AOGCM BGC AER

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  tracking_id :

  hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a5048ff496

  variable_id :

  tas

  variant_info :

  CMIP6 CESM2-FV2 historical experiment (1850-2014) with CAM6-FV2 and interactive land CLM5 at 1.9 degree latitude x 2.5 degree longitude, coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1).

  variant_label :

  r2i1p1f1

  netcdf_tracking_ids :

  hdl:21.14100/99cdfde8-5b6d-452b-9b78-62a5048ff496

  version_id :

  v20200226

Plot the Data

Plot a map from a specific date:

ds.tas.sel(time='1950-01').squeeze().plot()

<matplotlib.collections.QuadMesh at 0x7fdb4019ad70>

The global mean of a lat-lon field needs to be weighted by the area of each grid cell, which is proportional to the cosine of its latitude.

def global_mean(field):
    weights = np.cos(np.deg2rad(field.lat))
    return field.weighted(weights).mean(dim=['lat', 'lon'])

We can pass all of the temperature data through this function:

ta_timeseries = global_mean(ds.tas)
ta_timeseries

<xarray.DataArray 'tas' (time: 1980)> Size: 16kB
dask.array<truediv, shape=(1980,), dtype=float64, chunksize=(990,), chunktype=numpy.ndarray>
Coordinates:
  * time     (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:00:00

xarray.DataArray

'tas'

• time: 1980

• dask.array<chunksize=(990,), meta=np.ndarray>

  Array Chunk Bytes 15.47 kiB 7.73 kiB Shape (1980,) (990,) Dask graph 2 chunks in 15 graph layers Data type float64 numpy.ndarray

• Coordinates: (1)
  • time
    (time)
    object
    1850-01-15 12:00:00 ... 2014-12-...

    array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,
                 1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,
                 1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,
                 1850-10-15 12:00:00,
                 ...
                 2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,
                 2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,
                 2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,
                 2014-12-15 12:00:00],
                dtype='object', length=1980, calendar='noleap', freq=None))

• Attributes: (0)

By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly.

%time ta_timeseries.load()

CPU times: user 268 ms, sys: 95.7 ms, total: 364 ms
Wall time: 643 ms

<xarray.DataArray 'tas' (time: 1980)> Size: 16kB
array([285.53603312, 285.63958225, 286.27324086, ..., 288.15781771,
       287.18662389, 286.87765827])
Coordinates:
  * time     (time) object 16kB 1850-01-15 12:00:00 ... 2014-12-15 12:00:00

xarray.DataArray

'tas'

• time: 1980

• 285.5 285.6 286.3 287.2 288.1 288.7 ... 289.4 288.9 288.2 287.2 286.9

  array([285.53603312, 285.63958225, 286.27324086, ..., 288.15781771,
         287.18662389, 286.87765827])

• Coordinates: (1)
  • time
    (time)
    object
    1850-01-15 12:00:00 ... 2014-12-...

    array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-15 12:00:00, 1850-02-14 00:00:00, 1850-03-15 12:00:00,
                 1850-04-15 00:00:00, 1850-05-15 12:00:00, 1850-06-15 00:00:00,
                 1850-07-15 12:00:00, 1850-08-15 12:00:00, 1850-09-15 00:00:00,
                 1850-10-15 12:00:00,
                 ...
                 2014-03-15 12:00:00, 2014-04-15 00:00:00, 2014-05-15 12:00:00,
                 2014-06-15 00:00:00, 2014-07-15 12:00:00, 2014-08-15 12:00:00,
                 2014-09-15 00:00:00, 2014-10-15 12:00:00, 2014-11-15 00:00:00,
                 2014-12-15 12:00:00],
                dtype='object', length=1980, calendar='noleap', freq=None))

• Attributes: (0)

ta_timeseries.plot(label='monthly')
ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean', color='k')
plt.legend()
plt.grid()
plt.title('Global Mean Surface Air Temperature')

Text(0.5, 1.0, 'Global Mean Surface Air Temperature')

---

Summary

In this notebook, we opened a CESM2 dataset with fsspec and zarr. We calculated and plotted global average surface air temperature.

What’s next?

We will open a dataset with ESGF and OPenDAP.

Resources and references

• Original notebook in the Pangeo Gallery by Henri Drake and Ryan Abernathey