3.6 Climate Model Data with Intake-ESM

Note

The code for downloading climate model data in this section was adapted from this Pangeo tutorial and the Project Pythia CMIP6 Cookbook Thanks to Brian Rose and Pascal Bourgault for suggesting the addition of this section.

In addition to the ESGF archive and direct access to the Google Cloud CMIP data archive with xr.open_zarr, the Python package intake-esm can be useful for accessing climate model output. intake-esm works by accessing what’s called an ESM Collection Specification (also see this page), which describes a database of climate model data. One such databases we could access, which is maintained for the Pangeo project, is hosted on Google Cloud Services. We’ll work through how to access and search the data catalog and load the data to your local machine.

As usual, we’ll import the required packages.

import numpy as np
import xarray as xr
import gcsfs
import intake
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import xclim.ensembles as xce

# URLs for the Google Cloud CMIP6 ESM Collection Spec
url_google = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"

First let’s take a look at the data catalog for the Google CMIP6 archive. This shows a summary of all the CMIP6 data available from this database.

catalog = intake.open_esm_datastore(url_google)
catalog

pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

	unique
activity_id	18
institution_id	36
source_id	88
experiment_id	170
member_id	657
table_id	37
variable_id	700
grid_label	10
zstore	514818
dcpp_init_year	60
version	736
derived_variable_id	0

Now let’s search for a particular output variable from a certain model. The interface is similar to what we saw in Section 3.5, but we don’t need to use a long string to query a dataframe. intake-esm has a function for that. To switch things up, let’s now look for daily precipitation output for the historical and SSP3-7.0 simulations from the model IPSL-CM6A-LR.

search_result = catalog.search(source_id = "IPSL-CM6A-LR",
                               experiment_id=['historical', 'ssp370'], 
                               table_id='day', 
                               variable_id='pr')
# we can convert the search results to a pandas dataframe and print out the results
search_result.df

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	CMIP	IPSL	IPSL-CM6A-LR	historical	r8i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
1	CMIP	IPSL	IPSL-CM6A-LR	historical	r2i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
2	CMIP	IPSL	IPSL-CM6A-LR	historical	r7i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
3	CMIP	IPSL	IPSL-CM6A-LR	historical	r31i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
4	CMIP	IPSL	IPSL-CM6A-LR	historical	r5i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
5	CMIP	IPSL	IPSL-CM6A-LR	historical	r26i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
6	CMIP	IPSL	IPSL-CM6A-LR	historical	r29i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
7	CMIP	IPSL	IPSL-CM6A-LR	historical	r6i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
8	CMIP	IPSL	IPSL-CM6A-LR	historical	r25i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
9	CMIP	IPSL	IPSL-CM6A-LR	historical	r20i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
10	CMIP	IPSL	IPSL-CM6A-LR	historical	r24i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
11	CMIP	IPSL	IPSL-CM6A-LR	historical	r28i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
12	CMIP	IPSL	IPSL-CM6A-LR	historical	r23i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
13	CMIP	IPSL	IPSL-CM6A-LR	historical	r21i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
14	CMIP	IPSL	IPSL-CM6A-LR	historical	r30i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
15	CMIP	IPSL	IPSL-CM6A-LR	historical	r22i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
16	CMIP	IPSL	IPSL-CM6A-LR	historical	r18i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
17	CMIP	IPSL	IPSL-CM6A-LR	historical	r17i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
18	CMIP	IPSL	IPSL-CM6A-LR	historical	r11i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
19	CMIP	IPSL	IPSL-CM6A-LR	historical	r15i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
20	CMIP	IPSL	IPSL-CM6A-LR	historical	r16i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
21	CMIP	IPSL	IPSL-CM6A-LR	historical	r19i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
22	CMIP	IPSL	IPSL-CM6A-LR	historical	r1i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
23	CMIP	IPSL	IPSL-CM6A-LR	historical	r4i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
24	CMIP	IPSL	IPSL-CM6A-LR	historical	r27i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
25	CMIP	IPSL	IPSL-CM6A-LR	historical	r3i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
26	CMIP	IPSL	IPSL-CM6A-LR	historical	r9i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
27	CMIP	IPSL	IPSL-CM6A-LR	historical	r14i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
28	CMIP	IPSL	IPSL-CM6A-LR	historical	r10i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
29	CMIP	IPSL	IPSL-CM6A-LR	historical	r13i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
30	CMIP	IPSL	IPSL-CM6A-LR	historical	r12i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
31	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r2i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
32	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r1i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
33	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r10i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
34	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r3i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
35	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r4i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
36	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r5i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
37	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r6i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
38	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r7i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
39	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r9i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
40	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r8i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
41	CMIP	IPSL	IPSL-CM6A-LR	historical	r32i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20190802
42	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r14i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20191122

In principle, intake-esm can load all of these datasets in one function call using search_result.to_dataset_dict(zarr_kwargs={'consolidated': True}). We’ll do this, but to save time we’ll reduce the size of the data request by selecting only two ensemble members first.

# search again,specifying the ensemble members we want. Then print the dataframe again.
search_result_small = search_result.search(member_id = ['r1i1p1f1', 'r2i1p1f1'])
search_result_small.df

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	CMIP	IPSL	IPSL-CM6A-LR	historical	r2i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
1	CMIP	IPSL	IPSL-CM6A-LR	historical	r1i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
2	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r2i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119
3	ScenarioMIP	IPSL	IPSL-CM6A-LR	ssp370	r1i1p1f1	day	pr	gr	gs://cmip6/CMIP6/ScenarioMIP/IPSL/IPSL-CM6A-LR...	NaN	20190119

# now load the data into a dictionary and print the keys
ds_dict = search_result_small.to_dataset_dict(zarr_kwargs={'consolidated': True})
list(ds_dict.keys())

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

100.00% [2/2 00:07<00:00]

['ScenarioMIP.IPSL.IPSL-CM6A-LR.ssp370.day.gr',
 'CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr']

intake-esm organized the datasets into a dictionary with two entries: one for the SSP3-7.0 scenario and one for the historical-forcing simulation. Did it put both realizations into a single dataset for each period? Let’s see:

ds_dict['CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr']

<xarray.Dataset> Size: 10GB
Dimensions:         (member_id: 2, dcpp_init_year: 1, time: 60265, lat: 143,
                     lon: 144, axis_nbounds: 2)
Coordinates:
  * lat             (lat) float32 572B -90.0 -88.73 -87.46 ... 87.46 88.73 90.0
  * lon             (lon) float32 576B 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5
  * time            (time) datetime64[ns] 482kB 1850-01-01T12:00:00 ... 2014-...
    time_bounds     (time, axis_nbounds) datetime64[ns] 964kB dask.array<chunksize=(30133, 1), meta=np.ndarray>
  * member_id       (member_id) object 16B 'r1i1p1f1' 'r2i1p1f1'
  * dcpp_init_year  (dcpp_init_year) float64 8B nan
Dimensions without coordinates: axis_nbounds
Data variables:
    pr              (member_id, dcpp_init_year, time, lat, lon) float32 10GB dask.array<chunksize=(1, 1, 843, 143, 144), meta=np.ndarray>
Attributes: (12/53)
    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e
    Conventions:                      CF-1.7 CMIP-6.2
    EXPID:                            historical
    NCO:                              "4.6.0"
    activity_id:                      CMIP
    branch_method:                    standard
    ...                               ...
    intake_esm_attrs:variable_id:     pr
    intake_esm_attrs:grid_label:      gr
    intake_esm_attrs:version:         20180803
    intake_esm_attrs:_data_format_:   zarr
    variant_info:                     Restart from another point in piControl...
    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr

xarray.Dataset

• Dimensions:
  • member_id: 2
  • dcpp_init_year: 1
  • time: 60265
  • lat: 143
  • lon: 144
  • axis_nbounds: 2
• Coordinates: (6)
  • lat
    (lat)
    float32
    -90.0 -88.73 -87.46 ... 88.73 90.0

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.73239 , -87.46479 , -86.19718 , -84.92958 , -83.66197 ,
           -82.39436 , -81.12676 , -79.85915 , -78.59155 , -77.323944, -76.056335,
           -74.788734, -73.521126, -72.253525, -70.985916, -69.71831 , -68.45071 ,
           -67.1831  , -65.91549 , -64.64789 , -63.380283, -62.112675, -60.84507 ,
           -59.577465, -58.30986 , -57.042255, -55.774647, -54.507042, -53.239437,
           -51.971832, -50.704224, -49.43662 , -48.169014, -46.90141 , -45.633804,
           -44.366196, -43.09859 , -41.830986, -40.56338 , -39.295776, -38.028168,
           -36.760563, -35.492958, -34.225353, -32.957745, -31.690142, -30.422535,
           -29.15493 , -27.887323, -26.619719, -25.352112, -24.084507, -22.816902,
           -21.549295, -20.28169 , -19.014084, -17.746479, -16.478872, -15.211267,
           -13.943662, -12.676056, -11.408451, -10.140845,  -8.87324 ,  -7.605634,
            -6.338028,  -5.070423,  -3.802817,  -2.535211,  -1.267606,   0.      ,
             1.267606,   2.535211,   3.802817,   5.070423,   6.338028,   7.605634,
             8.87324 ,  10.140845,  11.408451,  12.676056,  13.943662,  15.211267,
            16.478872,  17.746479,  19.014084,  20.28169 ,  21.549295,  22.816902,
            24.084507,  25.352112,  26.619719,  27.887323,  29.15493 ,  30.422535,
            31.690142,  32.957745,  34.225353,  35.492958,  36.760563,  38.028168,
            39.295776,  40.56338 ,  41.830986,  43.09859 ,  44.366196,  45.633804,
            46.90141 ,  48.169014,  49.43662 ,  50.704224,  51.971832,  53.239437,
            54.507042,  55.774647,  57.042255,  58.30986 ,  59.577465,  60.84507 ,
            62.112675,  63.380283,  64.64789 ,  65.91549 ,  67.1831  ,  68.45071 ,
            69.71831 ,  70.985916,  72.253525,  73.521126,  74.788734,  76.056335,
            77.323944,  78.59155 ,  79.85915 ,  81.12676 ,  82.39436 ,  83.66197 ,
            84.92958 ,  86.19718 ,  87.46479 ,  88.73239 ,  90.      ],
          dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    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], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1850-01-01T12:00:00 ... 2014-12-...

    axis :

    T

    bounds :

    time_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1850-01-01 00:00:00

    array(['1850-01-01T12:00:00.000000000', '1850-01-02T12:00:00.000000000',
           '1850-01-03T12:00:00.000000000', ..., '2014-12-29T12:00:00.000000000',
           '2014-12-30T12:00:00.000000000', '2014-12-31T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_bounds
    (time, axis_nbounds)
    datetime64[ns]
    dask.array<chunksize=(30133, 1), meta=np.ndarray>

    Array Chunk Bytes 0.92 MiB 235.41 kiB Shape (60265, 2) (30133, 1) Dask graph 4 chunks in 7 graph layers Data type datetime64[ns] numpy.ndarray

  • member_id
    (member_id)
    object
    'r1i1p1f1' 'r2i1p1f1'

    array(['r1i1p1f1', 'r2i1p1f1'], dtype=object)

  • dcpp_init_year
    (dcpp_init_year)
    float64
    nan

    array([nan])

• Data variables: (1)
  • pr
    (member_id, dcpp_init_year, time, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 843, 143, 144), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    description :

    at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)

    history :

    none

    interval_operation :

    900 s

    interval_write :

    1 d

    long_name :

    Precipitation

    online_operation :

    average

    standard_name :

    precipitation_flux

    units :

    kg m-2 s-1

    Array Chunk Bytes 9.25 GiB 66.22 MiB Shape (2, 1, 60265, 143, 144) (1, 1, 843, 143, 144) Dask graph 144 chunks in 7 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • lat
    PandasIndex

    PandasIndex(Index([             -90.0, -88.73239135742188, -87.46479034423828,
           -86.19718170166016, -84.92958068847656, -83.66197204589844,
           -82.39436340332031, -81.12676239013672,  -79.8591537475586,
             -78.591552734375,
           ...
              78.591552734375,   79.8591537475586,  81.12676239013672,
            82.39436340332031,  83.66197204589844,  84.92958068847656,
            86.19718170166016,  87.46479034423828,  88.73239135742188,
                         90.0],
          dtype='float32', name='lat', length=143))

  • 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='float32', name='lon', length=144))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1850-01-01 12:00:00', '1850-01-02 12:00:00',
                   '1850-01-03 12:00:00', '1850-01-04 12:00:00',
                   '1850-01-05 12:00:00', '1850-01-06 12:00:00',
                   '1850-01-07 12:00:00', '1850-01-08 12:00:00',
                   '1850-01-09 12:00:00', '1850-01-10 12:00:00',
                   ...
                   '2014-12-22 12:00:00', '2014-12-23 12:00:00',
                   '2014-12-24 12:00:00', '2014-12-25 12:00:00',
                   '2014-12-26 12:00:00', '2014-12-27 12:00:00',
                   '2014-12-28 12:00:00', '2014-12-29 12:00:00',
                   '2014-12-30 12:00:00', '2014-12-31 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=60265, freq=None))

  • member_id
    PandasIndex

    PandasIndex(Index(['r1i1p1f1', 'r2i1p1f1'], dtype='object', name='member_id'))

  • dcpp_init_year
    PandasIndex

    PandasIndex(Index([nan], dtype='float64', name='dcpp_init_year'))

• Attributes: (53)

  CMIP6_CV_version :

  cv=6.2.3.5-2-g63b123e

  Conventions :

  CF-1.7 CMIP-6.2

  EXPID :

  historical

  NCO :

  "4.6.0"

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  contact :

  ipsl-cmip6@listes.ipsl.fr

  data_specs_version :

  01.00.21

  description :

  CMIP6 historical

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  day

  grid :

  LMDZ grid

  grid_label :

  gr

  initialization_index :

  1

  institution :

  Institut Pierre Simon Laplace, Paris 75252, France

  institution_id :

  IPSL

  license :

  CMIP6 model data produced by IPSL is licensed under a Creative Commons Attribution-NonCommercial-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) and at https://cmc.ipsl.fr/. 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

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  IPSL-CM6A-LR

  parent_time_units :

  days since 1850-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realm :

  atmos

  source :

  IPSL-CM6A-LR (2017): atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 40000 m) land: ORCHIDEE (v2.0, Water/Carbon/Energy mode) ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m) ocnBgchem: NEMO-PISCES seaIce: NEMO-LIM3

  source_id :

  IPSL-CM6A-LR

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  day

  title :

  IPSL-CM6A-LR model output prepared for CMIP6 / CMIP historical

  variable_id :

  pr

  status :

  2019-11-03;created;by nhn2@columbia.edu

  version_id :

  v20180803

  intake_esm_vars :

  ['pr']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  IPSL

  intake_esm_attrs:source_id :

  IPSL-CM6A-LR

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:table_id :

  day

  intake_esm_attrs:variable_id :

  pr

  intake_esm_attrs:grid_label :

  gr

  intake_esm_attrs:version :

  20180803

  intake_esm_attrs:_data_format_ :

  zarr

  variant_info :

  Restart from another point in piControl.. Information provided by this attribute may in some cases be flawed. Users can find more comprehensive and up-to-date documentation via the further_info_url global attribute.

  intake_esm_dataset_key :

  CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr

Yes it did, awesome! The dimension member_id in the dataset represents the different ensemble members. You can see how using intake-esm to load data is extremely convenient.

Some post-processing will be necessary to turn the data from a dictionary of different xr.Datasets to a single xr.Dataset. For example, we may wish to concatenate the historical and SSP3-7.0 output into a single time series. This is fairly trivial, but we’ll demonstrate how to do it anyway:

# extract each of the two datasets from the dictionary, and select an abritrary sample location
ds_historical = ds_dict['CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr']
ds_ssp370 = ds_dict['ScenarioMIP.IPSL.IPSL-CM6A-LR.ssp370.day.gr']

# concatenate in time
ds_full_record = xr.concat([ds_historical, ds_ssp370], dim = 'time')
ds_full_record 

<xarray.Dataset> Size: 15GB
Dimensions:         (member_id: 2, dcpp_init_year: 1, time: 91676, lat: 143,
                     lon: 144, axis_nbounds: 2)
Coordinates:
  * lat             (lat) float32 572B -90.0 -88.73 -87.46 ... 87.46 88.73 90.0
  * lon             (lon) float32 576B 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5
  * time            (time) datetime64[ns] 733kB 1850-01-01T12:00:00 ... 2100-...
    time_bounds     (time, axis_nbounds) datetime64[ns] 1MB dask.array<chunksize=(30133, 1), meta=np.ndarray>
  * member_id       (member_id) object 16B 'r1i1p1f1' 'r2i1p1f1'
  * dcpp_init_year  (dcpp_init_year) float64 8B nan
Dimensions without coordinates: axis_nbounds
Data variables:
    pr              (member_id, dcpp_init_year, time, lat, lon) float32 15GB dask.array<chunksize=(1, 1, 843, 143, 144), meta=np.ndarray>
Attributes: (12/53)
    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e
    Conventions:                      CF-1.7 CMIP-6.2
    EXPID:                            historical
    NCO:                              "4.6.0"
    activity_id:                      CMIP
    branch_method:                    standard
    ...                               ...
    intake_esm_attrs:variable_id:     pr
    intake_esm_attrs:grid_label:      gr
    intake_esm_attrs:version:         20180803
    intake_esm_attrs:_data_format_:   zarr
    variant_info:                     Restart from another point in piControl...
    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr

xarray.Dataset

• Dimensions:
  • member_id: 2
  • dcpp_init_year: 1
  • time: 91676
  • lat: 143
  • lon: 144
  • axis_nbounds: 2
• Coordinates: (6)
  • lat
    (lat)
    float32
    -90.0 -88.73 -87.46 ... 88.73 90.0

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.73239 , -87.46479 , -86.19718 , -84.92958 , -83.66197 ,
           -82.39436 , -81.12676 , -79.85915 , -78.59155 , -77.323944, -76.056335,
           -74.788734, -73.521126, -72.253525, -70.985916, -69.71831 , -68.45071 ,
           -67.1831  , -65.91549 , -64.64789 , -63.380283, -62.112675, -60.84507 ,
           -59.577465, -58.30986 , -57.042255, -55.774647, -54.507042, -53.239437,
           -51.971832, -50.704224, -49.43662 , -48.169014, -46.90141 , -45.633804,
           -44.366196, -43.09859 , -41.830986, -40.56338 , -39.295776, -38.028168,
           -36.760563, -35.492958, -34.225353, -32.957745, -31.690142, -30.422535,
           -29.15493 , -27.887323, -26.619719, -25.352112, -24.084507, -22.816902,
           -21.549295, -20.28169 , -19.014084, -17.746479, -16.478872, -15.211267,
           -13.943662, -12.676056, -11.408451, -10.140845,  -8.87324 ,  -7.605634,
            -6.338028,  -5.070423,  -3.802817,  -2.535211,  -1.267606,   0.      ,
             1.267606,   2.535211,   3.802817,   5.070423,   6.338028,   7.605634,
             8.87324 ,  10.140845,  11.408451,  12.676056,  13.943662,  15.211267,
            16.478872,  17.746479,  19.014084,  20.28169 ,  21.549295,  22.816902,
            24.084507,  25.352112,  26.619719,  27.887323,  29.15493 ,  30.422535,
            31.690142,  32.957745,  34.225353,  35.492958,  36.760563,  38.028168,
            39.295776,  40.56338 ,  41.830986,  43.09859 ,  44.366196,  45.633804,
            46.90141 ,  48.169014,  49.43662 ,  50.704224,  51.971832,  53.239437,
            54.507042,  55.774647,  57.042255,  58.30986 ,  59.577465,  60.84507 ,
            62.112675,  63.380283,  64.64789 ,  65.91549 ,  67.1831  ,  68.45071 ,
            69.71831 ,  70.985916,  72.253525,  73.521126,  74.788734,  76.056335,
            77.323944,  78.59155 ,  79.85915 ,  81.12676 ,  82.39436 ,  83.66197 ,
            84.92958 ,  86.19718 ,  87.46479 ,  88.73239 ,  90.      ],
          dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    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], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1850-01-01T12:00:00 ... 2100-12-...

    axis :

    T

    bounds :

    time_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1850-01-01 00:00:00

    array(['1850-01-01T12:00:00.000000000', '1850-01-02T12:00:00.000000000',
           '1850-01-03T12:00:00.000000000', ..., '2100-12-29T12:00:00.000000000',
           '2100-12-30T12:00:00.000000000', '2100-12-31T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_bounds
    (time, axis_nbounds)
    datetime64[ns]
    dask.array<chunksize=(30133, 1), meta=np.ndarray>

    Array Chunk Bytes 1.40 MiB 235.41 kiB Shape (91676, 2) (30133, 1) Dask graph 8 chunks in 16 graph layers Data type datetime64[ns] numpy.ndarray

  • member_id
    (member_id)
    object
    'r1i1p1f1' 'r2i1p1f1'

    array(['r1i1p1f1', 'r2i1p1f1'], dtype=object)

  • dcpp_init_year
    (dcpp_init_year)
    float64
    nan

    array([nan])

• Data variables: (1)
  • pr
    (member_id, dcpp_init_year, time, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 843, 143, 144), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    description :

    at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)

    history :

    none

    interval_operation :

    900 s

    interval_write :

    1 d

    long_name :

    Precipitation

    online_operation :

    average

    standard_name :

    precipitation_flux

    units :

    kg m-2 s-1

    Array Chunk Bytes 14.07 GiB 66.93 MiB Shape (2, 1, 91676, 143, 144) (1, 1, 852, 143, 144) Dask graph 290 chunks in 17 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • lat
    PandasIndex

    PandasIndex(Index([             -90.0, -88.73239135742188, -87.46479034423828,
           -86.19718170166016, -84.92958068847656, -83.66197204589844,
           -82.39436340332031, -81.12676239013672,  -79.8591537475586,
             -78.591552734375,
           ...
              78.591552734375,   79.8591537475586,  81.12676239013672,
            82.39436340332031,  83.66197204589844,  84.92958068847656,
            86.19718170166016,  87.46479034423828,  88.73239135742188,
                         90.0],
          dtype='float32', name='lat', length=143))

  • 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='float32', name='lon', length=144))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1850-01-01 12:00:00', '1850-01-02 12:00:00',
                   '1850-01-03 12:00:00', '1850-01-04 12:00:00',
                   '1850-01-05 12:00:00', '1850-01-06 12:00:00',
                   '1850-01-07 12:00:00', '1850-01-08 12:00:00',
                   '1850-01-09 12:00:00', '1850-01-10 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='datetime64[ns]', name='time', length=91676, freq=None))

  • member_id
    PandasIndex

    PandasIndex(Index(['r1i1p1f1', 'r2i1p1f1'], dtype='object', name='member_id'))

  • dcpp_init_year
    PandasIndex

    PandasIndex(Index([nan], dtype='float64', name='dcpp_init_year'))

• Attributes: (53)

  CMIP6_CV_version :

  cv=6.2.3.5-2-g63b123e

  Conventions :

  CF-1.7 CMIP-6.2

  EXPID :

  historical

  NCO :

  "4.6.0"

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  contact :

  ipsl-cmip6@listes.ipsl.fr

  data_specs_version :

  01.00.21

  description :

  CMIP6 historical

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  day

  grid :

  LMDZ grid

  grid_label :

  gr

  initialization_index :

  1

  institution :

  Institut Pierre Simon Laplace, Paris 75252, France

  institution_id :

  IPSL

  license :

  CMIP6 model data produced by IPSL is licensed under a Creative Commons Attribution-NonCommercial-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) and at https://cmc.ipsl.fr/. 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

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  IPSL-CM6A-LR

  parent_time_units :

  days since 1850-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realm :

  atmos

  source :

  IPSL-CM6A-LR (2017): atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 40000 m) land: ORCHIDEE (v2.0, Water/Carbon/Energy mode) ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m) ocnBgchem: NEMO-PISCES seaIce: NEMO-LIM3

  source_id :

  IPSL-CM6A-LR

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  day

  title :

  IPSL-CM6A-LR model output prepared for CMIP6 / CMIP historical

  variable_id :

  pr

  status :

  2019-11-03;created;by nhn2@columbia.edu

  version_id :

  v20180803

  intake_esm_vars :

  ['pr']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  IPSL

  intake_esm_attrs:source_id :

  IPSL-CM6A-LR

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:table_id :

  day

  intake_esm_attrs:variable_id :

  pr

  intake_esm_attrs:grid_label :

  gr

  intake_esm_attrs:version :

  20180803

  intake_esm_attrs:_data_format_ :

  zarr

  variant_info :

  Restart from another point in piControl.. Information provided by this attribute may in some cases be flawed. Users can find more comprehensive and up-to-date documentation via the further_info_url global attribute.

  intake_esm_dataset_key :

  CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr

In order to handle data from multiple models, we can use xclim.ensembles to combine datasets for separate models into a single xr.Dataset.

# search for data from two models, one ensemble member.
search_result_multimodel = catalog.search(source_id = ["IPSL-CM6A-LR" ,'CESM2'],
                                          experiment_id='historical',
                                          member_id = 'r1i1p1f1',
                                          table_id='day', 
                                          variable_id='pr')
# print the results
search_result_multimodel.df

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	CMIP	IPSL	IPSL-CM6A-LR	historical	r1i1p1f1	day	pr	gr	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...	NaN	20180803
1	CMIP	NCAR	CESM2	historical	r1i1p1f1	day	pr	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190401

# access the data
ds_dict_multimodel = search_result_multimodel.to_dataset_dict(zarr_kwargs={'consolidated': True})
list(ds_dict_multimodel.keys())

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

100.00% [2/2 00:00<00:00]

['CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr',
 'CMIP.NCAR.CESM2.historical.day.gn']

# put both models into a single dataset
ds_multimodel = xce.create_ensemble([ds_dict_multimodel[k] for k in ds_dict_multimodel.keys()],                             
                                    realizations = ["IPSL-CM6A-LR" ,'CESM2'],
                                    calendar = 'noleap')
ds_multimodel

<xarray.Dataset> Size: 92GB
Dimensions:         (realization: 2, member_id: 1, dcpp_init_year: 1,
                     time: 120451, lat: 333, lon: 288, axis_nbounds: 2, nbnd: 2)
Coordinates:
  * lat             (lat) float64 3kB -90.0 -89.06 -88.73 ... 88.73 89.06 90.0
  * lon             (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8
  * time            (time) object 964kB 1850-01-01 00:00:00 ... 2015-01-01 00...
  * member_id       (member_id) object 8B 'r1i1p1f1'
  * dcpp_init_year  (dcpp_init_year) float64 8B nan
    time_bounds     (time, axis_nbounds) datetime64[ns] 2MB dask.array<chunksize=(60227, 1), meta=np.ndarray>
    lat_bnds        (lat, nbnd) float64 5kB dask.array<chunksize=(333, 2), meta=np.ndarray>
    lon_bnds        (lon, nbnd) float64 5kB dask.array<chunksize=(288, 2), meta=np.ndarray>
    time_bnds       (time, nbnd) object 2MB dask.array<chunksize=(60226, 1), meta=np.ndarray>
  * realization     (realization) <U12 96B 'IPSL-CM6A-LR' 'CESM2'
Dimensions without coordinates: axis_nbounds, nbnd
Data variables:
    pr              (realization, member_id, dcpp_init_year, time, lat, lon) float32 92GB dask.array<chunksize=(1, 1, 1, 1100, 333, 288), meta=np.ndarray>
Attributes: (12/67)
    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e
    Conventions:                      CF-1.7 CMIP-6.2
    EXPID:                            historical
    NCO:                              "4.6.0"
    activity_id:                      CMIP
    branch_method:                    standard
    ...                               ...
    intake_esm_attrs:variable_id:     pr
    intake_esm_attrs:grid_label:      gr
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR...
    intake_esm_attrs:version:         20180803
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr

xarray.Dataset

• Dimensions:
  • realization: 2
  • member_id: 1
  • dcpp_init_year: 1
  • time: 120451
  • lat: 333
  • lon: 288
  • axis_nbounds: 2
  • nbnd: 2
• Coordinates: (10)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.73 ... 89.06 90.0

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057592, -88.732391, ...,  88.732391,  89.057592,
            90.      ])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • time
    (time)
    object
    1850-01-01 00:00:00 ... 2015-01-...

    axis :

    T

    bounds :

    time_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1850-01-01 00:00:00

    array([cftime.DatetimeNoLeap(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 1, 1, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 1, 2, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(2014, 12, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 31, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • member_id
    (member_id)
    object
    'r1i1p1f1'

    array(['r1i1p1f1'], dtype=object)

  • dcpp_init_year
    (dcpp_init_year)
    float64
    nan

    array([nan])

  • time_bounds
    (time, axis_nbounds)
    datetime64[ns]
    dask.array<chunksize=(60227, 1), meta=np.ndarray>

    Array Chunk Bytes 1.84 MiB 470.52 kiB Shape (120451, 2) (60227, 1) Dask graph 4 chunks in 11 graph layers Data type datetime64[ns] numpy.ndarray

  • lat_bnds
    (lat, nbnd)
    float64
    dask.array<chunksize=(333, 2), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 5.20 kiB 5.20 kiB Shape (333, 2) (333, 2) Dask graph 1 chunks in 10 graph layers Data type float64 numpy.ndarray

  • lon_bnds
    (lon, nbnd)
    float64
    dask.array<chunksize=(288, 2), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 4.50 kiB 4.50 kiB Shape (288, 2) (288, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(60226, 1), meta=np.ndarray>

    Array Chunk Bytes 1.84 MiB 470.52 kiB Shape (120451, 2) (60226, 1) Dask graph 4 chunks in 10 graph layers Data type object numpy.ndarray

  • realization
    (realization)
    <U12
    'IPSL-CM6A-LR' 'CESM2'

    axis :

    E

    array(['IPSL-CM6A-LR', 'CESM2'], dtype='<U12')

• Data variables: (1)
  • pr
    (realization, member_id, dcpp_init_year, time, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 1, 1100, 333, 288), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    description :

    at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)

    history :

    none

    interval_operation :

    900 s

    interval_write :

    1 d

    long_name :

    Precipitation

    online_operation :

    average

    standard_name :

    precipitation_flux

    units :

    kg m-2 s-1

    Array Chunk Bytes 86.07 GiB 402.43 MiB Shape (2, 1, 1, 120451, 333, 288) (1, 1, 1, 1100, 333, 288) Dask graph 362 chunks in 45 graph layers Data type float32 numpy.ndarray

• Indexes: (6)
  • lat
    PandasIndex

    PandasIndex(Index([             -90.0, -89.05759162303664, -88.73239135742188,
            -88.1151832460733, -87.46479034423828, -87.17277486910994,
            -86.2303664921466, -86.19718170166016, -85.28795811518324,
           -84.92958068847656,
           ...
            84.92958068847656,  85.28795811518324,  86.19718170166016,
             86.2303664921466,  87.17277486910996,  87.46479034423828,
            88.11518324607329,  88.73239135742188,  89.05759162303664,
                         90.0],
          dtype='float64', name='lat', length=333))

  • lon
    PandasIndex

    PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,
            11.25,
           ...
            347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,
           358.75],
          dtype='float64', name='lon', length=288))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-01 00:00:00, 1850-01-01 12:00:00, 1850-01-02 00:00:00,
                 1850-01-02 12:00:00, 1850-01-03 00:00:00, 1850-01-03 12:00:00,
                 1850-01-04 00:00:00, 1850-01-04 12:00:00, 1850-01-05 00:00:00,
                 1850-01-05 12:00:00,
                 ...
                 2014-12-27 12:00:00, 2014-12-28 00:00:00, 2014-12-28 12:00:00,
                 2014-12-29 00:00:00, 2014-12-29 12:00:00, 2014-12-30 00:00:00,
                 2014-12-30 12:00:00, 2014-12-31 00:00:00, 2014-12-31 12:00:00,
                 2015-01-01 00:00:00],
                dtype='object', length=120451, calendar='noleap', freq='12h'))

  • member_id
    PandasIndex

    PandasIndex(Index(['r1i1p1f1'], dtype='object', name='member_id'))

  • dcpp_init_year
    PandasIndex

    PandasIndex(Index([nan], dtype='float64', name='dcpp_init_year'))

  • realization
    PandasIndex

    PandasIndex(Index(['IPSL-CM6A-LR', 'CESM2'], dtype='object', name='realization'))

• Attributes: (67)

  CMIP6_CV_version :

  cv=6.2.3.5-2-g63b123e

  Conventions :

  CF-1.7 CMIP-6.2

  EXPID :

  historical

  NCO :

  "4.6.0"

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  21914.0

  contact :

  ipsl-cmip6@listes.ipsl.fr

  creation_date :

  2018-07-11T07:27:15Z

  data_specs_version :

  01.00.21

  description :

  CMIP6 historical

  dr2xml_md5sum :

  f1e40c1fc5d8281f865f72fbf4e38f9d

  dr2xml_version :

  1.11

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  day

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-CM6A-LR.historical.none.r1i1p1f1

  grid :

  LMDZ grid

  grid_label :

  gr

  history :

  Sat Dec 1 12:16:31 2018: ncatted -O -a realization_index,global,m,i,1 /ccc/work/cont003/cmip6/cmip6/onhold/CM61-LR-histEXT-03.1910/files+ext/pr_day_IPSL-CM6A-LR_historical_r1i1p1f1_gr_18500101-20141231.nc Sat Dec 1 12:09:56 2018: ncatted -O -a realization_index,global,m,i,1 /ccc/work/cont003/cmip6/cmip6/onhold/CM61-LR-hist-03.1910/files/pr_day_IPSL-CM6A-LR_historical_r1i1p1f1_gr_18500101-20141231.nc Sat Dec 1 11:04:28 2018: ncatted -O -a realization_index,global,m,i,1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r1i1p1f1_gr_18500101-20141231.nc Fri Nov 30 16:51:32 2018: ncatted -O -a realization_index,global,m,s,1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r1i1p1f1_gr_18500101-20141231.nc Thu Nov 29 16:52:55 2018: ncatted -O -a variant_label,global,m,c,r1i1p1f1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r3i1p1f1_gr_18500101-20141231.nc Thu Nov 29 16:52:55 2018: ncatted -O -a further_info_url,global,m,c,https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-CM6A-LR.historical.none.r1i1p1f1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r3i1p1f1_gr_18500101-20141231.nc Thu Nov 29 16:52:55 2018: ncatted -O -a name,global,m,c,/ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r1i1p1f1_gr_%start_date%-%end_date% /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r3i1p1f1_gr_18500101-20141231.nc Mon Sep 3 14:51:09 2018: ncatted -O -a parent_variant_label,global,m,c,r1i1p1f1 pr_day_IPSL-CM6A-LR_historical_r3i1p1f1_gr_18500101-20141231.nc none

  initialization_index :

  1

  institution :

  Institut Pierre Simon Laplace, Paris 75252, France

  institution_id :

  IPSL

  license :

  CMIP6 model data produced by IPSL is licensed under a Creative Commons Attribution-NonCommercial-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) and at https://cmc.ipsl.fr/. 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_version :

  6.1.5

  name :

  /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/ATM/pr_day_IPSL-CM6A-LR_historical_r1i1p1f1_gr_%start_date%-%end_date%

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  IPSL-CM6A-LR

  parent_time_units :

  days since 1850-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  source :

  IPSL-CM6A-LR (2017): atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 40000 m) land: ORCHIDEE (v2.0, Water/Carbon/Energy mode) ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m) ocnBgchem: NEMO-PISCES seaIce: NEMO-LIM3

  source_id :

  IPSL-CM6A-LR

  source_type :

  AOGCM BGC

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  day

  title :

  IPSL-CM6A-LR model output prepared for CMIP6 / CMIP historical

  tracking_id :

  hdl:21.14100/41b69e94-c1e8-4950-8f04-9a4cbe5c700c

  variable_id :

  pr

  variant_label :

  r1i1p1f1

  status :

  2019-11-03;created;by nhn2@columbia.edu

  netcdf_tracking_ids :

  hdl:21.14100/41b69e94-c1e8-4950-8f04-9a4cbe5c700c

  version_id :

  v20180803

  intake_esm_vars :

  ['pr']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  IPSL

  intake_esm_attrs:source_id :

  IPSL-CM6A-LR

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  day

  intake_esm_attrs:variable_id :

  pr

  intake_esm_attrs:grid_label :

  gr

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r1i1p1f1/day/pr/gr/v20180803/

  intake_esm_attrs:version :

  20180803

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.IPSL.IPSL-CM6A-LR.historical.day.gr