EERIE Data at DKRZ

This notebook guides EERIE data users and explains how to find and load data available at DKRZ.

The notebook works well within the python3/unstable kernel.

All data relevant for the project is referenced in the main DKRZ-EERIE Catalog:

import intake

eerie_cat = intake.open_catalog(
    "https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/eerie.yaml"
)
eerie_cat

eerie:
  args:
    path: https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/eerie.yaml
  description: ''
  driver: intake.catalog.local.YAMLFileCatalog
  metadata: {}

We use a catalog reference syntax, i.e. a path name template:

hpc.hardware.product.source_id.experiment_id.realm.grid_type

Opened with python, the catalog is a nested dictionary of catalog sources. The lowest level will finally contain data sources which can be opened as xarray datasets with to_dask().

You can browse through the catalog by listing the catalog and selecting keys:

print(list(eerie_cat))
print(list(eerie_cat["dkrz"]))

['jasmin', 'dkrz']
['disk', 'archive', 'cloud', 'main', 'dkrz_ngc3']

Entries can be joined with a ‘.’ so that you can access deeper level entries from the highest catalog level:

eerie_cat["dkrz.disk"]

disk:
  args:
    path: https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/dkrz/disk/main.yaml
  description: Use this catalog if you are working on Levante. This catalog contains
    datasets for all raw data in /work/bm1344 and accesses the data via kerchunks
    in /work/bm1344/DKRZ/kerchunks.
  driver: intake.catalog.local.YAMLFileCatalog
  metadata:
    catalog_dir: https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/dkrz

Note that there is the autocompletion feature catalogs when pushing tab.

For model-output stored on DKRZ’s disk, you can get a table-like overview from a “data base” csv file opened with pandas:

data_base = eerie_cat["dkrz"]["disk"]["model-output"]["csv"].read()
# equivalent to
data_base = eerie_cat["dkrz.disk.model-output.csv"].read()
data_base.head()

/sw/spack-levante/mambaforge-23.1.0-1-Linux-x86_64-3boc6i/lib/python3.10/site-packages/dask/dataframe/io/csv.py:542: UserWarning: Warning gzip compression does not support breaking apart files
Please ensure that each individual file can fit in memory and
use the keyword ``blocksize=None to remove this message``
Setting ``blocksize=None``
  warn(

	format	grid_id	member_id	institution_id	institution	references	simulation_id	variable-long_names	variables	source_id	experiment_id	realm	grid_lable	aggregation	urlpath
0	zarr	5aff0578-9bd9-11e8-8e4a-af3d880818e6	r1i1p1f1	MPI-M	Max Planck Institute for Meteorology/Deutscher...	see MPIM/DWD publications	erc1011	['10m windspeed', 'temperature in 2m']	['sfcwind', 'tas']	icon-esm-er	eerie-control-1950	atmos	gr025	2d_daily_max	reference:://work/bm1344/DKRZ/kerchunks_batche...
1	zarr	5aff0578-9bd9-11e8-8e4a-af3d880818e6	r1i1p1f1	MPI-M	Max Planck Institute for Meteorology/Deutscher...	see MPIM/DWD publications	erc1011	['total cloud cover', 'dew point temperature i...	['clt', 'dew2', 'evspsbl', 'hfls', 'hfss', 'pr...	icon-esm-er	eerie-control-1950	atmos	gr025	2d_daily_mean	reference:://work/bm1344/DKRZ/kerchunks_batche...
2	zarr	5aff0578-9bd9-11e8-8e4a-af3d880818e6	r1i1p1f1	MPI-M	Max Planck Institute for Meteorology/Deutscher...	see MPIM/DWD publications	erc1011	['temperature in 2m']	['tas']	icon-esm-er	eerie-control-1950	atmos	gr025	2d_daily_min	reference:://work/bm1344/DKRZ/kerchunks_batche...
3	zarr	5aff0578-9bd9-11e8-8e4a-af3d880818e6	r1i1p1f1	MPI-M	Max Planck Institute for Meteorology/Deutscher...	see MPIM/DWD publications	erc1011	['vertically integrated cloud ice', 'verticall...	['clivi', 'cllvi', 'clt', 'dew2', 'evspsbl', '...	icon-esm-er	eerie-control-1950	atmos	gr025	2d_monthly_mean	reference:://work/bm1344/DKRZ/kerchunks_batche...
4	zarr	5aff0578-9bd9-11e8-8e4a-af3d880818e6	r1i1p1f1	MPI-M	Max Planck Institute for Meteorology/Deutscher...	see MPIM/DWD publications	erc1011	['specific cloud ice content', 'specific cloud...	['cli', 'clw', 'gpsm', 'height_bnds', 'hus', '...	icon-esm-er	eerie-control-1950	atmos	gr025	model-level_monthly_mean	reference:://work/bm1344/DKRZ/kerchunks_batche...

from IPython.display import display, Markdown

drs = "source_id.experiment_id.realm.grid_lable"
for c in drs.split("."):
    display(Markdown(f"## Unique entries for *{c}*:"))
    display(Markdown("- " + "\n- ".join(data_base[c].unique())))

Unique entries for source_id:

• icon-esm-er

• ifs-fesom2-sr

• ifs-amip

• ifs-nemo

• hadgem3-gc5-n640-orca12

• hadgem3-gc5-n216-orca025

Unique entries for experiment_id:

• eerie-control-1950

• eerie-spinup-1950

• amip-hist-obs

• amip-hist-obs-lr30

• amip-hist-obs-c-lr30-a-0

• amip-hist-obs-c-lr30-a-lr30

• amip-ng-obs

• amip-ng-obs-lr30

• eerie-picontrol

Unique entries for realm:

• atmos

• ocean

• land

Unique entries for grid_lable:

• gr025

• native

Go to Browse with intake-esm to understand more about how to work with the dataframe data_base.

Note that not all combinations of the DRS exist. One example is:

eerie_cat["dkrz.disk.model-output.icon-esm-er.eerie-control-1950.atmos.gr025"]

gr025:
  args:
    path: https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/dkrz/disk/model-output/icon-esm-er/eerie-control-1950/atmos/gr025/main.yaml
  description: This catalog contains atmospheric EERIE ICON-ESM-ER eerie-control-1950
    output on 0.25deg grid available at DKRZ disk.
  driver: intake.catalog.local.YAMLFileCatalog
  metadata:
    catalog_dir: https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/dkrz/disk/model-output/icon-esm-er/eerie-control-1950/atmos

Use list to explore the content of a catalog

list(eerie_cat)

['jasmin', 'dkrz']

You can print a short description of each collection with its describe function:

for col in eerie_cat:
    print(f"Description of {col}:")
    print(eerie_cat[col].describe()["description"])

Description of jasmin:
This catalog contains datasets for EERIE stored on JASMIN
Description of dkrz:
This catalog contains datasets for EERIE stored on DKRZ

Datasets saved on DKRZ are in the DKRZ catalog:

eerie_dkrz = eerie_cat["dkrz"]
for col in eerie_dkrz:
    print(f"Description of {col}:")
    print(eerie_dkrz[col].describe()["description"])

Description of disk:
Use this catalog if you are working on Levante. This catalog contains datasets for all raw data in /work/bm1344 and accesses the data via kerchunks in /work/bm1344/DKRZ/kerchunks.
Description of archive:
Only use this catalog if your desired data is not available on diks and needs to be retrieved from DKRZ's tape archive. This catalog contains datasets for archived data in /arch/bm1344
Description of cloud:
Use this catalog if you are NOT working on Levante. This catalog contains the same datasets as *dkrz_eerie_kerchunk* but data access is via the xpublish server *eerie.cloud.dkrz.de*
Description of main:
DKRZ master catalog for all /pool/data catalogs available
Description of dkrz_ngc3:
NextGEMs Cycle 3 data

The DKRZ catalogs distinguish between the storage location on DKRZ.

```{note}

If you are accessing the data from remote, you can use the cloud catalog. For accessing other data, you have to be logged in on DKRZ´s HPC.

```

eerie_dkrz_disk = eerie_dkrz["disk"]
for col in eerie_dkrz_disk:
    print(f"Description of {col}:")
    print(eerie_dkrz_disk[col].describe()["description"])

Description of model-output:
EERIE Earth System Model output available on DKRZ's Levante File System. This catalog contains datasets for all raw data in /work/bm1344 and accesses the data via kerchunks in /work/bm1344/DKRZ/kerchunks
Description of observations:
This catalog contains observational data that is used for EERIE evaluation.

We continue to work with the eerie-dkrz-disk-model-output catalog to show how to

• browse

• open and load

• subset

data.

cat = eerie_dkrz_disk["model-output"]
list(cat)

['icon-esm-er',
 'ifs-fesom2-sr',
 'ifs-amip',
 'ifs-nemo',
 'hadgem3-gc5-n640-orca12',
 'hadgem3-gc5-n216-orca025',
 'csv',
 'esm-json']

We have two options:

• continue working with yaml files and the intake-xarray plugins (easier to load)

• switching to intake-esm (easier to browse)

Browse

With intake-esm

We can use a json+csv from the intake catalog to generate an intake-esm catalog:

import json

esmjson = json.loads("".join(cat["esm-json"].read()))
dkrz_disk_model_esm = intake.open_esm_datastore(
    obj=dict(esmcat=esmjson, df=data_base),
    columns_with_iterables=["variables", "variable-long_names", "urlpath"],
)
dkrz_disk_model_esm

dkrz-catalogue catalog with 110 dataset(s) from 112 asset(s):

	unique
format	3
grid_id	3
member_id	1
institution_id	1
institution	2
references	1
simulation_id	3
variable-long_names	54
variables	57
source_id	6
experiment_id	9
realm	3
grid_lable	2
aggregation	51
urlpath	112
derived_variables	0

Intake-esm uses the data_base dataframe under the hood accessible via .df which make things easier to browse through the catalog.

• what query keywords do exist?

dkrz_disk_model_esm.df.columns

Index(['format', 'grid_id', 'member_id', 'institution_id', 'institution',
       'references', 'simulation_id', 'variable-long_names', 'variables',
       'source_id', 'experiment_id', 'realm', 'grid_lable', 'aggregation',
       'urlpath'],
      dtype='object')

Intake-esm uses a pandas dataframe under the hood which make things easier to browse through the catalog.

• which models are available in the catalog?

dkrz_disk_model_esm.unique()["source_id"]

['icon-esm-er',
 'ifs-fesom2-sr',
 'ifs-amip',
 'ifs-nemo',
 'hadgem3-gc5-n640-orca12',
 'hadgem3-gc5-n216-orca025']

Search with wild cards:

subcat_esm = dkrz_disk_model_esm.search(
    **{
        "source_id": "icon-esm-er",
        "experiment_id": "eerie-control-1950",
        "grid_lable": "gr025",
        "realm": "atmos",
        "variable-long_names": "temperature*",
        "aggregation": "monthly*",
    }
)
subcat_esm

dkrz-catalogue catalog with 1 dataset(s) from 1 asset(s):

	unique
format	1
grid_id	1
member_id	1
institution_id	1
institution	1
references	1
simulation_id	1
variable-long_names	1
variables	1
source_id	1
experiment_id	1
realm	1
grid_lable	1
aggregation	1
urlpath	1
derived_variables	0

Pure Intake

Intake offers users a free text search field. We can search for example for the control run. Intake returns another catalog.

searchdict = dict(
    model="ICON",
    realm="atmos",
    exp="eerie-control-1950",
    var="temperature",
    frequency="monthly",
)
subcat = cat["icon-esm-er"]
for v in searchdict.values():
    subcat = subcat.search(v)
list(subcat)

# note that `search` has a keyword argument *depth* (default: 2) which indicates how many subcatalogs should be searched.
# if you use a high level catalog, adapt that argument to your needs

['eerie-control-1950.atmos.gr025.2d_monthly_mean',
 'eerie-control-1950.atmos.gr025.model-level_monthly_mean',
 'eerie-control-1950.atmos.gr025.plev19_monthly_mean',
 'eerie-control-1950.atmos.native.2d_monthly_mean',
 'eerie-control-1950.atmos.native.model-level_monthly_mean',
 'eerie-control-1950.atmos.native.plev19_monthly_mean']

Use the GUI:

cat.gui

Flatten a catalog by finding all data sources in all levels:

from copy import deepcopy


def find_data_sources(catalog, name=None):
    newname = ".".join([a for a in [name, catalog.name] if a])
    data_sources = []

    for key, entry in catalog.items():
        if isinstance(entry, intake.catalog.Catalog):
            if newname == "main":
                newname = None
            # If the entry is a subcatalog, recursively search it
            data_sources.extend(find_data_sources(entry, newname))
        elif isinstance(entry, intake.source.base.DataSource):
            data_sources.append(newname + "." + key)

    return data_sources

for entry in find_data_sources(cat["hadgem3-gc5-n216-orca025"]):
    print(entry)

hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.gr025.daily
hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.native.atmos_monthly_aermon
hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.native.atmos_monthly_amon_center
hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.native.atmos_monthly_amon_edge
hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.native.atmos_monthly_emon
hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.native.atmos_daily_center
hadgem3-gc5-n216-orca025.eerie-picontrol.atmos.native.atmos_daily_edge
hadgem3-gc5-n216-orca025.eerie-picontrol.ocean.gr025.daily
hadgem3-gc5-n216-orca025.eerie-picontrol.ocean.gr025.monthly

Open and load

You can open a dataset from the catalog similar to a dictionary entry. This gives you a lot of metadata information:

cat["icon-esm-er.eerie-control-1950.atmos.gr025.2d_monthly_mean"]

2d_monthly_mean:
  args:
    chunks: auto
    consolidated: false
    storage_options:
      lazy: true
      remote_protocol: file
    urlpath: reference:://work/bm1344/DKRZ/kerchunks_batched/erc1011/atm_2d_1mth_mean_remap025/combined.parq
  description: ''
  driver: intake_xarray.xzarr.ZarrSource
  metadata:
    CDI: Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)
    CDO: Climate Data Operators version 2.2.2 (https://mpimet.mpg.de/cdo)
    Conventions: CF-1.6
    DOKU_License: CC BY 4.0
    DOKU_Name: EERIE ICON-ESM-ER eerie-control-1950 run
    DOKU_authors: "Putrasahan, D.; Kr\xF6ger, J.; Wachsmann, F."
    DOKU_responsible_person: Fabian Wachsmann
    DOKU_summary: EERIE ICON-ESM-ER eerie-control-1950 run
    activity_id: EERIE
    catalog_dir: https://raw.githubusercontent.com/eerie-project/intake_catalogues/main/dkrz/disk/model-output/icon-esm-er/eerie-control-1950/atmos/gr025
    cdo_openmp_thread_number: 16
    comment: Sapphire Dyamond (k203123) on l40335 (Linux 4.18.0-372.32.1.el8_6.x86_64
      x86_64)
    experiment: eerie-control-1950
    experiment_id: eerie-1950control
    format: netcdf
    frequency: 1month
    grid_id: 5aff0578-9bd9-11e8-8e4a-af3d880818e6
    grid_label: gn
    history: deleted for convenience
    institution: Max Planck Institute for Meteorology/Deutscher Wetterdienst
    institution_id: MPI-M
    level_type: 2d
    member_id: r1i1p1f1
    plots:
      quicklook:
        aspect: 1
        cmap: jet
        coastline: 50m
        geo: true
        groupby: time
        kind: image
        use_dask: true
        width: 800
        x: lon
        y: lat
        z: clivi
    project: EERIE
    project_id: EERIE
    realm: atm
    references: see MPIM/DWD publications
    simulation_id: erc1011
    source: git@gitlab.dkrz.de:icon/icon-mpim.git@450227788f06e837f1238ebed27af6e2365fa673
    source_id: ICON-ESM
    source_type: AOGCM
    time_max: 2188799
    time_min: 44639
    time_reduction: mean
    title: ICON simulation
    variable-long_names:
    - vertically integrated cloud ice
    - vertically integrated cloud water
    - total cloud cover
    - dew point temperature in 2m
    - evaporation
    - latent heat flux
    - sensible heat flux
    - precipitation flux
    - vertically integrated water vapour
    - surface pressure
    - mean sea level pressure
    - surface downwelling longwave radiation
    - surface downwelling clear-sky longwave radiation
    - surface upwelling longwave radiation
    - toa outgoing longwave radiation
    - toa outgoing clear-sky longwave radiation
    - surface downwelling shortwave radiation
    - surface downwelling clear-sky shortwave radiation
    - toa incident shortwave radiation
    - surface upwelling shortwave radiation
    - surface upwelling clear-sky shortwave radiation
    - toa outgoing shortwave radiation
    - toa outgoing clear-sky shortwave radiation
    - 10m windspeed
    - temperature in 2m
    - maximum 2m temperature
    - minimum 2m temperature
    - u-momentum flux at the surface
    - v-momentum flux at the surface
    - surface temperature
    - zonal wind in 10m
    - meridional wind in 10m

You can load the data with the to_dask() function.

ds = cat["icon-esm-er.eerie-control-1950.atmos.gr025.2d_monthly_mean"].to_dask()
ds

<xarray.Dataset>
Dimensions:   (time: 372, lat: 721, lon: 1440, height: 1, height_2: 1)
Coordinates:
  * height    (height) float64 2.0
  * height_2  (height_2) float64 10.0
  * lat       (lat) float64 -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75 90.0
  * lon       (lon) float64 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8
  * time      (time) datetime64[ns] 2002-01-31T23:59:00 ... 2032-12-31T23:59:00
Data variables: (12/32)
    clivi     (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    cllvi     (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    clt       (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    dew2      (time, height, lat, lon) float32 dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>
    evspsbl   (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    hfls      (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    ...        ...
    tasmin    (time, height, lat, lon) float32 dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>
    tauu      (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    tauv      (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    ts        (time, lat, lon) float32 dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>
    uas       (time, height_2, lat, lon) float32 dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>
    vas       (time, height_2, lat, lon) float32 dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>
Attributes: (12/33)
    CDI:                       Climate Data Interface version 2.2.4 (https://...
    CDO:                       Climate Data Operators version 2.2.2 (https://...
    Conventions:               CF-1.6
    DOKU_License:              CC BY 4.0
    DOKU_Name:                 EERIE ICON-ESM-ER eerie-control-1950 run
    DOKU_authors:              Putrasahan, D.; Kröger, J.; Wachsmann, F.
    ...                        ...
    source_id:                 ICON-ESM
    source_type:               AOGCM
    time_max:                  2188799
    time_min:                  44639
    time_reduction:            mean
    title:                     ICON simulation

xarray.Dataset

• Dimensions:
  • time: 372
  • lat: 721
  • lon: 1440
  • height: 1
  • height_2: 1
• Coordinates: (5)
  • height
    (height)
    float64
    2.0

    axis :

    Z

    long_name :

    height

    positive :

    up

    standard_name :

    height

    units :

    m

    array([2.])

  • height_2
    (height_2)
    float64
    10.0

    axis :

    Z

    long_name :

    height

    positive :

    up

    standard_name :

    height

    units :

    m

    array([10.])

  • lat
    (lat)
    float64
    -90.0 -89.75 -89.5 ... 89.75 90.0

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ])

  • lon
    (lon)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • time
    (time)
    datetime64[ns]
    2002-01-31T23:59:00 ... 2032-12-...

    axis :

    T

    standard_name :

    time

    array(['2002-01-31T23:59:00.000000000', '2002-02-28T23:59:00.000000000',
           '2002-03-31T23:59:00.000000000', ..., '2032-10-31T23:59:00.000000000',
           '2032-11-30T23:59:00.000000000', '2032-12-31T23:59:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (32)
  • clivi
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    atmosphere

    long_name :

    vertically integrated cloud ice

    param :

    70.1.0

    standard_name :

    total_cloud_ice

    units :

    kg m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cllvi
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    atmosphere

    long_name :

    vertically integrated cloud water

    param :

    69.1.0

    standard_name :

    total_cloud_water

    units :

    kg m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • clt
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    total cloud cover

    param :

    1.6.0

    standard_name :

    clt

    units :

    m2 m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dew2
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    dew point temperature in 2m

    param :

    6.0.0

    standard_name :

    dew2

    units :

    K

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • evspsbl
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    evaporation

    param :

    6.1.0

    standard_name :

    evap

    units :

    kg m-2 s-1

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hfls
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    latent heat flux

    param :

    10.0.0

    standard_name :

    lhflx

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hfss
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    sensible heat flux

    param :

    11.0.0

    standard_name :

    shflx

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pr
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    precipitation flux

    param :

    52.1.0

    standard_name :

    pr

    units :

    kg m-2 s-1

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prw
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    atmosphere

    long_name :

    vertically integrated water vapour

    param :

    64.1.0

    standard_name :

    total_vapour

    units :

    kg m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ps
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface pressure

    param :

    0.3.0

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • psl
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    mean sea level pressure

    param :

    1.3.0

    standard_name :

    mean sea level pressure

    units :

    Pa

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlds
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface downwelling longwave radiation

    param :

    3.5.0

    standard_name :

    surface_downwelling_longwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rldscs
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface downwelling clear-sky longwave radiation

    param :

    203.5.0

    standard_name :

    surface_downwelling_longwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlus
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface upwelling longwave radiation

    param :

    199.5.0

    standard_name :

    surface_upwelling_longwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlut
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing longwave radiation

    param :

    4.5.0

    standard_name :

    toa_outgoing_longwave_flux

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlutcs
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing clear-sky longwave radiation

    param :

    204.5.0

    standard_name :

    toa_outgoing_longwave_flux_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsds
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface downwelling shortwave radiation

    param :

    7.4.0

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsdscs
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface downwelling clear-sky shortwave radiation

    param :

    207.4.0

    standard_name :

    surface_downwelling_shortwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsdt
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa incident shortwave radiation

    param :

    201.4.0

    standard_name :

    toa_incoming_shortwave_flux

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsus
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface upwelling shortwave radiation

    param :

    199.4.0

    standard_name :

    surface_upwelling_shortwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsuscs
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface upwelling clear-sky shortwave radiation

    param :

    209.4.0

    standard_name :

    surface_upwelling_shortwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsut
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing shortwave radiation

    param :

    8.4.0

    standard_name :

    toa_outgoing_shortwave_flux

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsutcs
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing clear-sky shortwave radiation

    param :

    208.4.0

    standard_name :

    toa_outgoing_shortwave_flux_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sfcwind
    (time, height_2, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10m windspeed

    param :

    1.2.0

    standard_name :

    sfcwind

    units :

    m s-1

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tas
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    temperature in 2m

    param :

    0.0.0

    standard_name :

    tas

    units :

    K

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmax
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    maximum 2m temperature

    param :

    4.0.0

    standard_name :

    tasmax

    units :

    K

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmin
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    minimum 2m temperature

    param :

    5.0.0

    standard_name :

    tasmin

    units :

    K

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tauu
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    u-momentum flux at the surface

    param :

    17.2.0

    standard_name :

    u_stress

    units :

    N m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tauv
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    v-momentum flux at the surface

    param :

    18.2.0

    standard_name :

    v_stress

    units :

    N m-2

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ts
    (time, lat, lon)
    float32
    dask.array<chunksize=(32, 721, 1440), meta=np.ndarray>

    long_name :

    surface temperature

    param :

    0.0.0

    standard_name :

    surface_temperature

    units :

    K

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 721, 1440) (32, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uas
    (time, height_2, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    zonal wind in 10m

    param :

    2.2.0

    standard_name :

    uas

    units :

    m s-1

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vas
    (time, height_2, lat, lon)
    float32
    dask.array<chunksize=(32, 1, 721, 1440), meta=np.ndarray>

    long_name :

    meridional wind in 10m

    param :

    3.2.0

    standard_name :

    vas

    units :

    m s-1

    Array Chunk Bytes 1.44 GiB 126.74 MiB Shape (372, 1, 721, 1440) (32, 1, 721, 1440) Dask graph 12 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • height
    PandasIndex

    PandasIndex(Index([2.0], dtype='float64', name='height'))

  • height_2
    PandasIndex

    PandasIndex(Index([10.0], dtype='float64', name='height_2'))

  • lat
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float64', name='lat', length=721))

  • lon
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-01-31 23:59:00', '2002-02-28 23:59:00',
                   '2002-03-31 23:59:00', '2002-04-30 23:59:00',
                   '2002-05-31 23:59:00', '2002-06-30 23:59:00',
                   '2002-07-31 23:59:00', '2002-08-31 23:59:00',
                   '2002-09-30 23:59:00', '2002-10-31 23:59:00',
                   ...
                   '2032-03-31 23:59:00', '2032-04-30 23:59:00',
                   '2032-05-31 23:59:00', '2032-06-30 23:59:00',
                   '2032-07-31 23:59:00', '2032-08-31 23:59:00',
                   '2032-09-30 23:59:00', '2032-10-31 23:59:00',
                   '2032-11-30 23:59:00', '2032-12-31 23:59:00'],
                  dtype='datetime64[ns]', name='time', length=372, freq=None))

• Attributes: (33)

  CDI :

  Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)

  CDO :

  Climate Data Operators version 2.2.2 (https://mpimet.mpg.de/cdo)

  Conventions :

  CF-1.6

  DOKU_License :

  CC BY 4.0

  DOKU_Name :

  EERIE ICON-ESM-ER eerie-control-1950 run

  DOKU_authors :

  Putrasahan, D.; Kröger, J.; Wachsmann, F.

  DOKU_responsible_person :

  Fabian Wachsmann

  DOKU_summary :

  EERIE ICON-ESM-ER eerie-control-1950 run

  activity_id :

  EERIE

  cdo_openmp_thread_number :

  16

  comment :

  Sapphire Dyamond (k203123) on l40335 (Linux 4.18.0-372.32.1.el8_6.x86_64 x86_64)

  experiment_id :

  eerie-1950control

  format :

  netcdf

  frequency :

  1month

  grid_id :

  5aff0578-9bd9-11e8-8e4a-af3d880818e6

  grid_label :

  gn

  history :

  deleted for convenience

  institution :

  Max Planck Institute for Meteorology/Deutscher Wetterdienst

  institution_id :

  MPI-M

  level_type :

  2d

  member_id :

  r1i1p1f1

  project :

  EERIE

  project_id :

  EERIE

  realm :

  atm

  references :

  see MPIM/DWD publications

  simulation_id :

  erc1011

  source :

  git@gitlab.dkrz.de:icon/icon-mpim.git@450227788f06e837f1238ebed27af6e2365fa673

  source_id :

  ICON-ESM

  source_type :

  AOGCM

  time_max :

  2188799

  time_min :

  44639

  time_reduction :

  mean

  title :

  ICON simulation

Load with intake-esm

subcat_esm.to_dataset_dict(
    xarray_open_kwargs=dict(backend_kwargs=dict(consolidated=False)),
    storage_options=dict(remote_protocol="file", lazy=True)
    #    ),
).popitem()[1]

--> The keys in the returned dictionary of datasets are constructed as follows:
        'source_id.experiment_id.realm.grid_lable.aggregation'

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

<xarray.Dataset>
Dimensions:   (time: 372, lat: 721, lon: 1440, height: 1, height_2: 1)
Coordinates:
  * height    (height) float64 2.0
  * height_2  (height_2) float64 10.0
  * lat       (lat) float64 -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75 90.0
  * lon       (lon) float64 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8
  * time      (time) datetime64[ns] 2002-01-31T23:59:00 ... 2032-12-31T23:59:00
Data variables: (12/32)
    clivi     (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    cllvi     (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    clt       (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    dew2      (time, height, lat, lon) float32 dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>
    evspsbl   (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    hfls      (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    ...        ...
    tasmin    (time, height, lat, lon) float32 dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>
    tauu      (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    tauv      (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    ts        (time, lat, lon) float32 dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>
    uas       (time, height_2, lat, lon) float32 dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>
    vas       (time, height_2, lat, lon) float32 dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>
Attributes: (12/50)
    CDI:                                   Climate Data Interface version 2.2...
    CDO:                                   Climate Data Operators version 2.2...
    Conventions:                           CF-1.6
    DOKU_License:                          CC BY 4.0
    DOKU_Name:                             EERIE ICON-ESM-ER eerie-control-19...
    DOKU_authors:                          Putrasahan, D.; Kröger, J.; Wachsm...
    ...                                    ...
    intake_esm_attrs:experiment_id:        eerie-control-1950
    intake_esm_attrs:realm:                atmos
    intake_esm_attrs:grid_lable:           gr025
    intake_esm_attrs:aggregation:          2d_monthly_mean
    intake_esm_attrs:urlpath:              reference:://work/bm1344/DKRZ/kerc...
    intake_esm_dataset_key:                icon-esm-er.eerie-control-1950.atm...

xarray.Dataset

• Dimensions:
  • time: 372
  • lat: 721
  • lon: 1440
  • height: 1
  • height_2: 1
• Coordinates: (5)
  • height
    (height)
    float64
    2.0

    axis :

    Z

    long_name :

    height

    positive :

    up

    standard_name :

    height

    units :

    m

    array([2.])

  • height_2
    (height_2)
    float64
    10.0

    axis :

    Z

    long_name :

    height

    positive :

    up

    standard_name :

    height

    units :

    m

    array([10.])

  • lat
    (lat)
    float64
    -90.0 -89.75 -89.5 ... 89.75 90.0

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ])

  • lon
    (lon)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • time
    (time)
    datetime64[ns]
    2002-01-31T23:59:00 ... 2032-12-...

    axis :

    T

    standard_name :

    time

    array(['2002-01-31T23:59:00.000000000', '2002-02-28T23:59:00.000000000',
           '2002-03-31T23:59:00.000000000', ..., '2032-10-31T23:59:00.000000000',
           '2032-11-30T23:59:00.000000000', '2032-12-31T23:59:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (32)
  • clivi
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    atmosphere

    long_name :

    vertically integrated cloud ice

    param :

    70.1.0

    standard_name :

    total_cloud_ice

    units :

    kg m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cllvi
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    atmosphere

    long_name :

    vertically integrated cloud water

    param :

    69.1.0

    standard_name :

    total_cloud_water

    units :

    kg m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • clt
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    total cloud cover

    param :

    1.6.0

    standard_name :

    clt

    units :

    m2 m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dew2
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    dew point temperature in 2m

    param :

    6.0.0

    standard_name :

    dew2

    units :

    K

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • evspsbl
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    evaporation

    param :

    6.1.0

    standard_name :

    evap

    units :

    kg m-2 s-1

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hfls
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    latent heat flux

    param :

    10.0.0

    standard_name :

    lhflx

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • hfss
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    sensible heat flux

    param :

    11.0.0

    standard_name :

    shflx

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pr
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    precipitation flux

    param :

    52.1.0

    standard_name :

    pr

    units :

    kg m-2 s-1

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prw
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    atmosphere

    long_name :

    vertically integrated water vapour

    param :

    64.1.0

    standard_name :

    total_vapour

    units :

    kg m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ps
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface pressure

    param :

    0.3.0

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • psl
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    mean sea level pressure

    param :

    1.3.0

    standard_name :

    mean sea level pressure

    units :

    Pa

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlds
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface downwelling longwave radiation

    param :

    3.5.0

    standard_name :

    surface_downwelling_longwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rldscs
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface downwelling clear-sky longwave radiation

    param :

    203.5.0

    standard_name :

    surface_downwelling_longwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlus
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface upwelling longwave radiation

    param :

    199.5.0

    standard_name :

    surface_upwelling_longwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlut
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing longwave radiation

    param :

    4.5.0

    standard_name :

    toa_outgoing_longwave_flux

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlutcs
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing clear-sky longwave radiation

    param :

    204.5.0

    standard_name :

    toa_outgoing_longwave_flux_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsds
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface downwelling shortwave radiation

    param :

    7.4.0

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsdscs
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface downwelling clear-sky shortwave radiation

    param :

    207.4.0

    standard_name :

    surface_downwelling_shortwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsdt
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa incident shortwave radiation

    param :

    201.4.0

    standard_name :

    toa_incoming_shortwave_flux

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsus
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface upwelling shortwave radiation

    param :

    199.4.0

    standard_name :

    surface_upwelling_shortwave_flux_in_air

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsuscs
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface upwelling clear-sky shortwave radiation

    param :

    209.4.0

    standard_name :

    surface_upwelling_shortwave_flux_in_air_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsut
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing shortwave radiation

    param :

    8.4.0

    standard_name :

    toa_outgoing_shortwave_flux

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rsutcs
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    level_type :

    toa

    long_name :

    toa outgoing clear-sky shortwave radiation

    param :

    208.4.0

    standard_name :

    toa_outgoing_shortwave_flux_assuming_clear_sky

    units :

    W m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sfcwind
    (time, height_2, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    10m windspeed

    param :

    1.2.0

    standard_name :

    sfcwind

    units :

    m s-1

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tas
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    temperature in 2m

    param :

    0.0.0

    standard_name :

    tas

    units :

    K

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmax
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    maximum 2m temperature

    param :

    4.0.0

    standard_name :

    tasmax

    units :

    K

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tasmin
    (time, height, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    minimum 2m temperature

    param :

    5.0.0

    standard_name :

    tasmin

    units :

    K

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tauu
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    u-momentum flux at the surface

    param :

    17.2.0

    standard_name :

    u_stress

    units :

    N m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tauv
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    v-momentum flux at the surface

    param :

    18.2.0

    standard_name :

    v_stress

    units :

    N m-2

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ts
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 182, 1440), meta=np.ndarray>

    long_name :

    surface temperature

    param :

    0.0.0

    standard_name :

    surface_temperature

    units :

    K

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 721, 1440) (1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • uas
    (time, height_2, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    zonal wind in 10m

    param :

    2.2.0

    standard_name :

    uas

    units :

    m s-1

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vas
    (time, height_2, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 182, 1440), meta=np.ndarray>

    long_name :

    meridional wind in 10m

    param :

    3.2.0

    standard_name :

    vas

    units :

    m s-1

    Array Chunk Bytes 1.44 GiB 1.00 MiB Shape (372, 1, 721, 1440) (1, 1, 182, 1440) Dask graph 1488 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • height
    PandasIndex

    PandasIndex(Index([2.0], dtype='float64', name='height'))

  • height_2
    PandasIndex

    PandasIndex(Index([10.0], dtype='float64', name='height_2'))

  • lat
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float64', name='lat', length=721))

  • lon
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-01-31 23:59:00', '2002-02-28 23:59:00',
                   '2002-03-31 23:59:00', '2002-04-30 23:59:00',
                   '2002-05-31 23:59:00', '2002-06-30 23:59:00',
                   '2002-07-31 23:59:00', '2002-08-31 23:59:00',
                   '2002-09-30 23:59:00', '2002-10-31 23:59:00',
                   ...
                   '2032-03-31 23:59:00', '2032-04-30 23:59:00',
                   '2032-05-31 23:59:00', '2032-06-30 23:59:00',
                   '2032-07-31 23:59:00', '2032-08-31 23:59:00',
                   '2032-09-30 23:59:00', '2032-10-31 23:59:00',
                   '2032-11-30 23:59:00', '2032-12-31 23:59:00'],
                  dtype='datetime64[ns]', name='time', length=372, freq=None))

• Attributes: (50)

  CDI :

  Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)

  CDO :

  Climate Data Operators version 2.2.2 (https://mpimet.mpg.de/cdo)

  Conventions :

  CF-1.6

  DOKU_License :

  CC BY 4.0

  DOKU_Name :

  EERIE ICON-ESM-ER eerie-control-1950 run

  DOKU_authors :

  Putrasahan, D.; Kröger, J.; Wachsmann, F.

  DOKU_responsible_person :

  Fabian Wachsmann

  DOKU_summary :

  EERIE ICON-ESM-ER eerie-control-1950 run

  activity_id :

  EERIE

  cdo_openmp_thread_number :

  16

  comment :

  Sapphire Dyamond (k203123) on l40335 (Linux 4.18.0-372.32.1.el8_6.x86_64 x86_64)

  experiment_id :

  eerie-1950control

  format :

  netcdf

  frequency :

  1month

  grid_id :

  5aff0578-9bd9-11e8-8e4a-af3d880818e6

  grid_label :

  gn

  history :

  deleted for convenience

  institution :

  Max Planck Institute for Meteorology/Deutscher Wetterdienst

  institution_id :

  MPI-M

  level_type :

  2d

  member_id :

  r1i1p1f1

  project :

  EERIE

  project_id :

  EERIE

  realm :

  atm

  references :

  see MPIM/DWD publications

  simulation_id :

  erc1011

  source :

  git@gitlab.dkrz.de:icon/icon-mpim.git@450227788f06e837f1238ebed27af6e2365fa673

  source_id :

  ICON-ESM

  source_type :

  AOGCM

  time_max :

  2188799

  time_min :

  44639

  time_reduction :

  mean

  title :

  ICON simulation

  intake_esm_vars :

  ["['clivi', 'cllvi', 'clt', 'dew2', 'evspsbl', 'hfls', 'hfss', 'pr', 'prw', 'ps', 'psl', 'rlds', 'rldscs', 'rlus', 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rsdt', 'rsus', 'rsuscs', 'rsut', 'rsutcs', 'sfcwind', 'tas', 'tasmax', 'tasmin', 'tauu', 'tauv', 'ts', 'uas', 'vas']"]

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_attrs:grid_id :

  5aff0578-9bd9-11e8-8e4a-af3d880818e6

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:institution_id :

  MPI-M

  intake_esm_attrs:institution :

  Max Planck Institute for Meteorology/Deutscher Wetterdienst

  intake_esm_attrs:references :

  see MPIM/DWD publications

  intake_esm_attrs:simulation_id :

  erc1011

  intake_esm_attrs:variable-long_names :

  ['vertically integrated cloud ice', 'vertically integrated cloud water', 'total cloud cover', 'dew point temperature in 2m', 'evaporation', 'latent heat flux', 'sensible heat flux', 'precipitation flux', 'vertically integrated water vapour', 'surface pressure', 'mean sea level pressure', 'surface downwelling longwave radiation', 'surface downwelling clear-sky longwave radiation', 'surface upwelling longwave radiation', 'toa outgoing longwave radiation', 'toa outgoing clear-sky longwave radiation', 'surface downwelling shortwave radiation', 'surface downwelling clear-sky shortwave radiation', 'toa incident shortwave radiation', 'surface upwelling shortwave radiation', 'surface upwelling clear-sky shortwave radiation', 'toa outgoing shortwave radiation', 'toa outgoing clear-sky shortwave radiation', '10m windspeed', 'temperature in 2m', 'maximum 2m temperature', 'minimum 2m temperature', 'u-momentum flux at the surface', 'v-momentum flux at the surface', 'surface temperature', 'zonal wind in 10m', 'meridional wind in 10m']

  intake_esm_attrs:variables :

  ['clivi', 'cllvi', 'clt', 'dew2', 'evspsbl', 'hfls', 'hfss', 'pr', 'prw', 'ps', 'psl', 'rlds', 'rldscs', 'rlus', 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rsdt', 'rsus', 'rsuscs', 'rsut', 'rsutcs', 'sfcwind', 'tas', 'tasmax', 'tasmin', 'tauu', 'tauv', 'ts', 'uas', 'vas']

  intake_esm_attrs:source_id :

  icon-esm-er

  intake_esm_attrs:experiment_id :

  eerie-control-1950

  intake_esm_attrs:realm :

  atmos

  intake_esm_attrs:grid_lable :

  gr025

  intake_esm_attrs:aggregation :

  2d_monthly_mean

  intake_esm_attrs:urlpath :

  reference:://work/bm1344/DKRZ/kerchunks_batched/erc1011/atm_2d_1mth_mean_remap025/combined.parq

  intake_esm_dataset_key :

  icon-esm-er.eerie-control-1950.atmos.gr025.2d_monthly_mean

Subset

Use - isel for index selection - sel for value selection. Note that you can use method=nearest to do a nearest neighbour interpolation - groupby for statistics

# get the latest time step
tas_last_timestep = ds["tas"].isel(time=-1)
tas_last_timestep

<xarray.DataArray 'tas' (height: 1, lat: 721, lon: 1440)>
dask.array<getitem, shape=(1, 721, 1440), dtype=float32, chunksize=(1, 721, 1440), chunktype=numpy.ndarray>
Coordinates:
  * height   (height) float64 2.0
  * lat      (lat) float64 -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75 90.0
  * lon      (lon) float64 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8
    time     datetime64[ns] 2032-12-31T23:59:00
Attributes:
    long_name:      temperature in 2m
    param:          0.0.0
    standard_name:  tas
    units:          K

xarray.DataArray

'tas'

• height: 1
• lat: 721
• lon: 1440

• dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

  Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (1, 721, 1440) (1, 721, 1440) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • height
    (height)
    float64
    2.0

    axis :

    Z

    long_name :

    height

    positive :

    up

    standard_name :

    height

    units :

    m

    array([2.])

  • lat
    (lat)
    float64
    -90.0 -89.75 -89.5 ... 89.75 90.0

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ])

  • lon
    (lon)
    float64
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02])

  • time
    ()
    datetime64[ns]
    2032-12-31T23:59:00

    axis :

    T

    standard_name :

    time

    array('2032-12-31T23:59:00.000000000', dtype='datetime64[ns]')

• Indexes: (3)
  • height
    PandasIndex

    PandasIndex(Index([2.0], dtype='float64', name='height'))

  • lat
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float64', name='lat', length=721))

  • lon
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float64', name='lon', length=1440))

• Attributes: (4)

  long_name :

  temperature in 2m

  param :

  0.0.0

  standard_name :

  tas

  units :

  K

# select a coordinate by values and with nearest neighbor look up:
import hvplot.xarray

tas_last_timestep_northsea = ds["tas"].sel(
    **dict(lat="54", lon="8.2"), method="nearest"
)
tas_last_timestep_northsea.hvplot.line()

tas_last_timestep_northsea_yearmean = tas_last_timestep_northsea.groupby(
    "time.year"
).mean()

tas_last_timestep_northsea_yearmean.hvplot.line()

Handling of ICON native grid for georeferenced plots:

dsnative = cat["icon-esm-er.eerie-control-1950.ocean.native.2d_monthly_mean"].to_dask()

import numpy as np

dsnative["lat"] = np.rad2deg(dsnative["lat"].load())
dsnative["lon"] = np.rad2deg(dsnative["lon"].load())

import hvplot.xarray

dsnative["to"].isel(time=0).squeeze().load().hvplot.scatter(
    x="lon", y="lat", c="to", rasterize=True, datashade=True
)