4. Ensembles

4.1. Ensemble reduction

This tutorial will explore ensemble reduction (also known as ensemble selection) using xscen. This will use pre-computed annual mean temperatures from xclim.testing.

from xclim.testing import open_dataset

import xscen as xs

datasets = {
    "ACCESS": "EnsembleStats/BCCAQv2+ANUSPLIN300_ACCESS1-0_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc",
    "BNU-ESM": "EnsembleStats/BCCAQv2+ANUSPLIN300_BNU-ESM_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc",
    "CCSM4-r1": "EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc",
    "CCSM4-r2": "EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r2i1p1_1950-2100_tg_mean_YS.nc",
    "CNRM-CM5": "EnsembleStats/BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp45_r1i1p1_1970-2050_tg_mean_YS.nc",
}

for d in datasets:
    ds = open_dataset(datasets[d]).isel(lon=slice(0, 4), lat=slice(0, 4))
    ds = xs.climatological_mean(ds, window=30, periods=[[1981, 2010], [2021, 2050]])
    datasets[d] = xs.compute_deltas(ds, reference_horizon="1981-2010")
    datasets[d].attrs["cat:id"] = d  # Required by build_reduction_data
    datasets[d].attrs["cat:xrfreq"] = "AS-JAN"

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/clisops/core/regrid.py:42: UserWarning: xarray version >= 2023.03.0 is not supported for regridding operations with cf-time indexed arrays. Please use xarray version < 2023.03.0. For more information, see: https://github.com/pydata/xarray/issues/7794.
  warnings.warn(

ERROR 1: PROJ: proj_create_from_database: Open of /home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/share/proj failed
INFO:xclim:Fetching remote file: EnsembleStats/BCCAQv2+ANUSPLIN300_ACCESS1-0_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc

INFO:xclim:Fetching remote file md5: EnsembleStats/BCCAQv2+ANUSPLIN300_ACCESS1-0_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc.md5

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/xscen/aggregate.py:90: FutureWarning: xs.climatological_mean is deprecated and will be abandoned in a future release. Use xs.climatological_op with option op=='mean' instead.
INFO:xclim:Fetching remote file: EnsembleStats/BCCAQv2+ANUSPLIN300_BNU-ESM_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc

INFO:xclim:Fetching remote file md5: EnsembleStats/BCCAQv2+ANUSPLIN300_BNU-ESM_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc.md5

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/xscen/aggregate.py:90: FutureWarning: xs.climatological_mean is deprecated and will be abandoned in a future release. Use xs.climatological_op with option op=='mean' instead.
INFO:xclim:Fetching remote file: EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc

INFO:xclim:Fetching remote file md5: EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r1i1p1_1950-2100_tg_mean_YS.nc.md5

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/xscen/aggregate.py:90: FutureWarning: xs.climatological_mean is deprecated and will be abandoned in a future release. Use xs.climatological_op with option op=='mean' instead.
INFO:xclim:Fetching remote file: EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r2i1p1_1950-2100_tg_mean_YS.nc

INFO:xclim:Fetching remote file md5: EnsembleStats/BCCAQv2+ANUSPLIN300_CCSM4_historical+rcp45_r2i1p1_1950-2100_tg_mean_YS.nc.md5

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/xscen/aggregate.py:90: FutureWarning: xs.climatological_mean is deprecated and will be abandoned in a future release. Use xs.climatological_op with option op=='mean' instead.
INFO:xclim:Fetching remote file: EnsembleStats/BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp45_r1i1p1_1970-2050_tg_mean_YS.nc

INFO:xclim:Fetching remote file md5: EnsembleStats/BCCAQv2+ANUSPLIN300_CNRM-CM5_historical+rcp45_r1i1p1_1970-2050_tg_mean_YS.nc.md5

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/xscen/aggregate.py:90: FutureWarning: xs.climatological_mean is deprecated and will be abandoned in a future release. Use xs.climatological_op with option op=='mean' instead.

4.1.1. Preparing the data

Ensemble reduction is built upon climate indicators that are relevant to represent the ensemble’s variability for a given application. In this case, we’ll use the mean temperature delta between 2021-2050 and 1981-2010.

However, the functions implemented in xclim.ensembles._reduce require a very specific 2-D DataArray of dimensions “realization” and “criteria”. That means that all the variables need to be combined and renamed, and that all dimensions need to be stacked together.

xs.build_reduction_data can be used to prepare the data for ensemble reduction. Its arguments are:

• datasets (dict, list)

• xrfreqs are the unique frequencies of the indicators.

• horizons is used to instruct on which horizon(s) to build the data from.

Because a simulation could have multiple datasets (in the case of multiple frequencies), an attempt will be made to decipher the ID and frequency from the metadata.

data = xs.build_reduction_data(
    datasets=datasets,
    xrfreqs=["AS-JAN"],
    horizons=["2021-2050"],
)

data

/home/docs/checkouts/readthedocs.org/user_builds/xscen/conda/v0.8.2/lib/python3.11/site-packages/xscen/reduce.py:140: FutureWarning: updating coordinate 'criteria' with a PandasMultiIndex would leave the multi-index level coordinates ['lat', 'lon', 'time'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['criteria', 'lat', 'lon', 'time'])` before assigning new coordinate values.

<xarray.DataArray 'values' (realization: 5, criteria: 16)>
array([[1.6385803, 1.6387024, 1.6386108, 1.6383972, 1.6400146, 1.6401978,
        1.6411133, 1.6411743, 1.6436768, 1.6435242, 1.6429443, 1.6429138,
        1.6479797, 1.6470337, 1.6461182, 1.6454468],
       [2.0663452, 2.0625305, 2.059143 , 2.056244 , 2.0645447, 2.0613098,
        2.0582886, 2.0549316, 2.0631104, 2.0596619, 2.056549 , 2.0528564,
        2.0624084, 2.0589905, 2.055359 , 2.0518494],
       [1.6872864, 1.690033 , 1.6922913, 1.6943359, 1.6863403, 1.689331 ,
        1.6921997, 1.6947937, 1.685852 , 1.6888733, 1.6918335, 1.6954346,
        1.6836548, 1.6868286, 1.6904602, 1.6944885],
       [1.6043091, 1.6021423, 1.6007996, 1.5988159, 1.6131592, 1.611084 ,
        1.6099854, 1.6082764, 1.6217651, 1.620697 , 1.6195679, 1.618042 ,
        1.6308289, 1.6300964, 1.629364 , 1.62854  ],
       [1.3090515, 1.3095093, 1.3098145, 1.3091736, 1.3128052, 1.3133545,
        1.3131714, 1.3131714, 1.3164062, 1.3165588, 1.316864 , 1.3173828,
        1.3200073, 1.3203735, 1.3208923, 1.3215027]], dtype=float32)
Coordinates:
  * realization  (realization) <U8 'ACCESS' 'BNU-ESM' ... 'CCSM4-r2' 'CNRM-CM5'
  * criteria     (criteria) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Attributes:
    long_name:  criteria for ensemble selection

xarray.DataArray

'values'

• realization: 5
• criteria: 16

• 1.639 1.639 1.639 1.638 1.64 1.64 ... 1.317 1.32 1.32 1.321 1.322

  array([[1.6385803, 1.6387024, 1.6386108, 1.6383972, 1.6400146, 1.6401978,
          1.6411133, 1.6411743, 1.6436768, 1.6435242, 1.6429443, 1.6429138,
          1.6479797, 1.6470337, 1.6461182, 1.6454468],
         [2.0663452, 2.0625305, 2.059143 , 2.056244 , 2.0645447, 2.0613098,
          2.0582886, 2.0549316, 2.0631104, 2.0596619, 2.056549 , 2.0528564,
          2.0624084, 2.0589905, 2.055359 , 2.0518494],
         [1.6872864, 1.690033 , 1.6922913, 1.6943359, 1.6863403, 1.689331 ,
          1.6921997, 1.6947937, 1.685852 , 1.6888733, 1.6918335, 1.6954346,
          1.6836548, 1.6868286, 1.6904602, 1.6944885],
         [1.6043091, 1.6021423, 1.6007996, 1.5988159, 1.6131592, 1.611084 ,
          1.6099854, 1.6082764, 1.6217651, 1.620697 , 1.6195679, 1.618042 ,
          1.6308289, 1.6300964, 1.629364 , 1.62854  ],
         [1.3090515, 1.3095093, 1.3098145, 1.3091736, 1.3128052, 1.3133545,
          1.3131714, 1.3131714, 1.3164062, 1.3165588, 1.316864 , 1.3173828,
          1.3200073, 1.3203735, 1.3208923, 1.3215027]], dtype=float32)

• Coordinates: (2)
  • realization
    (realization)
    <U8
    'ACCESS' 'BNU-ESM' ... 'CNRM-CM5'

    axis :

    E

    array(['ACCESS', 'BNU-ESM', 'CCSM4-r1', 'CCSM4-r2', 'CNRM-CM5'], dtype='<U8')

  • criteria
    (criteria)
    int64
    0 1 2 3 4 5 6 ... 10 11 12 13 14 15

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])

• Indexes: (2)
  • realization
    PandasIndex

    PandasIndex(Index(['ACCESS', 'BNU-ESM', 'CCSM4-r1', 'CCSM4-r2', 'CNRM-CM5'], dtype='object', name='realization'))

  • criteria
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype='int64', name='criteria'))

• Attributes: (1)

  long_name :

  criteria for ensemble selection

The number of criteria corresponds to: indicators x horizons x longitude x latitude, but criteria that are purely NaN across all realizations are removed.

Note that xs.spatial_mean could have been used prior to calling that function to remove the spatial dimensions.

4.1.2. Selecting a reduced ensemble

NOTE

Ensemble reduction in xscen is built upon xclim.ensembles. For more information on basic usage and available methods, please consult their documentation.

Ensemble reduction through xscen.reduce_ensemble consists in a simple call to xclim. The arguments are: - data, which is the 2D DataArray that is created by using xs.build_reduction_data. - method is either kkz or kmeans. See the link above for further details on each technique. - kwargs is a dictionary of arguments to send to the method chosen.

selected, clusters, fig_data = xs.reduce_ensemble(
    data=data, method="kmeans", kwargs={"method": {"n_clusters": 3}}
)

The method always returns 3 outputs (selected, clusters, fig_data): - selected is a DataArray of dimension ‘realization’ listing the selected simulations. - clusters (kmeans only) groups every realization in their respective clusters in a python dictionary. - fig_data (kmeans only) can be used to call xclim.ensembles.plot_rsqprofile(fig_data)

selected

<xarray.DataArray 'realization' (realization: 3)>
array(['ACCESS', 'BNU-ESM', 'CNRM-CM5'], dtype='<U8')
Coordinates:
  * realization  (realization) <U8 'ACCESS' 'BNU-ESM' 'CNRM-CM5'
Attributes:
    axis:     E

xarray.DataArray

'realization'

• realization: 3

• 'ACCESS' 'BNU-ESM' 'CNRM-CM5'

  array(['ACCESS', 'BNU-ESM', 'CNRM-CM5'], dtype='<U8')

• Coordinates: (1)
  • realization
    (realization)
    <U8
    'ACCESS' 'BNU-ESM' 'CNRM-CM5'

    axis :

    E

    array(['ACCESS', 'BNU-ESM', 'CNRM-CM5'], dtype='<U8')

• Indexes: (1)
  • realization
    PandasIndex

    PandasIndex(Index(['ACCESS', 'BNU-ESM', 'CNRM-CM5'], dtype='object', name='realization'))

• Attributes: (1)

  axis :

  E

# To see the clusters in more details
clusters

{0: <xarray.DataArray 'realization' (realization: 1)>
 array(['BNU-ESM'], dtype='<U8')
 Coordinates:
   * realization  (realization) <U8 'BNU-ESM'
 Attributes:
     axis:     E,
 1: <xarray.DataArray 'realization' (realization: 3)>
 array(['ACCESS', 'CCSM4-r1', 'CCSM4-r2'], dtype='<U8')
 Coordinates:
   * realization  (realization) <U8 'ACCESS' 'CCSM4-r1' 'CCSM4-r2'
 Attributes:
     axis:     E,
 2: <xarray.DataArray 'realization' (realization: 1)>
 array(['CNRM-CM5'], dtype='<U8')
 Coordinates:
   * realization  (realization) <U8 'CNRM-CM5'
 Attributes:
     axis:     E}

from xclim.ensembles import plot_rsqprofile

plot_rsqprofile(fig_data)

4.2. Ensemble partition

This tutorial will show how to use xscen to create the input for xclim partition functions.

# Get catalog
from pathlib import Path

output_folder = Path().absolute() / "_data"
cat = xs.DataCatalog(str(output_folder / "tutorial-catalog.json"))

# create a dictionnary of datasets wanted for the partition
input_dict = cat.search(variable="tas", member="r1i1p1f1").to_dataset_dict(
    xarray_open_kwargs={"engine": "h5netcdf"}
)

--> The keys in the returned dictionary of datasets are constructed as follows:
        'id.domain.processing_level.xrfreq'

100.00% [4/4 00:00<00:00]

From a dictionary of datasets, the function creates a dataset with new dimensions in partition_dim(["source", "experiment", "bias_adjust_project"], if they exist). In this toy example, we only have different experiments. - By default, it translates the xscen vocabulary (eg. experiment) to the xclim partition vocabulary (eg. scenario). It is possible to pass rename_dict to rename the dimensions with other names. - If the inputs are not on the same grid, they can be regridded through regrid_kw or subset to a point through subset_kw. The functions assumes that if there are different bias_adjust_project, they will be on different grids (with all source on the same grid). If there is one or less bias_adjust_project, the assumption is thatsource have different grids. - You can also compute indicators on the data if the input is daily. This can be especially useful when the daily input data is on different calendars.

# build a single dataset
import xclim as xc

ds = xs.ensembles.build_partition_data(
    input_dict,
    subset_kw=dict(name="mtl", method="gridpoint", lat=[45.5], lon=[-73.6]),
    indicators_kw={"indicators": [xc.atmos.tg_mean]},
)
ds

INFO:xscen.indicators:Computing 1 indicators.

INFO:xscen.indicators:1 - Computing tg_mean.

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:xscen.indicators:Computing 1 indicators.

INFO:xscen.indicators:1 - Computing tg_mean.

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:xscen.indicators:Computing 1 indicators.

INFO:xscen.indicators:1 - Computing tg_mean.

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:xscen.indicators:Computing 1 indicators.

INFO:xscen.indicators:1 - Computing tg_mean.

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:Entering _validate_reindex: reindex is None

INFO:flox:Leaving _validate_reindex: reindex is False

INFO:flox:_choose_engine: Choosing 'flox'

INFO:flox:find_group_cohorts: blockwise is preferred.

INFO:flox:Entering _validate_reindex: reindex is False

INFO:flox:Leaving _validate_reindex: reindex is False

<xarray.Dataset>
Dimensions:   (time: 2, scenario: 4, model: 1)
Coordinates:
  * time      (time) datetime64[ns] 2001-01-01 2002-01-01
  * scenario  (scenario) object 'ssp126' 'ssp245' 'ssp370' 'ssp585'
  * model     (model) <U23 'NCC_NorESM2-MM_r1i1p1f1'
    lat       float64 45.0
    lon       int64 -74
Data variables:
    tg_mean   (scenario, model, time) float64 dask.array<chunksize=(4, 1, 2), meta=np.ndarray>
Attributes: (12/22)
    comment:                 This is a test file created for the xscen tutori...
    version:                 v20191108
    intake_esm_vars:         ['tas']
    cat:id:                  CMIP6_ScenarioMIP_NCC_NorESM2-MM_ssp245_r1i1p1f1...
    cat:type:                simulation
    cat:processing_level:    indicators
    ...                      ...
    cat:date_start:          2001-01-01 00:00:00
    cat:date_end:            2002-12-31 00:00:00
    cat:_data_format_:       nc
    cat:path:                /home/docs/checkouts/readthedocs.org/user_builds...
    intake_esm_dataset_key:  CMIP6_ScenarioMIP_NCC_NorESM2-MM_ssp245_r1i1p1f1...
    history:                 [2024-02-12 21:51:12] gridpoint spatial subsetti...

xarray.Dataset

• Dimensions:
  • time: 2
  • scenario: 4
  • model: 1
• Coordinates: (5)
  • time
    (time)
    datetime64[ns]
    2001-01-01 2002-01-01

    array(['2001-01-01T00:00:00.000000000', '2002-01-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • scenario
    (scenario)
    object
    'ssp126' 'ssp245' 'ssp370' 'ssp585'

    array(['ssp126', 'ssp245', 'ssp370', 'ssp585'], dtype=object)

  • model
    (model)
    <U23
    'NCC_NorESM2-MM_r1i1p1f1'

    array(['NCC_NorESM2-MM_r1i1p1f1'], dtype='<U23')

  • lat
    ()
    float64
    45.0

    units :

    degrees_north

    description :

    Latitude.

    standard_name :

    latitude

    axis :

    Y

    array(45.)

  • lon
    ()
    int64
    -74

    units :

    degrees_east

    description :

    Longitude.

    standard_name :

    longitude

    axis :

    X

    array(-74)

• Data variables: (1)
  • tg_mean
    (scenario, model, time)
    float64
    dask.array<chunksize=(4, 1, 2), meta=np.ndarray>

    description :

    Annual mean of daily mean temperature.

    standard_name :

    air_temperature

    cell_methods :

    time: mean time: mean over days

    units :

    K

    history :

    [2024-02-12 21:51:12] tg_mean: TG_MEAN(tas=tas, freq='YS') with options check_missing=any - xclim version: 0.47.0

    long_name :

    Mean daily mean temperature

    Array Chunk Bytes 64 B 64 B Shape (4, 1, 2) (4, 1, 2) Dask graph 1 chunks in 123 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2001-01-01', '2002-01-01'], dtype='datetime64[ns]', name='time', freq='AS-JAN'))

  • scenario
    PandasIndex

    PandasIndex(Index(['ssp126', 'ssp245', 'ssp370', 'ssp585'], dtype='object', name='scenario'))

  • model
    PandasIndex

    PandasIndex(Index(['NCC_NorESM2-MM_r1i1p1f1'], dtype='object', name='model'))

• Attributes: (22)

  comment :

  This is a test file created for the xscen tutorial. This file is not a real CMIP6 file.

  version :

  v20191108

  intake_esm_vars :

  ['tas']

  cat:id :

  CMIP6_ScenarioMIP_NCC_NorESM2-MM_ssp245_r1i1p1f1_example-region

  cat:type :

  simulation

  cat:processing_level :

  indicators

  cat:mip_era :

  CMIP6

  cat:activity :

  ScenarioMIP

  cat:institution :

  NCC

  cat:source :

  NorESM2-MM

  cat:experiment :

  ssp245

  cat:member :

  r1i1p1f1

  cat:xrfreq :

  AS-JAN

  cat:frequency :

  yr

  cat:variable :

  ('tg_mean',)

  cat:domain :

  mtl

  cat:date_start :

  2001-01-01 00:00:00

  cat:date_end :

  2002-12-31 00:00:00

  cat:_data_format_ :

  nc

  cat:path :

  /home/docs/checkouts/readthedocs.org/user_builds/xscen/checkouts/v0.8.2/docs/notebooks/_data/path_builder_examples/simulation/raw/CMIP6/ScenarioMIP/example-region/NCC/NorESM2-MM/ssp245/r1i1p1f1/day/tas/tas_day_CMIP6_ScenarioMIP_example-region_NCC_NorESM2-MM_ssp245_r1i1p1f1_2001-2002.nc

  intake_esm_dataset_key :

  CMIP6_ScenarioMIP_NCC_NorESM2-MM_ssp245_r1i1p1f1_example-region.example-region.raw.D

  history :

  [2024-02-12 21:51:12] gridpoint spatial subsetting on 1 coordinates - clisops v0.12.2

Pass the input to an xclim partition function.

# compute uncertainty partitionning
mean, uncertainties = xc.ensembles.hawkins_sutton(ds.tg_mean)
uncertainties

<xarray.DataArray (uncertainty: 4, time: 4)>
dask.array<concatenate, shape=(4, 4), dtype=float64, chunksize=(1, 2), chunktype=numpy.ndarray>
Coordinates:
    lat          float64 45.0
    lon          int64 -74
  * time         (time) object 2001-01-01 00:00:00 ... 2004-01-01 00:00:00
  * uncertainty  (uncertainty) object 'variability' 'model' 'scenario' 'total'

xarray.DataArray

• uncertainty: 4
• time: 4

• dask.array<chunksize=(1, 2), meta=np.ndarray>

  Array Chunk Bytes 128 B 16 B Shape (4, 4) (1, 2) Dask graph 8 chunks in 245 graph layers Data type float64 numpy.ndarray

• Coordinates: (4)
  • lat
    ()
    float64
    45.0

    array(45.)

  • lon
    ()
    int64
    -74

    array(-74)

  • time
    (time)
    object
    2001-01-01 00:00:00 ... 2004-01-...

    array([cftime.DatetimeGregorian(2001, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2002, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2003, 1, 1, 0, 0, 0, 0, has_year_zero=False),
           cftime.DatetimeGregorian(2004, 1, 1, 0, 0, 0, 0, has_year_zero=False)],
          dtype=object)

  • uncertainty
    (uncertainty)
    object
    'variability' 'model' ... 'total'

    array(['variability', 'model', 'scenario', 'total'], dtype=object)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2001-01-01 00:00:00, 2002-01-01 00:00:00, 2003-01-01 00:00:00,
                 2004-01-01 00:00:00],
                dtype='object', length=4, calendar='standard', freq='AS-JAN'))

  • uncertainty
    PandasIndex

    PandasIndex(Index(['variability', 'model', 'scenario', 'total'], dtype='object', name='uncertainty'))

• Attributes: (0)

NOTE

Note that the figanos library provides a function fg.partition to plot the uncertainties.