CMIP Downscaled (0.5 deg)

In [1]:

# Uncomment to install climdata in Google Colab or other environments
# !pip install climdata

# Uncomment to install climdata in Google Colab or other environments # !pip install climdata

Example 1: Basic Usage - Point Extraction¶

Extract climate projection data for a specific location.

In [2]:

from climdata.utils.config import load_config
from climdata.datasets.CMIP_W5E5 import CMIPW5E5
from climdata import ClimData

# Configure for Berlin, Germany
overrides = [
    "dataset=cmip_w5e5",                  # Select CMIP-W5E5 dataset
    "lat=52.52",                          # Berlin latitude
    "lon=13.40",                          # Berlin longitude
    "experiment_id=ssp585",               # Climate scenario (ssp585 = high emissions)
    "source_id=gfdl-esm4",                # CMIP6 model
    "time_range.start_date=2015-01-01",   # Future period
    "time_range.end_date=2015-12-31",
    "variables=[tas,tasmin,tasmax,pr]",   # Temperature and precipitation
    "data_dir=./data",                    # Local directory for downloaded files
]

extractor = ClimData(overrides=overrides)

# Initialize CMIP-W5E5 dataset
cmip_w5e5 = CMIPW5E5(extractor.cfg)

# Fetch data from ISIMIP repository
cmip_w5e5.fetch()

# Load into xarray
cmip_w5e5.load()

# Extract for point location
cmip_w5e5.extract(point=(extractor.cfg.lon, extractor.cfg.lat))

from climdata.utils.config import load_config from climdata.datasets.CMIP_W5E5 import CMIPW5E5 from climdata import ClimData # Configure for Berlin, Germany overrides = [ "dataset=cmip_w5e5", # Select CMIP-W5E5 dataset "lat=52.52", # Berlin latitude "lon=13.40", # Berlin longitude "experiment_id=ssp585", # Climate scenario (ssp585 = high emissions) "source_id=gfdl-esm4", # CMIP6 model "time_range.start_date=2015-01-01", # Future period "time_range.end_date=2015-12-31", "variables=[tas,tasmin,tasmax,pr]", # Temperature and precipitation "data_dir=./data", # Local directory for downloaded files ] extractor = ClimData(overrides=overrides) # Initialize CMIP-W5E5 dataset cmip_w5e5 = CMIPW5E5(extractor.cfg) # Fetch data from ISIMIP repository cmip_w5e5.fetch() # Load into xarray cmip_w5e5.load() # Extract for point location cmip_w5e5.extract(point=(extractor.cfg.lon, extractor.cfg.lat))

🔍 Searching for CMIP6 datasets in ISIMIP repository...
   Model: gfdl-esm4, Experiment: ssp585

📥 Fetching tas...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tas_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tas_global_daily_2015_2020.nc

📥 Fetching tasmin...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmin_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmin_global_daily_2015_2020.nc

📥 Fetching tasmax...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmax_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmax_global_daily_2015_2020.nc

📥 Fetching pr...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_pr_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_pr_global_daily_2015_2020.nc

✅ Downloaded 4 files
📂 Loading 4 CMIP6 files...
  Loading tas from 1 file(s)...

<frozen importlib._bootstrap>:241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject

  Loading tasmin from 1 file(s)...
  Loading tasmax from 1 file(s)...
  Loading pr from 1 file(s)...
✅ Loaded dataset with 4 variables

In [ ]:

overrides = [
    "dataset=cmip_w5e5",  # Choose the MSWX dataset for extraction
    "lat=52",
    "lon=13",
    f"time_range.start_date=1989-01-01",  # Start date for data extraction
    f"time_range.end_date=1989-12-31",    # End date for data extraction
    "variables=[tasmin]",       # Variables to extract: min/max temp and precipitation
    "data_dir=./data",    # Local directory to store raw/intermediate files
    "source_id=GFDL-ESM4",
    # "dsinfo.mswx.params.google_service_account=./.climdata_conf/service.json",  # optional . required for MSWS data download
    "index=tn10p",  # Climate extreme index to calculate
    "impute=BRITS"
]

# -----------------------------
# Step 3: Define the workflow sequence
# -----------------------------
seq = ["extract", "impute", "calc_index", "to_nc"]

# -----------------------------
# Step 4: Initialize the ClimData extractor
# -----------------------------
extractor = ClimData(overrides=overrides)

# -----------------------------
# Step 5: Run the Multi-Step workflow
# -----------------------------
result = extractor.run_workflow(
    actions=seq,
)

overrides = [ "dataset=cmip_w5e5", # Choose the MSWX dataset for extraction "lat=52", "lon=13", f"time_range.start_date=1989-01-01", # Start date for data extraction f"time_range.end_date=1989-12-31", # End date for data extraction "variables=[tasmin]", # Variables to extract: min/max temp and precipitation "data_dir=./data", # Local directory to store raw/intermediate files "source_id=GFDL-ESM4", # "dsinfo.mswx.params.google_service_account=./.climdata_conf/service.json", # optional . required for MSWS data download "index=tn10p", # Climate extreme index to calculate "impute=BRITS" ] # ----------------------------- # Step 3: Define the workflow sequence # ----------------------------- seq = ["extract", "impute", "calc_index", "to_nc"] # ----------------------------- # Step 4: Initialize the ClimData extractor # ----------------------------- extractor = ClimData(overrides=overrides) # ----------------------------- # Step 5: Run the Multi-Step workflow # ----------------------------- result = extractor.run_workflow( actions=seq, )

Out[ ]:

<xarray.Dataset> Size: 9kB
Dimensions:  (time: 365)
Coordinates:
    lat      float64 8B 52.75
  * time     (time) datetime64[ns] 3kB 2015-01-01 2015-01-02 ... 2015-12-31
    lon      float64 8B 13.25
Data variables:
    tas      (time) float32 1kB ...
    tasmin   (time) float32 1kB ...
    tasmax   (time) float32 1kB ...
    pr       (time) float32 1kB ...
Attributes:
    institution:    Potsdam Institute for Climate Impact Research (PIK)
    contact:        ISIMIP cross-sectoral science team <info@isimip.org> <htt...
    references:     Lange (2019) <https://doi.org/10.5194/gmd-12-3055-2019> a...
    title:          ISIMIP3b bias-adjusted climate input data
    project:        Inter-Sectoral Impact Model Intercomparison Project phase...
    summary:        CMIP6 daily output data bias-adjusted and statistically d...
    source:         CMIP6 gfdl-esm4 via ISIMIP
    dataset:        CMIP6-W5E5
    experiment_id:  ssp585
    source_id:      gfdl-esm4
    member_id:      r1i1p1f1
    description:    CMIP6 ssp585 scenario from gfdl-esm4 in W5E5 format

xarray.Dataset

• Dimensions:
  • time: 365
• Coordinates: (3)
  • lat
    ()
    float64
    52.75

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array(52.75)

  • time
    (time)
    datetime64[ns]
    2015-01-01 ... 2015-12-31

    standard_name :

    time

    long_name :

    time

    axis :

    T

    array(['2015-01-01T00:00:00.000000000', '2015-01-02T00:00:00.000000000',
           '2015-01-03T00:00:00.000000000', ..., '2015-12-29T00:00:00.000000000',
           '2015-12-30T00:00:00.000000000', '2015-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    ()
    float64
    13.25

    array(13.25)

• Data variables: (4)
  • tas
    (time)
    float32
    ...

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    units :

    K

    [365 values with dtype=float32]

  • tasmin
    (time)
    float32
    ...

    standard_name :

    air_temperature

    long_name :

    Daily Minimum Near-Surface Air Temperature

    units :

    K

    [365 values with dtype=float32]

  • tasmax
    (time)
    float32
    ...

    standard_name :

    air_temperature

    long_name :

    Daily Maximum Near-Surface Air Temperature

    units :

    K

    [365 values with dtype=float32]

  • pr
    (time)
    float32
    ...

    standard_name :

    precipitation_flux

    long_name :

    Precipitation

    units :

    kg m-2 s-1

    [365 values with dtype=float32]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',
                   '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',
                   '2015-01-09', '2015-01-10',
                   ...
                   '2015-12-22', '2015-12-23', '2015-12-24', '2015-12-25',
                   '2015-12-26', '2015-12-27', '2015-12-28', '2015-12-29',
                   '2015-12-30', '2015-12-31'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

• Attributes: (12)

  institution :

  Potsdam Institute for Climate Impact Research (PIK)

  contact :

  ISIMIP cross-sectoral science team <info@isimip.org> <https://www.isimip.org>

  references :

  Lange (2019) <https://doi.org/10.5194/gmd-12-3055-2019> and Lange (2021) <https://doi.org/10.5281/zenodo.4686991> for ISIMIP3BASD; Cucchi et al. (2020) <https://doi.org/10.5194/essd-2020-28> and Lange et al. (2021) <https://doi.org/10.48364/ISIMIP.342217> for W5E5

  title :

  ISIMIP3b bias-adjusted climate input data

  project :

  Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b)

  summary :

  CMIP6 daily output data bias-adjusted and statistically downscaled to 0.5 degree horizontal resolution using ISIMIP3BASD v2.5.0 and W5E5 v2.0

  source :

  CMIP6 gfdl-esm4 via ISIMIP

  dataset :

  CMIP6-W5E5

  experiment_id :

  ssp585

  source_id :

  gfdl-esm4

  member_id :

  r1i1p1f1

  description :

  CMIP6 ssp585 scenario from gfdl-esm4 in W5E5 format

Example 2: Explore Available Models and Scenarios¶

Discover what CMIP6 experiments and models are available.

In [4]:

from climdata.datasets.CMIP_W5E5 import CMIPW5E5
from climdata.utils.config import load_config

cmip_w5e5 = CMIPW5E5(extractor.cfg)

# Get available experiment IDs
experiments = cmip_w5e5.get_experiment_ids()
print("Available CMIP6 Experiments:")
for exp in experiments:
    print(f"  - {exp}")

from climdata.datasets.CMIP_W5E5 import CMIPW5E5 from climdata.utils.config import load_config cmip_w5e5 = CMIPW5E5(extractor.cfg) # Get available experiment IDs experiments = cmip_w5e5.get_experiment_ids() print("Available CMIP6 Experiments:") for exp in experiments: print(f" - {exp}")

🔍 Fetching available experiment IDs from ISIMIP...
✅ Found 1 experiment IDs: ['historical']
Available CMIP6 Experiments:
  - historical

In [5]:

# Get available models for a specific experiment
models = cmip_w5e5.get_source_ids(experiment_id='ssp585')
print("\nAvailable Models for SSP5-8.5:")
for model in models:
    print(f"  - {model}")

# Get available models for a specific experiment models = cmip_w5e5.get_source_ids(experiment_id='ssp585') print("\nAvailable Models for SSP5-8.5:") for model in models: print(f" - {model}")

🔍 Fetching available source IDs for experiment 'ssp585'...
✅ Found 5 source IDs: ['gfdl-esm4', 'ipsl-cm6a-lr', 'mpi-esm1-2-hr', 'mri-esm2-0', 'ukesm1-0-ll']

Available Models for SSP5-8.5:
  - gfdl-esm4
  - ipsl-cm6a-lr
  - mpi-esm1-2-hr
  - mri-esm2-0
  - ukesm1-0-ll

Example 3: Compare Multiple Scenarios¶

Compare low and high emission scenarios for the same location.

In [6]:

import xarray as xr
import matplotlib.pyplot as plt
from climdata import ClimData
from climdata.datasets.CMIP_W5E5 import CMIPW5E5
# Function to load data for a scenario
def load_scenario(scenario, model='gfdl-esm4'):
    overrides = [
        "dataset=cmip_w5e5",
        "lat=52.52",
        "lon=13.40",
        f"experiment_id={scenario}",
        f"source_id={model}",
        "time_range.start_date=2050-01-01",
        "time_range.end_date=2050-12-31",
        "variables=[tasmin]",
        "data_dir=./data",
    ]
    extractor = ClimData(overrides=overrides)
    cmip = CMIPW5E5(extractor.cfg)
    cmip.fetch()
    cmip.load()
    cmip.extract(point=(extractor.cfg.lon, extractor.cfg.lat))
    return cmip.ds

# Load low and high emission scenarios
ds_low = load_scenario('ssp126')  # Low emissions
ds_high = load_scenario('ssp585') # High emissions

# Plot comparison
plt.figure(figsize=(12, 6))
ds_low['tasmin'].plot(label='SSP1-2.6 (Low emissions)', alpha=0.7)
ds_high['tasmin'].plot(label='SSP5-8.5 (High emissions)', alpha=0.7)
plt.title('Future Temperature Projections for Berlin')
plt.ylabel('Temperature (K)')
plt.xlabel('Year')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

import xarray as xr import matplotlib.pyplot as plt from climdata import ClimData from climdata.datasets.CMIP_W5E5 import CMIPW5E5 # Function to load data for a scenario def load_scenario(scenario, model='gfdl-esm4'): overrides = [ "dataset=cmip_w5e5", "lat=52.52", "lon=13.40", f"experiment_id={scenario}", f"source_id={model}", "time_range.start_date=2050-01-01", "time_range.end_date=2050-12-31", "variables=[tasmin]", "data_dir=./data", ] extractor = ClimData(overrides=overrides) cmip = CMIPW5E5(extractor.cfg) cmip.fetch() cmip.load() cmip.extract(point=(extractor.cfg.lon, extractor.cfg.lat)) return cmip.ds # Load low and high emission scenarios ds_low = load_scenario('ssp126') # Low emissions ds_high = load_scenario('ssp585') # High emissions # Plot comparison plt.figure(figsize=(12, 6)) ds_low['tasmin'].plot(label='SSP1-2.6 (Low emissions)', alpha=0.7) ds_high['tasmin'].plot(label='SSP5-8.5 (High emissions)', alpha=0.7) plt.title('Future Temperature Projections for Berlin') plt.ylabel('Temperature (K)') plt.xlabel('Year') plt.legend() plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

🔍 Searching for CMIP6 datasets in ISIMIP repository...
   Model: gfdl-esm4, Experiment: ssp126

📥 Fetching tasmin...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp126_tasmin_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp126_tasmin_global_daily_2041_2050.nc

✅ Downloaded 1 files
📂 Loading 1 CMIP6 files...
  Loading tasmin from 1 file(s)...
✅ Loaded dataset with 1 variables
🔍 Searching for CMIP6 datasets in ISIMIP repository...
   Model: gfdl-esm4, Experiment: ssp585

📥 Fetching tasmin...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmin_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmin_global_daily_2041_2050.nc

✅ Downloaded 1 files
📂 Loading 1 CMIP6 files...
  Loading tasmin from 1 file(s)...
✅ Loaded dataset with 1 variables

Example 4: Regional Extraction with Bounding Box¶

Extract data for a region instead of a single point.

In [7]:

from climdata.datasets.CMIP_W5E5 import CMIPW5E5
from climdata import ClimData

# Configure for Central Europe region
overrides = [
    "dataset=cmip_w5e5",
    "experiment_id=ssp126",                # Middle-of-the-road scenario
    "source_id=gfdl-esm4",               # UK Earth System Model
    "time_range.start_date=2050-01-01",
    "time_range.end_date=2050-12-31",
    "variables=[tas,pr]",
    "data_dir=./data",
]

extractor = ClimData(overrides=overrides)
cmip_w5e5 = CMIPW5E5(extractor.cfg)

# Fetch and load
cmip_w5e5.fetch()
cmip_w5e5.load()

# Extract for Central Europe bounding box
box = {
    'lon_min': 5.0,   # Western boundary
    'lon_max': 15.0,  # Eastern boundary
    'lat_min': 47.0,  # Southern boundary
    'lat_max': 55.0   # Northern boundary
}
cmip_w5e5.extract(box=box)

# View spatial data
cmip_w5e5.ds

from climdata.datasets.CMIP_W5E5 import CMIPW5E5 from climdata import ClimData # Configure for Central Europe region overrides = [ "dataset=cmip_w5e5", "experiment_id=ssp126", # Middle-of-the-road scenario "source_id=gfdl-esm4", # UK Earth System Model "time_range.start_date=2050-01-01", "time_range.end_date=2050-12-31", "variables=[tas,pr]", "data_dir=./data", ] extractor = ClimData(overrides=overrides) cmip_w5e5 = CMIPW5E5(extractor.cfg) # Fetch and load cmip_w5e5.fetch() cmip_w5e5.load() # Extract for Central Europe bounding box box = { 'lon_min': 5.0, # Western boundary 'lon_max': 15.0, # Eastern boundary 'lat_min': 47.0, # Southern boundary 'lat_max': 55.0 # Northern boundary } cmip_w5e5.extract(box=box) # View spatial data cmip_w5e5.ds

🔍 Searching for CMIP6 datasets in ISIMIP repository...
   Model: gfdl-esm4, Experiment: ssp126

📥 Fetching tas...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp126_tas_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp126_tas_global_daily_2041_2050.nc

📥 Fetching pr...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp126_pr_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp126_pr_global_daily_2041_2050.nc

✅ Downloaded 2 files
📂 Loading 2 CMIP6 files...
  Loading tas from 1 file(s)...
  Loading pr from 1 file(s)...
✅ Loaded dataset with 2 variables

Out[7]:

<xarray.Dataset> Size: 938kB
Dimensions:  (lat: 16, time: 365, lon: 20)
Coordinates:
  * lat      (lat) float64 128B 47.25 47.75 48.25 48.75 ... 53.75 54.25 54.75
  * time     (time) datetime64[ns] 3kB 2050-01-01 2050-01-02 ... 2050-12-31
  * lon      (lon) float64 160B 5.25 5.75 6.25 6.75 ... 13.25 13.75 14.25 14.75
Data variables:
    tas      (time, lat, lon) float32 467kB ...
    pr       (time, lat, lon) float32 467kB ...
Attributes:
    institution:    Potsdam Institute for Climate Impact Research (PIK)
    contact:        ISIMIP cross-sectoral science team <info@isimip.org> <htt...
    references:     Lange (2019) <https://doi.org/10.5194/gmd-12-3055-2019> a...
    title:          ISIMIP3b bias-adjusted climate input data
    project:        Inter-Sectoral Impact Model Intercomparison Project phase...
    summary:        CMIP6 daily output data bias-adjusted and statistically d...
    source:         CMIP6 gfdl-esm4 via ISIMIP
    dataset:        CMIP6-W5E5
    experiment_id:  ssp126
    source_id:      gfdl-esm4
    member_id:      r1i1p1f1
    description:    CMIP6 ssp126 scenario from gfdl-esm4 in W5E5 format

xarray.Dataset

• Dimensions:
  • lat: 16
  • time: 365
  • lon: 20
• Coordinates: (3)
  • lat
    (lat)
    float64
    47.25 47.75 48.25 ... 54.25 54.75

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    axis :

    Y

    array([47.25, 47.75, 48.25, 48.75, 49.25, 49.75, 50.25, 50.75, 51.25, 51.75,
           52.25, 52.75, 53.25, 53.75, 54.25, 54.75])

  • time
    (time)
    datetime64[ns]
    2050-01-01 ... 2050-12-31

    standard_name :

    time

    long_name :

    time

    axis :

    T

    array(['2050-01-01T00:00:00.000000000', '2050-01-02T00:00:00.000000000',
           '2050-01-03T00:00:00.000000000', ..., '2050-12-29T00:00:00.000000000',
           '2050-12-30T00:00:00.000000000', '2050-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float64
    5.25 5.75 6.25 ... 14.25 14.75

    array([ 5.25,  5.75,  6.25,  6.75,  7.25,  7.75,  8.25,  8.75,  9.25,  9.75,
           10.25, 10.75, 11.25, 11.75, 12.25, 12.75, 13.25, 13.75, 14.25, 14.75])

• Data variables: (2)
  • tas
    (time, lat, lon)
    float32
    ...

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    units :

    K

    [116800 values with dtype=float32]

  • pr
    (time, lat, lon)
    float32
    ...

    standard_name :

    precipitation_flux

    long_name :

    Precipitation

    units :

    kg m-2 s-1

    [116800 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([47.25, 47.75, 48.25, 48.75, 49.25, 49.75, 50.25, 50.75, 51.25, 51.75,
           52.25, 52.75, 53.25, 53.75, 54.25, 54.75],
          dtype='float64', name='lat'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2050-01-01', '2050-01-02', '2050-01-03', '2050-01-04',
                   '2050-01-05', '2050-01-06', '2050-01-07', '2050-01-08',
                   '2050-01-09', '2050-01-10',
                   ...
                   '2050-12-22', '2050-12-23', '2050-12-24', '2050-12-25',
                   '2050-12-26', '2050-12-27', '2050-12-28', '2050-12-29',
                   '2050-12-30', '2050-12-31'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

  • lon
    PandasIndex

    PandasIndex(Index([ 5.25,  5.75,  6.25,  6.75,  7.25,  7.75,  8.25,  8.75,  9.25,  9.75,
           10.25, 10.75, 11.25, 11.75, 12.25, 12.75, 13.25, 13.75, 14.25, 14.75],
          dtype='float64', name='lon'))

• Attributes: (12)

  institution :

  Potsdam Institute for Climate Impact Research (PIK)

  contact :

  ISIMIP cross-sectoral science team <info@isimip.org> <https://www.isimip.org>

  references :

  Lange (2019) <https://doi.org/10.5194/gmd-12-3055-2019> and Lange (2021) <https://doi.org/10.5281/zenodo.4686991> for ISIMIP3BASD; Cucchi et al. (2020) <https://doi.org/10.5194/essd-2020-28> and Lange et al. (2021) <https://doi.org/10.48364/ISIMIP.342217> for W5E5

  title :

  ISIMIP3b bias-adjusted climate input data

  project :

  Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b)

  summary :

  CMIP6 daily output data bias-adjusted and statistically downscaled to 0.5 degree horizontal resolution using ISIMIP3BASD v2.5.0 and W5E5 v2.0

  source :

  CMIP6 gfdl-esm4 via ISIMIP

  dataset :

  CMIP6-W5E5

  experiment_id :

  ssp126

  source_id :

  gfdl-esm4

  member_id :

  r1i1p1f1

  description :

  CMIP6 ssp126 scenario from gfdl-esm4 in W5E5 format

In [8]:

# Plot spatial map for a specific day
import matplotlib.pyplot as plt
import hvplot.xarray
# Select first day
cmip_w5e5.ds['tas'].isel(time=0).hvplot(figsize=(10, 8),coastline=True,geo=True)

# Plot spatial map for a specific day import matplotlib.pyplot as plt import hvplot.xarray # Select first day cmip_w5e5.ds['tas'].isel(time=0).hvplot(figsize=(10, 8),coastline=True,geo=True)

WARNING:param.main: hvPlot does not have the concept of a figure, and the figsize keyword will be ignored. The size of each subplot in a layout is set individually using the width and height options.

Out[8]:

Example 5: Save Data to File¶

Export extracted data to NetCDF or CSV format.

In [9]:

from climdata.datasets.CMIP_W5E5 import CMIPW5E5
from climdata import ClimData

# Configure and extract
overrides = [
    "dataset=cmip_w5e5",
    "lat=52.52",
    "lon=13.40",
    "experiment_id=ssp370",
    "source_id=mpi-esm1-2-hr",
    "time_range.start_date=2080-01-01",
    "time_range.end_date=2080-12-31",
    "variables=[tas,pr,rsds]",
    "data_dir=./data",
]

extractor = ClimData(overrides=overrides)
cmip_w5e5 = CMIPW5E5(extractor.cfg)
cmip_w5e5.fetch()
cmip_w5e5.load()
cmip_w5e5.extract(point=(extractor.cfg.lon, extractor.cfg.lat))

# Save as NetCDF
cmip_w5e5.save_netcdf('berlin_ssp370_2080.nc')

# Save as CSV
cmip_w5e5.save_csv('berlin_ssp370_2080.csv')

print("✅ Data saved successfully!")

from climdata.datasets.CMIP_W5E5 import CMIPW5E5 from climdata import ClimData # Configure and extract overrides = [ "dataset=cmip_w5e5", "lat=52.52", "lon=13.40", "experiment_id=ssp370", "source_id=mpi-esm1-2-hr", "time_range.start_date=2080-01-01", "time_range.end_date=2080-12-31", "variables=[tas,pr,rsds]", "data_dir=./data", ] extractor = ClimData(overrides=overrides) cmip_w5e5 = CMIPW5E5(extractor.cfg) cmip_w5e5.fetch() cmip_w5e5.load() cmip_w5e5.extract(point=(extractor.cfg.lon, extractor.cfg.lat)) # Save as NetCDF cmip_w5e5.save_netcdf('berlin_ssp370_2080.nc') # Save as CSV cmip_w5e5.save_csv('berlin_ssp370_2080.csv') print("✅ Data saved successfully!")

🔍 Searching for CMIP6 datasets in ISIMIP repository...
   Model: mpi-esm1-2-hr, Experiment: ssp370

📥 Fetching tas...
✅ Found dataset: mpi-esm1-2-hr_r1i1p1f1_w5e5_ssp370_tas_global_daily
  ⬇️ Downloading: mpi-esm1-2-hr_r1i1p1f1_w5e5_ssp370_tas_global_daily_2071_2080.nc

📥 Fetching pr...
✅ Found dataset: mpi-esm1-2-hr_r1i1p1f1_w5e5_ssp370_pr_global_daily
  ⬇️ Downloading: mpi-esm1-2-hr_r1i1p1f1_w5e5_ssp370_pr_global_daily_2071_2080.nc

📥 Fetching rsds...
✅ Found dataset: mpi-esm1-2-hr_r1i1p1f1_w5e5_ssp370_rsds_global_daily
  ⬇️ Downloading: mpi-esm1-2-hr_r1i1p1f1_w5e5_ssp370_rsds_global_daily_2071_2080.nc

✅ Downloaded 3 files
📂 Loading 3 CMIP6 files...
  Loading tas from 1 file(s)...
  Loading pr from 1 file(s)...
  Loading rsds from 1 file(s)...
✅ Loaded dataset with 3 variables
💾 Saved to: berlin_ssp370_2080.nc
💾 Saved to: berlin_ssp370_2080.csv
✅ Data saved successfully!

Example 6: Using ClimateExtractor Workflow¶

Use the high-level ClimateExtractor workflow for end-to-end processing.

In [10]:

from climdata.utils.wrapper_workflow import ClimateExtractor

# Configure with overrides
overrides = [
    "dataset=cmip_w5e5",
    "lat=12.89",
    "lon=24.25",
    "experiment_id=ssp585",
    "source_id=gfdl-esm4",
    "time_range.start_date=2050-01-01",
    "time_range.end_date=2050-12-31",
    "variables=[tasmin,tasmax,pr]",
    "data_dir=./data",
]

# Initialize extractor
extractor = ClimateExtractor(overrides=overrides)

# Extract data
ds = extractor.extract()

# Convert to DataFrame
df = extractor.to_dataframe()

# Save results
result = extractor.save(format='csv')

print(f"Data extracted and saved to: {result.filename}")
df.head()

from climdata.utils.wrapper_workflow import ClimateExtractor # Configure with overrides overrides = [ "dataset=cmip_w5e5", "lat=12.89", "lon=24.25", "experiment_id=ssp585", "source_id=gfdl-esm4", "time_range.start_date=2050-01-01", "time_range.end_date=2050-12-31", "variables=[tasmin,tasmax,pr]", "data_dir=./data", ] # Initialize extractor extractor = ClimateExtractor(overrides=overrides) # Extract data ds = extractor.extract() # Convert to DataFrame df = extractor.to_dataframe() # Save results result = extractor.save(format='csv') print(f"Data extracted and saved to: {result.filename}") df.head()

🔍 Searching for CMIP6 datasets in ISIMIP repository...
   Model: gfdl-esm4, Experiment: ssp585

📥 Fetching tasmin...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmin_global_daily
  ✓ Already exists: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmin_global_daily_2041_2050.nc

📥 Fetching tasmax...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmax_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_tasmax_global_daily_2041_2050.nc

📥 Fetching pr...
✅ Found dataset: gfdl-esm4_r1i1p1f1_w5e5_ssp585_pr_global_daily
  ⬇️ Downloading: gfdl-esm4_r1i1p1f1_w5e5_ssp585_pr_global_daily_2041_2050.nc

✅ Downloaded 3 files
📂 Loading 3 CMIP6 files...
  Loading tasmin from 1 file(s)...
  Loading tasmax from 1 file(s)...
  Loading pr from 1 file(s)...
✅ Loaded dataset with 3 variables

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[10], line 26
     23 df = extractor.to_dataframe()
     25 # Save results
---> 26 result = extractor.save(format='csv')
     28 print(f"Data extracted and saved to: {result.filename}")
     29 df.head()

AttributeError: 'ClimateExtractor' object has no attribute 'save'

Example 7: Calculate Climate Indices¶

Compute extreme indices like frost days or heat waves from CMIP-W5E5 data.

In [ ]:

from climdata.utils.wrapper_workflow import ClimateExtractor

# Configure with climate index
overrides = [
    "dataset=cmip_w5e5",
    "lat=52.52",
    "lon=13.40",
    "experiment_id=ssp585",
    "source_id=gfdl-esm4",
    "time_range.start_date=2050-01-01",
    "time_range.end_date=2079-12-31",  # 30 years for climate indices
    "variables=[tasmin,tasmax,pr]",
    "index=tn10p",  # Cold nights (10th percentile of minimum temperature)
    "data_dir=./data",
]

extractor = ClimateExtractor(overrides=overrides)

# Extract base data
ds = extractor.extract()

# Calculate index
index_ds = extractor.calc_index()

# View results
print("Climate index calculated:")
index_ds

from climdata.utils.wrapper_workflow import ClimateExtractor # Configure with climate index overrides = [ "dataset=cmip_w5e5", "lat=52.52", "lon=13.40", "experiment_id=ssp585", "source_id=gfdl-esm4", "time_range.start_date=2050-01-01", "time_range.end_date=2079-12-31", # 30 years for climate indices "variables=[tasmin,tasmax,pr]", "index=tn10p", # Cold nights (10th percentile of minimum temperature) "data_dir=./data", ] extractor = ClimateExtractor(overrides=overrides) # Extract base data ds = extractor.extract() # Calculate index index_ds = extractor.calc_index() # View results print("Climate index calculated:") index_ds

Key Features¶

Scenarios Available¶

• historical: Historical period (typically 1850-2014)
• ssp126: Low emissions (sustainability pathway)
• ssp245: Middle-of-the-road (moderate emissions)
• ssp370: Medium-high emissions
• ssp585: High emissions (fossil-fueled development)

Models Available in ISIMIP3b¶

• gfdl-esm4: NOAA Geophysical Fluid Dynamics Laboratory
• ipsl-cm6a-lr: Institut Pierre-Simon Laplace
• mpi-esm1-2-hr: Max Planck Institute for Meteorology
• mri-esm2-0: Meteorological Research Institute
• ukesm1-0-ll: UK Earth System Modeling

Variables¶

Standard CMIP6 variables are available:

• tas: Near-surface air temperature (K)
• tasmin: Daily minimum temperature (K)
• tasmax: Daily maximum temperature (K)
• pr: Precipitation (kg m⁻² s⁻¹)
• rsds: Surface downwelling shortwave radiation (W m⁻²)
• hurs: Near-surface relative humidity (%)
• sfcWind: Near-surface wind speed (m s⁻¹)
• ps: Surface air pressure (Pa)

Notes¶

1. Data Size: CMIP6 files can be large. Start with short time periods for testing.
2. ISIMIP Client: Requires isimip-client package for data access.
3. Resolution: Data is provided at 0.5° (~55 km) spatial resolution.
4. Bias Adjustment: ISIMIP3b data is bias-adjusted to W5E5 observations.
5. Attribution: When using this data, cite both CMIP6 and ISIMIP appropriately.

References¶

• ISIMIP: https://www.isimip.org/
• CMIP6: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6
• W5E5: https://doi.org/10.48364/ISIMIP.342217