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Climate data analysis, modeling workflows, and carbon neutrality research met...
Passed 19 of 23 test cases with the skill, and 19 of 23 without it.
0 fixed19 passed either way4 still failing0 broke
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A research skill for analyzing climate data, working with climate model outputs, and conducting carbon-related studies. Covers data sources, standard analytical workflows, and visualization techniques used in climate science publications.
| Dataset | Variables | Resolution | Period | Source |
|---|---|---|---|---|
| ERA5 | Temperature, precipitation, wind, etc. | 0.25 deg, hourly | 1940-present | ECMWF/Copernicus |
| GPCP | Precipitation | 2.5 deg, monthly | 1979-present | NASA |
| HadCRUT5 | Surface temperature anomaly | 5 deg, monthly | 1850-present | Met Office |
| NOAA GHCN | Station temperature, precipitation | Point data | 1850-present | NOAA |
| CRU TS | Temperature, precipitation, vapor pressure | 0.5 deg, monthly | 1901-present | UEA CRU |
import xarray as xr
def load_cmip6_data(model: str, experiment: str, variable: str,
member: str = 'r1i1p1f1') -> xr.Dataset:
"""
Load CMIP6 model output from a local or cloud archive.
Args:
model: Model name (e.g., 'CESM2', 'UKESM1-0-LL')
experiment: SSP scenario (e.g., 'ssp245', 'ssp585', 'historical')
variable: Variable name (e.g., 'tas', 'pr', 'tos')
member: Ensemble member ID
"""
# Using Pangeo cloud catalog
import intake
catalog = intake.open_esm_datastore(
"https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
)
query = catalog.search(
source_id=model,
experiment_id=experiment,
test cases passed, vs 19 / 23 without
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