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SKILL.md
Climate Science Guide
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.
Climate Data Sources
Observational Datasets
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
CMIP6 Model Outputs
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,
variable_id=variable,
member_id=member,
table_id='Amon' # Monthly atmospheric data
)
ds = query.to_dataset_dict(zarr_kwargs={'consolidated': True})
key = list(ds.keys())[0]
return ds[key]
Temperature Trend Analysis
Computing Global Mean Temperature Anomaly
import numpy as np
def compute_global_mean_anomaly(ds: xr.Dataset, var: str = 'tas',
baseline: tuple = (1850, 1900)) -> xr.DataArray:
"""
Compute area-weighted global mean temperature anomaly
relative to a baseline period.
"""
# Area weighting by latitude
weights = np.cos(np.deg2rad(ds.lat))
weights = weights / weights.sum()
# Global mean
global_mean = ds[var].weighted(weights).mean(dim=['lat', 'lon'])
# Baseline climatology
baseline_mean = global_mean.sel(
time=slice(str(baseline[0]), str(baseline[1]))
).mean('time')
anomaly = global_mean - baseline_mean
return anomaly
# Usage
# anomaly = compute_global_mean_anomaly(historical_ds)
# anomaly.plot() # produces a time series of temperature anomaly
Carbon Budget Analysis
Emissions and Remaining Budget
Track cumulative CO2 emissions against the remaining carbon budget for temperature targets:
def carbon_budget_tracker(cumulative_emissions_gtco2: float,
target_warming: float = 1.5) -> dict:
"""
Estimate remaining carbon budget.
Based on IPCC AR6 estimates.
"""
# IPCC AR6 remaining budget from 2020 (GtCO2)
budgets = {
1.5: {'50pct': 500, '67pct': 400, '83pct': 300},
2.0: {'50pct': 1350, '67pct': 1150, '83pct': 900}
}
budget = budgets[target_warming]
remaining = {prob: val - cumulative_emissions_gtco2
for prob, val in budget.items()}
# At ~40 GtCO2/year current rate
years_left = {prob: max(0, val / 40) for prob, val in remaining.items()}
return {'remaining_budget_GtCO2': remaining, 'years_at_current_rate': years_left}
result = carbon_budget_tracker(cumulative_emissions_gtco2=200, target_warming=1.5)
print(result)
