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)

Climate Visualization

Spatial Maps with Cartopy

import matplotlib.pyplot as plt
import cartopy.crs as ccrs

def plot_climate_map(data: xr.DataArray, title: str,
                      cmap: str = 'RdBu_r', vmin: float = None,
                      vmax: float = None):
    """Publication-quality climate map."""
    fig = plt.figure(figsize=(12, 6))
    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
    ax.coastlines(linewidth=0.5)
    ax.gridlines(draw_labels=True, linewidth=0.3, alpha=0.5)

    im = data.plot(ax=ax, transform=ccrs.PlateCarree(),
                   cmap=cmap, vmin=vmin, vmax=vmax,
                   add_colorbar=False)
    cbar = plt.colorbar(im, ax=ax, orientation='horizontal',
                         pad=0.05, shrink=0.7)
    cbar.set_label(data.attrs.get('units', ''))
    ax.set_title(title, fontsize=14)
    plt.tight_layout()
    return fig

Best Practices

• Always report uncertainties: use multi-model ensembles and provide confidence intervals
• Document data preprocessing steps for reproducibility
• Use standardized calendar handling (cftime) for model outputs with non-standard calendars
• Apply bias correction (e.g., quantile mapping) when comparing model outputs to observations
• Follow FAIR data principles and cite datasets using their DOIs