Computational replication of Arctic Amplification — Rantanen et al. 2022
see draft for the full numbered list — 7 items covering extended time window, Python vs R, GISTEMP boundary, CDS API v2, HadCRUT5 version, boundary sensitivity scope, no CMIP comparison
Annual mean temperature anomalies for the Arctic region (66.5°N–90°N) and the global mean are extracted from each of four observational datasets: ERA5 monthly 2-metre temperature (Copernicus CDS), GISTEMP v4 zonal means (NASA GISS), Berkeley Earth Land+Ocean monthly gridded data, and HadCRUT5. An ordinary least-squares (OLS) linear trend is fitted to the full available annual series starting from 1979, separately for the Arctic and global means. The Arctic Amplification ratio for each dataset is the Arctic OLS slope divided by the global OLS slope (dimensionless). The headline result is the unweighted mean of the four per-dataset AA ratios. All computations are implemented in Python using xarray, numpy, and scipy.stats.linregress. The full pipeline (data download → cleaning → analysis → figures) is automated via Snakemake and reproduced with pixi-managed dependencies.
The observational component of the Arctic Amplification claim: whether the ratio of the Arctic (66.5°N–90°N) linear warming trend to the global mean warming trend, computed from the same four publicly available observational datasets, exceeds 3× for the period starting in 1979. The model comparison component of the original paper (showing that CMIP5/CMIP6 models underestimate the observed AA) is explicitly out of scope.