Fingerprints are key tools in climate change detection and attribution (D&A)
that are used to determine whether changes in observations are different from
internal climate variability (detection), and whether observed changes can be
assigned to specific external drivers (attribution). We propose a direct D&A
approach based on supervised learning to extract fingerprints that lead to
robust predictions under relevant interventions on exogenous variables, i.e.,
climate drivers other than the target. We employ anchor regression, a
distributionally-robust statistical learning method inspired by causal
inference that extrapolates well to perturbed data under the interventions
considered. The residuals from the prediction achieve either uncorrelatedness
or mean independence with the exogenous variables, thus guaranteeing
robustness. We define D&A as a unified hypothesis testing framework that relies
on the same statistical model but uses different targets and test statistics.
In the experiments, we first show that the CO2 forcing can be robustly
predicted from temperature spatial patterns under strong interventions on the
solar forcing. Second, we illustrate attribution to the greenhouse gases and
aerosols while protecting against interventions on the aerosols and CO2
forcing, respectively. Our study shows that incorporating robustness
constraints against relevant interventions may significantly benefit detection
and attribution of climate change
