Explainable Cloud Feedback Evaluation in Earth System Models

Cloud feedback is one of the key sources of uncertainty in the sensitivity of climate projections to anthropogenic forcing in Earth system models (ESMs). Improving its representation remains challenging because clouds sit at the intersection of radiation, dynamics, and microphysics, and small errors in any of these can strongly affect climate sensitivity.. Consequently, analysing and understanding errors in simulated cloud feedback, evaluated against observations, is essential for advancing cloud parameterizations in ESMs.

In this work, we explore methodological frameworks for evaluating cloud feedback in climate models that move beyond simple model–observation comparisons toward physically interpretable insights into model properties and dynamics. We propose two advances: (1) improved cloud regime identification by extending standard k-means clustering to Wasserstein k-means, and (2) the use of explainable machine-learning methods to evaluate the extent the ESMs capture the realistic sensitivity between the cloud radiative anomalies and key cloud-controlling factors. We demonstrate these approaches by evaluating different versions of the HadGEM model in AMIP experiments against observations, illustrating their potential to support more physically grounded diagnosis of cloud-feedback behaviour in climate models.