Year: 2026
Type: preprint
Abstract: . The latest Earth System Models (ESMs) are much more complex than previous generations, and are better at reproducing aspects of the current observed climate. However, in general, the latest ESMs do not reproduce the historical record of global warming with as much fidelity as the previous generation of models. We hypothesise that this is because the techniques used to calibrate ESMs have not kept track with the sophistication of the models, a phenomenon that we term the calibration deficit. Here we summarize the current state of the art in ESM calibration and discuss key challenges and opportunities introduced by Machine Learning and Artificial Intelligence. We outline how hybrid approaches that make use of process-based models for out-of-sample climate projections, but use ML/AI approaches for model calibration, have huge potential to revolutionise Earth System Modelling.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225632558