Earth system models (ESMs) have grown tremendously in realism, yet key uncertainties persist in the climate response to greenhouse-gas forcing, particularly due to cloud radiative feedbacks. In addition, their software architecture was not designed for accelerator hardware or modern artificial intelligence (AI). Here w...
Pierre Gentine, D. Balwada, Aytaç Paçal et al.· 0 citations
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physics-based, data-driven, or hybrid climate model on equal foot...
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal et al.· 0 citations
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state w...
Jurij Schönfeld, Tom Beucler, Julien Savre et al.· 0 citations
This paper employs a hybrid quantum neural network (QNN) on a dataset on cloud microphysics, containing processes for phase transitions of water in the atmosphere and its related temperature changes, which are highly relevant for accurate climate predictions and projections.
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello et al.· 0 citations
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