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Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine
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@article{yu2026climsim,
title = {ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation},
author = {Sungduk Yu and Walter M. Hannah and L. Peng and Jerry Lin and Mohamed Aziz Bhouri and Ritwik Gupta and Björn Lütjens and Justus C. Will and G. Behrens and Julius Busecke and Nora Loose and Charles Stern and Tom Beucler and B. Harrop and B. Hillman and A. M. Jenney and Savannah L. Ferretti and Na Liu and A. Anandkumar and Noah D. Brenowitz and Veronika Eyring and N. Geneva and Pierre Gentine and Stephan Mandt and Jaideep Pathak and Akshay Subramaniam and Carl Vondrick and Rose Yu and L. Zanna and Tian Zheng and R. Abernathey and Fiaz Ahmed and D. C. Bader and Pierre Baldi and Elizabeth A. Barnes and Christopher S. Bretherton and Peter M. Caldwell and W. Chuang and Yilun Han and Yu Huang and Fernando Iglesias‐Suarez and Sanket R. Jantre and K. Kashinath and M. Khairoutdinov and Thorsten Kurth and Nicholas J. Lutsko and Po‐Lun Ma and G. Mooers and J. Neelin and David A. Randall and S. Shamekh and Mark A. Taylor and N. Urban and J. Yuval and Guang Zhang and Mike Pritchard},
year = {2026},
doi = {10.52202/075280-0968},
url = {https://doi.org/10.52202/075280-0968},
journal = {Neural Information Processing Systems}
}