1.1k citations · 3k across the 33 of their papers we have counts for
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Multi-fidelity climate model parameterization for better generalization and extrapolation
Mohamed Aziz Bhouri, Liran Peng, Michael S. Pritchard +1
Machine-learning-based parameterizations (i.e. representation of sub-grid processes) of global climate models or turbulent simulations have recently been proposed as a powerful alt…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
Climate-Invariant Machine Learning
Tom Beucler, Pierre Gentine, Janni Yuval +10
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models requir…
On the Generalization of Agricultural Drought Classification from Climate Data
Julia Gottfriedsen, Max Berrendorf, Pierre Gentine +4
Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and…
Recovering the parameters underlying the Lorenz-96 chaotic dynamics
Soukayna Mouatadid, Pierre Gentine, Wei Yu +1
Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to res…