Climate Modeling

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Climate modeling represents the atmosphere, ocean, land, and ice to explain climate processes and estimate how conditions may change under different forcings. Alongside general circulation and Earth system models, researchers use machine-learning emulators, hybrid physical-neural models, generative methods for ensembles and downscaling, and learned parameterizations of unresolved processes. Models are assessed against observations and reanalysis, and tested for long-term stability, physical consistency, extremes, uncertainty, and performance across resolutions and scenarios. A central challenge is to gain speed and finer detail without losing reliable responses to unfamiliar climates, rare events, or changes in external forcing.

Latest papers

69 papers9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
Mar 11, 2026Savannah L. Ferretti, Jerry Lin, Sara Shamekh +3Operator Learning Spatiotemporal Forecasting
Department of Earth System Science, University of California Irvine, Irvine, USA · Department of Computing and Data Science, Boston University, Boston, USA · The Center for Atmosphere Ocean Science, New York University, New York, USA +4
CarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning
Mar 10, 2026Aleksei Rozanov, Arvind Renganathan, Yimeng Zhang +1Cross-Domain Generalization Zero-Shot Learning
Department of Computer Science & Engineering, University of Minnesota – Twin Cities, Minneapolis, MN, USA
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach
Mar 5, 2026Rares Grozavescu, Pengyu Zhang, Mark Girolami +1Spatiotemporal Forecasting PDE Surrogate Modeling
Department of Engineering, University of Cambridge, Cambridge, UK · Alan Turing Institute, London, UK · INRIA, Paris, France
Spatially Aware Deep Learning for Microclimate Prediction from High-Resolution Geospatial Imagery
Feb 27, 2026Idan Sulami, Alon Itzkovitch, Michael R. Kearney +2Spatial Context Modeling Climate Modeling
Tel Aviv University, Faculty of Life Sciences, School of Zoology, Tel Aviv, Israel · School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia · Tel Aviv University, Center for Artificial Intelligence & Data Science, Tel Aviv, Israel
Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Feb 17, 2026Ankur Mahesh, William D. Collins, Travis A. O'Brien +10Climate Emulation Climate Science
Earth and Planetary Science, University of California, Berkeley, Berkeley, CA, USA. · Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. · Earth and Atmospheric Sciences, Indiana University, Bloomington, IN, USA. +4
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
Feb 13, 2026Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman +3Climate Science Climate Modeling
1Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania · Department of Applied Mathematics, University of California, Santa Cruz, California · 3Allen Institute for Artificial Intelligence (AI2), Seattle, Washington +1
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
Feb 4, 2026Ziyu Zhou, Yuchen Fang, Weilin Ruan +4Climate Science Time Series Forecasting
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · DAMO Academy, Alibaba Group, Hangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China
Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM
Oct 9, 2025Oskar Bohn Lassen, Serio Angelo Maria Agriesti, Filipe Rodrigues +2Neural Surrogate Modeling Surrogate Modeling
Technical University of Denmark Kongens Lyngby, Denmark
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
Sep 19, 2025Graham Clyne, Guillaume Couairon, Guillaume Gastineau +2Neural Surrogate Modeling Climate Science
ARCHES, INRIA, Paris, France · UMR LOCEAN, IPSL, Sorbonne Universit´e, IRD, CNRS, MNHN, Paris, France · Department of Computer Science, University of Colorado Boulder, USA

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Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning

Savannah L. Ferretti, Jerry Lin, Sara Shamekh, Jane W. Baldwin+2

Department of Earth System Science, University of California Irvine, Irvine, USA · Department of Computing and Data Science, Boston University, Boston, USA · The Center for Atmosphere Ocean Science, New York University, New York, USA+4

Operator Learning Spatiotemporal Forecasting Explainable Time Series Forecasting Climate Modeling

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Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While this can improve predictive skill, it makes learned relationships difficult to interpret and prone to overfitting as the extent of nonlocal information grows. We address this challenge by introducing data-driven integration kernels, a framework that adds structure to nonlocal operator learning by explicitly separating nonlocal information aggregation from local nonlinear prediction. Each spatiotemporal predictor field is first integrated using learnable kernels (defined as continuous weighting functions over horizontal space, height, and/or time), after which a local nonlinear mapping is applied only to the resulting kernel-integrated features and optional local inputs. This design confines nonlinear interactions to a small set of integrated features and makes each kernel directly interpretable as a weighting pattern that reveals which horizontal locations, vertical levels, and/or past timesteps contribute most to the prediction. We demonstrate the framework for South Asian monsoon precipitation using a hierarchy of neural network models with increasing structure, including baseline, nonparametric kernel, and parametric kernel models. Across this hierarchy, kernel models achieve near-baseline performance with far fewer trainable parameters, indicating that much of the relevant nonlocal information can be captured through a small set of interpretable integrations when appropriate structural constraints are imposed.