The impact of internal variability on benchmarking deep learning climate emulators

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The impact of internal variability on benchmarking deep learning climate emulators

9 August 2024

Björn Lütjens

Raffaele Ferrari

Duncan Watson-Parris

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Papers citing "The impact of internal variability on benchmarking deep learning climate emulators"

8 / 8 papers shown

Title
DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models Seth Bassetti Brian Hutchinson Claudia Tebaldi Ben Kravitz 87 5 0 17 Sep 2024
Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model Chenggong Wang Michael S. Pritchard Noah D. Brenowitz Y. Cohen Boris Bonev Thorsten Kurth Dale Durran Jaideep Pathak BDL AI4Cl 76 15 0 12 Jun 2024
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning Julia Kaltenborn C. E. E. Lange Venkatesh Ramesh Philippe Brouillard Yaniv Gurwicz Chandni Nagda Jakob Runge Peer Nowack David Rolnick AILaw 77 18 0 07 Nov 2023
ACE: A fast, skillful learned global atmospheric model for climate prediction Oliver Watt‐Meyer Gideon Dresdner Jeremy McGibbon Spencer K. Clark Brian Henn ... K. Kashinath Michael S. Pritchard Boris Bonev Matthew E. Peters Christopher S. Bretherton AI4Cl 116 47 0 03 Oct 2023
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation S. Bouabid Dino Sejdinovic D. Watson‐Parris 61 5 0 14 Jul 2023
ClimaX: A foundation model for weather and climate Tung Nguyen Johannes Brandstetter Ashish Kapoor Jayesh K. Gupta Aditya Grover AI4Cl AI4CE 115 271 0 24 Jan 2023
Enhancing Computational Fluid Dynamics with Machine Learning Ricardo Vinuesa Steven L. Brunton AI4CE 184 385 0 05 Oct 2021
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 705 8,044 0 13 Jun 2015