ClimateBench v1.0: A benchmark for data-driven climate projections

Watson-Parris, Duncan, Rao, Yuhan, Olivie, Dirk, Seland, Øyvind, Nowack, Peer ORCID: https://orcid.org/0000-0003-4588-7832, Camps-Valls, Gustau, Stier, Philip, Bouabid, Shahine, Dewey, Maura, Fons, Emilie, Gonzalez, Jessenia, Harder, Paula, Jeggle, Kai, Lenhardt, Julien, Manshausen, Peter, Novitasari, Maria, Ricard, Lucile and Roesch, Carla (2022) ClimateBench v1.0: A benchmark for data-driven climate projections. Journal of Advances in Modeling Earth Systems. ISSN 1942-2466 (In Press)

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Abstract

Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench - a benchmarking framework based on a suite of CMIP, AerChemMIP and DAMIP simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, consistency and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage others to tackle this important and demanding challenge.

Item Type: Article
Additional Information: Funding information: DWP and PS acknowledge funding from NERC projects NE/P013406/1 (A-CURE) and NE/S005390/1 (ACRUISE). DWP, GCV, PS, SB, MD, EF, PH, KJ, JL, PM, MN, LR, CR and JV acknowledge funding from the European Union’s Horizon 2020 research and innovation programme iMIRACLI under Marie Skłodowska-Curie grant agreement No 860100. PS additionally acknowledges support from the ERC project RECAP and the FORCeS project under the European Union’s Horizon 2020 research programme with grant agreements 724602 and 821205. GCV was partly supported by the European Research Council (ERC) Synergy Grant “Understanding and Modelling the Earth System with Machine Learning (USMILE)” under the Horizon 2020 research and innovation programme (Grant agreement No. 855187). YR was supported by NOAA through the Cooperative Institute for Satellite Earth System Studies under Cooperative Agreement NA19NES4320002. This research was supported, in part, by the National Science Foundation under Grant No. NSF PHY-579 1748958.
Uncontrolled Keywords: sdg 7 - affordable and clean energy,sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy
Faculty \ School: Faculty of Science > School of Environmental Sciences
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Depositing User: LivePure Connector
Date Deposited: 03 Sep 2022 00:22
Last Modified: 22 Sep 2022 14:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/87693
DOI:

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