TY - JOUR
T1 - Validating Climate Models with Spherical Convolutional Wasserstein Distance
AU - Garrett, Robert C.
AU - Harris, Trevor
AU - Wang, Zhuo
AU - Li, Bo
N1 - We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. NCEP/DOE Reanalysis II data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov. This work is partially supported by the National Science Foundation grants NSF-DMS-1830312, NSF-DGE-1922758, and NSF-DMS-2124576 as well as the National Oceanic and Atmospheric Administration (NOAA) grant NA18OAR4310271. Lastly, we thank the referees and area chairs for their time and valuable feedback.
PY - 2024
Y1 - 2024
N2 - The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data. This new similarity measure accounts for spatial variability using convolutional projections and quantifies local differences in the distribution of climate variables. We apply this method to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members by comparing them to observational and reanalysis data products. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climatologies.
AB - The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data. This new similarity measure accounts for spatial variability using convolutional projections and quantifies local differences in the distribution of climate variables. We apply this method to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members by comparing them to observational and reanalysis data products. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climatologies.
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M3 - Conference article
AN - SCOPUS:105000545609
SN - 1049-5258
VL - 37
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
T2 - 38th Conference on Neural Information Processing Systems, NeurIPS 2024
Y2 - 9 December 2024 through 15 December 2024
ER -