A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coar...

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Bibliographic Details
Main Authors: B. Barthel Sorensen, A. Charalampopoulos, S. Zhang, B. E. Harrop, L. R. Leung, T. P. Sapsis
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-03-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
climate modeling
extreme event statistics
debiasing
nudging
machine learning
Online Access:https://doi.org/10.1029/2023MS004122

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