A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics
Name
J Adv Model Earth Syst - 2024 - Barthel Sorensen - A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time.pdf
Size
4.68 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Barthel Sorensen, B.
Charalampopoulos, A.
Zhang, S.
Harrop, B. E.
Leung, L. R.
Sapsis, T. P.
Date Issued
March 2024
Journal
Journal of Advances in Modeling Earth Systems
Publisher
American Geophysical Union
Citation
Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., & Sapsis, T. P. (2024). A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics. Journal of Advances in Modeling Earth Systems, 16, e2023MS004122.
Version
Final published version
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, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.
Subjects
General Earth and Planetary Sciences
Environmental Chemistry
Global and Planetary Change
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2023ms004122
