A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact Weather Systems in E3SM Atmosphere Model
Name
J Adv Model Earth Syst - 2024 - Zhang - A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact.pdf
Description
Published version
Size
20.51 MB
Format
Adobe PDF
Checksum (MD5)
a11f2df7cecd2888d8b5dcd5c5452e8e
Author(s)
Zhang, Shixuan
Harrop, Bryce
Leung, L Ruby
Charalampopoulos, Alexis‐Tzianni
Barthel Sorensen, Benedikt
Xu, Wenwei
Sapsis, Themistoklis
Date Issued
August 8, 2024
Journal
Journal of Advances in Modeling Earth Systems
Publisher
American Geophysical Union
Citation
Zhang, S., Harrop, B., Leung, L. R., Charalampopoulos, A.-T., Barthel Sorensen, B., Xu, W., & Sapsis, T. (2024). A machine learning bias correction on large-scale environment of high-impact weather systems in E3SM atmosphere model. Journal of Advances in Modeling Earth Systems, 16, e2023MS004138.
Abstract
Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
10.1029/2023ms004138
