{"title":"A Machine Learning Bias Correction on Large-Scale Environment of High-Impact Weather Systems in E3SM Atmosphere Model","authors":"Shixuan Zhang, Bryce Harrop, L. Ruby Leung, Alexis-Tzianni Charalampopoulos, Benedikt Barthel Sorensen, Wenwei Xu, Themistoklis Sapsis","doi":"10.1029/2023MS004138","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004138","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advances in Modeling Earth Systems","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2023MS004138","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
大尺度动力学和热力学过程是造成极端天气事件的高影响天气系统的常见环境驱动因素。然而,这种大尺度环境条件在气候模拟中经常出现系统性偏差,给评估高影响天气系统和极端天气事件带来了挑战。本文采用机器学习(ML)方法对能源超大规模地球系统模式(ESM)大气部分模拟的大尺度风、温度和湿度进行了 1° 分辨率的偏差校正。通过重点研究热带气旋(TC)、外热带气旋(ETC)和大气河流(AR)等三种影响较大的天气系统,证明了 ML 方法在极端天气分析中的实用性。我们的研究表明,ML 模式可以有效减少大尺度风、温度和湿度的气候偏差,同时保持它们对外加气候变化扰动的响应。我们发现,偏差校正可直接改善与 ARs 相关的水汽输送,以及与 ETCs 相关的热动力流的表示。当偏差校正后的大尺度风被用来驱动大西洋盆地上空的合成热气旋路径预报模式时,所得到的热气旋路径密度与观测风驱动的热气旋路径模式的密度更为一致。此外,ML 模式对大尺度风暴环境的平均气候变化信号以及三个天气系统的发生和强度干扰不大。这项研究表明,所提出的 ML 方法可以通过提供低分辨率气候模式所模拟的更真实的大尺度风暴环境来改进极端天气事件的降尺度处理。
期刊介绍:
The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community.
Open access. Articles are available free of charge for everyone with Internet access to view and download.
Formal peer review.
Supplemental material, such as code samples, images, and visualizations, is published at no additional charge.
No additional charge for color figures.
Modest page charges to cover production costs.
Articles published in high-quality full text PDF, HTML, and XML.
Internal and external reference linking, DOI registration, and forward linking via CrossRef.