Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates

降水 气候学 气候变化 环境科学 北半球 前线(军事) 强迫(数学) 气候模式 极端天气 大气科学 气象学 地质学 地理 海洋学
作者
Katherine Dagon,John Truesdale,J. Biard,Kenneth E. Kunkel,Gerald A. Meehl,María J. Molina
出处
期刊:Journal Of Geophysical Research: Atmospheres [Wiley]
卷期号:127 (21) 被引量:1
标识
DOI:10.1029/2022jd037038
摘要

Extreme precipitation events, including those associated with weather fronts, have wide-ranging impacts across the world. Here we use a deep learning algorithm to identify weather fronts in high resolution Community Earth System Model (CESM) simulations over the contiguous United States (CONUS), and evaluate the results using observational and reanalysis products. We further compare results between CESM simulations using present-day and future climate forcing, to study how these features might change with climate change. We find that detected front frequencies in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation such as the jet stream. We also associate the detected fronts with precipitation and find that total and extreme frontal precipitation mostly decreases with climate change, with some seasonal and regional differences. Decreases in Northern Hemisphere summer frontal precipitation are largely driven by changes in the frequency of different front types, especially cold and stationary fronts. On the other hand, Northern Hemisphere winter exhibits some regional increases in frontal precipitation that are largely driven by changes in frontal precipitation intensity. While CONUS mean and extreme precipitation generally increase during all seasons in these climate change simulations, the likelihood of frontal extreme precipitation decreases, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change.
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