Open AccessContrasting Responses of Hailstorms to Anthropogenic Climate Change in Different Synoptic Weather Systems
Author(s) -
Fan Jiwen,
Zhang Yuwei,
Wang Jingyu,
Jeong JongHoon,
Chen Xiaodong,
Zhang Shixuan,
Lin Yun,
Feng Zhe,
AdamsSelin Rebecca
Publication year - 2022
Publication title -
earth's future
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.641
H-Index - 39
ISSN - 2328-4277
DOI - 10.1029/2022ef002768
Subject(s) - precipitation , storm , convective storm detection , environmental science , climatology , synoptic scale meteorology , winter storm , convection , jet stream , climate change , severe weather , atmospheric sciences , meteorology , jet (fluid) , geography , geology , oceanography , physics , thermodynamics
Hailstones and extreme precipitation generate substantial economic losses across the United States (US) and the globe. Their strong association with short‐lived, intense convective storms poses a great challenge in predicting their future changes. Here, we conducted model simulations at 1.2 km grid spacing for severe convective storms with large hail and heavy precipitation that occurred in two typical types of synoptic‐scale environments in spring seasons over the central US under both current and future climate conditions. We find that the responses of large hail (diameters >2.5 cm) to anthropogenic climate change (ACC) are markedly different between the hailstorms developed in the two types of synoptic‐scale environments, with over 110% increase in large hail occurrences for the frontal systems, whereas less than 30% increase for the Great Plains low‐level jet (GPLLJ) systems. This is explained by the larger increase in convective intensity and updraft width and a smaller increase in warm cloud depth in the frontal storms compared with the GPLLJ storms. Interestingly, the occurrences and intensity of heavy precipitation (rain rate >20 mm hr −1 ) in both types of systems are similarly sensitive to ACC (e.g., 40% and 33% increases in the occurrences for the frontal and GPLLJ systems, respectively). These results advance our knowledge of hail projection and have important implications for managing risks for future hail.