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Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California

机译:加利福尼亚州极端大气河风暴的极高分辨率集成镇压的未来降水量

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Precipitation extremes will likely intensify under climate change. However, much uncertainty surrounds intensification of high-magnitude events that are often inadequately resolved by global climate models. In this analysis, we develop a framework involving targeted dynamical downscaling of historical and future extreme precipitation events produced by a large ensemble of a global climate model. This framework is applied to extreme “atmospheric river” storms in California. We find a substantial (10 to 40%) increase in total accumulated precipitation, with the largest relative increases in valleys and mountain lee-side areas. We also report even higher and more spatially uniform increases in hourly maximum precipitation intensity, which exceed Clausius-Clapeyron expectations. Up to 85% of this increase arises from thermodynamically driven increases in water vapor, with a smaller contribution by increased zonal wind strength. These findings imply substantial challenges for water and flood management in California, given future increases in intense atmospheric river-induced precipitation extremes.
机译:降水极端可能会在气候变化下加剧。然而,很多不确定性环绕着全球气候模型往往不充分解决的高级事件的强化。在此分析中,我们开发了一个涉及目标动态俯卧的历史和未来极端降水事件的框架,由全球气候模型的大集合产生的历史和未来的极端降水事件。该框架应用于加利福尼亚州的极端“大气河”风暴。我们发现总累积降水量大幅增加(10%至40%),具有最大的相对增加的山谷和山地李侧区域。我们还报告了更高且更空间的均匀增加的时间最大降水强度,超过Clausius-Clapyyron期望。在水蒸气中的热力驱动增加,高达85%的增加产生的增加,通过增加的区域风强度增加了较小的贡献。由于强烈的大气河流引起的降水极端,因此,这些研究结果暗示了加利福尼亚水和洪水管理的大量挑战。

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