Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

IF 3.8 2区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Geophysical Research: Atmospheres Pub Date : 2024-10-26 DOI:10.1029/2024JD041010
S. Wolvin, C. Strong, S. Rupper, W. J. Steenburgh
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Abstract

Within High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier-scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve-fitting daily precipitation as a function of elevation from a 15-year, 4-km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu-Kush mountain ranges. To facilitate precipitation curve-fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion-corrected values and an F-test p-value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using k $k$ -means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra-seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically-forced heavy-precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically-based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

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根据动态降尺度得出的亚洲高山地区降水梯度气候学数据
在亚洲高山地区(HMA),冰川和积雪每年都会融化,为下游居民提供重要的淡水。亚洲高山地区的降水会改变冰川和积雪的质量平衡,从而直接影响该地区的淡水供应。然而,现有的再分析和降尺度模拟缺乏了解重要的冰川尺度降水变化所需的分辨率。这项研究的目的是通过对 15 年、4 公里网格间距的天气研究和预报(WRF)模型模拟的日降水量与海拔高度的函数关系进行曲线拟合,确定地貌降水梯度(OPG)的当前特征,重点是喜马拉雅山脉、喀喇昆仑山脉和兴都库什山脉。为便于降水曲线拟合,WRF 模型网格点被划分为方向相似的区域,称为面。阿凯克信息准则校正值和 F 检验 p 值表明,需要一个曲率项来解释随海拔高度变化的 OPG。通过对月平均 OPG 系数进行 k $k$ -均值聚类,发现了具有相似季节变化的区域。喜马拉雅山脉中部斜坡的 OPG 季节内变化取决于同步尺度条件,在这种条件下,周期性强降水事件使降水量随海拔高度呈强烈的亚线性增长。使用月系数对降水量估算进行的初步测试表明,在降尺度化 WRF 日降水量方面取得了可喜的成果;平均而言,每个网格点的日平均绝对误差比日平均降水总量的误差要小。研究结果为正在开发的机器学习算法提供了一个基于物理的背景,该算法用于预测 OPG 和降尺度全球气候模式在 HMA 上的降水输出。
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来源期刊
Journal of Geophysical Research: Atmospheres
Journal of Geophysical Research: Atmospheres Earth and Planetary Sciences-Geophysics
CiteScore
7.30
自引率
11.40%
发文量
684
期刊介绍: JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.
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