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Attribution of Snowpack Errors to Simulated Temperature and Precipitation in E3SMv1 Over the Contiguous United States
Journal of Advances in Modeling Earth Systems ( IF 4.4 ) Pub Date : 2021-09-12 , DOI: 10.1029/2021ms002640
Michael A. Brunke 1 , Joshua Welty 1 , Xubin Zeng 1
Affiliation  

Snow water equivalent (SWE), temperature, and precipitation biases and trends are evaluated in the atmosphere-land simulations of the Energy Exascale Earth System Model version 1 (E3SMv1) in comparison to the Community Earth System Model version 2 (CESM2) and two other models using the ground measurement-based University of Arizona (UA) snow product. SWE, temperature, and precipitation biases are highest in magnitude in the Western contiguous United States (CONUS). SWE errors are attributed to temperature and precipitation through multiple linear regressions of normalized errors, the coefficients of which represent the sensitivities to temperature and precipitation errors. SWE errors are more sensitive to temperature errors throughout the CONUS. Model SWE and temperature trends are generally opposite from UA product trends in the Western CONUS. SWE trend errors are also attributed to temperature and precipitation trend errors using multiple linear regressions of normalized trend errors. SWE trend errors are more sensitive to those of precipitation at higher elevations (>1,500 m) in the Western CONUS in these simulations. Thus, the sensitivity to temperature and precipitation differ for SWE errors and its trend errors. Furthermore, the SWE trend errors are more sensitive to temperature and precipitation in the atmosphere-ocean coupled simulations in which the atmosphere-land is coupled to an active ocean model. These results suggest that both errors in simulated temperature and precipitation contribute to SWE errors.

中文翻译:

将积雪误差归因于美国本土 E3SMv1 中模拟的温度和降水

与社区地球系统模型第 2 版 (CESM2) 和其他两个模型相比,雪水当量 (SWE)、温度和降水偏差和趋势在能源百亿亿级地球系统模型第 1 版 (E3SMv1) 的大气-陆地模拟中得到评估使用基于地面测量的亚利桑那大学 (UA) 雪产品的模型。SWE、温度和降水偏差在美国西部毗邻地区 (CONUS) 的幅度最大。SWE误差通过归一化误差的多元线性回归归因于温度和降水,其系数代表对温度和降水误差的敏感性。SWE 误差对整个 CONUS 的温度误差更为敏感。模型 SWE 和温度趋势与西部 CONUS 的 UA 产品趋势大体相反。SWE 趋势误差也归因于温度和降水趋势误差,使用归一化趋势误差的多元线性回归。在这些模拟中,SWE 趋势误差对西部 CONUS 较高海拔 (>1,500 m) 的降水更为敏感。因此,对温度和降水的敏感性因 SWE 误差及其趋势误差而异。此外,在大气-陆地耦合到活动海洋模型的大气-海洋耦合模拟中,SWE 趋势误差对温度和降水更敏感。这些结果表明模拟温度和降水的误差都会导致 SWE 误差。在这些模拟中,SWE 趋势误差对西部 CONUS 较高海拔 (>1,500 m) 的降水更为敏感。因此,对温度和降水的敏感性因 SWE 误差及其趋势误差而异。此外,在大气-陆地耦合到活动海洋模型的大气-海洋耦合模拟中,SWE 趋势误差对温度和降水更敏感。这些结果表明模拟温度和降水的误差都会导致 SWE 误差。在这些模拟中,SWE 趋势误差对西部 CONUS 较高海拔 (>1,500 m) 的降水更为敏感。因此,对温度和降水的敏感性因 SWE 误差及其趋势误差而异。此外,在大气-陆地耦合到活动海洋模型的大气-海洋耦合模拟中,SWE 趋势误差对温度和降水更敏感。这些结果表明,模拟温度和降水的误差都会导致 SWE 误差。在大气-海洋耦合模拟中,SWE 趋势误差对温度和降水更敏感,在该模拟中,大气-陆地与活动的海洋模型耦合。这些结果表明模拟温度和降水的误差都会导致 SWE 误差。在大气-海洋耦合模拟中,SWE 趋势误差对温度和降水更敏感,在该模拟中,大气-陆地与活动的海洋模型耦合。这些结果表明模拟温度和降水的误差都会导致 SWE 误差。
更新日期:2021-09-29
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