Evaluation of the performance of WRF9km in simulating climate over the upper Yellow River Basin  

作　　者：Yi-Jia LI Xue-Jia WANG Xiao-Hua GOU Qi WANG Tinghai OU Guo-Jin PANG Mei-Xue YANG Lan-Ya LIU Li-Ya QIE Tao WANG Jia-Yu WANG Si-Hao WEI Xiao-Lai CHENG 

机构地区：[1]Key Laboratory of Western China's Environmental Systems(Ministry of Education),College of Earth and Environmental Sciences,Lanzhou University,Lanzhou 730000,China [2]Gansu Liancheng Forest Ecosystem Field Observation and Research Station,Lanzhou University,Lanzhou 730333,China [3]Regional Climate Group,Department of Earth Sciences,University of Gothenburg,Gothenburg 40530,Sweden [4]Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China [5]Key Laboratory of Cryospheric Science and Frozen Soil Engineering,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China [6]School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai 519082,China

出　　处：《Advances in Climate Change Research》2025年第1期58-72,共15页气候变化研究进展(英文版)

基　　金：financed by the National Natural Science Foundation of China(42161025,U21A2006,42477483,42442055);the Young Doctor Support Project of Education Department of Gansu Province(2023QB-060);the Start-up Funds for Introduced Talent at Lanzhou University(561120217).

摘　　要：Understanding the current climate in the Yellow River Basin is essential for accurately predicting future climate change and assessing its impacts on water resources and ecosystems;however,existing models exhibit notable biases in this region,primarily due to low resolution and errors in driving data and model domains.Using in-situ station observation data,CN05.1 gridded meteorological observation dataset,along with the ERA5 and MERRA2 reanalysis datasets,the performance of the WRF9km in simulating temperature and precipitation from 1980 to 2016 was comprehensively evaluated.Results indicate that the WRF9km model effectively captures the spatial pattern of air temperature,with a spatial correlation exceeding 0.86(at the 95%confidence level)and a cold bias of−2.8℃compared to CN05.1.This bias is primarily due to the underestimation of downward radiation and the overestimation of surface albedo.However,the WRF9km model fails to reproduce the observed warming trend across the entire region,especially during the summer.For precipitation,the WRF9km model generally reproduces the observed spatial pattern,with spatial correlation coefficients above 0.80 for all seasons except winter(at the 95%confidence level).However,the model overestimates precipitation relative to CN05.1 and underestimates it when compared to MERRA2.The precipitation bias is mainly attributed to the misrepresentation of wind fields and moisture by the WRF9km model.Regarding precipitation trends,different datasets yield divergent results,indicating substantial inter-annual variability that is difficult for the WRF9km to capture.Compared to the driving ERA5 data,the WRF9km model reduces cold biases between November and December,as well as wet biases across all seasons.The model also better simulates the winter warming trend in the western part of the UYRB and the summer wetting trend in the northern part.The evaluation of the WRF9km model provides valuable insights for the development of dynamical downscaling in terrain complex regions,especially fo

关 键 词：WRF Climate change Upper Yellow River Basin Air temperature Precipitation 

分 类 号：P467[天文地球—大气科学及气象学]