Improving the simulation of terrestrial water storage anomalies over China using a Bayesian model averaging ensemble approach  

作　　者：LIU Jian-Guo JIA Bing-Hao XIE Zheng-Hui SHI Chun-Xiang 

机构地区：[1]School of Mathematics and Computational Science, and Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua University [2]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences [3]National Meteorological Information Center, China Meteorological Administration

出　　处：《Atmospheric and Oceanic Science Letters》2018年第4期322-329,共8页大气和海洋科学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.41405083 and 91437220);the Natural Science Foundation of Hunan Province,China(Grant No.2015JJ3098);the Key Research Program of Frontier Sciences,CAS(QYZDY-SSW-DQC012);the Fund Project for The Education Department of Hunan Province(Grant No.16A234)

摘　　要：The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.作为全球能量和水分循环的关键参量,陆地水储量包括土壤水、地表水、地下水、积雪和生物体水等,在水文、气候、农业、生态等众多领域起重要影响。与地面观测和遥感反演相比,陆面模式在刻画陆地水储量的时空变率等方面具有明显优势。然而不同模式参数化方案以及大气强迫驱动导致陆地水储量模拟存在不确定。为了减少陆地水储量模拟不确定性,本研究建立了基于贝叶斯模型平均(BMA)和多强迫多模式集合的陆地水储量模拟系统,获得了中国区域1979–2008年陆地水储量数据集。选取2004–08年的数据与GRACE重力卫星数据比较分析,结果显示BMA集合模拟的陆地水储量异常(Terrestrial water storage anomalies,TWSA)优于所有单个模拟结果,与GRACE观测的TWSA有更高的相关系数和更小的误差。

关 键 词：Terrestrial water storage anomalies multi-forcing and multi-model ensemble simulation Bayesian model averaging spatiotemporal variation UNCERTAINTY 

分 类 号：P[天文地球]