Consistency and discrepancy of global surface soil moisture changes from multiple model-based datasets against satellite observations
Xihui Gu1,2, Jianfeng Li2*, Yongqin David Chen3,4, Dongdong Kong5, Jianyu Liu6
Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Department of Geography, Hong Kong Baptist University, Hong Kong, China
School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, China
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
Abstract: A large population of global soil moisture datasets generated by a variety of models is compared with the latest satellite-based Essential Climate Variable (ECV) soil moisture product in a common framework. The model-based surface soil moisture datasets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Models (GCMs), and Inter-Sectoral Impact Model Intercomparison Project (including observation-driven outputs ISI-MIP_OBS and GCM-driven outputs ISI-MIP_GCM). We evaluate the model-based surface soil moisture against ECV with focuses on spatial patterns, temporal correlations, long-term trends, and relationships with precipitation and Normalized Difference Vegetation Index (NDVI). The results indicate that all datasets reach a good agreement on the spatial patterns of surface soil moisture which are also consistent with the spatial pattern of precipitation. However, datasets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture relative to ECV. Specifically, CMIP5 GCMs tend to underestimate the absolute values of surface soil moisture. However, in comparisons that remove the influence of absolute values (e.g. unbiased Root Mean Square Difference Error), all model-based datasets show comparable and acceptable performances against ECV. GLDAS, reanalysis, and ISI-MIP_OBS datasets show significant positive temporal correlations with ECV. Model-based datasets and ECV consistently indicate widespread drying trends during 1980-2005, but the regional trends vary in different datasets. Compared to ECV, GLDAS and reanalysis datasets exhibit more intensive drying trends, while CMIP5 and ISI-MIP_GCM tend to underestimate the drying. In most of the regions, the wetting/drying trends are consistent with the increases/decreases in precipitation and NDVI.
Published by Journal of Geophysical Research: Atmospheres, DOI: 10.1029/2018JD029304
