Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations. Issue 3 (12th February 2019)

• Record Type: Journal Article
• Authors: Gu, Xihui; Li, Jianfeng; Chen, Yongqin David; Kong, Dongdong; Liu, Jianyu
• Is Part Of: Journal of geophysical research. Volume 124:Issue 3(2019)
• Page Start: 1474
• Page End: 1495
• Publication Date: 2019-02-12
• Subjects: surface soil moisture -- spatiotemporal changes -- Global Climate Models -- hydrological models -- reanalysis data sets -- remote sensing; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD029304
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: A large population of global soil moisture data sets 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 data sets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 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. The results indicate that all data sets reach a good agreement on the spatial patterns of surface soil moisture, which are also consistent with that of precipitation. However, data sets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture. Specifically, GCMs tend to underestimate the absolute values of surface soil moisture relative to ECV. In comparisons that remove the influence of absolute values (e.g., unbiased root‐mean‐square error), all model‐based data sets show comparable performances against ECV. GLDAS, reanalysis, and ISI‐MIP_OBS data sets show significant positive temporal correlations with ECV. Model‐based data sets and ECV consistently indicate widespread drying trends during 1980–2005, but the regional trends vary in different data sets. Compared to ECV, GLDAS and reanalysis data sets exhibit more intensive drying trends, while Coupled Model Intercomparison Project Phase 5 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 Normalized Difference Vegetation Index. Key Points: The model‐based and ECV data sets agree on the spatial patterns of soil moisture, but CMIP5 underestimates soil moisture values GLDAS, reanalysis, and ISI‐MIP driven by observations show significant positive temporal correlations with ECV soil moisture All data sets exhibit widespread drying trends with more intensive drying in GLDAS and reanalysis but weaker trends in CMIP5 and ISI‐MIP