TY - JOUR
T1 - Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model-Based Data Sets Against Satellite Observations
AU - Gu, Xihui
AU - Li, Jianfeng
AU - Chen, Yongqin David
AU - Kong, Dongdong
AU - Liu, Jianyu
N1 - Funding Information:
This work is financially supported by the Strategic Priority Research Program Grant of the Chinese Academy of Sciences (grant XDA19070402), the National Key Research and Development Program of China (grant 2018YFA0605603), the grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project HKBU22301916), the Direct Grant from Chinese University of Hong Kong (Project 4052134), and the Fundamental Research Funds for the Central Universities and China University of Geosciences (Wuhan; CUG180614 and CUGCJ1702). We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP and the ISI‐MIP coordination team, and we thank the climate modeling groups for developing and making their model output avail able. Data used in the study can be accessed in the repositories introduced in the main text. Last but not the least, our cordial gratitude should also be extended to the Editor, Ruby Leung, and the anonymous reviewers for their pertinent and professional comments and suggestions, which are greatly helpful for further improvement of the quality of this manuscript.
PY - 2019/2/16
Y1 - 2019/2/16
N2 - 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.
AB - 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.
KW - Global Climate Models
KW - hydrological models
KW - reanalysis data sets
KW - remote sensing
KW - spatiotemporal changes
KW - surface soil moisture
UR - http://www.scopus.com/inward/record.url?scp=85061429506&partnerID=8YFLogxK
U2 - 10.1029/2018JD029304
DO - 10.1029/2018JD029304
M3 - Journal article
AN - SCOPUS:85061429506
SN - 2169-897X
VL - 124
SP - 1474
EP - 1495
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
IS - 3
ER -
