Articles | Volume 21, issue 4
Research article
24 Apr 2017
Research article |  | 24 Apr 2017

Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations

Shanshui Yuan and Steven M. Quiring

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources is used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multimodel ensemble means and the performance of individual models are assessed at a monthly timescale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the amount of both near-surface and soil column soil moisture in the western US and underestimate it in the eastern US. There are large variations across models, especially for the near-surface soil moisture. There are significant regional variations in performance as well. Results of a regional analysis show that in the deeper soil layers, the CMIP5 soil moisture simulations tend to be most skillful in the southern US. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

Short summary
This study has significant practical implications because soil moisture from ESMs can be used for operational drought monitoring, calibrating/validating satellites and documenting how soil moisture influences the climate system on seasonal to interannual timescales. This article shows the spatial and temporal patterns of biases of simulated soil moisture. This could help better develop the models and provide better predictions of future soil-moisture-related climatic events.