Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data

There are significant biases and uncertainties in the simulated soil moisture with land surface models. Here we evaluate multimodel differences in Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products on different time scales. The variability of surface soil moisture (SSM) within three frequency bands (7-30 days, 30-90 days, and 90-365 days) after normalization is quantified using Fourier transform for the evaluation. Compared to the SMAP observations, the simulated SSM variability within CMIP5 is underestimated in the two higher frequency bands (by 72% and 56%, respectively) and overestimated in the lowest frequency band (by 113%). In addition, these differences concentrate in regions with larger SSM. Finally, these multimodel differences are found to be significantly correlated with mean climate conditions rather than soil texture. This study identifies the spatiotemporal distribution of the model deficiencies within CMIP5 and finds they are systematic in the long-term simulation on a global scale. Plain Language Summary Soil moisture has been largely regarded as a key variable in Earth system and plays an important role in climate prediction. However, land surface models have large uncertainties in simulating soil moisture. This study identifies that (a) land surface models underestimate soil moisture variability on weekly to seasonal time scales and overestimate it on seasonal to annual time scales compared to a remote sensing observation, (b) both the underestimation and overestimation are concentrated in the wetter regions, and (c) the differences between these models and the observation are more closely related to vegetation condition and surface temperature than soil sand and clay content. Using satellite observed data, this study reveals the deficiencies of land surface models in simulating temporal variability of soil moisture, which will help improve the soil moisture predictability of these models.