Soil Moisture Memory of Land Surface Models Utilized in Major Reanalyses Differ Significantly From SMAP Observation

Weather and climate forecast predictability relies on hydrometeorological processes occurring at different time scales. However, evaluation of parameterizations of such processes in current land surface models (LSMs) is challenging since they are complex, and large‐scale observations are scarce and uncommon. Recent advancements in satellite observations, in this light, provide a unique opportunity to evaluate the models' performances at large spatial scales. Using 5‐year soil moisture memory (SMM) from Soil Moisture Active and Passive observations, we evaluate hydrometeorological behaviors of four LSMs in major reanalyses. Multi‐model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water‐limited processes whereas underestimate SMM controlled by energy‐limitations. Large model spreads in SMM are also observed between individual models. The SMM biases are highly dependent on models' parameterizations, while showing minor relevance to the models' online/offline simulating schemes. Further analyses of two important terrestrial water cycle‐related variables indicate current LSMs may underestimate soil moisture that is directly available for water cycle rate and water‐limited evapotranspiration. Finally, the comparison of two soil moisture thresholds indicates the soil parameters utilized in LSMs play an essential role in producing the model's biases. This study provides an observation‐based reference for hydrometeorological evaluations in multiple LSMs, shedding lights on improving land surface simulations in future.