Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large-scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in-situ geological measurements. Fortunately, recent advancements of satellite technology provide a unique opportunity to constrain the soil texture datasets by introducing observed information at large spatial scales. Here, two major soil texture baseline datasets (Global Soil Datasets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite-estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon content increases globally especially over regions with dense vegetation cover. The optimized soil texture datasets are then used to run simulations in one example LSM, i.e., Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite-optimized soil texture maps are improved at both grid and in-situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation-oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation Earth System Models.

Aerodynamic roughness length (z0) fundamentally affects land surface momentum loss and wind resource simulation, but ground truth data of z0 are sparse in space, causing z0 datasets used in atmospheric models are empirically estimated from land cover types through a look-up table. In this study, we derived z0 values from 101 anemometer towers in China. Taking them as ground truth, we show that existing gridded z0 datasets determined from either a look-up table or a machine-learning method contain considerable uncertainty and fail to capture the variability of z0 within each land cover type, although the latter performs better. Even for the widely-used ERA5, its z0 is overestimated in wind-rich regions of China, causing an underestimation of near-surface wind speed. This highlights the urgency to improve z0 data in atmospheric models. Current rapidly expanding anemometer towers may substantially enrich z0 truth data and thus provide potential to improve wind resource modeling.

Weather and climate forecast predictability relies on Land-Atmosphere (L-A) interactions occurring at different time scales. However, evaluation of L-A coupling parameterizations in current land surface models (LSMs) is challenging since the physical processes 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 (SMAP) observations, we evaluate L-A coupling performances in 4 prevailing LSMs with both coupled and offline simulations. Multi-model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water-limited processes and vice versa. 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’ soil layer depths or 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 evapotranspiration and global flood risks. Finally, a comparison of two soil moisture thresholds indicates that the soil parameters employed in LSMs play an essential role in producing the model’s biases. The satellite estimation of ET at the water-limited stage and soil hydraulic parameters provides readily available information to constrain LSMs, which are essentially important to improve the models’ L-A coupling simulations, as well as other land surface processes such as terrestrial hydrological cycles.