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.

The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides the long-term soil moisture (SM) products and this study conducts a comprehensive assessment of SM products of multiple CMIP6 model simulations over conterminous China. Both near-surface (0-10 cm) SM simulations from 40 models and root-zone (0-100 cm) SM from 25 models are compared with a set of station measurements in the growing season (April to September) for 1992-2013 in term of magnitude, spatial and temporal variability, and the long-term trend and interannual variability of near-surface SM for 1961-2014 are further evaluated with an offline land surface modeling dataset. Simulations from most models broadly capture the spatial characteristics of observation and the multi-model mean (MME) well reproduces seasonal variations over majority regions regardless of large-spread across models. Models from the same institution likely manifest similar performances and the land surface scheme plays a dominant role in the SM reproduction. The majority of models well simulate the overall drying trend in China as a whole and the signs of SM trend are highly consistent across models, but the areas with significant wetting/drying trends vary with models. The spatial patterns of SM interannual variability are model-dependent in term of spatial patterns. MME is overall superior to the simulations of individual model and may have potential applications in the future research. The heterogeneity SM performances across models reveal the complexity in modeling land surface variables, suggesting the need for improving representations of land surface processes in the coupled models.

Land skin temperature (LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other nonclimatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 to 2017. The high-quality land surface air temperature (LSAT) dataset is used to correct the LST warming biases in cold months in regions north of 40ºN due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization (MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68×103 significant breakpoints in 1.65×106 monthly records are detected. A large number of these significant breakpoints are located over large parts of the Sichuan Basin and southern China. After MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/- 0.5 ºC, and 10% of the breakpoints are adjusted over 1.5 ºC. Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we preliminarily analyze the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend (0.22 ºC decadal-1). The homogenized LST dataset can be further adopted for a variety of applications (e.g., model evaluation and extreme event characterization).