The Tibetan Plateau (TP) region experiences strong land‐atmosphere interactions, and as an elevated heating source, significantly influences the formation of the Asian monsoon. Those interactions are not well represented in current land‐surface models (LSMs), partly due to difficulties in representing heterogeneities in soil structures in LSM. Simulations using the Noah with multiparameterization options (Noah‐MP) LSM are employed to assess the relative importance of parameterizing vertical soil heterogeneity, organic matter, and soil rhizosphere and their impacts on seasonal evolution of soil temperature, soil moisture, and surface energy and water budgets at the sparsely vegetated Amdo site located in central TP. The LSM spin‐up time at the central TP depends on the complexity of the model physics, ranging from 4 years with simplest soil physics to 30 years with the addition of organic matter and spare to dense rhizosphere parameterization in Noah‐MP. Representing layered soil texture and organic matter does not improve low biases in topsoil moisture. Reducing the saturated conductivity from the mucilage in the rhizosphere produces better results. Surface sensible and latent heat fluxes are better simulated in the monsoon season as well. Adding layered soil texture and organic matter in Noah‐MP retard the thawing in deep soil layers, and the rhizosphere effect delays thawing even more in the transient season. Uncertainties in soil initialization significantly affect deep‐soil temperature and moisture, but uncertainties in atmospheric forcing conditions mainly affect topsoil variables and consequently the surface energy fluxes. Differing land‐surface physics cause 36% uncertainty in the accumulated evapotranspiration and subsurface runoff.

The increased mean temperature and temperature variability under global warming can have many adverse impacts on human and natural systems. However, how soil moisture‐atmosphere interaction can affect future climate change over East Asia remains largely unclear. We use long‐term regional climate simulations with the Community Climate System Model‐Weather Research and Forecasting (CCSM‐WRF) model system to investigate the role of soil moisture feedbacks in future summer surface air temperature change over East Asia. Results show that summer surface air temperature and precipitation are projected to increase over most of East Asia by the end of 21st century under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. Soil moisture feedbacks lead to an increased summer mean temperature of 0.15°C averaged over East Asia, with key regions appearing over the northern part of Tibetan Plateau, Sichuan Basin, and the middle and lower reaches of the Yangtze River Basin during 2071‐2100 under the RCP8.5 scenario. And regionally averaged interannual temperature variability induced by soil moisture feedbacks is substantially enhanced as a whole. Regarding the spatial distribution, soil moisture feedbacks on interannual temperature variability are projected to strengthen notably over the northern part of Tibetan Plateau, the Hexi Corridor, and Sichuan Basin and weaken over the south Tibetan Plateau, southeast China, and the areas surrounding the Lake Baikal. The stronger responses of surface heat and the atmospheric boundary layer to dry anomalies than wet anomalies lead to an obvious dry‐wet asymmetry of soil moisture feedbacks on summer surface air temperature over the key areas of East Asia.

The ongoing climate change has posed severe threat to the natural system and human society. However, how soil temperature feedbacks matter for climate change projections is not yet well explored. In this study, we assess the role of soil temperature feedbacks for summer air temperature variability over East Asia under global warming. Regional climate model simulations with and without soil temperature‐atmosphere interactions were performed to isolate the role of soil temperature feedbacks under historical (1976–2005) and future (2071–2100) warming conditions. Results indicate that soil temperature feedbacks can largely enhance interannual variability of summer daily mean and minimum surface air temperatures over East Asia, with strongest impacts over Mongolia and many areas of northern Tibetan Plateau and northern China, in the historical period. The soil temperature feedback strength below 925‐hPa mainly depends on changes in longwave radiation, surface heat flux partitioning, and temperature advections, while it is largely determined by the diabatic heating processes in the lower troposphere. The spatial distribution of soil temperature feedbacks is projected to change notably over East Asia under future warming condition. In particular, the impacts of soil temperature feedbacks on summer temperature are shown to be stronger in the lower troposphere under future condition. These results imply the important role of soil temperature feedbacks on air temperature variability for regional climate modeling especially in the context of climate change.

In this study, we perform two regional climate simulations with the Weather Research and Forecasting model driven by outputs from the Community Climate System Model (CCSM‐WRF) to investigate the role of soil moisture feedbacks in summer surface air temperature variability over East Asia for the period of 1976−2005. Strong soil moisture feedbacks on the daily mean and maximum temperatures identified by the CCSM‐WRF model system mainly appear over the region from southern Siberia through eastern Mongolia to northeast China, the Tibetan Plateau, and most areas of central and east China, accounting for 30%−70% of the total variances. Meanwhile, the simulated soil moisture feedbacks on daily minimum temperature are shown to be much weaker than those on daily mean and maximum temperatures. The soil moisture‐temperature feedbacks in the CCSM‐WRF model system are generally well validated with those from two reanalysis products. The analysis of the physical processes shows that soil moisture feedback strength is mainly determined by the ability of soil moisture to influence the local surface heat fluxes and planetary boundary layer processes. The reasonable simulations of present soil moisture‐temperature feedbacks indicate that the CCSM‐WRF model system can be further applied to understand the role of soil moisture in influencing projected climate change over East Asia.

Multi‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense sampling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.