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The Tibetan Plateau (TP) is the highest land in the world and has a very complex topography. However, the influence of topography on TP snow cover simulations has not been adequately addressed in most land surface models. The analysis of satellite observations indicates that snow cover fraction (SCF) simulation biases increase with the topography complexity, and this increasing trend slows down when the standard deviation of topography is greater than 200 m. The result also shows that using the SCF schemes without consideration of topography leads to a consistent overestimation in winter. To account for the topographic effects, we introduce a modified topographic factor to the SCF schemes. Then we conduct regional simulations using the Simplified Simple Biosphere Model version 3 (SSiB3) and evaluate the results at the location of observation sites to reduce the uncertainty induced by forcing data. Compared with the default SCF scheme, the mean winter SCF bias is reduced from 3.83% to −0.10%. The optimization of SCF simulations further improves the winter surface albedo and land surface temperature (LST) simulations. The winter surface albedo bias over the TP is reduced from 0.020 to 0.007, with a maximum reduction by −0.133. The winter LST bias is reduced from −3.33 to −3.04 K, with a maximum reduction by 3.60 K. This study highlights the importance of topographic effects in simulating snow cover distribution and land surface energy budget for reducing the "cold bias" in winter climate simulations over the TP. Plain Language Summary: The Tibetan Plateau (TP) has a very complex topography, which greatly influences the snow cover distribution in winter. However, the influence of topography on snow cover simulations has not been adequately addressed in most land surface models. Based on the satellite data, we find that ignoring topography leads to a significant overestimation of the simulated snow cover distribution over the TP in winter, and the overestimation increases with the topographic complexity until the terrain complexity reaches a certain level. By applying this relationship, the winter snow cover distribution simulations are greatly improved over the TP, and this improvement further leads to the improvement of land surface energy budget simulations in winter. Taking the topographic effect on snow cover distribution into account contributes to reducing the "cold bias" in winter climate simulations over the TP. Key Points: The complex topography over the Tibetan Plateau greatly influences the snow cover distribution in winterUsing the snow cover fraction (SCF) schemes without topography leads to a consistent positive bias of SCF simulations in winterBy adding a topographic factor, the simulations of winter SCF and surface albedo are significantly improved [ABSTRACT FROM AUTHOR]

In this study, a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyu station in Jilin Province, China, combined with a sophisticated LSM (common land model, CoLM). Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification, salination, and degradation since late 1960s. In this study, three key land-surface parameters, namely, soil color, proportion of sand or clay in soil, and leaf-area index were chosen as parameters to be optimized. Our study comprised three experiments: First, a single-parameter optimization was performed, while the second and third experiments performed triple- and six-parameter optimizations, respectively. Notable improvements in simulating sensible heat flux (SH), latent heat flux (LH), soil temperature (TS), and moisture (MS) at shallow layers were achieved using the optimized parameters. The multiple-parameter optimization experiments performed better than the single-parameter experminent. All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM. Moreover, clear mathematical meaning, simple design structure, and rapid computability give this method great potential for further application to parameter optimization in LSMs. [ABSTRACT FROM AUTHOR]

Numerical simulations of the atmospheric boundary layer require careful representation of the surface heterogeneity, which involves the upscaling parameterization scheme for the heterogeneous surface parameters. In this study, the sensitivity comparisons of an effective aerodynamic parameter scheme against the area-weighted average scheme in simulating the land-atmosphere interaction over heterogeneous terrain were carried out by conducting multinested simulations with the Weather Research and Forecasting (WRF) Model at coarse and fine resolutions, for a typical sea-land breeze case in the Bohai Gulf of China. The results show that the limited-area model is sensitive to the aerodynamic parameter scheme and the effective aerodynamic parameter scheme exhibits a better performance in simulating the variables and parameters in the land-atmosphere interaction process, such as surface wind speed, sensible heat flux, latent heat flux, friction velocity, and surface air temperature, among others, for short-term simulations. Particularly, the underestimation of sensible heat flux and overestimation of latent heat flux over heterogeneous terrain with area-weighted average scheme for aerodynamic parameters can be improved with the effective parameter scheme in the coastal regions, where the mean simulation error with the effective parameter scheme is about one-half of that with the average scheme for sensible heat flux and one-third for latent heat flux. [ABSTRACT FROM AUTHOR]