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Snow cover over the Tibetan Plateau (TP) plays an important role in Asian climate. State‐of‐the‐art models, however, show significant simulation biases. In this study, we assess the main uncertainty associated with model physics in snow cover modeling over the TP using ground‐based observations and high‐resolution snow cover satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FengYun‐3B (FY3B). We first conducted 10‐km simulations using the Noah with multiparameterization (Noah‐MP) land surface model by optimizing physics‐scheme options, which reduces 8.2% absolute bias of annual snow cover fraction (SCF) compared with the default model settings. Then, five SCF parameterizations in Noah‐MP were optimized and assessed, with three of them further reducing the annual SCF biases from around 15% to less than 2%. Thus, optimizing SCF parameterizations appears to be more important than optimizing physics‐scheme options in reducing the uncertainty of snow modeling. As a result of improved SCF, the positive bias of simulated surface albedo decreases significantly compared to the GLASS albedo data, particularly in high‐elevation regions. This substantially enhances the absorbed solar radiation and further reduces the annual mean biases of ground temperature from −3.5 to −0.8°C and snow depth from 4.2 to 0.2 mm. However, the optimized model still overestimates SCF in the western TP and underestimates SCF in the eastern TP. Further analysis using a higher‐resolution (4 km) simulation driven by topographically adjusted air temperature shows slight improvement, suggesting a rather limited contribution of the finer‐scale land surface characteristics to SCF uncertainty. Key Points: Optimizing Noah‐MP physics‐scheme options reduces about 8% absolute bias of the annual snow cover fraction (SCF) compared to MODIS SCFOptimizing SCF parameterizations further reduces around 13% SCF absolute bias, which is higher than optimizing physics‐scheme optionsContributions of high‐resolution topographically adjusted air temperature to the SCF simulation uncertainty are limited [ABSTRACT FROM AUTHOR]

Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions. Key Points: Convection‐permitting WRF results capture the observed precipitation, SWE, and snow depth in western and northern portions of WUS mountainsAll datasets produce large negative bias in precipitation, SWE, and snow depth in interior WUS mountains with fair performance for WRFConvection‐permitting modeling has added values in providing precipitation for snow simulations over mountains without enough observations [ABSTRACT FROM AUTHOR]

Abstract: We have augmented the existing capabilities of the integrated Weather Research and Forecasting (WRF)‐urban modeling system by coupling three urban canopy models (UCMs) available in the WRF model with the new community Noah with multiparameterization options (Noah‐MP) land surface model (LSM). The WRF‐urban modeling system's performance has been evaluated by conducting six numerical experiments at high spatial resolution (1 km horizontal grid spacing) during a 15 day clear‐sky summertime period for a semiarid urban environment. To assess the relative importance of representing urban surfaces, three different urban parameterizations are used with the Noah and Noah‐MP LSMs, respectively, over the two major cities of Arizona: Phoenix and Tucson metropolitan areas. Our results demonstrate that Noah‐MP reproduces somewhat better than Noah the daily evolution of surface skin temperature and near‐surface air temperature (especially nighttime temperature) and wind speed. Concerning the urban areas, bulk urban parameterization overestimates nighttime 2 m air temperature compared to the single‐layer and multilayer UCMs that reproduce more accurately the daily evolution of near‐surface air temperature. Regarding near‐surface wind speed, only the multilayer UCM was able to reproduce realistically the daily evolution of wind speed, although maximum winds were slightly overestimated, while both the single‐layer and bulk urban parameterizations overestimated wind speed considerably. Based on these results, this paper demonstrates that the new community Noah‐MP LSM coupled to an UCM is a promising physics‐based predictive modeling tool for urban applications. [ABSTRACT FROM AUTHOR]

Abstract: The air conditioning (AC) electric loads and their impacts on local weather over Beijing during a 5 day heat wave event in 2010 are investigated by using the Weather Research and Forecasting (WRF) model, in which the Noah land surface model with multiparameterization options (Noah‐MP) is coupled to the multilayer Building Effect Parameterization and Building Energy Model (BEP+BEM). Compared to the legacy Noah scheme coupled to BEP+BEM, this modeling system shows a better performance, decreasing the root‐mean‐square error of 2 m air temperature to 1.9°C for urban stations. The simulated AC electric loads in suburban and rural districts are significantly improved by introducing the urban class‐dependent building cooled fraction. Analysis reveals that the observed AC electric loads in each district are characterized by a common double peak at 3 p.m. and at 9 p.m. local standard time, and the incorporation of more realistic AC working schedules helps reproduce the evening peak. Waste heat from AC systems has a smaller effect (~1°C) on the afternoon 2 m air temperature than the evening one (1.5~2.4°C) if AC systems work for 24 h and vent sensible waste heat into air. Influences of AC systems can only reach up to ~400 m above the ground for the evening air temperature and humidity due to a shallower urban boundary layer than daytime. Spatially varying maps of AC working schedules and the ratio of sensible to latent waste heat release are critical for correctly simulating the cooling electric loads and capturing the thermal stratification of urban boundary layer. [ABSTRACT FROM AUTHOR]