Impacts of maximum snow albedo and land cover changes on meteorological variables during winter in northeast China

The meteorological conditions in northeast China exhibit particular characteristics associated with snow cover in winter and dramatic land cover transformations. In this study, the Weather Research and Forecasting Model (WRF) is used to assess the influences of snow albedo and land cover changes on wind speed, temperature, relative humidity, and planetary boundary layer height (PBLH). Two new static geographical datasets, a maximum snow albedo dataset (MSA) developed by Beijing Normal University (BNU_MSA) and a land cover dataset developed by Tsinghua University (TU_LC), are employed for updating previous datasets in WRF. Besides, a case study for local anthropogenic heat (AH) parameters is conducted to examine its influences on temperature in winter. Modeled scenarios for 13 December 2017 to 15 January 2018 show that TU_LC yields a lower mean absolute error of 0.55 m/s for simulated wind speed, suggesting the usefulness of the TU_LC in modeled 10-m wind speed. The change in the wind speed is closely related to land cover transformations occurring over urban and forest regions. The spatially varying temperature agrees well with changes in the MSA. Simulation with updated AH parameters induces higher temperature over downtown Harbin and Changchun. Increasing PBLH is observed as compared with that using default datasets because of increasing temperature and heat fluxes induced by the decreases in MSA. The maximum differences of the wind speed and temperature between updated and default datasets in the verticle direction are confined within 600-m height above the ground surface. •The new land cover and maximum snow albedo datasets show good effects on different modeled meteorological variable as compared with WRF default datasets.•The changes of meteorological variables are significant in urban and forest regions in winter.•The maximum differences of the wind speed and temperature between updated and default datasets in the vertical direction are identified in 600 m height.