Response of surface air temperature to the change of leaf area index in the source region of the Yellow River by the WRF model
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Leaf area index (LAI) is a crucial land-atmosphere exchange parameter. LAI change can cause a variation of other land surface parameter. In this research, three experiments were conducted to investigate the impact of LAI and albedo change on surface air temperature in the source region of the Yellow River (SRYR) using the Weather Research and Forecasting (WRF) model. Three experiments used the same settings, initial and boundary conditions except for the LAI and albedo data. The control simulation (CTL) used the WRF model climatological LAI data, the second simulation (LAI) used the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data, and the third simulation (LAIALB) used the MODIS LAI and MODIS albedo. The results show MODIS LAI is greater by 34.5% than WRF climatological LAI, and the MODIS albedo is lower by 24.3% than WRF climatological albedo over the whole growing season of 2006 in SRYR. All the experiments can simulate the surface air temperature (Ta) spatial distribution characteristics, but underestimate the values of 1.3 °C in CTL experiment and 0.6 °C in LAIALB experiment in SRYR. The simulated Ta by LAI experiment is lower 0.1 °C than by the CTL experiment, but the simulated Ta by the LAIALB experiment is obviously higher 0.6 °C than the CTL experiment. The LAI experiment shows a cooling effect because the higher MODIS LAI decreases the canopy resistance, which induces a positive average 2.1 Wm−2 latent heat flux (LH) and a negative average − 2.1 Wm−2 sensible heat flux (Hs). The LAIALB experiment presents a warming effect because of low MODIS albedo comparing WRF albedo, which changes the radiation components and results in an obvious negative − 14.0 Wm−2 upward short wave radiation, a positive 11.7 Wm−2 net radiation, and a positive 10.9 Wm−2 heat flux. In fact, more precipitation produces more snow and high surface albedo in the WRF model, which results in a cold temperature bias in SRYR.
The authors thank Professor Xianhong Meng and Doctor Lin Zhao for providing the help of remote sensing data and the model initial data processing, and the authors also acknowledge computing resources and time on the Supercomputing Center of Northwest Institute of Eco-Environment and Resources of Chinese Academy of Sciences.
This research was financially supported by the National Key R&D Program of China (2017YFC1502101) and the National Natural Science Foundation of China (91537105, 91537211, 41205076, 41805079, 91637107, GZ1259).
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