Sciences in Cold and Arid Regions ›› 2018, Vol. 10 ›› Issue (5): 379-391.doi: 10.3724/SP.J.1226.2018.00379

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Numerical simulation of the climate effect of high-altitude lakes on the Tibetan Plateau

YinHuan Ao1,ShiHua Lyu2,3,ZhaoGuo Li1,*(),LiJuan Wen1,Lin Zhao1   

  1. 1 Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-En-vironment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2 Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China
    3 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China
  • Received:2018-01-16 Accepted:2018-08-16 Online:2018-11-19 Published:2018-11-21
  • Contact: ZhaoGuo Li
  • Supported by:
    This research was supported by the National Natural Science Foundation of China (Nos. 91637107, 41605011, 41675020, 91537214 and 41775016), Sino-German Research Project (No. GZ1259), the Science and Technology Service Network Initiative of CAREERI, Chinese Academy of Sciences (No. 6516-71001).


Lakes regulate the water and heat exchange between the ground and the atmosphere on different temporal and spatial scales. However, studies of the lake effect in the high-altitude Tibetan Plateau (TP) rarely have been performed until recently, and little attention has been paid to modelling of frozen lakes. In this study, the Weather Research and Forecasting Model (WRF v. 3.6.1) is employed to conduct three numerical experiments in the Ngoring Lake Basin (the original experiment, an experiment with a tuned model, and a no-lake experiment) to investigate the influences of parameter optimization on the lake simulation and of the high-altitude lake on the regional climate. After the lake depth, the roughness lengths, and initial surface temperature are corrected in the model, the simulation of the air temperature is distinctly improved. In the experiment using a tuned model, the simulated sensible-heat flux (H) is clearly improved, especially during periods of ice melting (from late spring to early summer) and freezing (late fall). The improvement of latent-heat flux (LE) is mainly manifested by the sharp increase in the correlation coefficient between simulation and observation, whereas the improvement in the average value is small. The optimization of initial surface temperature shows the most prominent effect in the first year and distinctly weakens after a freezing period. After the lakes become grassland in the model, the daytime temperature clearly increases during the freezing and melting periods; but the nocturnal cooling appears in other stages, especially from September to October. The annual mean H increases by 6.4 times in the regions of the Ngoring Lake and the Gyaring Lake, and the LE declines by 56.2%. The sum of H and LE increases from 71.2 W/m2 (with lake) to 84.6 W/m2 (no lake). For the entire simulation region, the sum of H and LE also increases slightly. After the lakes are removed, the air temperature increases significantly from June to September over the area corresponding to the two lakes, and an abnormal convergence field appears; at the same time, the precipitation clearly increases over the two lakes and surrounding areas.

Key words: Lake-surface temperature, roughness length, turbulent flux, Ngoring Lake, Tibetan Plateau

Table 1

Parameters of grids in the nested domains"

Grid domain Central coordinates Number of point grid Horizontal grid size (km) Time step (s)
1 97.4°E, 34.7°N 70×60 50 300
2 97.4°E, 34.7°N 81×71 10 60
3 97.4°E, 34.7°N 101×91 2 12

Figure 1

Domain of the WRF model simulations (a) and the horizontal discretization of the d03 domain (b): the blue lines (a) and the green grids (b) represent the lakes. Three stations (TS, LS, and MS) are marked in Figure 1b "

Figure 2

Original (a) and improved (b) lake depths of the Gyaring Lake (left) and the Ngoring Lake (right) in the d03 domain in the WRF model"

Figure 3

Observed and simulated half-monthly average air temperature and wind speed for the Ngoring Lake (a, c) and the Madoi Station (b, d) in Case 1 and Case 2"

Table 2

Correlation coefficient, root mean square error (RMSE), and mean of air temperatureand wind speed for the simulation and observation"

Observation Case 1 Case 2
Simulation Correlation RMSE Simulation Correlation RMSE
Lake Temperature ?0.13 ?1.09 0.97 2.25 ?0.83 0.97 2.33
Wind speed 4.51 4.73 0.32 1.40 4.84 0.32 1.37
Madoi Temperature ?0.19 ?1.59 0.98 2.09 ?1.54 0.98 2.09
Wind speed 2.75 4.67 0.20 1.34 4.65 0.19 1.36

Figure 4

Observed and simulated daily precipitation in Case 1 and Case 2 for the Ngoring Lake (a) and the Madoi Station (b)"

Figure 5

Simulated precipitation amount in the summer and fall seasons and the wind field at 550 hPa in Case 2 (a, c) and the difference between Case 1 and Case 2 (b, d)"

Figure 6

Observed and simulated sensible-heat (H) and latent-heat flux (LE) on the lake surface for Case 1 and Case 2, and the daily average lake surface temperature (LST)"

Figure 7

Simulated average temperature and wind speed (half-month average) in Case 2 and the difference between Case 2 and Case 3 (△Ta: Case 3–Case 2). The solid circle and open circle indicate Case 2 and Case 3, respectively, and the triangle symbols show the difference between two cases"

Figure 8

Monthly average sensible-heat and latent-heat fluxes in Case 2 and Case 3. The lines are the H and LE in the d03 region, and the columns are the H and LE in the area of the two lakes"

Figure 9

Simulated accumulative precipitation amount in Case 2 for 2011 (a) and 2012 (c) (from June 1 to September 30) and the difference between Case 3 and Case 2 during the same period (b and d)"

Figure 10

Simulated air temperature and wind field at 550 hPa for 2011 (a) and 2012 (c) in Case 2 (from June 1 to September 30), and the difference between Case 3 and Case 2 during the same period (b, d)"

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