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The ability of FLake, WRF-Lake, and CoLM-Lake models in simulating the thermal features of Lake Nam Co in Central Tibetan Plateau has been evaluated in this study. All the three models with default settings exhibited distinct errors in the simulated vertical temperature profile. Then model calibration was conducted by adjusting three (four) key parameters within FLake and CoLM-Lake (WRF-Lake) in a series of sensitive experiments. Results showed that each model's performance is sensitive to the key parameters and becomes much better when adjusting all the key parameters relative to tuning single parameter. Overall, setting the temperature of maximum water density to 1.1 degrees C instead of 4 degrees C in the three models consistently leads to improved vertical thermal structure simulation during cold seasons; reducing the light extinction coefficient in FLake results in much deeper mixed layer and warmer thermocline during warm seasons in better agreement with the observation. The vertical thermal structure can be clearly improved by decreasing the light extinction coefficient and increasing the turbulent mixing in WRF-Lake and CoLM-Lake during warm seasons. Meanwhile, the modeled water temperature profile in warm seasons can be significantly improved by further replacing the constant surface roughness lengths by a parameterized scheme in WRF-Lake. Further intercomparison indicates that among the three calibrated models, FLake (WRF-Lake) performs the best to simulate the temporal evolution and intensity of temperature in the layers shallower (deeper) than 10m, while WRF-Lake is the best at simulating the amplitude and pattern of the temperature variability at all depths.