Sun Shuai, Shi Chunxiang, Liang Xiao, et al. Assessment of ground temperature simulation in China by different land surface models based on station observations. J Appl Meteor Sci, 2017, 28(6): 737-749. DOI:  10.11898/1001-7313.20170609.
Citation: Sun Shuai, Shi Chunxiang, Liang Xiao, et al. Assessment of ground temperature simulation in China by different land surface models based on station observations. J Appl Meteor Sci, 2017, 28(6): 737-749. DOI:  10.11898/1001-7313.20170609.

Assessment of Ground Temperature Simulation in China by Different Land Surface Models Based on Station Observations

DOI: 10.11898/1001-7313.20170609
  • Received Date: 2017-04-18
  • Rev Recd Date: 2017-09-21
  • Publish Date: 2017-11-30
  • As an important physical quantity in the land surface process, the ground temperature plays an important role in climate change research, agricultural production and ecological environment. A set of simulation experiments are carried out, in which ground temperature are simulated by Community Land Model 3.5 (CLM3.5) land surface model and Noah-Multi Parameterization Land Surface Model (Noah-MP) of three different parameterization schemes, forced by China Meteorological Administration Land Data Assimilation System (CLDAS) atmosphere forcing data containing high-quality temperature, pressure, humidity, wind speed, precipitation and solar shortwave radiation. The different model-simulated ground temperature is verified by 2000 national ground temperature observation stations of China Meteorological Administration from 2009 to 2013. Results show that errors of different model-simulated ground temperature compared with observations behave seasonal fluctuations from the error analysis of time series. And the ground temperature simulated by CLM3.5 land surface model and Noah-MP land surface model can better represent the spatial distribution of ground temperature of China in seasonal climate state. The ground temperature is underestimated in general, and the underestimation in spring and autumn is smaller than that in summer and winter. On the spatial distribution, the error of the model-simulated ground temperature in the eastern China is smaller than that in the western China, and in the northeastern China and northern Xinjiang the error is even greater. Three different parameterization schemes of Noah-MP land surface model are selected to compare the simulation result. Results show that when the non-dynamic vegetation scheme remain unchanged, considering different radiation transferring schemes, the two-stream approximation radiative transferring scheme considering vegetation coverage of Noah-MP land surface model performs better than the radiative transferring scheme considering the solar altitude angle and vegetation 3D structures of Noah-MP surface land model. When the default two-stream approximation radiative transferring scheme in Noah-MP land model doesn't change, considering the dynamic vegetation scheme of Noah-MP land surface model, the result shows that the ground temperature choosing the dynamic vegetation scheme of Noah-MP land surface model is better than the non-dynamic vegetation scheme named of Noah-MP land model. Above all, the ground temperature simulated by the dynamic vegetation scheme of Noah-MP land surface model is better than the other two parameterization schemes of Noah-MP land model and the CLM3.5 land surface model.
  • Fig. 1  The spatial distribution of ground temperature simulated by Noah-MP2 in the seasonal average

    Fig. 2  Bias between the simulated and the observed daily average ground temperatures

    Fig. 3  The statistics histogram of bias between the simulated and the observed daily average ground temperatures

    Fig. 4  Root mean square error between the simulated and the observed daily average ground temperatures

    Fig. 5  The statistics histogram of root mean square error between the simulated and the observed daily average ground temperatures

    Fig. 6  The seasonal bias between the simulated by Noah-MP2 and the observed daily average ground temperatures

    Fig. 7  Bias between the simulated and the observed ground temperatures

    Fig. 8  The simulated and the observed daily average ground temperatures from 2009 to 2013

    Fig. 9  Comparisons between the simulated and the observed daily average ground temperatures

    (a)bias, (b)root mean square error, (c)correlation coefficient

    Fig. 10  Comparisons between the simulated and the observed monthly average ground temperatures

    (a)bias, (b)root mean square error, (c)correlation coefficient

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    • Received : 2017-04-18
    • Accepted : 2017-09-21
    • Published : 2017-11-30

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