Extended Application of the CNOP-P method in the Inner Mongolia using the Common Land Model
Bo Wang1,2,4, Zhenhua Huo3,4, Yujing Yuan5, and Shang Wu21Institute of Applied Mathematics, Henan University, 475004 Kaifeng, China 2School of Mathematics and Statistics, Henan University, 475004 Kaifeng, China 3University of Chinese Academy of Sciences, 100049, Beijing, China 4LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029, Beijing, China 5School of Mathematics, Shandong University, 250100 Jinan, China
Received: 19 Jan 2016 – Accepted for review: 22 Jan 2016 – Discussion started: 27 Jan 2016
Abstract. An extension method of the conditional nonlinear optimal perturbation about parameter (CNOP-P) is adopted to study the soil parameter optimization for the Hulunbeier Steppe within the common land model (CoLM) with the differential evolution (DE) method. Using National Center for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project-II (AMIP-II) 6-hourly Reanalysis Gaussian Grid data and National Meteorological Center (NMC) Reanalysis 6-hourly surface fluxes data, three experiments (I and II) were designed to study the impact of the percentages of sand and clay of the shallow soil in CoLM on simulating the shallow soil moisture. To study the shallow soil moisture and the latent heat flux simultaneously, experiment (III) is designed. The optimal parameters obtained by the extended CNOP-P method are used to predict the shallow soil moisture in the following month. In all the three experiments, after optimization stage, the optimal soil parameters could significantly improve the simulation ability of CoLM in the Inner Mongolia to the shallow soil moisture at the stage of prediction; the optimal parameters attained by the double-parameter optimal experiment could make CoLM simulate the shallow soil moisture better than the single-parameter optimal experiment in the optimization slot. Moreover, the results of experiments (I and II) justify the conclusion that the more accurate the atmospheric forcing data and observation data are, the more effective the results of optimization will be.
Wang, B., Huo, Z., Yuan, Y., and Wu, S.: Extended Application of the CNOP-P method in the Inner Mongolia using the Common Land Model, Nonlin. Processes Geophys. Discuss., doi:10.5194/npg-2016-13, in review, 2016.