Evaluation of CMIP5 Earth System Models in Reproducing Leaf Area Index and Vegetation Cover over the Tibetan Plateau

The abilities of 12 earth system models（ESMs） from the Coupled Model Intercomparison Project Phase5（CMIP5） to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau（TP） were examined.The results show that most of the models tend to overestimate the observed leaf area index（LAI）and vegetation carbon above the ground,with the possible reasons being overestimation of photosynthesis and precipitation.The model simulations show a consistent increasing trend with observed LAI over most of the TP during the reference period of 1986-2005,while they fail to reproduce the downward trend around the headstream of the Yellow River shown in the observation due to their coarse resolutions.Three of the models：CCSM4,CESM1-BGC,and NorESM1-ME,which share the same vegetation model,show some common strengths and weaknesses in their simulations according to our analysis.The model ensemble indicates a reasonable spatial distribution but overestimated land coverage,with a significant decreasing trend（-1.48%per decade） for tree coverage and a slight increasing trend（0.58%per decade） for bare ground during the period 1950-2005.No significant sign of variation is found for grass.To quantify the relative performance of the models in representing the observed mean state,seasonal cycle,and interannual variability,a model ranking method was performed with respect to simulated LAI.INMCM4,bcc-csm-1.1m,MPI-ESM-LR,IPSL CM5A-LR,HadGEM2-ES,and CCSM4 were ranked as the best six models in reproducing vegetation dynamics among the 12 models.

MODELING GENETIC REGULATORY NETWORKS:A DELAY DISCRETE DYNAMICAL MODEL APPROACH

Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.