## 1 Introduction
IAP-DGVM simulations showed that the model could effectively reproduce the global distribution of shrubs, trees, grass, and bare soil, but it overestimated the fraction of temperate forests [21]. [PERSON] et al. [4] used a modified BIOME DGVM to simulate the biome distribution on the TP; vegetation maps confirmed that the modified model did a better job of simulating biome patterns on the TP (grid cell agreement 52%) than the original BIOME4 model (35%). [PERSON] et al. [22] evaluated the CLM3.0-DGVM model, showing that the CLM3.0-DGVM predicted a relatively high population density with small individual trees in boreal forests. DGVMs perform differently in simulating vegetation distribution, and the models still have certain defects that cause deviations in simulations. Vegetation phenology considers the influence of climate and other environmental factors and shows the time pattern of vegetation seasonal growth and decadency. [PERSON] et al. [23] validated leaf area index (LAI) estimates from multiple land surface models (LSMs) and found that all the LSMs overestimated the mean LAI and the length of the vegetation growing season, primarily due to late dormancy as a result of late summer phenology. A more complete vegetation phenology parameterization in DGVMs is significant for simulating the vegetation distribution and carbon fluxes accurately. With their development and improvement, vegetation models have been added to Earth system models to study future changes in the global climate and terrestrial ecosystems. Dynamic vegetation models play a major role in simulating carbon, nitrogen, and water cycle processes in the ecosystem. Improving their accuracy is one of the focuses of vegetation model developers. Due to the high altitude and unique natural environment, DGVMs still encounter great uncertainty in simulating the vegetation distribution on the TP. In this study, we validated the performance of vegetation distribution and ecosystem productivity simulations on the TP by using the CLM5.0-DGVM. The CLM5.0-DGVM runs the CLM biogeochemistry model in combination with the LPJ-derived DGVM introduced in CLM3.0. The accuracy of soil moisture and temperature simulations in the model is significant for improving the ability to simulate ecosystem productivity. Following the work of [PERSON] et al. (2019), in which the soil moisture and temperature simulations in CLM5.0 were improved for the TP, the revised soil water and heat transfer schemes were used in all the simulations in this paper. Firstly, we modified the original code in the establishment and survival module, replaced the default vegetation parameters with observations (referred to as the BGCDV_CTL experiment), and evaluated the performance of the BGCDV_CTL experiment. In addition, we modified the vegetation phenology parameterization in the BGCDV_CTL experiment to conduct a comprehensive sensitivity analysis for the modified dynamic vegetation model. Secondly, we evaluated the vegetation productivity and vegetation distribution results from both remote sensing and simulations. Lastly, based on the improved model and the remote sensing GPP products, we investigated vegetation, climate change, and the response of the vegetation to regional climate change on the TP. Following this introduction Section 1, the data and model are briefly described in Section 2. An evaluation of the CLM5.0-DGVM and the GPP remote sensing products is conducted in Section 3. Section 4 analyzes the response of the vegetation to regional climate change on the TP. The conclusions are included in Section 5.