## 3 Results and Discussion
### Limitations and Potential Further Studies  
Based on the analysis of the four terrestrial biosphere models and the satellite-based NDVI data, we found that the detected NDVI trends are mostly consistent with model-based GPP trends in Asia. These detected NDVI and GPP trends were analyzed to identify the important processes driving changes in the NDVI and GPP using the terrestrial biosphere models.  
To fill the gaps in the results from the satellite-based NDVI and model-based GPP, additional analyses are required. First, the terrestrial biosphere models need to be further improved to reproduce observed carbon fluxes in Asia [69,72] and other regions, for example, North America [73] and Europe [74]. Further refinements to these models using ground-based observation networks [75] may improve the consistency between the modeled GPP and the satellite-based NDVI and inter-model consistency. Second, an analysis of human-induced disturbances and land-use changes, including afforestation and agriculture, should be assessed in future studies because afforestation in China [22] and the increased irrigation area and fertilization use in India [28] have been found to contribute to increases in terrestrial GPP and the NDVI. In addition, the terrestrial biosphere models used in this study do not include disturbance effects or land-use changes, including plantation and deforestation in the tropical Asia. Third, to assess each model's sensitivity to climate anomalies, terrestrial GPP anomalies in response to certain anomalous climate events should be analyzed. For example, the Boreal Eurasia South East region experienced cooler and cloudier meteorological conditions in the summer of 2003, corresponding to negative anomalies in GPP. All of the models showed the same negative GPP anomalies in 2003 (Figure 4d). The mechanisms should be carefully assessed using observations and models to determine if each model reproduces GPP anomalies through a similar mechanism. Fourth, a further assessment of model-data mismatches should be conducted to clarify the potential issues for both models and satellite-based observations. In this study, we demonstrated several model and data mismatches in Section 3.2. Identifying the mismatch causes may improve the performances of terrestrial biosphere models.  
A quantitative comparison of the models and data are also required in the further studies even though current qualitative assessments showed consistent variations in modeled GPPs and satellite-derived NDVI. To date, satellite-based vegetation indices have often been used for the evaluation of terrestrial biosphere models in continental-scale analysis [33]. A possible method to conduct a quantitative comparison is to use GPPs of empirically upscaled products that rely on the dense network of eddy-covariance observations and satellite-based data [12]. The eddy-covariance network dataset in Asia is insufficient to cover various ecosystems in Asia, especially in Siberia and Southeast and South Asia [70]; further improvements in the dataset are required.  
An improved solar radiation dataset is required to interpret the effects of solar radiation on the modeled GPP. In this study, we used empirically estimated surface radiation based on the MTCLIM model with corrections using satellite-based solar radiation data during the 2000-2010 period. The method relies on the assumption that variations in diurnal temperature changes are related to incoming solar radiation, which may produce large uncertainties in estimating solar radiation. Other satellite-based radiation data (e.g., SRB data; [76]) cover the period from 1983 to 2007; no data are available for the past 30 years. Solar radiation is one of the important variables that determines GPP [17]; improved solar radiation data are thus required to improve the analysis of GPP.