## 4 Response of Vegetation to Regional Climate Change on the TP
### Response of GPP to Climate Change  
Figure 8 shows partial correlations between different GPP products and precipitation. The results indicated that the GLASS, NIRv, FLUXCOM, FLUXCOM1, and MODIS GPP products did not significantly correlate with precipitation changes in most regions of the TP. Compared to the sub-humid regions of the TP, the remote sensing GPP products showed a relatively high correlation in the semi-arid regions of the TP, especially the FLUXCOM1 GPP product. The GPP simulated using CLM5.0-DGVM significantly correlated with precipitation changes in most regions of the TP. Among all the remote sensing products, the GLASS and FLUXCOM1 GPP products were most sensitive to precipitation in the semi-arid regions.  
Figure 9 shows partial correlations between different GPP products and temperature. Limited by the period covered by the FLUXCOM and MODIS GPP products, there was  
Figure 8: Partial correlations between GPP and precipitation.  
Figure 7: Trend of GPP from May to October on the TP.  
no significant correlation between GPP and temperature for these products. As shown in Figure 9, the GLASS GPP product significantly correlated with temperature changes in most regions of the TP. Partial correlations between the GLASS GPP product and temperature show that the increase in GPP was mainly caused by climate warming on the TP. The NIRv and FLUXCOM1 GPP data show positive correlations with temperature in the sub-humid and semi-arid regions of the TP, while the CLM5.0-DGVM GPP simulation shows a positive correlation in the arid regions of the TP. The overall results indicate that the increase in the remote sensing GPP was mainly caused by temperature, while the increase in the GPP simulated in CLM5.0-DGVM was mainly caused by precipitation in most regions of the TP.  
Figure 10 shows the annual change in the GPP anomalies based on the GLASS, NIRv, and FLUXCOM1 products, as well as the BGCDV_NEW experiment, with precipitation and temperature anomalies averaged over the TP. The correlations between the GLASS, NIRv, FLUXCOM, and BGCDV_NEW GPP anomalies and the temperature anomaly were 0.83, 0.80, 0.53, and 0.57, respectively, showing significant positive correlations between the GPP calculated by these products and temperature. The variances in precipitation and temperature were weaker for the GLASS and NIRv GPP products than in the GPP simulation. The correlations between the remote sensing product GPP anomalies and the temperature anomalies were greater than the correlations between the remote sensing GPP products and precipitation. Compared to precipitation, the remote sensing GPP products were more sensitive to temperature. The correlations between the GPP simulation from the BGCDV_NEW experiment and precipitation and temperature were 0.52 and 0.57, respectively. As shown in Figure 10, the correlation between the BGCDV_NEW experiment GPP simulation and precipitation was smaller than that of the GLASS GPP. In addition, the variances in the remote sensing GPPs were weaker than in the simulated GPP from the BGCDV_NEW experiment. The correlation between the BGCDV_NEW experiment GPP and temperature was smaller than those of the GLASS and NIRv GPPs, which indicates that the GLASS and NIRv GPPs were more sensitive to temperature than the simulated GPP from the BGCDV_NEW experiment. A negative precipitation anomaly was recovered from 1998, while the simulated GPP from the BGCDV_NEW experiment dramatically increased from 1996, which mainly caused the significantly positive precipitation anomaly in 1996.  
Figure 9: Partial correlations between GPP and temperature.