## 3 Results
### Interannual Variability in NPP  
Past studies have indicated that the interannual variability of terrestrial NPP over different parts of the globe have been related strongly to the interannual variability of climatic parameters [8, 40]. The  
Figure 3: _Cont._  
spatial pattern of correlation between observed NPP and precipitation and NPP and temperature (Figure 4(a,b)) shows that NPP has a positive correlation with precipitation almost everywhere except in the Himalayan forest region in the north and negative correlation with temperature except in central eastern India and the Himalayan forest areas. The correlation shown in Figure 4(a,b) has contributions from correlations in trend and variability. In order to show only the contribution from variability, we subtract the trends before calculating the correlations (Figure 4(c,d)). Correlation with temperature is negative for temperature in most regions (Figure 4(c)). The spatial pattern of correlation between NPP and precipitation in Figure 4(d) is in good agreement with a recent study [18] which concluded that there is a significantly large correlation between NPP and precipitation. Our results are also similar to the findings by another study [11] which concluded that interannual variations in NPP are positively correlated with precipitation and negatively with temperature everywhere except high latitudes (\(>\)50\({}^{\circ}\)N) where NPP is enhanced by warming. The spatial pattern of the temperature trend over India from 1982 to 1999 also indicates negative correlation with the NPP trend (negative temperature trend in Figure 1 and positive NPP trend in Figure 2 in [8]). Figure 4: Correlation of NPP with (**a**) temperature and (**b**) precipitation. Correlation of interannual NPP anomaly (trend and mean removed) with (**c**) temperature anomaly and (**d**) precipitation anomaly. In order to cleanly separate interannual variability from the effects of trends, we subtracted the linear trend and mean from the observed time series of annual mean NPP, precipitation, temperature, soil water, solar radiation in India and global mean CO\({}_{2}\) to create interannual anomalies. The correlation and multivariate analysis is subsequently performed on these anomalies (Figures 2 and 3). We find that the correlation between observed NPP and precipitation anomalies is 0.35 (R\({}^{2}\) = 0.12) but the correlation is only \(-\)0.22 (R\({}^{2}\) = 0.05) between NPP and temperature anomalies (Figure 3; Table 1). This analysis clearly indicates that the interannual variations in NPP are positively correlated with precipitation and negatively but weakly with temperature. The correlation between NPP and CO\({}_{2}\) anomalies is also weak and negative at \(-\)0.27 (R\({}^{2}\) = 0.07) which suggests that interannual variations in NPP are inversely correlated to interannual variations in global mean CO\({}_{2}\).While the correlation with soil water is positive (Figure 2(j); r = 0.24; R\({}^{2}\) = 0.06), the correlation with solar radiation (Figure 2(i); r = \(-\)0.23, R\({}^{2}\) = 0.05) has the same sign as with temperature (Figure 2(f); r = \(-\)0.22; R\({}^{2}\) = 0.05), thus indicating the dependence of temperature on solar radiation. The correlation between NPP and multivariate anomaly is 0.38 (R\({}^{2}\) = 0.14) when the multivariate NPP anomaly is created using precipitation and temperature as independent variables (Table 1 and Figure 3). The correlation increases to 0.48 (R\({}^{2}\) = 0.23) when CO\({}_{2}\) is also included as an independent variable along with precipitation and temperature (Table 1).The correlation increases marginally when soil water (r = 0.49, R\({}^{2}\) = 0.23) is added and further when solar radiation is added (r = 0.56, R\({}^{2}\) = 0.31). The main inference on the interannual anomalies is that precipitation variability is the main driver for interannual variations in NPP in India since precipitation anomaly has the largest correlation with NPP anomaly in the single variable regression analysis (Table 1).