## 3 Results
### Trends in NPP
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Single Variable** & **Multivariate** & **Single Variable** & **Multivariate** & **Single Variable** & **Multivariate** \\
**Linear** & **Regression of** & **Regression of** & **Regression of** & **Regression of** & **Regression of** \\
**Regression of** & **NPP** & **Interannual NPP** & **Interannual NPP** & **Seasonal NPP** & **Seasonal NPP** \\ NPP & & **Anomaly** & **Anomaly** & **Anomaly** & **Anomaly** \\ \hline
**0.18 (0.03) [P]** & 0.50 (0.25) (P and T) & 0.35 (0.12) (P) & 0.38 (0.14) (P and T) & 0.52 (0.27) (P) & 0.65 (0.42) (P and T) \\
**0.42 (0.18) \{T\}** & 0.80 (0.63) & \(-\)0.22 (0.05) \{T\} & 0.48 (0.23) & 0.01 (0.0001) & 0.95 (0.91) \\  & \{P, T and CO\({}_{2}\}\) & & \{P, T and CO\({}_{2}\}\) & \{T\} & \{SW and SR\} \\
**0.76 (0.58) (CO\({}_{2}\))** & 0.80 (0.64) & \(-\)0.27 (0.07) & 0.49 (0.23) & 0.95 (0.90) \{SW\} & 0.98 (0.96) \\  & [P, T, CO\({}_{2}\)and SW] & [CO\({}_{2}\)] & [P, T, CO\({}_{2}\)and SW] & & & \\ \hline
**-0.61 (0.37) \{SW\}** & 0.80 (0.65) & 0.24 (0.06) \{SW\} & 0.56 (0.31) & \(-\)0.63 (0.40) & 0.98 (0.96) \\  & \{P, T, CO\({}_{2}\), SWand & & \{P, T, CO\({}_{2}\), SWand & & \{SR\} & \{P, T, SW and SR\} \\  & SR\} & & SR\} & \\ \hline
**-0.72 (0.51) (SR)** & & \(-\)0.23 (0.05) & & & \\  & \{SR\} & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Correlation coefficient (r) between observed NPP and climate variables and multivariate NPP for various cases. R\({}^{2}\) (coefficient of determination) values are given in parenthesis. P, T, CO\({}_{2}\), SW and SR in curly brackets refer to the independent climate variables precipitation, temperature, global mean CO\({}_{2}\), soil water in the top 3.4 m soil and downward solar radiation at the surface, respectively in Equation (1). We caution that a statistical analysis like this cannot establish firm casual relationships and thus we cannot make robust attribution statements. Recently, it was found that the increase in NPP in India is mainly contributed by enhancements in cropland areas [18]. Therefore, it is possible that land use  
Figure 2: Temporal evolutions of domain-averaged NPP and (**a**) temperature, (**b**) and precipitation, (**c**) global-mean atmospheric CO\({}_{2}\), (**d**) downward solar radiation at the surface and (**e**) soil moisture in the top 3.4 m soil for 1982–2006. Panels (**f-j**) are similar to (a–e) except the variables are now interannual anomalies (_i.e._, mean and trends are removed from the original data). In each panel, the correlation (r) between the two time series is also shown. management practices may have also played an important role in NPP increase. We discuss in detail the role of land use management practices on NPP increase in forest and non-forest areas in Section 3.5. When precipitation and temperature are used as independent variables in Equation (1), the correlation between the observed and multivariate NPP is 0.50 and R\({}^{2}\) is 0.25 (Figure 3, Table 1) which indicates that a combination of temperature and precipitation have a stronger control on NPP than either precipitation or temperature individually. The correlation and R\({}^{2}\) increase to 0.80 and 0.63, respectively (Figure 3, Table 1) when multivariate NPP is generated from precipitation, temperature and CO\({}_{2}\) confirming the stronger relationship between observed NPP and CO\({}_{2}\). The correlation and R\({}^{2}\) do not increase any further when soil water and surface solar radiation are used in the multivariate regression (Table 1). Figure 3: Scatter plots between the annual-mean observed NPP over India and (**a**) temperature, (**b**) precipitation, (**c**) CO\({}_{2}\) concentration, (**d**) multivariate NPP using precipitation and temperature as independent variables and (**e**) multivariate NPP using precipitation, temperature and CO\({}_{2}\) as independent variables.