## 3 Results and Discussion  
### Climate, NPP, and Growth Projections  
Figure 2 provides temporal trends in GCM-simulated temperature, precipitation and NPP for each grid cell across the study area over the course of the current century. An increasing temperature trend was projected by all three models, although the rate of temperature increase varied slightly among the GCMs. Over the study area, the average rate of temperature increase was 0.76, 1.03 and 1.16 \({}^{\circ}\)C per decade as simulated by CanESM2, MIROC and MPI models, respectively (\(p\)-values < 0.05). Temperature projections by the MPI model were slightly lower than those of the other two models. There was also a slight increasing trend in the precipitation projections, though this is largely masked by significant spatial and temporal variability in the precipitation estimates. The average rate of precipitation increase was 0.33, 1.20 and 0.63 mm per decade as simulated by CanESM2, MIROC and MPI models. CanESM2 and MPI exhibited gradual increases in NPP over time of 1.53 and 2.17 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) per decade, respectively (\(p\)-values = 0.18 and 0.05), while MIROC-simulated NPP showed a curvilinear relationship over the course of the century (Figure 2f). [PERSON] et al. [41] reported an average NPP in eastern Canada of approximately 259 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) year\({}^{-1}\); the GCM-simulated NPP values reported here are smaller in comparison, reaching a maximum of \(\sim\)170 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) year\({}^{-1}\). This issue may arise because the GCMs use single class representations of land cover to describe large grid cells, which can lead to prediction errors [42]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Discount rate (\%) & 4, (2, 8) * \\ Establishment costs (\$-ha\({}^{-1}\)) & 500, (0, 200, 1000) * \\ Timber price (\$-m\({}^{-3}\)) & 20, (50) * \\ Price for carbon (\$-ton\({}^{-1}\)-CO\({}_{2}\)) & 5, (10, 20) * \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values used in the baseline scenario and sensitivity analyses. There was a clear spatial gradient in NPP across the study area, with higher average NPP values (82, 101 and 123 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)year\({}^{-1}\) for CanESM2, MICRO and MPI, respectively) in the southeast and lower values (43, 60 and 94 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)year\({}^{-1}\), respectively) in the northwest (Figure 3). Pearson's linear correlation analysis indicated a positive correlation between temperature and NPP as well as between precipitation and NPP as reported in previous studies [43; 44; 45]. ANOVA indicated that 43% of the variability in NPP is explained by temperature and precipitation (29.7% and 12.7% respectively). Experimental studies have also reported that carbon assimilation by black spruce may increase due to climate warming [46; 47], although it is possible that such a response may be inhibited by other warming-induced processes affecting plant growth, such as drought-related impacts on photosynthesis [48; 49; 50]. Similar to NPP, projections of merchantable wood volume were highest in the southern part of the study area and lowest in the north (Figure 4). The projected average yield volumes in 2100 for CanESM2, MIROC, and MPI in southern Ontario were 270, 277, and 253 m\({}^{3}\).ha\({}^{-1}\), respectively. In contrast, the projected average yield volumes in 2100 for CanESM2, MIROC, and MPI in northern  
Figure 2: Time series of simulated temperature, precipitation and Net Primary Production (NPP) from three global climate models; (**a–c**) CanESM2 (Canadian); (**d–f**) MIROC (Japanese); and (**g–i**) MPI (European) for IPCC future climate scenario RCP 8.5 for each grid (\(\sim\)10 km \(\times\) 10 km) over the study area. Each line represents a grid-specific value from 2006 to 2100. Fitted average lines are also shown. Ontario were 170, 170, and 156 m\({}^{3}\).ha\({}^{-1}\), respectively.