Incorporating weather sensitivity in inventory-based estimates of boreal forest productivity: A meta-analysis of process model results

Abstract Weather effects on forest productivity are not normally represented in inventory-based models for carbon accounting. To represent these effects, a meta-analysis was conducted on modeling results of five process models ( ecosys , CN-CLASS , Can-IBIS , InTEC and TRIPLEX ) as applied to a 6275 ha boreal forest landscape in Eastern Canada. Process model results showed that higher air temperature ( T a ) caused gains in CO 2 uptake in spring, but losses in summer, both of which were corroborated by CO 2 fluxes measured by eddy covariance (EC). Seasonal changes in simulated CO 2 fluxes and resulting inter-annual variability in NEP corresponded to those derived from EC measurements. Simulated long-term changes in above-ground carbon (AGC) resulting from modeled NEP and disturbance responses were close to those estimated from inventory data. A meta-analysis of model results indicates a robust positive correlation between simulated annual NPP and mean maximum daily air temperature ( T amax ) during May–June in four of the process models. We therefore, derived a function to impart climate sensitivity to inventory-based models of NPP: NPP′ i  = NPP i  + 9.5 ( T amax −16.5) where NPP i and NPP′ i ; are the current and temperature-adjusted NPP, 16.5 is the long-term mean T amax during May–June, and T amax is that for the current year. The sensitivity of net CO 2 exchange to T a is nonlinear. Although, caution should be exercised while extrapolating this algorithm to regions beyond the conditions studied in this landscape, results of our study are scalable to other regions with a humid continental boreal climate dominated by black spruce. Collectively, such regions comprise one of the largest climatic zones in the 450 Mha North American boreal forest ecosystems.