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

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 6275ha boreal forest landscape in Eastern Canada. Process model results showed that higher air temperature (T a ) caused gains in CO2 uptake in spring, but losses in summer, both of which were corroborated by CO2 fluxes measured by eddy covariance (EC). Seasonal changes in simulated CO2 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 CO2 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 450Mha North American boreal forest ecosystems.