Sources of variability in net primary production predictions at a regionalscale: A comparison using PnET-II and TEM 4.0 in northeastern US forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimat es of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest tha t attention can be focused more clearly on understanding the relative stren gths of predicted net primary productivity (NPP) limitation by energy, wate r, and nutrients. We evaluate the sources of variability among model predic tions of NPP with a regional-scale comparison between estimates made by PnE T-II (a forest ecosystem process model previously applied to the northeaste rn region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predict ions made by PnET-II and TEM were remarkably similar, and at the biome leve l, model predictions agreed fairly well with NPP estimates developed from f ield measurements. However, TEM. 4.0 predictions were more sensitive to reg ional variations in temperature as a result of feedbacks between temperatur e and belowground N availability. In PnET-II, the direct link between trans piration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stres s was relieved substantially when soil water holding capacity (WHC) was inc reased. Increasing soil WHC had little effect on TEM 4.0 predictions becaus e soil water storage was already sufficient to meet plant demand with basel ine WHC values, and because predicted N availability under baseline conditi ons in this region was not limited by water. Because NPP predictions were c losely keyed to forest cover type, the relative coverage of low- versus hig h-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell s ize and differences in the methods used to aggregate from fine to coarse re solution were important to NPP predictions insofar as they changed the rela tive proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP p redictions will result if models applied to this region use land cover inpu t data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate an d soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic inte ractions between water stress, N availability, and forest productivity in t his region will enable models to make more accurate predictions of future c arbon stocks and fluxes.