Response of North American ecosystem models to multi-annual periodicities in temperature and precipitation

Ecosystem models typically use input temperature and precipitation data generated stochastically from weather station means and variances. Although the weather station data are based on measurements taken over a few decades, model simulations are usually on the order of centuries. Consequently, observed periodicities in temperature and precipitation at the continental scale that have been correlated with largscale forcings, such as ocean-atmosphere dynamics and lunar and sunspot cycles, are ignored. We investigated how these natural climatic fluctuations affect aboveground biomass in ecosystem models by incorporating some of the more pronounced continental-scale cycles in temperature (4, 11, 80, 180 year periods) and precipitation (11 and 19 year periods) into models of three North American forests (using LINKAGES) and one North American grassland (using STEPPE). Even without inclusion of periodicities in climate, long-term dynamics of these models were characterized by internal frequencies resulting from vegetation birth, growth and death processes. Our results indicate that long-term temperature cycles result in significantly lower predictions of forest biomass than observed in the control case for a forest on a biome transition (northern hardwoods/boreal forest). Lower-frequency, higher-amplitude temperature oscillation caused amplification of forest biomass response in forests containing hardwood species. Shortgrass prairie and boreal ecosystems, dominated by species with broad stress tolerance ranges, were relatively insensitive to climatic oscillations. Our results suggest periodicities in climate should be incorporated within long-term simulations of ecosystems with strong internal frequencies, particularly for systems on biome transitions.