What factor generates greater uncertainty in predicting carbon flux for North America: climate characterization or model choice?

Numerous efforts have begun to characterize a variety of sources of uncertainty in carbon flux estimates from both forward-modeling and inverse modeling approaches. One source of uncertainty is structural, created by the variety of approaches taken to select and characterize the most important biogeochemical processes. To begin to explore this structural uncertainty, we have used an ensemble of well-known models including CASA (Potter et al. (1993), version 2003.04.29), LPJ (Sitch et al. (2003), version 3.1.1-0.9.02), and BGC (White et al. (2000), version 5.0) with a consistent set of inputs for the period 1982-2006 for North America. Initially, the ensemble was run using input climate data interpolated from maximum, minimum and dew-point temperatures, precipitation, vapor pressure deficit, and incident daily solar radiation at stations from the National Climate Data Center's Global Summary of the Day, incorporating on average about 1900 stations. NCDC's Cooperative Summary of the Day data, available over the United States only, yielded a combined data set of approximately 9000 stations that was then used for the ensemble runs. The combined data set resulted in a significantly wetter surface than with the sparser set, resulting in noticeably larger gross primary production (GPP) estimates by models in the ensemble. Mexico and Canada remain significantly undersampled. Uncertainty due to the choice of a relatively sparse or dense station network was smaller than the structural uncertainty due to model choice.