GLOBAL NET CARBON EXCHANGE AND INTRAANNUAL ATMOSPHERIC CO2 CONCENTRATIONS PREDICTED BY AN ECOSYSTEM PROCESS MODEL AND 3-DIMENSIONAL ATMOSPHERIC TRANSPORT MODEL

A generalized terrestrial ecosystem process model, BIOME-BGC (for BIOM E BioGeoChemical Cycles), was used to simulate the global fluxes of CO 2 resulting from photosynthesis, autotrophic respiration, and heterotr ophic respiration. Daily meteorological data for the year 1987, gridde d to 1 degrees by 1 degrees, were used to drive the model simulations. From the maximum value of the normalized difference vegetation index (NDVI) for 1987, the leaf area index for each grid cell was computed. Global NPP was estimated to be 52 Pg C, and global R(h) was estimated to be 66 Pg C. Model predictions of the stable carbon isotopic ratio C -13/C-12 for C-3 and C-4 vegetation were in accord with values publish ed in the literature, suggesting that our computations of total net ph otosynthesis, and thus NPP, are more reliable than R(h). For each grid cell, daily R(h) was adjusted so that the annual total was equal to a nnual NPP, and the resulting net carbon fluxes were used as inputs to a three-dimensional atmospheric transport model (TM2) using wind data from 1987. We compared the spatial and seasonal patterns of NPP with a diagnostic NDVI model, where NPP was derived from biweekly NDVI data and Rh was tuned to fit atmospheric CO2 observations from three northe rn stations. To an encouraging degree, predictions from the BIOME-BGC model agreed in phase and amplitude with observed atmospheric CO2 conc entrations for 20 degrees to 55 degrees N, the zone in which the most complete data on ecosystem processes and meteorological input data are available. However, in the tropics and high northern latitudes, disag reements between simulated and measured CO2 concentrations indicated a reas where the model could be improved. We present here a methodology by which terrestrial ecosystem models can be tested globally, not by c omparisons to homogeneous-plot data, but by seasonal and spatial consi stency with a diagnostic NDVI model and atmospheric CO2 observations.