The Predictive Uncertainty of Land Surface Fluxes in Response to Increasing Ambient Carbon Dioxide

Abstract

The exchange of water vapor and carbon dioxide (CO2) between the land surface and the atmosphere plays an important role in numerical weather forecasting and climate change prediction using general circulation models. In this study, a typical representation of photosynthesis as used in recent soil–vegetation–atmosphere transfer schemes has been analyzed within a Monte Carlo–based uncertainty estimation framework to estimate the predictive uncertainty of land surface fluxes in response to increasing levels of ambient CO2. The comparison of predicted latent heat and carbon fluxes with measurements from a two-week concentrated field campaign within the Northern Hemisphere Climate Processes Land Surface Experiment (NOPEX) project identified the problem of model equifinality in that many different model parameterizations are shown to be able to reproduce the observed data acceptably well. The same parameter sets, however, lead to the prediction of a wide range of possible responses of latent heat and carbon fluxes when the boundary conditions are changed to doubled ambient CO2 concentrations.