EXE: a Climatically Sensitive Model to Study Climate Change and CO₂ Enhancement Effects on Forests

Abstract

Vegetation plays a significant role in determining the local and regional hydrology of ice-free continental surfaces and the dynamics of the atmosphere above it. Vegetation also influences the global climate directly by affecting atmospheric chemistry. In particular, it partially controls the carbon cycle. In turn, vegetation is influenced by climate and changes in the ambient concentration of CO₂. This may have important consequences for agriculture and natural resource exploitation. A formal recognition of atmosphere/biosphere interrelationships is crucial but insufficient. Systematic investigations of the interactions between climate, plant physiology and ecology are badly needed. In this spirit, this paper presents the results of numerical simulations performed with the Energy, water and momentum eXchange, and Ecological dynamics (EXE) model at a local scale over periods of 400-800 (simulation) years. EXE constitutes a first attempt to couple a physiologically based water budget and an explicit treatment of ecological dynamics. In principle, EXE could be forced by the output of an atmospheric general circulation model (GCM). Within this context, the paper demonstrates through the examples it analyses that both potential stomata1 response to CO₂ and the possible range of changes in atmospheric relative humidity are likely major factors in determining the ecosystem response to greenhouse warming. Consequently, they should be considered in future studies of this kind. The paper also provides explanations regarding the movement of ecotones, defined as the transition zones between different vegetation assemblages. Taking the North American forest/prairie boundary as a case study, the analysis of the results shows how, in a greenhouse warmed world, St Paul, MN, might look like North Platte, NE. Finally, building on the previous example by using two different models, this study illustrates that results can be strongly model dependent and encourages extreme caution in their interpretation.