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This paper presents a conceptual modeling study on the behaviors of terrestrial biosphere-atmosphere systems as they relate to multiple equilibrium states and climate variability, and emphasizes their implications for physically based climate modeling. The conceptual biosphere-atmosphere model consists of equilibrium re- sponses of vegetation and precipitation to each other, dynamics of the vegetation system, and stochastic forcing of precipitation representing the impact of atmospheric internal variability. Using precipitation as the atmospheric variable in describing the biosphere-atmosphere interactions, this model pertains to regions where vegetation growth is limited by water. Low moisture convergence in the atmosphere combined with high sensitivity of the atmospheric climate to vegetation changes provides the most favorable condition for the existence of multiple equilibrium states. In a coupled biosphere-atmosphere system with multiple equilibria, experiments varying the stochastic forcing indicate that atmospheric internal variability is an important factor in the long-term variability of the model climate and in its sensitivity to initial conditions. Specifically, the enhancement of low-frequency rainfall variability by vegetation dynamics is most pronounced with a moderate magnitude of atmospheric internal variability and is less pronounced if internal variability is either too large or too small; detecting the existence of multiple equilibria by examining the sensitivity of the coupled model climate to initial conditions is not always reliable since too large an internal variability reduces or even eliminates the model sensitivity to initial conditions. Findings from the conceptual model are confirmed using results from a physically based, synchro- nously coupled biosphere-atmosphere model. This study provides guidance for interpreting and understanding the model dependence of biosphere-atmosphere interaction studies using complex climate system models.

A Conceptual Modeling Study on Biosphere–Atmosphere Interactions and Its Implications for Physically Based Climate Modeling