Climate-biosphere feedbacks over South Africa

A key consideration in climate variability is the role played by land cover. The land surface is important to the climate system for the exchanges of moisture, momentum and heat. A high percentage of water fluxes from the surface to the atmosphere move through vegetation. There is an interactive effect between the climate and biosphere, with the feedback effect resonating through both systems. The response of the land surface to climate system changes is two-fold, there being short-term physiological responses (e.g. rates of photosynthesis) and longer term ecological threshold responses (threshold values vary for different species).

This research uses a terrestrial biosphere model known as the Integrated Biosphere Simulator (IBIS), which was developed by J.A. Foley and others at the University of Wisconsin-Madison in the USA. IBIS attempts to achieve an integrated dynamic approach to modelling the biosphere, incorporating into it land surface, equilibrium vegetation and terrestrial biogeochemistry models. IBIS has several components operating at different time steps, including land surface and physiological processes, phenological behaviour and leaf display, as well as transient changes in carbon balance and vegetation structure.

Various atmospheric variables are used as inputs into IBIS (e.g. mean temperature, precipitation, number of wet days in a month), which can be modified to reflect a different climate state. The model is therefore potentially useful in climate change studies. In this research a transient run (1995-2085) is initiated with IBIS, using smoothed HadCM2 data from the Unified Model. These data are on a monthly temporal scale at a spatial resolution of 2.5 by 3.75 degrees, and are from a greenhouse gas climate change run performed with a 1% compound increase in carbon dioxide.

The output from the transient run with IBIS is examined in the light of vegetation boundary shifts through time over the South African domain. The class or biome level response is examined. In addition to the mean transient vegetation boundary shift response, some degree of confidence is achieved by calculating the 95% confidence limit standard deviations either side of the mean of the input atmospheric data. These are used to initiate 2 further IBIS model runs. The results of the 3 runs are then compared over time. In this manner, a greater level of understanding of the vegetation class level response to a transient climate change simulation is achieved.