HR: 1340h
AN: GC43A-0946    [Abstracts]
TI: Assessing Climate Change Risks Using a Multi-Model Approach
AU: Knorr, W
EM: wolfgang.knorr@bristol.ac.uk
AF: University of Bristol, QUEST - Dep. of Earth Sciences Wills Memorial Building, Queen's Road, Bristol, BS8 1RJ, United Kingdom
AU: * Scholze, M
EM: marko.scholze@bristol.ac.uk
AF: University of Bristol, QUEST - Dep. of Earth Sciences Wills Memorial Building, Queen's Road, Bristol, BS8 1RJ, United Kingdom
AU: Prentice, C
EM: coln.prentice@bristol.ac.uk
AF: University of Bristol, QUEST - Dep. of Earth Sciences Wills Memorial Building, Queen's Road, Bristol, BS8 1RJ, United Kingdom
AB: We quantify the risks of climate-induced changes in key ecosystem processes during the 21st century by forcing a dynamic global vegetation model with multiple scenarios from the IPCC AR4 data archive using 16 climate models and mapping the proportions of model runs showing exceedance of natural variability in wildfire frequency and freshwater supply or shifts in vegetation cover. Our analysis does not assign probabilities to scenarios. Instead, we consider the distribution of outcomes within three sets of model runs grouped according to the amount of global warming they simulate: < 2 degree C (including committed climate change simulations), 2-3 degree C, and >3 degree C. Here, we are contrasting two different methods for calculating the risks: first we use an equal weighting approach giving every model within one of the three sets the same weight, and second, we weight the models according to their ability to model ENSO. The differences are underpinning the need for the development of more robust performance metrics for global climate models.
DE: 1626 Global climate models (3337, 4928)
DE: 1630 Impacts of global change (1225)
DE: 1632 Land cover change
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting