Studies of the Earth's history show that the large climate changes of the past have been associated with equally large changes in the biosphere, the deep ocean, the large continental ice sheets, and the Earth's carbon cycle. This award supports a project designed to study climate changes of the past using numerical models of the atmosphere, the land surface, terrestrial ecosystems, oceans, ice-sheets, and components of the carbon cycle. Specifically, the project will focus on the most recent period of major deglaciation and major global warming in the geologic record, which occurred during the Permian, about 280 million years ago. In the Permian, large climate changes were probably initiated by tectonically-related changes in land-ocean distribution and topography. By prescribing changes in these "external" forcings of climate, it is possible to use models to simulate the climate's direct response, and to study potential feedback mechanisms involving the biosphere, the hydrosphere, or the cryosphere that might amplify or dampen the direct response. The great advantage of using the past as a "testing ground" for studies of large climate change is that geological records from terrestrial and marine environments can be compared with the results of the simulations to evaluate the accuracy of the models under very different boundary conditions, as well as to test our understanding of fundamental climate processes. Large changes in the atmospheric concentration of the CO2 probably played a role in the Permian global warming event and will be an important focus of the project. Equally important will be the use of climate system/earth systems models to evaluate the role that major changes in ocean currents, ocean heat transport, and vegetation may have played in amplifying the warming trend. The data sets of Permian climate indicators already developed at the University of Chicago will be used to evaluate the results. The results of this work will lead to improved understanding of the mechanisms involved in large climate changes of the past and will help assess the accuracy of models being developed to estimate possible large
