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Climate model benchmarking with glacial and mid-Holocene climates
Authors:S P Harrison  P J Bartlein  S Brewer  I C Prentice  M Boyd  I Hessler  K Holmgren  K Izumi  K Willis
Institution:1. Department of Biological Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
2. Centre for Past Climate Change and School of Archaeology, Geography and Environmental Sciences, University of Reading, Whiteknights, Reading, RG6 6AH, UK
3. Department of Geography, University of Oregon, Eugene, OR, USA
4. Geography Department, University of Utah, Salt Lake City, UT, USA
5. AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences and Grantham Institute for Climate Change, Imperial College, Silwood Park, Ascot, SL5 7PY, UK
6. Bert Bolin Centre for Climate Research, Stockholm University, 106 91, Stockholm, Sweden
7. Department of Physical Geography and Quaternary Geology, Stockholm University, 106 91, Stockholm, Sweden
8. MARUM, Centre for Marine Environmental Sciences, University of Bremen, Bremen, Germany
Abstract:Past climates provide a test of models’ ability to predict climate change. We present a comprehensive evaluation of state-of-the-art models against Last Glacial Maximum and mid-Holocene climates, using reconstructions of land and ocean climates and simulations from the Palaeoclimate Modelling and Coupled Modelling Intercomparison Projects. Newer models do not perform better than earlier versions despite higher resolution and complexity. Differences in climate sensitivity only weakly account for differences in model performance. In the glacial, models consistently underestimate land cooling (especially in winter) and overestimate ocean surface cooling (especially in the tropics). In the mid-Holocene, models generally underestimate the precipitation increase in the northern monsoon regions, and overestimate summer warming in central Eurasia. Models generally capture large-scale gradients of climate change but have more limited ability to reproduce spatial patterns. Despite these common biases, some models perform better than others.
Keywords:
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