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Statistical problems in the probabilistic prediction of climate change

Authors


  • This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.

David B. Stephenson, Exeter Climate Systems, Mathematics Research Institute, University of Exeter, Exeter, U.K. E-mail d.b.stephenson@exeter.ac.uk

Abstract

Future climate change projections are constructed from simulated numerical output from a small set of global climate models—samples of opportunity known as multi-model ensembles. Climate models do not produce probabilities, nor are they perfect representations of the real climate, and there are complex inter-relationships due to shared model features. This creates interesting statistical challenges for making inference about the real climate.

These issues were the focus of discussions at an Isaac Newton Institute workshop on probabilistic prediction of climate change held at the University of Exeter on 20–23 September 2010. This article presents a summary of the issues discussed between the statisticians, mathematicians, and climate scientists present at the workshop. In addition, we also report the discussion that took place on how to define the concept of climate. Copyright © 2012 John Wiley & Sons, Ltd.

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