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Google Scholar Crossref Search ADS Google Preview WorldCat COPAC In the Brier Score, we can use this gross value instead of the contract-specific value HT ht . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 2 Hastie T., Tibshirani R., & Friedman J. (2009). [Extracted from the article]

In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is a striking observation that the ensemble mean often outperforms at least half of the ensemble members in mean squared error (measured with respect to observations). In fact, as demonstrated in the most recent IPCC report, the ensemble mean often outperforms all or almost all of the ensemble members across a range of climate variables. This paper shows that these could be mathematical results based on convexity and averaging but with implications for the properties of the current generation of climate simulators. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]