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Skill and uncertainty in climate models
Author(s) -
Hargreaves Julia C.
Publication year - 2010
Publication title -
wiley interdisciplinary reviews: climate change
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.678
H-Index - 75
eISSN - 1757-7799
pISSN - 1757-7780
DOI - 10.1002/wcc.58
Subject(s) - climate change , probabilistic logic , scale (ratio) , bayesian probability , climate model , computer science , general circulation model , econometrics , bayesian inference , estimation , climatology , artificial intelligence , economics , geography , geology , ecology , cartography , biology , management
Abstract Analyses of skill are widely used for assessing weather predictions, but the time scale and lack of validation data mean that it is not generally possible to investigate the predictive skill of today's climate models on the multidecadal time scale. The predictions made with early climate models can, however, be analyzed, and here we show that one such forecast did have skill. It seems reasonable to expect that predictions based on today's more advanced models will be at least as skillful. In general, assessments of predictions based on today's climate models should use Bayesian methods, in which the inevitable subjective decisions are made explicit. For the AR4, the Intergovernmental Panel on Climate Change (IPCC) recommended the Bayesian paradigm for making estimates of uncertainty and probabilistic statements, and here we analyze the way in which uncertainty was actually addressed in the report. Analysis of the ensemble of general circulation models (GCMs) used in the last IPCC report suggests there is little evidence to support the popular notion that the multimodel ensemble is underdispersive, which would imply that the spread of the ensemble may be a reasonable starting point for estimating uncertainty. It is important that the field of uncertainty estimation is developed in order that the best use is made of current scientific knowledge in making predictions of future climate. At the same time, it is only by better understanding the processes and inclusion of these processes in the models, the best estimates of future climate will be closer to the truth. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models