A Simple, coherent framework for partitioning uncertainty in climate predictions

Stan Yip, Christopher A.T. Ferro, David B. Stephenson, Ed Hawkins

Research output: Journal article publicationJournal articleAcademic researchpeer-review

131 Citations (Scopus)

Abstract

A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model-scenario interaction- the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.

Original languageEnglish
Pages (from-to)4634-4643
Number of pages10
JournalJournal of Climate
Volume24
Issue number17
DOIs
Publication statusPublished - 1 Sep 2011
Externally publishedYes

Keywords

  • Climate prediction
  • Coupled models
  • Ensembles
  • Model comparison
  • Statistical techniques

ASJC Scopus subject areas

  • Atmospheric Science

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