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Reliability of the CMIP3 ensemble
Author(s) -
Annan J. D.,
Hargreaves J. C.
Publication year - 2010
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2009gl041994
Subject(s) - probabilistic logic , contrast (vision) , interpretation (philosophy) , ensemble learning , ensemble forecasting , ensemble average , computer science , rank (graph theory) , histogram , reliability (semiconductor) , statistical physics , econometrics , artificial intelligence , mathematics , climatology , physics , geology , power (physics) , quantum mechanics , combinatorics , image (mathematics) , programming language
We consider paradigms for interpretation and analysis of the CMIP3 ensemble of climate model simulations. The dominant paradigm in climate science, of an ensemble sampled from a distribution centred on the truth, is contrasted with the paradigm of a statistically indistinguishable ensemble, which has been more commonly adopted in other fields. This latter interpretation (which gives rise to a natural probabilistic interpretation of ensemble output) leads to new insights about the evaluation of ensemble performance. Using the well‐known rank histogram method of analysis, we find that the CMIP3 ensemble generally provides a rather good sample under the statistically indistinguishable paradigm, although it appears marginally over‐dispersive and exhibits some modest biases. These results contrast strongly with the incompatibility of the ensemble with the truth‐centred paradigm. Thus, our analysis provides for the first time a sound theoretical foundation, with empirical support, for the probabilistic use of multi‐model ensembles in climate research.