Reliability and importance of structural diversity of climate model ensembles
Annan, James D.
Jackson, Charles S.
Webb, Mark J.
Hargreaves, Julia C.
Open Access. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
We investigate the performance of the newest generation multi-model ensemble (MME) from the Coupled Model Intercomparison Project (CMIP5). We compare the ensemble to the previous generation models (CMIP3) as well as several single model ensembles (SMEs), which are constructed by varying components of single models. These SMEs range from ensembles where parameter uncertainties are sampled (perturbed physics ensembles) through to an ensemble where a number of the physical schemes are switched (multi-physics ensemble). We focus on assessing reliability against present-day climatology with rank histograms, but also investigate the effective degrees of freedom (EDoF) of the fields of variables which makes the statistical test of reliability more rigorous, and consider the distances between the observation and ensemble members. We find that the features of the CMIP5 rank histograms, of general reliability on broad scales, are consistent with those of CMIP3, suggesting a similar level of performance for present-day climatology. The spread of MMEs tends towards being "over-dispersed" rather than "under-dispersed". In general, the SMEs examined tend towards insufficient dispersion and the rank histogram analysis identifies them as being statistically distinguishable from many of the observations. The EDoFs of the MMEs are generally greater than those of SMEs, suggesting that structural changes lead to a characteristically richer range of model behaviours than is obtained with parametric/physical-scheme-switching ensembles. For distance measures, the observations and models ensemble members are similarly spaced from each other for MMEs, whereas for the SMEs, the observations are generally well outside the ensemble. We suggest that multi-model ensembles should represent an important component of uncertainty analysis. © 2013 The Author(s).
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP. For CMIP the US Department of Energy’s Pro- gram for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. M.C. was partially supported by funding from NERC grants NE/I006524/1 and NE/I022841/1. MW is supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). T.Y., J.D.A, H.S., S.E., M.Y., J.C.H. were supported by the Global Environment Research Fund of the Ministry of the Environment of Japan (S-10, Integrated Climate Assessment – Risks,Uncertainties and Society, ICA-RUS).
Vol. 41, pp. 2745 - 2763