The interpretation and use of biases in decadal climate predictions

Hawkins, E. ORCID: https://orcid.org/0000-0001-9477-3677, Dong, B. ORCID: https://orcid.org/0000-0003-0809-7911, Robson, J. ORCID: https://orcid.org/0000-0002-3467-018X, Sutton, R. ORCID: https://orcid.org/0000-0001-8345-8583 and Smith, D. (2014) The interpretation and use of biases in decadal climate predictions. Journal of Climate, 27 (8). pp. 2931-2947. ISSN 1520-0442 doi: 10.1175/JCLI-D-13-00473.1

Abstract/Summary

Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/36469
Identification Number/DOI	10.1175/JCLI-D-13-00473.1
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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