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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 04 Jul 2018

Review article | 04 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).

Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing

Gab Abramowitz1,2, Nadja Herger1,3, Ethan Gutmann4, Dorit Hammerling4, Reto Knutti5, Martin Leduc6,7, Ruth Lorenz5, Robert Pincus8,9, and Gavin A. Schmidt10 Gab Abramowitz et al.
  • 1Climate Change Research Centre, UNSW Sydney, Australia
  • 2ARC Centre of Excellence for Climate Extremes, Australia
  • 3ARC Centre of Excellence for Climate System Science, Australia
  • 4National Center for Atmospheric Research, Boulder, Colorado, USA
  • 5Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
  • 6Ouranos, Montréal, Québec, Canada
  • 7Université du Québec à Montréal, Montréal, Québec, Canada
  • 8Cooperative Institute for Research in Environmental Sciences, University of Colorado, USA
  • 9NOAA Earth System Research Lab, Physical Sciences Division, USA
  • 10NASA Goddard Institute for Space Studies, New York, NY, USA

Abstract. The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates, so that a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognized by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.

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Gab Abramowitz et al.
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Publications Copernicus
Short summary
Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. Yet it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.
Best estimates of future climate projections typically rely on a range of climate models from...