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

Abstract

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; therefore, 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 recognised 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.

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

“…Twelve GCM outputs (shown in bold) ranking from the best performing to the worst are summarized in Table 2; the SU filter shows that the top four performing GCMs were ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M. These results further verified [16], who suggested that GCMs should meet these criteria. The top four GCMs were selected according to their higher SU weight and common performance.…”

Quantifying uncertainty in European climate projections using combined performance-independence weighting

“…It has been argued that using a measure for model interdependence is important in so-called 'ensembles of opportunity' such as CMIP5, which are not designed to represent independent realizations of the climate system (e.g. Abramowitz et al 2019). An in-depth investigation of the independence weighting, e.g.…”

A weighting scheme to incorporate large ensembles in multi-model ensemble projections

“…It therefore becomes important when incorporating SMILEs into a multi-model ensemble that uncertainty estimates reflect effective degrees of freedom in the ensemble (Pennell and Reichler, 2011). This can be achieved by down-weighting redundant information by a measure of independence (Abramowitz et al, 2019).…”