Abstract
Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single-model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs allow for the quantification of internal variability, a non-negligible component of uncertainty on regional scales, but may also serve to inappropriately narrow uncertainty by giving a single model many additional votes. In advance of the mixed multi-model, the SMILE Coupled Model Intercomparison version 6 (CMIP6) ensemble, we investigate weighting approaches to incorporate 50 members of the Community Earth System Model (CESM1.2.2-LE), 50 members of the Canadian Earth System Model (CanESM2-LE), and 100 members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. The weights assigned are based on ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) predictors are used to determine the weights, and relationships between present and future predictor behavior are discussed. The estimated residual thermodynamic trend is proposed as an alternative predictor to replace 50-year regional SAT trends, which are more susceptible to internal variability. Uncertainty in estimates of northern European winter and Mediterranean summer end-of-century warming is assessed in a CMIP5 and a combined SMILE–CMIP5 multi-model ensemble. Five different weighting strategies to account for the mix of initial condition (IC) ensemble members and individually represented models within the multi-model ensemble are considered. Allowing all multi-model ensemble members to receive either equal weight or solely a performance weight (based on the root mean square error (RMSE) between members and observations over nine predictors) is shown to lead to uncertainty estimates that are dominated by the presence of SMILEs. A more suitable approach includes a dependence assumption, scaling either by 1∕N, the number of constituents representing a “model”, or by the same RMSE distance metric used to define model performance. SMILE contributions to the weighted ensemble are smallest (<10 %) when a model is defined as an IC ensemble and increase slightly (<20 %) when the definition of a model expands to include members from the same institution and/or development stream. SMILE contributions increase further when dependence is defined by RMSE (over nine predictors) amongst members because RMSEs between SMILE members can be as large as RMSEs between SMILE members and other models. We find that an alternative RMSE distance metric, derived from global SAT and hemispheric SLP climatology, is able to better identify IC members in general and SMILE members in particular as members of the same model. Further, more subtle dependencies associated with resolution differences and component similarities are also identified by the global predictor set.
Highlights
Projections of regional climate change are both key to climate adaptation policy and fundamentally uncertain due to the nature of the climate system (Deser et al, 2012; Kunreuther et al, 2013)
Because estimated residual thermodynamic Surface air temperature (SAT) trends in the broader European region are more comparable between members and observations due to the removal of an estimate of the influence of atmospheric variability that manifests on multidecadal timescales, we chose them as the ninth predictor in the definition of climate used in our performance weightings and RMSE independence weighting
To explicitly determine the contribution of the SMILEs, we show the fraction of total weight received by each SMILE and Coupled Model Intercomparison Project Phase 5 (CMIP5) in Fig. 3c and d
Summary
Projections of regional climate change are both key to climate adaptation policy and fundamentally uncertain due to the nature of the climate system (Deser et al, 2012; Kunreuther et al, 2013). Initial condition (IC) ensemble members that project climate trajectories which only differ by internal variability; similar trajectories are likely to exist amongst the 50 to 100 members of a SMILE It is important when assembling a multi-model ensemble that uncertainty estimates reflect the fact that not every member is an independent entity (Pennell and Reichler, 2011). We evaluate if a performance and independence weighting scheme (Knutti et al, 2017; Lorenz et al, 2018; Brunner et al, 2019) can be used to include three SMILEs in a CMIP5 multimodel ensemble and provide a justifiably constrained estimate of European regional end-of-century warming uncertainty. Though all products are observational estimates, we refer to them as “observations” or “OBS” to distinguish them from members of the multi-model ensemble
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