Reduced global warming from CMIP6 projections when weighting models by performance and independence

Abstract

Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 % in the likely range compared with the unweighted case.

Refinement of future Arctic sea-ice projections

“…Projections without model selection. In our study, we focus on both the high-emission SSP5-8.5 and low-emission SSP1-2.6 scenarios, which correspond to a global warming of around 4 • C and 1 • C, respectively, over this century (2081-2100 relative to 1995-2014) 23 . Averaged over 33 CMIP6 models (totalling 166 model members, Supplementary Table 1), the multi-model mean March Arctic sea-ice area and volume are reduced by 45 % and 78 %, respectively, in 2096-2100, compared to 2015-2019, in the high-emission scenario (Fig.…”

“…Different approaches have been taken to try to reduce the model spread in projections of Arctic sea-ice area for a given emission scenario. One such approach consists in giving a weight to each model based on its performance relative to observations during the historical period: models that strongly agree with observations receive more weight than models that poorly agree 23,25 . Another approach is to select models based on their historical performance and exclude models that do not satisfy the selection criteria 16,18,26 .…”

Natural variability is a large source of uncertainty in future projections of hypoxia in the Baltic Sea

“…Apart from their inclusion of new scenarios, in the CMIP6 models, the equilibrium climate sensitivity to a doubling of greenhouse gas concentrations is higher than in the predecessor CMIP5 models (1.5-4.5°C for CMIP5 vs 1.8-5.6°C for CMIP6 models 47 ), at least as determined in initial assessments. However, the CMIP6 models in which warming is highest at the end of the century do not well simulate the recorded warming trend during the historical period and should therefore be excluded from policy-relevant assessments 45,[48][49][50][51] .…”

Reducing uncertainty in local climate projections

“…Since the AR5, a new generation of climate models [7] has been used to provide a range of projections in response to different socio-economic scenarios [8]. Based on this new dataset, various studies have recently shown that uncertainty in global mean warming can be considerably reduced by using the information provided by recent observed warming trends via so-called "constraint" methods [9,10,11,12]. These studies consistently point towards a downward revision of the expected warming in all emission scenarios [11,9], with a decrease in model uncertainty of nearly 40% by 2100 [10], and even more at shorter lead times.…”

“…Based on this new dataset, various studies have recently shown that uncertainty in global mean warming can be considerably reduced by using the information provided by recent observed warming trends via so-called "constraint" methods [9,10,11,12]. These studies consistently point towards a downward revision of the expected warming in all emission scenarios [11,9], with a decrease in model uncertainty of nearly 40% by 2100 [10], and even more at shorter lead times. This is an important result as, until then, observations have failed to provide clear evidence in reducing the range of climate projections [13].…”