Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models

Abstract

Abstract. Climate sensitivity to CO2 remains the key uncertainty in projections of future climate change. Transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets. Equilibrium climate sensitivity (ECS) is vital for understanding longer-term climate change and stabilisation targets. In the IPCC 5th Assessment Report (AR5), the stated “likely” ranges (16 %–84 % confidence) of TCR (1.0–2.5 K) and ECS (1.5–4.5 K) were broadly consistent with the ensemble of CMIP5 Earth system models (ESMs) available at the time. However, many of the latest CMIP6 ESMs have larger climate sensitivities, with 5 of 34 models having TCR values above 2.5 K and an ensemble mean TCR of 2.0±0.4 K. Even starker, 12 of 34 models have an ECS value above 4.5 K. On the face of it, these latest ESM results suggest that the IPCC likely ranges may need revising upwards, which would cast further doubt on the feasibility of the Paris targets. Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to constrain the likely range of TCR to 1.3–2.1 K, with a central estimate of 1.68 K. We reach this conclusion through an emergent constraint approach which relates the value of TCR linearly to the global warming from 1975 onwards. This is a period when the signal-to-noise ratio of the net radiative forcing increases strongly, so that uncertainties in aerosol forcing become progressively less problematic. We find a consistent emergent constraint on TCR when we apply the same method to CMIP5 models. Our constraints on TCR are in good agreement with other recent studies which analysed CMIP ensembles. The relationship between ECS and the post-1975 warming trend is less direct and also non-linear. However, we are able to derive a likely range of ECS of 1.9–3.4 K from the CMIP6 models by assuming an underlying emergent relationship based on a two-box energy balance model. Despite some methodological differences; this is consistent with a previously published ECS constraint derived from warming trends in CMIP5 models to 2005. Our results seem to be part of a growing consensus amongst studies that have applied the emergent constraint approach to different model ensembles and to different aspects of the record of global warming.

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

“…The three worst performing models, MIROC-ES2L, CanESM5, and HadGEM3-GC31-LL, in turn, receive less than 1/20 of the equal weighting (about 0.001). Indeed, several recent studies have found that models which show more future warming per unit of greenhouse gas are less likely based on comparison with past observations (e.g., Jiménez-de-la Cuesta and Nijsse et al, 2020;Tokarska et al, 2020). Consistent with their findings, models with high TCR receive very low performance (and combined) weights (label colors in Fig.…”

“…A summary of weights and warming values for all models as well as all statistics can be found in Tables S2 and S3. Recent studies that use the historical temperature trend as an observational constraint for future warming (e.g., Nijsse et al, 2020;Tokarska et al, 2020) lead to similar conclusions, with lower constrained warming compared with unconstrained (both in the mean and upper percentiles of the distributions).…”

“…One aim of our study is to find an optimal combination of diagnostics that successfully constrains projections for our target quantity (global temperature change) while avoiding overconfidence or susceptibility to uncertainty from internal variability. For example, tasTREND is a powerful diagnostic due to its clear physical relationship and high correlation with projected warming (e.g., Nijsse et al, 2020;Tokarska et al, 2020). However, while it has the highest correlation with the target of all investigated diagnostics, it also has the largest uncertainty due to internal variability (i.e., the spread of tasTREND across ensemble members of the same model).…”

“…Uncertainty due to future emissions can easily be isolated by making projections conditional on scenarios such as the Shared Socioeconomic Pathways (SSPs) in CMIP6 (O'Neill et al, 2014) or the Representative Concentration Pathways (RCPs) in CMIP5 (van Vuuren et al, 2011). The other two sources of uncertainty are harder to quantify, as reliably separating them is often challenging (e.g., Kay et al, 2015;Maher et al, 2019).…”

“…Weights are based on (i) each model's performance with respect to simulating historical properties of the climate system, such as horizontally resolved anomaly, variability, and trend fields, and (ii) its independence from the other models in the ensemble, which is estimated based on the shared biases of climatology. In contrast to many other methods that constrain model projections based on only one observable quantity, such as the warming trend (e.g., Giorgi and Mearns, 2002;Ribes et al, 2017;Jiménez-de-la Cuesta and Mauritsen, 2019;Liang et al, 2020;Nijsse et al, 2020;Tokarska et al, 2020), ClimWIP is based on multiple diagnostics, representing different aspects of the climate system. These diagnostics are chosen to evaluate a model's performance with respect to simulating observed climatology, variability, and trend patterns.…”