Abstract
Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.
Highlights
A bulk measure of the sensitivity of the climate system to carbon dioxide in the atmosphere (CO2) is commonly expressed by the equilibrium climate sensitivity (ECS), an idealized metric defined as the mean global surface air temperature change that results from a doubling of the atmospheric CO2 concentration over pre-industrial levels once the climate system reached equilibrium
The best estimate of ECS averaged over all emergent constraints increases by 12 % when moving from relationships trained on Coupled Model Intercomparison Project Phase 5 (CMIP5) to those trained on CMIP6
Some increase is predicted by every constraint we analyzed and can be at least partly explained by the increased multimodel mean ECS of CMIP6, which was not accompanied by systematic changes in the constraint variables that could explain this increase, leading to regression fits with higher intercept values at observed constraint values
Summary
A bulk measure of the sensitivity of the climate system to carbon dioxide in the atmosphere (CO2) is commonly expressed by the equilibrium climate sensitivity (ECS), an idealized metric defined as the mean global surface air temperature change that results from a doubling of the atmospheric CO2 concentration over pre-industrial levels once the climate system reached equilibrium. Applied to the hydrological cycle and the snow– albedo feedback (Allen and Ingram, 2002; Hall and Qu, 2006), emergent constraints offer the possibility to constrain future projections of Earth system model (ESM) ensembles with observations. Their theoretical basis is an emergent relationship between an observable quantity in the past or present-day climate and a quantity related to the future climate (such as ECS). We follow up on the work of Caldwell et al (2018) by analyzing 11 published emergent constraints on ECS, which are summarized, and assessing whether they still hold for the new CMIP6 model ensemble (Eyring et al, 2016).
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