On the Robustness of Emergent Constraints Used in Multimodel Climate Change Projections of Arctic Warming

Abstract Statistical relationships between future and historical model runs in multimodel ensembles (MMEs) are increasingly exploited to make more constrained projections of climate change. However, such emergent constraints may be spurious and can arise because of shared (common) errors in a particular MME or because of overly influential models. This study assesses the robustness of emergent constraints used for Arctic warming by comparison of such constraints in ensembles generated by the two most recent Coupled Model Intercomparison Project (CMIP) experiments: CMIP3 and CMIP5. An ensemble regression approach is used to estimate emergent constraints in Arctic wintertime surface air temperature change over the twenty-first century under the Special Report on Emission Scenarios (SRES) A1B scenario in CMIP3 and the Representative Concentration Pathway (RCP) 4.5 scenario in CMIP5. To take account of different scenarios, this study focuses on polar amplification by using temperature responses at each grid point that are scaled by the global mean temperature response for each climate model. In most locations, the estimated emergent constraints are reassuringly similar in CMIP3 and CMIP5 and differences could have easily arisen from sampling variation. However, there is some indication that the emergent constraint and polar amplification is substantially larger in CMIP5 over the Sea of Okhotsk and the Bering Sea. Residual diagnostics identify one climate model in CMIP5 that has a notable influence on estimated emergent constraints over the Bering Sea and one in CMIP3 that that has a notable influence more widely along the sea ice edge and into midlatitudes over the western North Atlantic.