Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6

An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m−2 K−1, or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow.