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Assessing prior emergent constraints on surface albedo feedback in CMIP6

Presentation Date
Wednesday, December 9, 2020 at 5:38am



An emergent constraint (EC) is a widely used form of model evaluation, which offers the potential to reduce intermodel variability in projections of Arctic climate change. Two examples have previously been laid out for highly uncertain future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere snow cover (SAFsnow) and Arctic 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 robust across multiple model generations. On the other hand, the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. Notably, there is a change in the future months that best resemble the current seasonal cycle, stemming from larger biases in historical simulated sea ice thickness during summer within CMIP6. Moreover, ensemble mean SAFsnow and SAFice strengths are largely consistent with CMIP5, while any reduction in intermodel spread is limited by poor outlier simulations of snow cover extent and sea ice thickness. Despite minimal change in ensemble spread, a majority of models exhibit improved seasonal SAF. Lastly, we shed light on the land surface and sea ice model development steps that have likely translated to improved or worsened SAF. The hope is that this information will be useful for the model development community going forward.

Funding Program Area(s)