Why Do Models Produce Spread in Snow Albedo Feedback?

Snow albedo feedback (SAF) behaves similarly in the current and future climate contexts; thus, constraining the large intermodel variance in SAF will likely reduce uncertainty in climate projections. To better understand this intermodel spread, structural and parametric biases contributing to SAF variability are investigated. We find that structurally varying snowpack, vegetation, and albedo parameterizations drive most of the spread, while differences arising from model parameters are generally smaller. Models with the largest SAF biases exhibit clear structural or parametric errors. Additionally, despite widespread intermodel similarities, model interdependency has little impact on the strength of the relationship between SAF in the current and future climate contexts. Furthermore, many models now feature a more realistic SAF than in the prior generation, but shortcomings from two models limit the reduction in ensemble spread. Lastly, preliminary signs from ongoing model development are positive and suggest a likely reduction in SAF spread among upcoming models. Plain Language Summary Snow albedo feedback is a response of the Northern Hemisphere snowpack to a warming climate through reductions in snow cover and surface reflectance, and subsequently increased sunlight absorbed at the surface. Climate model simulations exhibit large differences in this feedback, leading to uncertainty in future climate change. To better understand the different feedback responses, we assess two types of errors in the simulations. One such error relates to how important land processes are structured, while the other pertains to differences in prescribed constants. We find that much of the spread is attributed to how models represent important snow and vegetation processes. The largest feedback errors are associated with both types of modeling issues. Furthermore, many models better represent this feedback than they did previously, but the failure to reduce model spread is due to two error-prone models. Current model development is promising and suggests a likely reduction in feedback variability among future climate models.