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.
A highly uncertain aspect of anthropogenic climate change is the rate at which the global hydrologic cycle intensifies. The future change in global‐mean precipitation per degree warming, or hydrologic sensitivity, exhibits a threefold spread (1–3%/K) in current global climate models. In this study, we find that the intermodel spread in this value is associated with a significant portion of variability in future projections of extreme precipitation in the tropics, extending also into subtropical atmospheric river corridors. Additionally, there is a very tight intermodel relationship between changes in extreme and nonextreme precipitation, whereby models compensate for increasing extreme precipitation events by decreasing weak‐moderate events. Another factor linked to changes in precipitation extremes is model resolution, with higher resolution models showing a larger increase in heavy extremes. These results highlight ways various aspects of hydrologic cycle intensification are linked in models and shed new light on the task of constraining precipitation extremes.
This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).