This study uses dynamical and statistical methods to understand end‐of‐century mean changes to Sierra Nevada snowpack. Dynamical results reveal mid‐elevation watersheds experience considerably more rain than snow during winter, leading to substantial snowpack declines by spring. Despite some high‐elevation watersheds receiving slightly more snow in January and February, the warming signal still dominates across the wet‐season and leads to notable declines by springtime. A statistical model is created to mimic dynamical results for April 1 snowpack, allowing for an efficient downscaling of all available General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5. For all GCMs and emissions scenarios, dramatic April 1 snowpack loss occurs at elevations below 2500 meters, despite increased precipitation in many GCMs. Only 36% (±12%) of historical April 1 total snow water equivalent volume remains at the century's end under a “business‐as‐usual” emissions scenario, with 70% (±12%) remaining under a realistic “mitigation” scenario.

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed.

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).