Abstract. 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).

The relationship between land surface temperature and snow cover extent trends is examined in three distinct types of ensembles over the 1981–2010 period: an observation‐based ensemble, a representative selection of CMIP5 coupled climate model output, and two large initial condition coupled climate model ensembles. Observation‐based estimates of snow cover sensitivity are stronger than simulated over midlatitude and alpine regions. Observed sensitivity estimates over Arctic regions are consistent with simulated values. Anomalous snow cover extent trends present in one data set, the NOAA climate record, obscure the relationship to surface temperature seen in the rest of the analyzed data. The spread in modeled snow cover trends reflects roughly equal contributions from intermodel variability and from natural variability. Together, the anomalous relationship between surface temperature and snow cover expressed in the NOAA climate record and the large influence of natural variability present in the simulations highlight the importance of ensemble‐based approaches.

This study presents a comprehensive evaluation of snow albedo feedback (SAF) in two generations of climate models (Coupled Model Intercomparison Project versions 3 (CMIP3) and 5 (CMIP5)). A comparison of the models is performed against a multiobservation‐based reference data set (mOBS) derived from the seasonal cycle of albedo, snow cover, and temperature. The observed total SAF shows low uncertainty and is generally well simulated by the CMIP3 and CMIP5 ensemble mean, except for a low (high) bias over the Arctic (northern boreal forest). Most CMIP5 models overestimate the snow cover component of SAF (SNC) and underestimate the temperature sensitivity component (TEM). The high bias in SNC is due to simulated snow albedos 4–5% brighter than observed driving unrealistically large albedo contrasts. However, overall representation of surface albedo—and mean climate—has improved, as fewer CMIP5 models exhibit large cold temperature, or high snow, biases. The low bias in TEM is related to overly persistent snow albedo during spring, particularly over southern Eurasia and North America. There is large observational uncertainty in the reference data set mOBS that is traced primarily to the different snow cover products, with a secondary contribution from the albedo products and a small contribution from the temperature products. The conclusion is that the model mean tends to simulate the multiobservation mean very closely; however, this masks considerable spread in both models and observations. There is clear motivation for producing improved submonthly snow cover products for the purpose of model evaluation.

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020–2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5–10 % per decade (or 15–30 % in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10 % per decade (30 % in total) are projected across southern Canada.

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