Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble

Lawrence Mudryk,Claire Brutel-Vuilmet,Gerhard Krinner,Chris Derksen,Mike Brady,María Santolaria-Otín,Richard Essery,Martin Ménégoz

doi:10.5194/tc-14-2495-2020

Abstract

Abstract. This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6). Where appropriate, the CMIP6 output is compared to CMIP5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six observation-based products is used to produce a new time series of historical Northern Hemisphere snow extent anomalies and trends; a subset of four of these products is used for snow mass. Trends in snow extent over 1981–2018 are negative in all months and exceed -50×103 km2 yr−1 during November, December, March, and May. Snow mass trends are approximately −5 Gt yr−1 or more for all months from December to May. Overall, the CMIP6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP5. Simulated snow extent and snow mass trends over the 1981–2014 period are stronger in CMIP6 than in CMIP5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all CMIP6 Shared Socioeconomic Pathways. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 % relative to the 1995–2014 level per degree Celsius of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast-response components of the cryosphere such as sea ice and near-surface permafrost extent.

Highlights

Introduction1. Like summer sea ice in the Arctic, spring snow cover over land has a cooling effect on the climate system (Flanner et al, 2011)
It is imperative that Earth system models properly treat seasonal snow cover in order to account for a number of important energy and water cycle processes.1
While the NOAA climate record represents an established “baseline” dataset for assessing climatological snow extent at the hemispheric scale, no such equivalent dataset exists for climatological snow mass

Summary

Introduction

1. Like summer sea ice in the Arctic, spring snow cover over land has a cooling effect on the climate system (Flanner et al, 2011). Like summer sea ice in the Arctic, spring snow cover over land has a cooling effect on the climate system (Flanner et al, 2011) The magnitude of this cooling influence has declined alongside observed reductions in spring snow cover over recent decades (Zhang et al, 2019; Letterly et al, 2018). 2. Snow cover influences the carbon balance across biomes and seasons. Across tundra regions in winter, snow cover insulation of the underlying soil is a key factor in driving winter season carbon fluxes from northern permafrost (Natali et al, 2019). The net effect of these processes on large-scale carbon budgets remains uncertain

Methods

Results

Discussion

Conclusion