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

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.

Snow cover duration trends observed at sites and predicted by multiple models

“…In preparation for the sixth IPCC assessment report, climate modelling centres have now performed CMIP6 coupled land-atmosphere-ocean simulations with their latest models. Mudryk et al (2020) report an overall better representation of Northern Hemisphere snow cover extent in the CMIP6 multi-model ensemble than in CMIP5, but a large spread remains in simulated trends.…”

“…Having been chosen for snow research in part because they have dependable seasonal snow cover, the ESM-SnowMIP sites are not in regions of marginal snow cover that are most vulnerable to warming. A compilation of multiple observation-based estimates of Northern Hemisphere snow cover extent shows maximum decreasing trends in November and March, coincident with peaks in surface temperature warming trends (Mudryk et al, 2017). Large-scale simulations are required for predicting large-scale trends in snow cover extent, but simulations at well-instrumented sites allow more insight into modelling of snow processes and impacts that are experienced on small scales.…”

Climate change in the High Mountain Asia in CMIP6

“…This dataset is adapted for continental-scale studies, but shows limitations over mountainous regions (Déry and Brown, 2007), even if the inclusion of Meteosat-5 data in 2001 significantly improved its quality over the Asian continent (Helfrich et al, 2007). Trend analyses based on NOAA CDR data must be taken with caution because of potential temporal heterogeneities related to changes of experimental protocols (Mudryk et al, 2020). To obtain monthly fractional values, we simply average the weekly binaries values included in each corresponding month.…”

Effect of Forest Canopy Structure on Wintertime Land Surface Albedo: Evaluating CLM5 Simulations With In‐Situ Measurements

“…Variations in snow vegetation masking have been found to be responsible for much of the spread of wintertime LSA in global climate models (Essery, 2013; Qu & Hall, 2007, 2014). The lack of dramatic model improvement in simulated LSA between the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) can be partially attributed to the effect the vegetation parameterization has on LSA over snow covered areas (Mudryk et al., 2020; Wang et al., 2016). Given the global warming‐induced reduction in snow cover extent (Derksen & Brown, 2012; Kunkel et al., 2016) and the interrelated implications on water resources (Barnett et al., 2005; Sturm et al., 2017), agriculture (Qin et al., 2020), carbon balance (Pulliainen et al., 2017) and LSA itself (Zhang et al., 2019), it is paramount to reduce such uncertainties in LSMs.…”