Editing IPCC:AR6/WGI/Chapter-3 (section)

=== 3.4.2 Snow Cover ===

<div id="h2-13-siblings" class="h2-siblings"></div>

Seasonal snow cover is a defining climate feature of the northern continents. It is therefore of considerable interest that climate models correctly simulate this feature. It is discussed in more detail in Section 9.5.3, and observational aspects of snow cover are assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] .

The AR5 noted the strong linear correlation between Northern Hemisphere snow cover extent (SCE) and annual-mean surface air temperature in CMIP5 models. It was assessed as ''likely'' that there had been an anthropogenic contribution to observed reductions in Northern Hemisphere snow cover since 1970 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). The AR5 assessed that CMIP5 models reproduced key features of observed snow cover well, including the seasonal cycle of snow cover over northern regions of Eurasia and North America, but had more difficulties in more southern regions with intermittent snow cover. The AR5 also found that CMIP5 models underestimated the observed reduction in spring snow cover over this period (Figure 3.22; see also [[#Brutel-Vuilmet--2013|Brutel-Vuilmet et al., 2013]] ; [[#Thackeray--2016|Thackeray et al., 2016]] ; [[#Santolaria-Otín--2020|Santolaria-Otín and Zolina, 2020]] ). This behaviour has been linked to how the snow-albedo feedback is represented in models ( [[#Thackeray--2018a|Thackeray et al., 2018a]] ). The CMIP5 multi-model ensemble has been shown to represent the snow-albedo feedback more realistically than CMIP3, although models from some individual modelling centres have not improved or have even got worse ( [[#Thackeray--2018a|Thackeray et al., 2018a]] ). There is still a systematic overestimation of the albedo of boreal forest covered by snow ( [[#Thackeray--2015|Thackeray et al., 2015]] ; Y. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ). Consequently, the snow albedo feedback might have been overestimated by CMIP5 models (Section 9.5.3; [[#Xiao--2017|Xiao et al., 2017]] ).

<div id="_idContainer053" class="_idGenObjectStyleOverride-1"></div>

[[File:d0f9a5bbcefe63d97aa9756c30b7ef89 IPCC_AR6_WGI_Figure_3_22.png]]

Figure 3.22 | '''Time series of Northern Hemisphere March–April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations.''' The observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), [[#Mudryk--2020|Mudryk et al. (2020)]] , and GLDAS2. CMIP5 '''(top)''' and CMIP6 '''(bottom)''' simulations of the response to natural plus anthropogenic forcing are shown in brown, natural forcing only in green, and the pre-industrial control simulation range is presented in blue. Five-year mean anomalies are shown for the 1923–2017 period with the x-axis representing the centre years of each five-year mean. CMIP5 all forcing simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading indicates 5th–95th percentile ranges for CMIP5 and CMIP6 all and natural forcings simulations, and solid lines are ensemble means, based on all available ensemble members with equal weight given to each model ( [[#3.2|Section 3.2]] ). The blue vertical bar indicates the mean 5th–95th percentile range of pre-industrial control simulation anomalies, based on non-overlapping segments. The numbers in brackets indicate the number of models used. Anomalies are relative to the average over 1971–2000. For models, SCE is restricted to grid cells with land fraction ≥50%. Greenland is excluded from the total area summation. Figure is modified from [[#Paik--2020|Paik and Min (2020)]] , their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

CMIP6 models improve on CMIP5 models in producing slightly increased SCE versus CMIP5, correcting the low bias in CMIP5 ( [[#Mudryk--2020|Mudryk et al., 2020]] ). The linear relationship noted above between GSAT and SCE also exists in CMIP6 ( [[#Mudryk--2020|Mudryk et al., 2020]] ). Like CMIP5, the CMIP6 models capture the negative trend in spring snow cover that has occurred in recent decades (Figure 3.22). However, the median CMIP6 model now produces slightly stronger post-1981 declines in the March to April mean SCE than the CMIP5 median ( [[#Mudryk--2020|Mudryk et al., 2020]] ). Until about 1980, the models produce a generally stable March to April SCE, but after that a substantial decline, reaching a loss of about 2 × 10 <sup>6</sup> km <sup>2</sup> in 2012–2017 relative to the 1971–2000 average. Compared to earlier studies which found that models underestimate observed trends for the 1979–2005 period ( [[#Brutel-Vuilmet--2013|Brutel-Vuilmet et al., 2013]] ), both CMIP5 and CMIP6 models show improved agreement with the observations over the period to 2017 (Figure 3.22). One remaining concern is a failure of most CMIP6 models to correctly represent the relationship between snow cover extent and snow mass, reflecting too slow seasonal increases and decreases of SCE in the models ( [[#Mudryk--2020|Mudryk et al., 2020]] ).

Several CMIP5 and CMIP6 based studies have consistently attributed the observed Northern Hemisphere spring SCE changes ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ) to anthropogenic influences ( [[#Rupp--2013|Rupp et al., 2013]] ; [[#Najafi--2016|Najafi et al., 2016]] ; [[#Paik--2020|Paik and Min, 2020]] ), with the observed changes being found to be inconsistent with natural variability alone. Similarly, spring snow thickness (Snow Water Equivalent) changes on the scale of the Northern Hemisphere have been attributed to greenhouse gas forcing ( [[#Jeong--2017|Jeong et al., 2017]] ). Using individual forcing simulations from multiple CMIP6 models, [[#Paik--2020|Paik and Min (2020)]] detected greenhouse gas influence in the observed decrease of early spring SCE between 1925 and 2019, which was found to be separable from the responses to other forcings.

In summary, it is ''very'' ''likely'' that anthropogenic influence contributed to the observed reductions in Northern Hemisphere springtime snow cover since 1950. CMIP6 models better represent the seasonality and geographical distribution of snow cover than CMIP5 simulations ( ''high confidence'' ). Both CMIP5 and CMIP6 models simulate strong declines in spring SCE during recent years, in general agreement with observations, causing the multi-model mean decreasing trend in spring SCE to now better agree with observations than in earlier evaluations. Evidence has yet to emerge that interactions between vegetation and snow, found problematic in CMIP5, have improved in CMIP6 models (Section 9.5.3). Such deficiencies in the representation of snow in climate models mean there is ''medium confidence'' in the simulation of snow cover over the northern continents in CMIP6 model simulations. The models consistently link snow extent to surface air temperature (Figure 9.24). With warming of near-surface air linked to anthropogenic influence, and particularly to greenhouse gas increases ( [[#3.3.1.1|Section 3.3.1.1]] ), this provides additional evidence that reductions in snow cover are also caused by human activity.

<div id="3.4.3" class="h2-container"></div>

<span id="glaciers-and-ice-sheets"></span>