Editing IPCC:AR6/WGI/Chapter-9 (section)

==== 9.5.3.2 Evaluation of Seasonal Snow in Climate Models ====

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Building on AR5 ( [[#Flato--2013|Flato et al., 2013]] ) and subsequent published work, SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) stated that CMIP5 models tended to underestimate the observed decrease of Northern Hemisphere spring SCE due to inappropriate parametrization of snow processes, misrepresentation of the snow-albedo feedback, underestimated temperature sensitivity, and biased climatological spring snow cover. Since AR5, progress in the observation, description and understanding of snow microstructure ( [[#Kinar--2015|Kinar and Pomeroy, 2015]] ; [[#Calonne--2017|Calonne et al., 2017]] ) and its links to physical (thermal and radiative) properties ( [[#Löwe--2013|Löwe et al., 2013]] ; [[#Calonne--2014|Calonne et al., 2014]] ) has prompted efforts to represent physical properties as a function of the evolving snow microstructure in models ( [[#Carmagnola--2014|Carmagnola et al., 2014]] ; [[#Calonne--2015|Calonne et al., 2015]] ). However, even state-of-the-art snow models intended for meteorological and climate applications still struggle to correctly represent the time evolution of the snow thermal properties, particularly of cold and dry tundra snow ( [[#Domine--2016|Domine et al., 2016]] ). Moreover, most, if not all, CMIP6 climate models do not explicitly represent the darkening of snow by deposition of black carbon and other light-absorbing aerosol species known to influence snow melt rates ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.3|Section 7.3.4.3]] ). Regardless of these shortcomings, snow modules of climate models continue to be improved. Recent progress includes the incorporation of multiple energy balances within the canopy and between sub-grid tiles with different snow heights ( [[#Aas--2017|Aas et al., 2017]] ; [[#Boone--2017|Boone et al., 2017]] ) and inclusion of advanced specific snow models in coupled climate models ( [[#Niwano--2018|Niwano et al., 2018]] ; [[#Voldoire--2019|Voldoire et al., 2019]] ), opening the prospect of future progress in quantifying snow-related feedbacks in a changing climate. Recently developed multi-physics snow models ( [[#Essery--2015|Essery, 2015]] ; [[#Lafaysse--2017|Lafaysse et al., 2017]] ), which are able to emulate the behaviour of a large number of models in a broad range of climates, allow model shortcomings and key parameter uncertainties, for example, concerning snow masking by vegetation or snow thermal conductivity, to be identified. Guidance for future model improvement can be provided by improved diagnostics, such as a concise metric of snow insulation (A.G. [[#Slater--2017|]] [[#Slater--2017|Slater et al., 2017]] ), which builds on an observed relation between effective seasonal mean SD and the dampening of winter season temperature decrease within the soil, and allows an efficient quantification of inaccuracies in the simulated snow insulation effect.

There is ''high confidence'' that large inter-model variations in the snow-cover sensitivity to temperature can largely be explained by inaccuracies in the simulated snow-albedo feedback ( [[#Qu--2014|Qu and Hall, 2014]] ); a multi-model sub-ensemble of CMIP5 models that simulate a correct magnitude of this feedback presents a 40% reduced spread in the projected 21st century Northern Hemisphere land warming trend ( [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ). Errors of the simulated feedback strength were linked to: (i) systematic positive albedo biases over the boreal forest belt, mostly due to unrealistic treatment of vegetation masking ( [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ); (ii) inaccurate prescribed tree cover fraction and inappropriate parametrization of leaf area index in some models ( [[#Loranty--2014|Loranty et al., 2014]] ; L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ); and (iii) low spatial resolution leading to inaccuracies in the strength of the simulated snow albedo feedback in mountainous regions ( [[#Letcher--2015|Letcher and Minder, 2015]] ). Although the representation of snow-albedo feedback improved in many CMIP5 models over CMIP3, some models deteriorated ( [[#Thackeray--2018|Thackeray et al., 2018]] ).

Analysis of the available CMIP6 historical simulations for the 1981–2014 period shows that, on average, CMIP6 models simulate well the observed SCE ( [[#Mudryk--2020|Mudryk et al., 2020]] ), except for outliers and a median low bias during the winter months (Figure 9.24a). This is an improvement over CMIP5 ( [[#Mudryk--2020|Mudryk et al., 2020]] ), where many snow-related biases were linked to inadequacies of the vegetation masking of snow cover over the boreal forests ( [[#Thackeray--2015|Thackeray et al., 2015]] ). A comparison between CMIP5 and CMIP6 results ( [[#Mudryk--2020|Mudryk et al., 2020]] ) shows that there is no notable progress in the quality of the representation of the observed 1981–2014 monthly snow cover trends.

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[[File:34d7c3435878107860cd421521aace20 IPCC_AR6_WGI_Figure_9_24.png]]

'''Figure 9.24''' '''|''' '''Simulated Coupled Model Intercomparison Project Phase 6 (CMIP6) and observed snow cover extent (SCE). (a)''' Simulated CMIP6 and observed ( [[#Mudryk--2020|Mudryk et al., 2020]] ) SCE (in millions of km <sup>2</sup> ) for 1981–2014. Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 1981–2014, with the red bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed interannual distribution over the period is represented in green, with the yellow bar indicating the median. '''(b)''' Spring (March to May) Northern Hemisphere SCE against global surface air temperature (GSAT) (relative to the 1995–2014 average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. Each data point is the mean for one CMIP6 simulation (first ensemble member for each available model) in the corresponding temperature bin. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

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