Editing IPCC:AR6/WGI/Chapter-7 (section)

==== 7.4.2.3 Surface-albedo Feedback ====

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Surface albedo is determined primarily by reflectance at Earth’s surface, but also by the spectral and angular distribution of incident solar radiation. Changes in surface albedo result in changes in planetary albedo that are roughly reduced by two-thirds, owing to atmospheric absorption and scattering, with variability and uncertainty arising primarily from clouds ( [[#Bender--2011|Bender, 2011]] ; [[#Donohoe--2011|Donohoe and Battisti, 2011]] ; [[#Block--2013|Block and Mauritsen, 2013]] ). Temperature change induces surface-albedo change through several direct and indirect means. In the present climate and at multi-decadal time scales, the largest contributions by far are changes in the extent of sea ice and seasonal snow cover, as these media are highly reflective and are located in regions that are close to the melting temperature (Sections 2.3.2.1 and 2.3.2.2). Reduced snow cover on sea ice may contribute as much to albedo feedback as reduced extent of sea ice ( [[#Zhang--2019|Zhang et al., 2019]] ). Changes in the snow metamorphic rate, which generally reduces snow albedo with warmer temperature, and warming-induced consolidation of light-absorbing impurities near the surface, also contribute secondarily to the albedo feedback ( [[#Flanner--2006|Flanner and Zender, 2006]] ; [[#Qu--2007|Qu and Hall, 2007]] ; [[#Doherty--2013|Doherty et al., 2013]] ; [[#Tuzet--2017|Tuzet et al., 2017]] ). Other contributors to albedo change include vegetation state (assessed separately in ( [[#7.4.2.5|Section 7.4.2.5]] ), soil wetness and ocean roughness.

Several studies have attempted to derive surface-albedo feedback from observations of multi-decadal changes in climate, but only over limited spatial and inconsistent temporal domains, inhibiting a purely observational synthesis of global surface-albedo feedback ( α A ). [[#Flanner--2011|Flanner et al. (2011)]] applied satellite observations to determine that the northern hemisphere (NH) cryosphere contribution to global α A over the period 1979–2008 was 0.48 [ ''likely'' range 0.29 to 0.78] W m <sup>–2</sup> °C <sup>–1</sup> , with roughly equal contributions from changes in land snow cover and sea ice. Since AR5, and over similar periods of observation, [[#Crook--2014|Crook and Forster (2014)]] found an estimate of 0.8 ± 0.3 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) for the total NH extratropical surface-albedo feedback, when averaged over global surface area. For Arctic sea ice alone, [[#Pistone--2014|Pistone et al. (2014)]] and [[#Cao--2015|Cao et al. (2015)]] estimated the contribution to global α A to be 0.31 ± 0.04 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) and 0.31 ± 0.08 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation), respectively, whereas [[#Donohoe--2020|Donohoe et al. (2020)]] estimated it to be only 0.16 ± 0.04 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation). Much of this discrepancy can be traced to different techniques and data used for assessing the attenuation of surface-albedo change by Arctic clouds. For the NH land snow, [[#Chen--2016|Chen et al. (2016)]] estimated that observed changes during 1982–2013 contributed (after converting from NH temperature change to global mean temperature change) by 0.1 W m <sup>–2</sup> °C <sup>–1</sup> to global α A , smaller than the estimate of 0.24 W m <sup>–2</sup> °C <sup>–1</sup> from [[#Flanner--2011|Flanner et al. (2011)]] . The contribution of the Southern Hemisphere (SH) to global α A is expected to be small because seasonal snow cover extent in the SH is limited, and trends in SH sea ice extent are relatively flat over much of the satellite record ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2|Section 2.3.2]] ).

CMIP5 and CMIP6 models show moderate spread in global α A , determined from century time scale changes <sub></sub> ( [[#Qu--2014|Qu and Hall, 2014]] ; [[#Schneider--2018|Schneider et al., 2018]] ; [[#Thackeray--2019|Thackeray and Hall, 2019]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ), owing to variations in modelled sea ice loss and snow cover response in boreal forest regions. The multi-model mean global-scale α A (from all contributions) over the 21st century in CMIP5 models under the RCP8.5 scenario was derived by [[#Schneider--2018|Schneider et al. (2018)]] to be 0.40 ± 0.10 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation). Moreover, they found that modelled α A does not decline over the 21st century, despite large losses of snow and sea ice, though a weakened feedback is apparent after 2100. Using the idealized ''abrupt 4xCO2'' , as for the other feedbacks, the estimate of the global-scale albedo feedback in the CMIP5 models is 0.35 ± 0.08 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Vial--2013|Vial et al., 2013]] ; [[#Caldwell--2016|Caldwell et al., 2016]] ). The CMIP6 multi-model mean varies from 0.3 to 0.5 W m <sup>–2</sup> °C <sup>–1</sup> depending on the kernel used ( [[#Zelinka--2020|Zelinka et al., 2020]] ). [[#Donohoe--2020|Donohoe et al. (2020)]] derived a multi-model mean α A and its inter-model spread of 0.37 ± 0.19 W m <sup>–2</sup> °C <sup>–1</sup> from the CMIP5 ''abrupt 4xCO2'' ensemble, employing model-specific estimates of atmospheric attenuation and thereby avoiding bias associated with use of a single radiative kernel.

The surface-albedo feedback estimates using centennial changes have been shown to be highly correlated to those using seasonal regional changes for NH land snow ( [[#Qu--2014|Qu and Hall, 2014]] ) and Arctic sea ice ( [[#Thackeray--2019|Thackeray and Hall, 2019]] ). For the NH land snow, because the physics underpinning this relationship are credible, this opens the possibility to use it as an emergent constraint ( [[#Qu--2014|Qu and Hall, 2014]] ). Considering only the eight models whose seasonal cycle of albedo feedback falls within the observational range does not change the multi-model mean contribution to global α A (0.08 W m <sup>–2</sup> °C <sup>–1</sup> ) but decreases the inter-model spread by a factor of two (from ±0.03 to ±0.015 W m <sup>–2</sup> °C <sup>–1</sup> ; [[#Qu--2014|Qu and Hall, 2014]] ). For Arctic sea ice, [[#Thackeray--2019|Thackeray and Hall (2019)]] show that the seasonal cycle also provides an emergent constraint, at least until mid-century when the relationship degrades. They find that the CMIP5 multi-model mean of the Arctic sea ice contribution to α A <sub></sub> is 0.13 W m <sup>–2</sup> °C <sup>–1</sup> and that the inter-model spread is reduced by a factor of two (from ±0.04 to ±0.02 W m <sup>–2</sup> °C <sup>–1</sup> ) when the emergent constraint is used. This model estimate is smaller than observational estimates ( [[#Pistone--2014|Pistone et al., 2014]] ; [[#Cao--2015|Cao et al., 2015]] ) except those of [[#Donohoe--2020|Donohoe et al. (2020)]] . This can be traced to CMIP5 models generally underestimating the rate of Arctic sea ice loss during recent decades ( [[IPCC:Wg1:Chapter:Chapter-9#9.3.1|Section 9.3.1]] ; [[#Stroeve--2012|Stroeve et al., 2012]] ; [[#Flato--2013|Flato et al., 2013]] ), though this may also be an expression of internal variability, since the observed behaviour is captured within large ensemble simulations ( [[#Notz--2015|Notz, 2015]] ). CMIP6 models better capture the observed Arctic sea ice decline ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.1|Section 3.4.1]] ). In the SH the opposite situation is observed. Observations show relatively flat trends in SH sea ice over the satellite era ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1|Section 2.3.2.1]] ) whereas CMIP5 models simulate a small decrease ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.1|Section 3.4.1]] ). SH α A is presumably larger in models than observations but only contributes about one quarter of the global α A . Thus, we assess that α A estimates are consistent, at global scale, in CMIP5 and CMIP6 models and satellite observations, though hemispheric differences and the role of internal variability need to be further explored.

Based on the multiple lines of evidence presented above that include observations, CMIP5 and CMIP6 models and theory, the global surface-albedo feedback is assessed to be positive with ''high confidence'' . The basic phenomena that drive this feedback are well understood and the different studies cover a large variety of hypotheses or behaviours, including how the evolution of clouds affects this feedback. The value of the global surface-albedo feedback is assessed to be α A <sub></sub> = 0.35 W m <sup>–2</sup> °C <sup>–1</sup> , with a ''very likely'' range from 0.10 to 0.60 W m <sup>–2</sup> °C <sup>–1</sup> and a ''likely'' range from 0.25 to 0.45 W m <sup>–2</sup> °C <sup>–1</sup> with ''high confidence'' .

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