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

=== 7.4.2 Assessing Climate Feedbacks ===

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This section provides an overall assessment of individual feedback parameters, α x , by combining different lines of evidence from observations, theory, process models and ESMs. To achieve this, we review the understanding of the key processes governing the feedbacks, why the feedback estimates differ among models, studies or approaches, and the extent to which these approaches yield consistent results. The individual terms assessed are the Planck response ( [[#7.4.2.1|Section 7.4.2.1]] ) and feedbacks associated with changes in water vapour and lapse rate ( [[#7.4.2.2|Section 7.4.2.2]] ), surface albedo ( [[#7.4.2.3|Section 7.4.2.3]] ), clouds ( [[#7.4.2.4|Section 7.4.2.4]] ), biogeophysical and non-CO <sub>2</sub> biogeochemical processes ( [[#7.4.2.5|Section 7.4.2.5]] ), and ice sheets ( [[#7.4.2.6|Section 7.4.2.6]] ). A synthesis is provided in ( [[#7.4.2.7|Section 7.4.2.7]] . Climate feedbacks in CMIP6 models are then evaluated in ( [[#7.4.2.8|Section 7.4.2.8]] , with an explanation of how they have been incorporated into the assessment.

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==== 7.4.2.1 Planck Response ====

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The Planck response represents the additional thermal or longwave (LW) emission to space arising from vertically uniform warming of the surface and the atmosphere. The Planck response α P , often called the Planck feedback, plays a fundamental stabilizing role in Earth’s climate and has a value that is strongly negative: a warmer planet radiates more energy to space. A crude estimate of α P can be made using the normalized greenhouse effect g̃ , defined as the ratio between the greenhouse effect ''G'' and the upwelling LW flux at the surface ( [[#Raval--1989|Raval and Ramanathan, 1989]] ). Current estimates ( [[#7.2|Section 7.2]] , Figure 7.2) give ''G'' = 159 W m <sup>–2</sup> and g̃ ≈ 0.4. Assuming g̃ is constant, one obtains for a surface temperature ''T'' s = 288 K, α P = ( g – 1) 4 σ ''T'' <sup>3</sup> s ≈ –3.3 W m <sup>–2</sup> °C <sup>–1</sup> , where σ is the Stefan–Boltzmann constant. This parameter α P is estimated more accurately using kernels obtained from meteorological reanalysis or climate simulations ( [[#Soden--2006|Soden and Held, 2006]] ; [[#Dessler--2013|Dessler, 2013]] ; [[#Vial--2013|Vial et al., 2013]] ; [[#Caldwell--2016|Caldwell et al., 2016]] ; [[#Colman--2017|Colman and Hanson, 2017]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ). Discrepancies among estimates primarily arise because differences in cloud distributions make the radiative kernels differ ( [[#Kramer--2019|Kramer et al., 2019]] ). Using six different kernels, [[#Zelinka--2020|Zelinka et al. (2020)]] obtained a spread of ±0.1 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation). Discrepancies among estimates secondarily arise from differences in the pattern of equilibrium surface temperature changes among ESMs. For the CMIP5 and CMIP6 models this introduces a spread of ±0.04 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation). The multi-kernel and multi-model mean of α P is equal to –3.20 W m <sup>–2</sup> °C <sup>–1</sup> for the CMIP5 and –3.22 W m <sup>–2</sup> °C <sup>–1</sup> for the CMIP6 models (Supplementary Material, Table 7.SM.5). Overall, there is ''high confidence'' in the estimate of the Planck response, which is assessed to be α P = –3.22 W m <sup>–2</sup> °C <sup>–1</sup> with a ''very likely'' range of –3.4 to –3.0 W m <sup>–2</sup> °C <sup>–1</sup> and a ''likely range'' of –3.3 to –3.1 W m <sup>–2</sup> °C <sup>–1</sup> .

The Planck temperature response Δ ''T'' P is the equilibrium temperature change in response to a forcing Δ ''F'' when the net feedback parameter is equal to the Planck response parameter: Δ ''T'' P ''= –'' Δ ''F /'' α P .

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==== 7.4.2.2 Water-vapour and Temperature Lapse-rate Feedbacks ====

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Two decompositions are generally used to analyse the feedbacks associated with a change in the water-vapour and temperature lapse-rate in the troposphere. As in any system, many feedback decompositions are possible, each of them highlighting a particular property or aspect of the system ( [[#Ingram--2010|Ingram, 2010]] ; [[#Held--2012|Held and Shell, 2012]] ; [[#Dufresne--2016|Dufresne and Saint-Lu, 2016]] ). The first decomposition considers separately the changes (and therefore feedbacks) in the lapse rate (LR) and specific humidity (WV). The second decomposition considers changes in the lapse rate assuming constant relative humidity (LR*) separately from changes in relative humidity (RH).

The specific humidity (WV) feedback, also known as the water-vapour feedback, quantifies the change in radiative flux at the TOA due to changes in atmospheric water vapour concentration associated with a change in global mean surface air temperature. According to theory, observations and models, the water vapour increase approximately follows the Clausius–Clapeyron relationship at the global scale with regional differences dominated by dynamical processes ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ; [[#Sherwood--2010a|Sherwood et al., 2010a]] ; [[#Chung--2014|Chung et al., 2014]] ; [[#Romps--2014|Romps, 2014]] ; R. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Schröder--2019|Schröder et al., 2019]] ). Greater atmospheric water vapour content, particularly in the upper troposphere, results in enhanced absorption of LW and SW radiation and reduced outgoing radiation. This is a positive feedback. Atmospheric moistening has been detected in satellite records ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.3|Section 2.3.1.3.3]] ), it is simulated by climate models ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.2|Section 3.3.2.2]] ), and the estimates agree within model and observational uncertainty ( [[#Soden--2005|Soden et al., 2005]] ; [[#Dessler--2013|Dessler, 2013]] ; [[#Gordon--2013|Gordon et al., 2013]] ; [[#Chung--2014|Chung et al., 2014]] ). The estimate of this feedback inferred from satellite observations is α WV <sub></sub> = 1.85 ± 0.32 W m <sup>–2</sup> °C <sup>–1</sup> (R. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ). This is consistent with the value α WV <sub></sub> = 1.77 ± 0.20 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) obtained with CMIP5 and CMIP6 models ( [[#Zelinka--2020|Zelinka et al., 2020]] ).

The lapse-rate (LR) feedback quantifies the change in radiative flux at the TOA due to a nonuniform change in the vertical temperature profile. In the tropics, the vertical temperature profile is mainly driven by moist convection and is close to a moist adiabat. The warming is larger in the upper troposphere than in the lower troposphere ( [[#Manabe--1975|Manabe and Wetherald, 1975]] ; [[#Santer--2005|Santer et al., 2005]] ; [[#Bony--2006|Bony et al., 2006]] ), leading to a larger radiative emission to space and therefore a negative feedback. This larger warming in the upper troposphere than at the surface has been observed over the last 20 years thanks to the availability of sufficiently accurate observations ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.2.2|Section 2.3.1.2.2]] ). In the extratropics, the vertical temperature profile is mainly driven by a balance between radiation, meridional heat transport and ocean heat uptake ( [[#Rose--2014|Rose et al., 2014]] ). Strong winter temperature inversions lead to warming that is larger in the lower troposphere ( [[#Payne--2015|Payne et al., 2015]] ; [[#Feldl--2017a|Feldl et al., 2017a]] ) and a positive LR feedback in polar regions ( [[#7.4.4.1|Section 7.4.4.1]] ; [[#Manabe--1975|Manabe and Wetherald, 1975]] ; [[#Bintanja--2012|Bintanja et al., 2012]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ). However, the tropical contribution dominates, leading to a negative global mean LR feedback ( [[#Soden--2006|Soden and Held, 2006]] ; [[#Dessler--2013|Dessler, 2013]] ; [[#Vial--2013|Vial et al., 2013]] ; [[#Caldwell--2016|Caldwell et al., 2016]] ). The LR feedback has been estimated at interannual time scales using meteorological reanalysis and satellite measurements of TOA fluxes ( [[#Dessler--2013|Dessler, 2013]] ). These estimates from climate variability are consistent between observations and ESMs ( [[#Dessler--2013|Dessler, 2013]] ; [[#Colman--2017|Colman and Hanson, 2017]] ). The mean and standard deviation of this feedback under global warming based on the cited studies are α LR <sub></sub> = –0.50 ± 0.20 W m <sup>–2</sup> °C <sup>–1</sup> ( [[#Dessler--2013|Dessler, 2013]] ; [[#Caldwell--2016|Caldwell et al., 2016]] ; [[#Colman--2017|Colman and Hanson, 2017]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ).

The second decomposition was proposed by [[#Held--2012|Held and Shell (2012)]] to separate the response that would occur under the assumption that relative humidity remains constant from that due to the change in relative humidity. The feedback is decomposed into three: (i) change in water vapour due to an identical temperature increase at the surface and throughout the troposphere assuming constant relative humidity, which will be called the Clausius–Clapeyron (CC) feedback here; (ii) change in LR assuming constant relative humidity (LR*); (iii) change in relative humidity (RH). Since AR5 it has been clarified that by construction, the sum of the temperature lapse rate and specific humidity (LR + WV) feedbacks is equal to the sum of the Clausius–Clapeyron feedback, the lapse rate feedback assuming constant relative humidity, and the feedback from changes in relative humidity (that is, CC + LR* + RH). Therefore, each of these two sums may simply be referred to as the ‘water-vapour plus lapse-rate’ feedback.

The CC feedback has a large positive value due to well understood thermodynamic and radiative processes: α CC <sub></sub> = 1.36 ± 0.04 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Held--2012|Held and Shell, 2012]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ). The lapse-rate feedback assuming a constant relative humidity (LR*) in CMIP6 models has small absolute values ( α LR <sub>*</sub> = –0.10 ± 0.07 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation)), as expected from theoretical arguments ( [[#Ingram--2010|Ingram, 2010]] , 2013). It includes the pattern effect of surface warming that modulates the lapse rate and associated specific humidity changes ( [[#Po-Chedley--2018b|Po-Chedley et al., 2018b]] ). The relative humidity feedback is close to zero ( α RH = 0.00 ± 0.06 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation)) and the spread among models is confined to the tropics ( [[#Sherwood--2010b|Sherwood et al., 2010b]] ; [[#Vial--2013|Vial et al., 2013]] ; [[#Takahashi--2016|Takahashi et al., 2016]] ; [[#Po-Chedley--2018b|Po-Chedley et al., 2018b]] ). The change in upper tropospheric RH is closely related to model representation of current climate ( [[#Sherwood--2010b|Sherwood et al., 2010b]] ; [[#Po-Chedley--2019|Po-Chedley et al., 2019]] ), and a reduction in model RH biases is expected to reduce the uncertainty of the RH feedback. At interannual time scales, it has been shown that the change in RH in the tropics is related to the change of the spatial organization of deep convection ( [[#Holloway--2017|Holloway et al., 2017]] ; [[#Bony--2020|Bony et al., 2020]] ).

Both decompositions allow estimates of the sum of the lapse-rate and specific humidity feedbacks α LR+WV . The multi-kernel and multi-model mean of α LR+WV <sub></sub> is equal to 1.24 and 1.26 W m <sup>–2</sup> °C <sup>–1</sup> respectively for CMIP5 and CMIP6 models, with a standard deviation of 0.10 W m <sup>–2</sup> °C <sup>–1</sup> ( [[#Zelinka--2020|Zelinka et al., 2020]] ). These values are larger than the recently assessed value of 1.15 W m <sup>–2</sup> °C <sup>–1</sup> by [[#Sherwood--2020|Sherwood et al. (2020)]] as a larger set of kernels, including those obtained from meteorological reanalysis, are used here.

Since AR5, the effect of the water vapour increase in the stratosphere as a result of global warming has been investigated by different studies. This increase produces a positive feedback between 0.1 and 0.3 W m <sup>–2</sup> °C <sup>–1</sup> if the stratospheric radiative response is computed assuming temperatures that are adjusted with fixed dynamical heating ( [[#Dessler--2013|Dessler et al., 2013]] ; [[#Banerjee--2019|Banerjee et al., 2019]] ). However, various feedbacks reduce this temperature adjustment and the overall physical (water vapour, temperature and dynamical) stratospheric feedback becomes much smaller (0.0 to 0.1 W m <sup>–2</sup> °C <sup>–1</sup> ; [[#Huang--2016|Huang et al., 2016]] , 2020; [[#Li--2020|Li and Newman, 2020]] ), with uncertainty arising from limitations of current ESMs in simulating stratospheric processes. The total stratospheric feedback is assessed at 0.05 ± 0.1 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation).

The combined ‘water-vapour plus lapse-rate’ feedback is positive. The main physical processes that drive this feedback are well understood and supported by multiple lines of evidence including models, theory and observations. The combined ‘water-vapour plus lapse-rate’ feedback parameter is assessed to be α LR+WV <sub></sub> = 1.30 W m <sup>–2</sup> °C <sup>–1</sup> , with a ''very likely'' range of 1.1 to 1.5 W m <sup>–2</sup> °C <sup>–1</sup> and a ''likely'' range of 1.2 to 1.4 W m <sup>–2</sup> °C <sup>–1</sup> with ''high confidence.''

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==== 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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==== 7.4.2.4 Cloud Feedbacks ====

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===== 7.4.2.4.1 Decomposition of clouds into regimes =====

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Clouds can be formed almost anywhere in the atmosphere when moist air parcels rise and cool, enabling the water vapour to condense. Clouds consist of liquid water droplets and/or ice crystals, and these droplets and crystals can grow into larger particles of rain, snow or drizzle. These microphysical processes interact with aerosols, radiation and atmospheric circulation, resulting in a highly complex set of processes governing cloud formation and life cycles that operate across a wide range of spatial and temporal scales.

Clouds have various types, from optically thick convective clouds to thin stratus and cirrus clouds, depending upon thermodynamic conditions and large-scale circulation (Figure 7.9). Over the equatorial warm pool and inter-tropical convergence zone (ITCZ) regions, high SSTs stimulate the development of deep convective cloud systems, which are accompanied by anvil and cirrus clouds near the tropopause where the convective air outflows. The large-scale circulation associated with these convective clouds leads to subsidence over the subtropical cool ocean, where deep convection is suppressed by a lower tropospheric inversion layer maintained by the subsidence and promoting the formation of shallow cumulus and stratocumulus clouds. In the extratropics, mid-latitude storm tracks control cloud formation, which occurs primarily in the frontal bands of extratropical cyclones. Since liquid droplets do not freeze spontaneously at temperatures warmer than approximately –40°C and ice nucleating particles that can aid freezing at warmer temperatures are scarce (see ( [[#7.3.3|Section 7.3.3]] ), extratropical clouds often consist both of super-cooled liquid and ice crystals, resulting in mixed-phase clouds.

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[[File:5aee661d7dc43dcdeffa6cfb9e858230 IPCC_AR6_WGI_Figure_7_9.png]]

'''Figure 7.9''' '''|''' '''Schematic cross section of diverse cloud responses to surface warming from the tropics to polar regions.''' Thick solid and dashed curves indicate the tropopause and the subtropical inversion layer in the current climate, respectively. Thin grey text and arrows represent robust responses in the thermodynamic structure to greenhouse warming, of relevance to cloud changes. Text and arrows in red, orange and green show the major cloud responses assessed with ''high'' , ''medium'' and ''low confidence'' , respectively, and the sign of their feedbacks to the surface warming is indicated in the parenthesis. Major advances since AR5 are listed in the box. Figure adapted from [[#Boucher--2013|Boucher et al. (2013)]] .

In the global energy budget at TOA, clouds affect shortwave (SW) radiation by reflecting sunlight due to their high albedo (cooling the climate system) and also longwave (LW) radiation by absorbing the energy from the surface and emitting at a lower temperature to space, that is, contributing to the greenhouse effect, warming the climate system. In general, the greenhouse effect of clouds strengthens with height whereas the SW reflection depends on the cloud optical properties. The effects of clouds on Earth’s energy budget are measured by the cloud radiative effect (CRE), which is the difference in the TOA radiation between clear and all skies (see ( [[#7.2.1|Section 7.2.1]] ). In the present climate, the SW CRE tends to be compensated by the LW CRE over the equatorial warm pool, leading to the net CRE pattern showing large negative values over the eastern part of the subtropical ocean and the extratropical ocean due to the dominant influence of highly reflective marine low-clouds.

In a first attempt to systematically evaluate equilibrium climate sensitivity (ECS) based on fully coupled general circulation models (GCMs) in AR4, diverging cloud feedbacks were recognized as a dominant source of uncertainty. An advance in understanding the cloud feedback was to assess feedbacks separately for different cloud regimes ( [[#Gettelman--2016|Gettelman and Sherwood, 2016]] ). A thorough assessment of cloud feedbacks in different cloud regimes was carried out in AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ), which assigned ''high'' or ''medium confidence'' for some cloud feedbacks but ''low'' or ''no'' ''confidence'' for others (Table 7.9). Many studies that estimate the net cloud feedback using CMIP5 simulations ( [[#Vial--2013|Vial et al., 2013]] ; [[#Caldwell--2016|Caldwell et al., 2016]] ; [[#Zelinka--2016|Zelinka et al., 2016]] ; [[#Colman--2017|Colman and Hanson, 2017]] ) show different values depending on the methodology and the set of models used, but often report a large inter-model spread of the feedback, with the 90% confidence interval spanning both weak negative and strong positive net feedbacks. Part of this diversity arises from the dependence of the model cloud feedbacks on the parametrization of clouds and their coupling to other sub-grid-scale processes ( [[#Zhao--2015|Zhao et al., 2015]] ).

Since AR5, community efforts have been undertaken to understand and quantify the cloud feedbacks in various cloud regimes coupled with large-scale atmospheric circulation ( [[#Bony--2015|Bony et al., 2015]] ). For some cloud regimes, alternative tools to ESMs, such as observations, theory, high-resolution cloud resolving models (CRMs), and large eddy simulations (LES), help quantify the feedbacks. Consequently, the net cloud feedback derived from ESMs has been revised by assessing the regional cloud feedbacks separately and summing them with weighting by the ratio of fractional coverage of those clouds over the globe to give the global feedback, following an approach adopted in [[#Sherwood--2020|Sherwood et al. (2020)]] . This ‘bottom-up’ assessment is explained below with a summary of updated confidence of individual cloud feedback components (Table 7.9). Dependence of cloud feedbacks on evolving patterns of surface warming will be discussed in ( [[#7.4.4|Section 7.4.4]] and is not explicitly taken into account in the assessment presented in this section.

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<span id="assessment-for-individual-cloud-regimes"></span>
===== 7.4.2.4.2 Assessment for individual cloud regimes =====

<div id="h4-8-siblings" class="h4-siblings"></div>

<span id="high-cloud-altitude-feedback"></span>
====== High-cloud altitude feedback ======

It has long been argued that cloud-top altitude rises under global warming, concurrent with the rising of the tropopause at all latitudes ( [[#Marvel--2015|Marvel et al., 2015]] ; [[#Thompson--2017|Thompson et al., 2017]] ). This increasing altitude of high-clouds was identified in early generation GCMs and the tropical high-cloud altitude feedback was assessed to be positive with ''high confidence'' in AR5 ( [[#Boucher--2013|Boucher et al., 2013]] ). This assessment is supported by a theoretical argument called the ‘fixed anvil temperature mechanism’, which ensures that the temperature of the convective detrainment layer does not change when the altitude of high-cloud tops increases with the rising tropopause ( [[#Hartmann--2002|Hartmann and Larson, 2002]] ). Because the cloud-top temperature does not change significantly with global warming, cloud LW emission does not increase even though the surface warms, resulting in an enhancement of the high-cloud greenhouse effect (a positive feedback; [[#Yoshimori--2020|Yoshimori et al. (2020)]] ). The upward shift of high-clouds with surface warming is detected in observed interannual variability and trends in satellite records for recent decades ( [[#Chepfer--2014|Chepfer et al., 2014]] ; [[#Norris--2016|Norris et al., 2016]] ; [[#Saint-Lu--2020|Saint-Lu et al., 2020]] ). The observational detection is not always successful ( [[#Davies--2017|Davies et al., 2017]] ), but the cloud altitude shifts similarly in many CRM experiments ( [[#Khairoutdinov--2013|Khairoutdinov and Emanuel, 2013]] ; [[#Tsushima--2014|Tsushima et al., 2014]] ; [[#Narenpitak--2017|Narenpitak et al., 2017]] ). The high-cloud altitude feedback was estimated to be 0.5 W m <sup>–2</sup> °C <sup>–1</sup> based on GCMs in AR5, but is revised, using a recent re-evaluation that excludes aliasing effects by reduced low-cloud amounts, downward to 0.22 ± 0.12 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Zhou--2014|Zhou et al., 2014]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ). In conclusion, there is ''high confidence'' in the positive high-cloud altitude feedback simulated in ESMs as it is supported by theoretical, observational, and process modelling studies.

<span id="tropical-high-cloud-amount-feedback"></span>
====== Tropical high-cloud amount feedback ======

Updrafts in convective plumes lead to detrainment of moisture at a level where the buoyancy diminishes, and thus deep convective clouds over high SSTs in the tropics are accompanied by anvil and cirrus clouds in the upper troposphere. These clouds, rather than the convective plumes themselves, play a substantial role in the global TOA radiation budget. In the present climate, the net CRE of these clouds is small due to a cancellation between the SW and LW components ( [[#Hartmann--2001|Hartmann et al., 2001]] ). However, high-clouds with different optical properties could respond to surface warming differently, potentially perturbing this radiative balance and therefore leading to a non-zero feedback.

A thermodynamic mechanism referred to as the ‘stability iris effect’ has been proposed to explain that the anvil cloud amount decreases with surface warming ( [[#Bony--2016|Bony et al., 2016]] ). In this mechanism, a temperature-mediated increase of static stability in the upper troposphere, where convective detrainment occurs, acts to balance a weakened mass outflow from convective clouds, and thereby reduce anvil cloud areal coverage (Figure 7.9). The reduction of anvil cloud amount is accompanied by enhanced convective aggregation that causes a drying of the surrounding air and thereby increases the LW emission to space that acts as a negative feedback ( [[#Bony--2020|Bony et al., 2020]] ). This phenomenon is found in many CRM simulations ( [[#Emanuel--2014|Emanuel et al., 2014]] ; [[#Wing--2014|Wing and Emanuel, 2014]] ; [[#Wing--2020|Wing et al., 2020]] ) and also identified in observed interannual variability ( [[#Stein--2017|Stein et al., 2017]] ; [[#Saint-Lu--2020|Saint-Lu et al., 2020]] ).

Despite the reduction of anvil cloud amount supported by several lines of evidence, estimates of radiative feedback due to high-cloud amount changes is highly uncertain in models. The assessment presented here is guided by combined analyses of TOA radiation and cloud fluctuations at interannual time scale using multiple satellite datasets. The observationally based local cloud amount feedback associated with optically thick high-clouds is negative, leading to its global contribution (by multiplying the mean tropical anvil cloud fraction of about 8%) of –0.24 ± 0.05 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) for LW ( [[#Vaillant%20de%20Guélis--2018|Vaillant de Guélis et al., 2018]] ). Also, there is a positive feedback due to increase of optically thin cirrus clouds in the tropopause layer, estimated to be 0.09 ± 0.09 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Zhou--2014|Zhou et al., 2014]] ). The negative LW feedback due to reduced amount of thick high-clouds is partly compensated by the positive SW feedback (due to less reflection of solar radiation), so that the tropical high-cloud amount feedback is assessed to be equal to or smaller than their sum. Consistently, the net high-cloud feedback in the tropical convective regime, including a part of the altitude feedback, is estimated to have the global contribution of –0.13 ± 0.06 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Williams--2017|Williams and Pierrehumbert, 2017]] ). The negative cloud LW feedback is considerably biased in CMIP5 GCMs ( [[#Mauritsen--2015|Mauritsen and]] [[#Stevens--2015|Stevens, 2015]] ; [[#Su--2017|Su et al., 2017]] ; [[#Li--2019|Li et al., 2019]] ) and highly uncertain, primarily due to differences in the convective parametrization ( [[#Webb--2015|Webb et al., 2015]] ). Furthermore, high-resolution CRM simulations cannot alone be used to constrain uncertainty because the results depend on parametrized cloud microphysics and turbulence ( [[#Bretherton--2014|Bretherton et al., 2014]] ; [[#Ohno--2019|Ohno et al., 2019]] ). Therefore, the tropical high-cloud amount feedback is assessed as negative but with ''low confidence'' given the lack of modelling evidence. Taking observational estimates altogether and methodological uncertainty into account, the global contribution of the high-cloud amount feedback is assessed to be –0.15 ± 0.2 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation).

<span id="subtropical-marine-low-cloud-feedback"></span>
====== Subtropical marine low-cloud feedback ======

It has long been argued that the response of marine boundary-layer clouds over the subtropical ocean to surface warming was the largest contributor to the spread among GCMs in the net cloud feedback ( [[#Boucher--2013|Boucher et al., 2013]] ). However, uncertainty of the marine low-cloud feedback has been reduced considerably since AR5 through combined knowledge from theoretical, modelling and observational studies ( [[#Klein--2017|Klein et al., 2017]] ). Processes that control the low-clouds are complex and involve coupling with atmospheric motions on multiple scales, from the boundary-layer turbulence to the large-scale subsidence, which may be represented by a combination of shallow and deep convective mixing ( [[#Sherwood--2014|Sherwood et al., 2014]] ).

In order to disentangle the large-scale processes that cause the cloud amount either to increase or decrease in response to the surface warming, the cloud feedback has been expressed in terms of several ‘cloud controlling factors’ ( [[#Qu--2014|Qu et al., 2014]] , 2015; [[#Zhai--2015|Zhai et al., 2015]] ; [[#Brient--2016|Brient and Schneider, 2016]] ; [[#Myers--2016|Myers and Norris, 2016]] ; [[#McCoy--2017a|McCoy et al., 2017a]] ). The advantage of this approach over conventional calculation of cloud feedbacks is that the temperature-mediated cloud response can be estimated without using information of the simulated cloud responses that are less well-constrained than the changes in the environmental conditions. Two dominant factors are identified for the subtropical low-clouds: a thermodynamic effect due to rising SST that acts to reduce low-cloud by enhancing cloud-top entrainment of dry air, and a stability effect accompanied by an enhanced inversion strength that acts to increase low-cloud ( [[#Qu--2014|Qu et al., 2014]] , 2015; [[#Kawai--2017|Kawai et al., 2017]] ). These controlling factors compensate with a varying degree in different ESMs, but can be constrained by referring to the observed seasonal or interannual relationship between the low-cloud amount and the controlling factors in the environment as a surrogate. The analysis leads to a positive local feedback that has the global contribution of 0.14 to 0.36 W m <sup>–2</sup> °C <sup>–1</sup> ( [[#Klein--2017|Klein et al., 2017]] ), to which the feedback in the stratocumulus regime dominates over the feedback in the trade cumulus regime ( [[#Cesana--2019|Cesana et al., 2019]] ; [[#Radtke--2021|Radtke et al., 2021]] ). The stratocumulus feedback may be underestimated because explicit simulations using LES show a larger local feedback of up to 2.5 W m <sup>–2</sup> °C <sup>–1</sup> , corresponding to the global contribution of 0.2 W m <sup>–2</sup> °C <sup>–1</sup> by multiplying the mean tropical stratocumulus fraction of about 8% ( [[#Bretherton--2015|Bretherton, 2015]] ). Supported by different lines of evidence, the subtropical marine low-cloud feedback is assessed as positive with ''high confidence'' . Based on the combined estimate using LESs and the cloud controlling factor analysis, the global contribution of the feedback due to marine low-clouds equatorward of 30° is assessed to be 0.2 ± 0.16 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation), for which the range reflects methodological uncertainties.

<span id="land-cloud-feedback"></span>
====== Land cloud feedback ======

Intensification of the global hydrological cycle is a robust feature of global warming, but at the same time, many land areas in the subtropics will experience drying at the surface and in the atmosphere ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|Section 8.2.2]] ). This occurs due to limited water availability in these regions, where the cloudiness is consequently expected to decrease. Reduction in clouds over land is consistently identified in the CMIP5 models and also in a GCM with explicit convection ( [[#Bretherton--2014|Bretherton et al., 2014]] ; [[#Kamae--2016a|Kamae et al., 2016a]] ). Because low-clouds make up the majority of subtropical land clouds, this reduced amount of low-clouds reflects less solar radiation and leads to a positive feedback similar to the marine low-clouds. The mean estimate of the global land cloud feedback in CMIP5 models is smaller than the marine low-cloud feedback, 0.08 ± 0.08 W m <sup>–2</sup> °C <sup>–1</sup> ( [[#Zelinka--2016|Zelinka et al., 2016]] ). These values are nearly unchanged in CMIP6 ( [[#Zelinka--2020|Zelinka et al., 2020]] ). However, ESMs still have considerable biases in the climatological temperature and cloud fraction over land, and the magnitude of this feedback has not yet been supported by observational evidence. Therefore, the feedback due to decreasing land clouds is assessed to be 0.08 ± 0.08 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) with ''low confidence'' .

<span id="mid-latitude-cloud-amount-feedback"></span>
====== Mid-latitude cloud amount feedback ======

Poleward shifts in the mid-latitude jets are evident since the 1980s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] ) and are a feature of the large-scale circulation change in future projections ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.6|Section 4.5.1.6]] ). Because mid-latitude clouds over the North Pacific, North Atlantic and Southern Ocean are induced mainly by extratropical cyclones in the storm tracks along the jets, it has been suggested that the jet shifts should be accompanied by poleward shifts in the mid-latitude clouds, which would result in a positive feedback through the reduced reflection of insolation ( [[#Boucher--2013|Boucher et al., 2013]] ). However, studies since AR5 have revealed that this proposed mechanism does not apply in practice ( [[#Ceppi--2015|Ceppi and Hartmann, 2015]] ). While a poleward shift of mid-latitude cloud maxima in the free troposphere has been identified in satellite and ground-based observations ( [[#Bender--2012|Bender et al., 2012]] ; [[#Eastman--2013|Eastman and Warren, 2013]] ), associated changes in net CRE are small because the responses in high and low-clouds to the jet shift act to cancel each other ( [[#Grise--2016|Grise and Medeiros, 2016]] ; [[#Tselioudis--2016|Tselioudis et al., 2016]] ; [[#Zelinka--2018|Zelinka et al., 2018]] ). This cancellation is not well captured in ESMs ( [[#Lipat--2017|Lipat et al., 2017]] ), but the above findings show that the mid-latitude cloud feedback is not dynamically driven by the poleward jet shifts, which are rather suggested to occur partly in response to changes in high clouds (Y. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ).

Thermodynamics play an important role in controlling extratropical cloud amount equatorward of about 50° latitude. Recent studies showed, using observed cloud controlling factors, that the mid-latitude low-cloud fractions decrease with rising SST, which also acts to weaken stability of the atmosphere unlike in the subtropics ( [[#McCoy--2017a|McCoy et al., 2017a]] ). ESMs consistently show a decrease of cloud amounts and a resultant positive SW feedback in the 30°–40° latitude bands, which can be constrained using observations of seasonal migration of cloud amount ( [[#Zhai--2015|Zhai et al., 2015]] ). Based on the qualitative agreement between observations and ESMs, the mid-latitude cloud amount feedback is assessed as positive with ''medium confidence.'' Following these emergent constraint studies using observations and CMIP5/6 models, the global contribution of net cloud amount feedback over 30°–60° ocean areas, covering 27% of the globe, is assessed at 0.09 ± 0.1 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation), in which the uncertainty reflects potential errors in models’ low-cloud response to changes in thermodynamic conditions.

<span id="extratropical-cloud-optical-depth-feedback"></span>
====== Extratropical cloud optical depth feedback ======

Mixed-phase clouds that consist of both liquid and ice are dominant over the Southern Ocean (50°S–80°S), which accounts for 20% of the net CRE in the present climate ( [[#Matus--2017|Matus and L’Ecuyer, 2017]] ). It has been argued that the cloud optical depth (opacity) will increase over the Southern Ocean as warming drives the replacement of ice-dominated clouds with liquid-dominated clouds ( [[#Tan--2019|Tan et al., 2019]] ). Liquid clouds generally consist of many small cloud droplets, while the crystals in ice clouds are orders of magnitude fewer in number and much larger, causing the liquid clouds to be optically thicker and thereby resulting in a negative feedback ( [[#Boucher--2013|Boucher et al., 2013]] ). However, this phase-change feedback works effectively only below freezing temperature ( [[#Lohmann--2018|Lohmann and Neubauer, 2018]] ; [[#Terai--2019|Terai et al., 2019]] ) and other processes that increase or decrease liquid water path (LWP) may also affect the optical depth feedback ( [[#McCoy--2019|McCoy et al., 2019]] ).

Due to insufficient amounts of super-cooled liquid water in the simulated atmospheric mean state, many CMIP5 models overestimated the conversion from ice to liquid clouds with climate warming and the resultant negative phase-change feedback ( [[#Kay--2016a|Kay et al., 2016a]] ; [[#Tan--2016|Tan et al., 2016]] ; [[#Lohmann--2018|Lohmann and Neubauer, 2018]] ). This feedback can be constrained using satellite-derived LWP observations over the past 20 years that enable estimates of both long-term trends and the interannual relationship with SST variability ( [[#Gordon--2014|Gordon and Klein, 2014]] ; [[#Ceppi--2016|Ceppi et al., 2016]] ; [[#Manaster--2017|Manaster et al., 2017]] ). The observationally-constrained SW feedback ranges from –0.91 to –0.46 W m <sup>–2</sup> °C <sup>–1</sup> over 40°S–70°S depending on the methodology ( [[#Ceppi--2016|Ceppi et al., 2016]] ; [[#Terai--2016|Terai et al., 2016]] ). In some CMIP6 models, representation of super-cooled liquid water content has been improved, leading to weaker negative optical depth feedback over the Southern Ocean closer to observational estimates ( [[#Bodas-Salcedo--2019|Bodas-Salcedo et al., 2019]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ). This improvement at the same time results in a positive optical depth feedback over other extratropical ocean where LWP decreased in response to reduced stability in those CMIP6 models ( [[#Zelinka--2020|Zelinka et al., 2020]] ). Given the accumulated observational estimates and an improved agreement between ESMs and observations, the extratropical optical depth feedback is assessed to be small negative with ''medium confidence.'' Quantitatively, the global contribution of this feedback is assessed to have a value of –0.03 ± 0.05 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation) by combining estimates based on observed interannual variability and the cloud controlling factors.

<span id="arctic-cloud-feedback"></span>
====== Arctic cloud feedback ======

Clouds in polar regions, especially over the Arctic, form at low altitude above or within a stable to neutral boundary layer and are known to co-vary with sea ice variability beneath. Because the clouds reflect sunlight during summer but trap LW radiation throughout the year, seasonality plays an important role in cloud effects on Arctic climate ( [[#Kay--2016b|Kay et al., 2016b]] ). AR5 assessed that Arctic low-cloud amount will increase in boreal autumn and winter in response to declining sea ice in a warming climate, due primarily to an enhanced upward moisture flux over open water. The cloudier conditions during these seasons result in more downwelling LW radiation, acting as a positive feedback on surface warming ( [[#Kay--2009|Kay and Gettelman, 2009]] ). Over recent years, further evidence of the cloud contribution to the Arctic amplification has been obtained ( [[#7.4.4.1|Section 7.4.4.1]] ; [[#Goosse--2018|Goosse et al., 2018]] ). Space-borne lidar (light detection and ranging) observations show that the cloud response to summer sea ice loss is small and cannot overcome the cloud effect in autumn ( [[#Taylor--2015|Taylor et al., 2015]] ; [[#Morrison--2019|Morrison et al., 2019]] ). The seasonality of the cloud response to sea ice variability is reproduced in GCM simulations ( [[#Laîné--2016|Laîné et al., 2016]] ; [[#Yoshimori--2017|Yoshimori et al., 2017]] ). The agreement between observations and models indicates that the Arctic cloud feedback is positive at the surface. This leads to an Arctic cloud feedback at TOA that is ''likely'' positive, but very small in magnitude, as found in some climate models ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Morrison--2019|Morrison et al., 2019]] ). The observational estimates are sensitive to the analysis period and the choice of reanalysis data, and a recent estimate of the TOA cloud feedback over 60°N–90°N using atmospheric reanalysis data and CERES satellite observations suggests a regional value ranging from –0.3 to +0.5 W m <sup>–2</sup> °C <sup>–1</sup> , which corresponds to a global contribution of –0.02 to +0.03 W m <sup>–2</sup> °C <sup>–1</sup> (R. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Based on the overall agreement between ESMs and observations, the Arctic cloud feedback is assessed to be small positive and has the value of 0.01 ± 0.05 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation). The assessed range indicates that a negative feedback is almost as probable as a positive feedback, and the assessment that the Arctic cloud feedback is positive is therefore given ''low confidence'' .

<div id="7.4.2.4.3" class="h4-container"></div>

<span id="synthesis-for-the-net-cloud-feedback"></span>
===== 7.4.2.4.3 Synthesis for the net cloud feedback =====

<div id="h4-9-siblings" class="h4-siblings"></div>

The understanding of the response of clouds to warming and associated radiative feedback has deepened since AR5 (Figure 7.9 and FAQ 7.2). Particular progress has been made in the assessment of the marine low-cloud feedback, which has historically been a major contributor to the cloud feedback uncertainty but is no longer the largest source of uncertainty. Multiple lines of evidence (theory, observations, emergent constraints and process modelling) are now available in addition to ESM simulations, and the positive low-cloud feedback is consequently assessed with ''high confidence'' .

The best estimate of net cloud feedback is obtained by summing feedbacks associated with individual cloud regimes and assessed to be α C = 0.42 W m <sup>–2</sup> °C <sup>–1</sup> . By assuming that the uncertainties of individual cloud feedbacks are independent of each other, their standard deviations are added in quadrature, leading to the ''likely'' range of 0.12 to 0.72 W m <sup>–2</sup> °C <sup>–1</sup> and the ''very likely'' range of –0.10 to +0.94 W m <sup>–2</sup> °C <sup>–1</sup> (Table 7.10). This approach potentially misses feedbacks from cloud regimes that are not assessed, but almost all the major cloud regimes were taken into consideration ( [[#Gettelman--2016|Gettelman and Sherwood, 2016]] ) and therefore additional uncertainty will be small. This argument is also supported by an agreement between the net cloud feedback assessed here and the net cloud feedback directly estimated using observations. The observational estimate, which is sensitive to the period considered and is based on two atmospheric reanalyses (ERA-Interim and MERRA) and TOA radiation budgets derived from the CERES satellite observations for the years 2000–2010, is 0.54 ± 0.7 W m <sup>–2</sup> °C <sup>–1</sup> (one standard deviation; [[#Dessler--2013|Dessler, 2013]] ). The observational estimate overlaps with the assessed range of the net cloud feedback. The assessed ''very likely'' range is reduced by about 50% compared to AR5, but is still wide compared to those of other climate feedbacks (Table 7.10). The largest contribution to this uncertainty range is the estimate of tropical high-cloud amount feedback which is not yet well quantified using models.

In reality, different types of cloud feedback may occur simultaneously in one cloud regime. For example, an upward shift of high-clouds associated with the altitude feedback could be coupled to an increase/decrease of cirrus/anvil cloud fractions associated with the cloud amount feedback. Alternatively, slowdown of the tropical circulation with surface warming ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.3|Section 4.5.3]] and Figure 7.9) could affect both high and low-clouds so that their feedbacks are co-dependent. Quantitative assessments of such covariances require further knowledge about cloud feedback mechanisms, which will further narrow the uncertainty range.

In summary, deepened understanding of feedback processes in individual cloud regimes since AR5 leads to an assessment of the positive net cloud feedback with ''high confidence'' . A small probability (less than 10%) of a net negative cloud feedback cannot be ruled out, but this would require an extremely large negative feedback due to decreases in the amount of tropical anvil clouds or increases in optical depth of extratropical clouds over the Southern Ocean; neither is supported by current evidence.

<div id="_idContainer041" class="Basic-Text-Frame"></div>

'''Table 7.9''' '''|''' '''Assessed sign and confidence level of cloud feedbacks in different regimes in AR5 and AR6.''' For some cloud regimes, the feedback was not assessed in AR5, indicated by N/A.

{| class="wikitable"
|-
| Feedback

| AR5

| AR6

|-
| High-cloud altitude feedback

| Positive ( ''high confidence'' )

| Positive ( ''high confidence'' )

|-
| Tropical high-cloud amount feedback

| N/A

| Negative ( ''low confidence'' )

|-
| Subtropical marine low-cloud feedback

| N/A ( ''low confidence'' )

| Positive ( ''high confidence'' )

|-
| Land cloud feedback

| N/A

| Positive ( ''low confidence'' )

|-
| Mid-latitude cloud amount feedback

| Positive ( ''medium confidence'' )

| Positive ( ''medium confidence'' )

|-
| Extratropical cloud optical depth feedback

| N/A

| Small negative ( ''medium confidence'' )

|-
| Arctic cloud feedback

| Small positive ( ''very low confidence'' )

| Small positive ( ''low confidence'' )

|-
| Net cloud feedback

| Positive ( ''medium confidence'' )

| Positive ( ''high confidence'' )

|}

<div id="7.4.2.5" class="h3-container"></div>

<span id="biogeophysical-and-non-co-2-biogeochemical-feedbacks"></span>
==== 7.4.2.5 Biogeophysical and Non-CO <sub>2</sub> Biogeochemical Feedbacks ====

<div id="h3-28-siblings" class="h3-siblings"></div>

The feedbacks presented in the previous sections (Sections 7.4.2.1–7.4.2.4) are directly linked to physical climate variables (for example temperature, water vapour, clouds, or sea ice). The central role of climate feedbacks associated with these variables has been recognized since early studies of climate change. However, in addition to these physical climate feedbacks, the Earth system includes feedbacks for which the effect of global mean surface temperature change on the TOA energy budget is mediated through other mechanisms, such as the chemical composition of the atmosphere, or by vegetation changes. Among these additional feedbacks, the most important is the CO <sub>2</sub> feedback that describes how a change of the global surface temperature affects the atmospheric CO <sub>2</sub> concentration. In ESM simulations in which CO <sub>2</sub> emissions are prescribed, changes in surface carbon fluxes affect the CO <sub>2</sub> concentration in the atmosphere, the TOA radiative energy budget, and eventually the global mean surface temperature. In ESM simulations in which the CO <sub>2</sub> concentration is prescribed, changes in the carbon cycle allow compatible CO <sub>2</sub> emissions to be calculated, that is, the CO <sub>2</sub> emissions that are compatible with both the prescribed CO <sub>2</sub> concentration and the representation of the carbon cycle in the ESM. The CO <sub>2</sub> feedback is assessed in ( [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] [[IPCC:Wg1:Chapter:Chapter-5#5.4|Section 5.4]] ). The framework presented in this chapter assumes that the CO <sub>2</sub> concentration is prescribed, and our assessment of the net feedback parameter, α , does not include carbon cycle feedbacks on the atmospheric CO <sub>2</sub> concentration ( [[#7.1|Section 7.1]] and Box 7.1). However, our assessment of α does include non-CO <sub>2</sub> biogeochemical feedbacks (including effects due to changes in atmospheric methane concentration; [[#7.4.2.5.1|Section 7.4.2.5.1]] ) and biogeophysical feedbacks ( [[#7.4.2.5.2|Section 7.4.2.5.2]] ). A synthesis of the combination of biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks is given in [[#7.4.2.5.3|Section 7.4.2.5.3]] .

<div id="7.4.2.5.1" class="h4-container"></div>

<span id="non-co-2-biogeochemical-feedbacks"></span>
===== 7.4.2.5.1 Non-CO <sub>2</sub> biogeochemical feedbacks =====

<div id="h4-10-siblings" class="h4-siblings"></div>

The chemical composition of the atmosphere (beyond CO <sub>2</sub> and water vapour changes) is expected to change in response to a warming climate. These changes in greenhouse gases (methane, nitrous oxide and ozone) and aerosol amount (including dust) have the potential to alter the TOA energy budget and are collectively referred to as ‘non-CO <sub>2</sub> biogeochemical feedbacks’. Methane (CH <sub>4</sub> ) and nitrous oxide (N <sub>2</sub> O) feedbacks arise partly from changes in their emissions from natural sources in response to temperature change; these are assessed in ( [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] [[IPCC:Wg1:Chapter:Chapter-5#5.4.7|Section 5.4.7]] ; see also Figure 5.29c). Here we exclude the permafrost CH <sub>4</sub> feedback ( [[IPCC:Wg1:Chapter:Chapter-5#5.4.9.1.2|Section 5.4.9.1.2]] ) because, although associated emissions are projected to increase under warming on multi-decadal to centennial time scales, on longer time scales these emissions would eventually substantially decline as the permafrost carbon pools were depleted ( [[#Schneider%20von%20Deimling--2012|Schneider von Deimling et al., 2012]] , 2015). This leaves the wetland CH <sub>4</sub> , land N <sub>2</sub> O, and ocean N <sub>2</sub> O feedbacks, the assessed mean values of which sum to a positive feedback parameter of +0.04 [0.02 to 0.06] W m <sup>–2</sup> °C <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-5#5.4.7|Section 5.4.7]] . Other non-CO <sub>2</sub> biogeochemical feedbacks that are relevant to the net feedback parameter are assessed in [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (Section 6.4.5 and Table 6.8). These feedbacks are associated with sea salt, dimethyl sulphide, dust, ozone, biogenic volatile organic compounds, lightning, and CH <sub>4</sub> lifetime, and sum to a negative feedback parameter of –0.20 [–0.41 to +0.01] W m <sup>–2</sup> °C <sup>–1</sup> . The overall feedback parameter for non-CO <sub>2</sub> biogeochemical feedbacks is obtained by summing the [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] and [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] assessments, which gives –0.16 [–0.37 to +0.05] W m <sup>–2</sup> °C <sup>–1</sup> . However, there is ''low confidence'' in the estimates of both the individual non-CO <sub>2</sub> biogeochemical feedbacks as well as their total effect, as evident from the large range in the magnitudes of α from different studies, which can be attributed to diversity in how models account for these feedbacks and limited process-level understanding.

<div id="7.4.2.5.2" class="h4-container"></div>

<span id="biogeophysical-feedbacks"></span>

===== 7.4.2.5.2 Biogeophysical feedbacks =====

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Biogeophysical feedbacks are associated with changes in the spatial distribution and/or biophysical properties of vegetation, induced by surface temperature change and attendant hydrological cycle change. These vegetation changes can alter radiative fluxes directly via albedo changes, or via surface momentum or moisture flux changes and hence changes in cloud properties. However, the direct physiological response of vegetation to changes in CO <sub>2</sub> , including changes in stomatal conductance, is considered part of the CO <sub>2</sub> effective radiative forcing rather than a feedback ( [[#7.3.2.1|Section 7.3.2.1]] ). The time scale on which vegetation responds to climate change is relatively uncertain but can be from decades to hundreds of years ( [[#Willeit--2014|Willeit et al., 2014]] ), and could occur abruptly or as a tipping point (Sections 5.4.9.1.1, 8.6.2.1 and 8.6.2.2); equilibrium only occurs when the soil system and associated nutrient and carbon pools equilibrate, which can take millennia ( [[#Brantley--2008|Brantley, 2008]] ; [[#Sitch--2008|Sitch et al., 2008]] ). The overall effects of climate-induced vegetation changes may be comparable in magnitude to those from anthropogenic land-use and land-cover change ( [[#Davies-Barnard--2015|Davies-Barnard et al., 2015]] ). Climate models that include a dynamical representation of vegetation (e.g., [[#Reick--2013|Reick et al., 2013]] ; [[#Harper--2018|Harper et al., 2018]] ) are used to explore the importance of biogeophysical feedbacks ( [[#Notaro--2007|Notaro et al., 2007]] ; [[#Brovkin--2009|Brovkin et al., 2009]] ; [[#O’ishi--2009|O’ishi et al., 2009]] ; [[#Port--2012|Port et al., 2012]] ; [[#Willeit--2014|Willeit et al., 2014]] ; [[#Alo--2017|Alo and Anagnostou, 2017]] ; W. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#Armstrong--2019|Armstrong et al., 2019]] ). In AR5, it was discussed that such model experiments predicted that expansion of vegetation in the high latitudes of the Northern Hemisphere would enhance warming due to the associated surface-albedo change, and that reduction of tropical forests in response to climate change would lead to regional surface warming, due to reduced evapotranspiration (M. [[#Collins--2013|]] [[#Collins--2013|Collins et al., 2013]] ), but there was no assessment of the associated feedback parameter. The SRCCL stated that regional climate change can be dampened or enhanced by changes in local land cover, but that this depends on the location and the season; however, in general the focus was on anthropogenic land-cover change, and no assessment of the biogeophysical feedback parameter was carried out. There are also indications of a marine biogeophysical feedback associated with surface-albedo change due to changes in phytoplankton ( [[#Frouin--2002|Frouin and Iacobellis, 2002]] ; [[#Park--2015|Park et al., 2015]] ), but there is not currently enough evidence to quantitatively assess this feedback.

Since AR5, several studies have confirmed that a shift from tundra to boreal forests and the associated albedo change leads to increased warming in Northern Hemisphere high latitudes ( ''high confidence'' ) ( [[#Willeit--2014|Willeit et al., 2014]] ; W. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#Armstrong--2019|Armstrong et al., 2019]] ). However, regional modelling indicates that vegetation feedbacks may act to cool climate in the Mediterranean ( [[#Alo--2017|Alo and Anagnostou, 2017]] ), and in the tropics and subtropics the regional response is in general not consistent across models. On a global scale, several modelling studies have either carried out a feedback analysis ( [[#Stocker--2013|Stocker et al., 2013]] ; [[#Willeit--2014|Willeit et al., 2014]] ) or presented simulations that allow a feedback parameter to be estimated ( [[#O’ishi--2009|O’ishi et al., 2009]] ; [[#Armstrong--2019|Armstrong et al., 2019]] ), in such a way that the physiological response can be accounted for as a forcing rather than a feedback. The central estimates of the biogeophysical feedback parameter from these studies range from close to zero ( [[#Willeit--2014|Willeit et al., 2014]] ) to +0.13 W m <sup>–2</sup> °C <sup>–1</sup> ( [[#Stocker--2013|Stocker et al., 2013]] ). An additional line of evidence comes from the mid-Pliocene warm period (MPWP, Chapter 2, Cross-Chapter Box 2.1), for which paleoclimate proxies provide evidence of vegetation distribution and CO <sub>2</sub> concentrations. Model simulations that include various combinations of modern versus MPWP vegetation and CO <sub>2</sub> allow an associated feedback parameter to be estimated, as long as account is also taken of the orographic forcing ( [[#Lunt--2010|Lunt et al., 2010]] , 2012b). This approach has the advantage over pure modelling studies in that the reconstructed vegetation is based on (paleoclimate) observations, and is in equilibrium with the CO <sub>2</sub> forcing. However, there are uncertainties in the vegetation reconstruction in regions with little or no proxy data, and it is uncertain how much of the vegetation change is associated with the physiological response to CO <sub>2</sub> . This paleoclimate approach gives an estimate for the biogeophysical feedback parameter of +0.3 W m <sup>–2</sup> °C <sup>–1</sup> .

Given the limited number of studies, we take the full range of estimates discussed above for the biogeophysical feedback parameter, and assess the ''very likely'' range to be from 0.0 to +0.3 W m <sup>–2</sup> °C <sup>–1</sup> , with a central estimate of +0.15 W m <sup>–2</sup> °C <sup>–1</sup> ( ''low confidence'' ). Although this assessment is based on evidence from both models and paleoclimate proxies, and the studies above agree on the sign of the change, there is nonetheless ''limited evidence'' . Higher confidence could be obtained if there were more studies that allowed calculation of a biogeophysical feedback parameter (particularly from paleoclimates), and if the partitioning between biogeophysical feedbacks and physiological forcing were clearer for all lines of evidence.

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<span id="synthesis-of-biogeophysical-and-non-co-2-biogeochemical-feedbacks"></span>
===== 7.4.2.5.3 Synthesis of biogeophysical and non-CO 2 biogeochemical feedbacks =====

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The non-CO <sub>2</sub> biogeochemical feedbacks are assessed in ( [[#7.4.2.5.1|Section 7.4.2.5.1]] to be –0.16 [–0.37 to +0.05] W m <sup>–</sup> <sup>2</sup> °C <sup>–1</sup> and the biogeophysical feedbacks are assessed in ( [[#7.4.2.5.2|Section 7.4.2.5.2]] to be +0.15 [0.0 to +0.3] W m <sup>–2</sup> °C <sup>–1</sup> . The sum of the biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks is assessed to have a central value of –0.01 W m <sup>–2</sup> °C <sup>–1</sup> and a ''very likely'' range from –0.27 to +0.25 W m <sup>–2</sup> °C <sup>–1</sup> (Table 7.10). Given the relatively long time scales associated with the biological processes that mediate the biogeophysical and many of the non-CO <sub>2</sub> biogeochemical feedbacks, in comparison with the relatively short time scale of many of the underlying model simulations, combined with the small number of studies for some of the feedbacks, and the relatively small signals, this overall assessment has ''low confidence'' .

Some supporting evidence for this overall assessment can be obtained from the CMIP6 ensemble, which provides some pairs of instantaneous 4×CO <sub>2</sub> simulations carried out using related models, with and without biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks. This is not a direct comparison because these pairs of simulations may differ by more than just their inclusion of these additional feedbacks; furthermore, not all biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks are fully represented. However, a comparison of the pairs of simulations does provide a first-order estimate of the magnitude of these additional feedbacks. [[#Séférian--2019|Séférian et al. (2019)]] find a slightly more negative feedback parameter in CNRM-ESM2-1 (with additional feedbacks) then in CNRM-CM6-1 (a decrease of 0.02 W m <sup>–2</sup> °C <sup>–1</sup> , using the linear regression method from years 10–150). [[#Andrews--2019|Andrews et al. (2019)]] also find a slightly more negative feedback parameter when these additional feedbacks are included (a decrease of 0.04 W m <sup>–2</sup> °C <sup>–1</sup> in UKESM1 compared with HadGEM3-GC3.1). Both of these studies suggest a small but slightly negative feedback parameter for the combination of biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks, but with relatively large uncertainty given (i) interannual variability and (ii) that feedbacks associated with natural terrestrial emissions of CH <sub>4</sub> and N <sub>2</sub> O were not represented in either pair.

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==== 7.4.2.6 Long-Term Radiative Feedbacks Associated with Ice Sheets ====

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Although long-term radiative feedbacks associated with ice sheets are not included in our definition of ECS (Box 7.1), the relevant feedback parameter is assessed here because the time scales on which these feedbacks act are relatively uncertain, and the long-term temperature response to CO <sub>2</sub> forcing of the entire Earth system may be of interest.

Earth’s ice sheets (Greenland and Antarctica) are sensitive to climate change ( [[IPCC:Wg1:Chapter:Chapter-9#9.4|Section 9.4]] ; [[#Pattyn--2018|Pattyn et al., 2018]] ). Their time evolution is determined by both their surface mass balance and ice dynamic processes, with the latter being particularly important for the West Antarctic Ice Sheet. Surface mass balance depends on the net energy and hydrological fluxes at their surface, and there are mechanisms of ice-sheet instability that depend on ocean temperatures and basal melt rates ( [[IPCC:Wg1:Chapter:Chapter-9#9.4.1.1|Section 9.4.1.1]] ). The presence of ice sheets affects Earth’s radiative budget, hydrology, and atmospheric circulation due to their characteristic high albedo, low roughness length, and high altitude, and they influence ocean circulation through freshwater input from calving and melt (e.g., [[#Fyke--2018|Fyke et al., 2018]] ). Ice-sheet changes also modify surface albedo through the attendant change in sea level and therefore land area ( [[#Abe-Ouchi--2015|Abe-Ouchi et al., 2015]] ). The time scale for ice sheets to reach equilibrium is of the order of thousands of years ( [[#Clark--2016|Clark et al., 2016]] ). Due to the long time scales involved, it is a major challenge to run coupled climate–ice sheet models to equilibrium, and as a result, long-term simulations are often carried out with lower complexity models, and/or are asynchronously coupled.

In AR5, it was described that both the Greenland and Antarctic ice sheets would continue to lose mass in a warming world (M. [[#Collins--2013|]] [[#Collins--2013|Collins et al., 2013]] ), with a continuation in sea level rise beyond the year 2500 assessed as ''virtually certain'' . However, there was ''low confidence'' in the associated radiative feedback mechanisms, and as such, there was no assessment of the magnitude of long-term radiative feedbacks associated with ice sheets. That assessment is consistent with SROCC, wherein it was stated that ‘with limited published studies to draw from and no simulations run beyond 2100, firm conclusions regarding the net importance of atmospheric versus ocean melt feedbacks on the long-term future of Antarctica cannot be made.’

The magnitude of the radiative feedback associated with changes to ice sheets can be quantified by comparing the global mean long-term equilibrium temperature response to increased CO <sub>2</sub> concentrations in simulations that include interactive ice sheets with that of simulations that do not include the associated ice sheet–climate interactions ( [[#Swingedouw--2008|Swingedouw et al., 2008]] ; [[#Vizcaíno--2010|Vizcaíno et al., 2010]] ; [[#Goelzer--2011|Goelzer et al., 2011]] ; [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ). These simulations indicate that on multi-centennial time scales, ice-sheet mass loss leads to freshwater fluxes that can modify ocean circulation ( [[#Swingedouw--2008|Swingedouw et al., 2008]] ; [[#Goelzer--2011|Goelzer et al., 2011]] ; [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ). This leads to reduced surface warming (by about 0.2°C in the global mean after 1000 years; [[#7.4.4.1.1|Section 7.4.4.1.1]] ; [[#Goelzer--2011|Goelzer et al., 2011]] ), although other work suggests no net global temperature effect of ice-sheet mass loss ( [[#Vizcaíno--2010|Vizcaíno et al., 2010]] ). However, model simulations in which the Antarctic Ice Sheet is removed completely in a paleoclimate context indicate a positive global mean feedback on multi-millennial time scales due primarily to the surface-albedo change ( [[#Goldner--2014a|Goldner et al., 2014a]] ; [[#Kennedy-Asser--2019|Kennedy-Asser et al., 2019]] ); in ( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] [[IPCC:Wg1:Chapter:Chapter-9#9.6.3|Section 9.6.3]] ) it is assessed that such ice-free conditions could eventually occur given 7°C–13°C of warming. This net positive feedback from ice-sheet mass loss on long time scales is also supported by model simulations of the mid-Pliocene Warm Period (MPWP; Cross-chapter Box 2.1) in which the volume and area of the Greenland and West Antarctic ice sheets are reduced in model simulations in agreement with geological data ( [[#Chandan--2018|Chandan and Peltier, 2018]] ), leading to surface warming. As such, overall, on multi-centennial time scales the feedback parameter associated with ice sheets is ''likely'' negative ( ''medium confidence'' ), but on multi-millennial time scales by the time the ice sheets reach equilibrium, the feedback parameter is ''very likely'' positive ( ''high confidence'' ) (Table 7.10). However, a relative lack of models carrying out simulations with and without interactive ice sheets over centennial to millennial time scales means that there is currently not enough evidence to quantify the magnitude of these feedbacks, or the time scales on which they act.

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==== 7.4.2.7 Synthesis ====

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Table 7.10 summarizes the estimates and the assessment of the individual and the net feedbacks presented in the above sections. The uncertainty range of the net climate feedback was obtained by adding standard deviations of individual feedbacks in quadrature, assuming that they are independent and follow the Gaussian distribution. It is ''virtually certain'' that the net climate feedback is negative, primarily due to the Planck temperature response, indicating that climate acts to stabilize in response to radiative forcing imposed to the system. Supported by the level of confidence associated with the individual feedbacks, it is also ''virtually certain'' that the sum of the non-Planck feedbacks is positive. Based on Table 7.10 these climate feedbacks amplify the Planck temperature response by about 2.8 [1.9 to 5.9] times ''.'' Cloud feedback remains the largest contributor to uncertainty of the net feedback, but the uncertainty is reduced compared to AR5. A secondary contribution to the net feedback uncertainty is the biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks, which together are assessed to have a central value near zero and thus do not affect the central estimate of ECS. The net climate feedback is assessed to be –1.16 W m <sup>–2</sup> °C <sup>–1</sup> , ''likely'' from –1.54 to –0.78 W m <sup>–2</sup> °C <sup>–1</sup> , and ''very likely'' from –1.81 to –0.51 W m <sup>–2</sup> °C <sup>–1</sup> ''.''

Feedback parameters in climate models are calculated assuming that they are independent of each other, except for a well-known co-dependency between the water vapour (WV) and lapse rate (LR) feedbacks. When the inter-model spread of the net climate feedback is computed by adding in quadrature the inter-model spread of individual feedbacks, it is 17% wider than the spread of the net climate feedback directly derived from the ensemble. This indicates that the feedbacks in climate models are partly co-dependent. Two possible co-dependencies have been suggested ( [[#Huybers--2010|Huybers, 2010]] ; [[#Caldwell--2016|Caldwell et al., 2016]] ). One is a negative covariance between the LR and longwave cloud feedbacks, which may be accompanied by a deepening of the troposphere ( [[#O’Gorman--2013|O’Gorman and Singh, 2013]] ; [[#Yoshimori--2020|Yoshimori et al., 2020]] ) leading both to greater rising of high-clouds and a larger upper-tropospheric warming. The other is a negative covariance between albedo and shortwave cloud feedbacks, which may originate from the Arctic regions: a reduction in sea ice enhances the shortwave cloud radiative effect because the ocean surface is darker than sea ice ( [[#Gilgen--2018|Gilgen et al., 2018]] ). This covariance is reinforced as the decrease of sea ice leads to an increase in low-level clouds ( [[#Mauritsen--2013|Mauritsen et al., 2013]] ). However, the mechanism causing these co-dependences between feedbacks is not well understood yet and a quantitative assessment based on multiple lines of evidence is difficult. Therefore, this synthesis assessment does not consider any co-dependency across individual feedbacks.

The assessment of the net climate feedback presented above is based on a single approach (i.e., process understanding) and directly results in a value for ECS given in ( [[#7.5.1|Section 7.5.1]] ; this is in contrast to the synthesis assessment of ECS in ( [[#7.5.5|Section 7.5.5]] which combines multiple approaches. The total (net) feedback parameter consistent with the final synthesis assessment of the ECS and Equation 7.1 (Box 7.1) is provided there.

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<span id="climate-feedbacks-in-esms"></span>
==== 7.4.2.8 Climate Feedbacks in ESMs ====

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Since AR5, many modelling groups have newly participated in CMIP experiments, leading to an increase in the number of models in CMIP6 [[IPCC:Wg1:Chapter:Chapter-1#1.5.4|Section 1.5.4]] ). Other modelling groups that contributed to CMIP5 also updated their ESMs for carrying out CMIP6 experiments. While some of the CMIP6 models share components and are therefore not independent, they are analysed independently when calculating climate feedbacks. This, and more subtle forms of model inter-dependence, creates challenges when determining appropriate model weighting schemes ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.4|Section 1.5.4]] ). Additionally, it must be kept in mind that the ensemble sizes of the CMIP5 and CMIP6 models are not sufficiently large to sample the full range of model uncertainty.

The multi-model mean values of all physical climate feedbacks are calculated using the radiative kernel method ( [[#7.4.1|Section 7.4.1]] ) and compared with the assessment in the previous sections (Figure 7.10). For CMIP models, there is a discrepancy between the net climate feedback calculated directly using the time evolutions of Δ ''T'' and Δ ''N'' in each model and the accumulation of individual feedbacks, but it is negligibly small (Supplementary Material 7.SM.4). Feedbacks due to biogeophysical and non-CO <sub>2</sub> biogeochemical processes are included in some models but neglected in the kernel analysis. In AR6, biogeophysical and non-CO <sub>2</sub> biogeochemical feedbacks are explicitly assessed ( [[#7.4.2.5|Section 7.4.2.5]] ).

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'''Table 7.10''' '''|''' '''Synthesis assessment of climate feedbacks (central estimate shown in bold).''' The mean values and their 90% ranges in CMIP5/6 models, derived using multiple radiative kernels ( [[#Zelinka--2020|Zelinka et al., 2020]] ) are also presented for comparison.

{| class="wikitable"
|-
| rowspan="2"| Feedback Parameter α x (W m <sup>–2</sup> °C <sup>–1</sup> )

| CMIP5 GCMs

| CMIP6 ESMs

| colspan="4"| AR6 Assessed Ranges

|-
| Mean and

5–95% Interval

| Mean and

5–95% Interval

| Central Estimate

| Very likely Interval

| Likely Interval

| Level of Confidence

|-
| Planck

| –3.20 [–3.3 to –3.1]

| –3.22 [–3.3 to –3.1]

| '''–3.22'''

| –3.4 to –3.0

| –3.3 to –3.1

| ''high''

|-
| WV+LR

| 1.24 [1.08 to 1.35]

| 1.25 [1.14 to 1.45]

| '''1.30'''

| 1.1 to 1.5

| 1.2 to 1.4

| ''high''

|-
| Surface albedo

| 0.41 [0.25 to 0.56]

| 0.39 [0.26 to 0.53]

| '''0.35'''

| 0.10 to 0.60

| 0.25 to 0.45

| ''medium''

|-
| Clouds

| 0.41 [–0.09 to 1.1]

| 0.49 [–0.08 to 1.1]

| '''0.42'''

| –0.10 to 0.94

| 0.12 to 0.72

| ''high''

|-
| Biogeophysical and non-CO <sub>2</sub> biogeochemical

| Not evaluated

| Not evaluated

| '''–0.01'''

| –0.27 to 0.25

| –0.16 to 0.14

| ''low''

|-
| Residual of kernel estimates

| 0.06 [–0.17 to 0.29]

| 0.05 [–0.18 to 0.28 ]

|
|-
| '''Net''' (i.e., relevant for ECS)

| –1.08 [–1.61 to –0.68]

| –1.03 [–1.54 to –0.62]

| '''–1.16'''

| –1.81 to –0.51

| –1.54 to –0.78

| ''medium''

|-
| Long-term ice-sheet feedbacks (millennial scale)

|
| &gt;0.0

|
| ''high''

|}

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[[File:abad03bd326a3f3e49cb28d9d362a7d5 IPCC_AR6_WGI_Figure_7_10.png]]

'''Figure 7.10''' '''|''' '''Global mean climate feedbacks estimated in''' ''abrupt 4xCO2'' '''simulations of 29 CMIP5 models (light blue) and 49 CMIP6 models (orange), compared with those assessed in this Report (red).''' Individual feedbacks for CMIP models are averaged across six radiative kernels as computed in [[#Zelinka--2020|Zelinka et al. (2020)]] . The white line, black box and vertical line indicate the mean, 66% and 90% ranges, respectively. The shading represents the probability distribution across the full range of GCM/ESM values and for the 2.5–97.5 percentile range of the AR6 normal distribution. The unit is W m <sup>–2</sup> °C <sup>–1</sup> . Feedbacks associated with biogeophysical and non-CO <sub>2</sub> biogeochemical processes are assessed in AR6, but they are not explicitly estimated from general circulation models (GCMs)/Earth system models (ESMs) in CMIP5 and CMIP6. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

All the physical climate feedbacks apart from clouds are very similar in the CMIP5 and CMIP6 model ensembles (see also Table 7.10). These values, where possible supported by other lines of evidence, are used for assessing feedbacks in Sections 7.4.2.1–7.4.2.3. A difference found between CMIP5 and CMIP6 models is the net cloud feedback, which is larger in CMIP6 by about 20%. This change is the major cause of less-negative values of the net climate feedback in CMIP6 than in CMIP5 and hence an increase in modelled ECs ( [[#7.5.1|Section 7.5.1]] ).

A remarkable improvement of cloud representation in some CMIP6 models is the reduced error of the too-weak negative shortwave CRE over the Southern Ocean ( [[#Bodas-Salcedo--2019|Bodas-Salcedo et al., 2019]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ) due to a more realistic simulation of supercooled liquid droplets and associated cloud optical depths that were biased low commonly in CMIP5 models ( [[#McCoy--2014a|McCoy et al., 2014a]] , b). Because the negative cloud optical depth feedback occurs due to ‘brightening’ of clouds via phase change from ice to liquid cloud particles in response to surface warming ( [[#Cesana--2017|Cesana and]] [[#Storelvmo--2017|Storelvmo, 2017]] ), the extratropical cloud shortwave feedback tends to be less negative or even slightly positive in models with reduced errors ( [[#Bjordal--2020|Bjordal et al., 2020]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ). The assessment of cloud feedbacks in ( [[#7.4.2.4|Section 7.4.2.4]] incorporates estimates from these improved ESMs. Yet, there still remain other shared model errors, such as in the subtropical low-clouds ( [[#Calisto--2014|Calisto et al., 2014]] ) and tropical anvil clouds ( [[#Mauritsen--2015|Mauritsen and]] [[#Stevens--2015|Stevens, 2015]] ), hampering an assessment of feedbacks associated with these cloud regimes based only on ESMs ( [[#7.4.2.4|Section 7.4.2.4]] ).

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