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

==== 7.4.2.4 Cloud Feedbacks ====

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<span id="decomposition-of-clouds-into-regimes"></span>
===== 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 =====

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<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'' .

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

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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.

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'''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'' )

|}

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<span id="biogeophysical-and-non-co-2-biogeochemical-feedbacks"></span>