Editing Cloud feedback

{{Short description|Type of climate change feedback mechanism}}
{{multiple image
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| image1 = Shortwave Radiation.jpg
| caption1 = During daytime, clouds scatter incoming [[shortwave radiation]] from the [[Sun]] due to their albedo, which results in substantial cooling
| image2 = Longwave Radiation.jpg
| caption2 = [[Water vapor]] in the clouds also absorbs [[Outgoing longwave radiation|longwave radiation]] from the Earth's surface and reemits it back. This effect is often weaker than the albedo cooling, but it is active day and night  
}}

'''Cloud feedback''' is a type of [[climate change feedback]], where the overall [[cloud]] frequency, height, and the relative fraction of the different types of clouds are altered due to [[climate change]], and these changes then affect the [[Earth's energy balance]].<ref name="IPCC glossary" />{{rp|2224}} On their own, clouds are already an important part of the [[climate system]], as they consist of [[water vapor]], which acts as a [[greenhouse gas]] and so contributes to warming; at the same time, they are bright and reflective of the Sun, which causes cooling.<ref name="Stephens2005">{{Cite journal|last=Stephens|first=Graeme L.|date=2005-01-01|title=Cloud Feedbacks in the Climate System: A Critical Review|journal=Journal of Climate|volume=18|issue=2|pages=237–273|doi=10.1175/JCLI-3243.1|issn=0894-8755|bibcode=2005JCli...18..237S|citeseerx=10.1.1.130.1415|s2cid=16122908 }}</ref> Clouds at low altitudes have a stronger cooling effect, and those at high altitudes have a stronger warming effect. Altogether, clouds make the Earth cooler than it would have been without them.<ref name="IPCC AR6 WG1 CH7">{{Cite report |last1=Forster |first1=P. |last2=Storelvmo |first2=T. |last3=Armour |first3=K. |last4=Collins |first4=W. |last5=Dufresne |first5=J.-L. |last6=Frame |first6=D. |last7=Lunt |first7=D.J. |last8=Mauritsen |first8=T. |last9=Watanabe |first9=M. |last10=Wild |first10=M. |last11=Zhang |first11=H. |date=2021 |editor-last=Masson-Delmotte |editor-first=V. |editor2-last=Zhai |editor2-first=P. |editor3-last=Pirani |editor3-first=A. |editor4-last=Connors |editor4-first=S. L. |editor5-last=Péan |editor5-first=C. |editor6-last=Berger |editor6-first=S. |editor7-last=Caud |editor7-first=N. |editor8-last=Chen |editor8-first=Y. |editor9-last=Goldfarb |editor9-first=L. |title=Chapter 7: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity |url=https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf |journal=Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change |publisher=Cambridge University Press, Cambridge, UK and New York, NY, US |pages=923–1054 |doi=10.1017/9781009157896.009 }}</ref>{{rp|1022}}

If climate change causes low-level cloud cover to become more widespread, then these clouds will increase planetary [[albedo]] and contribute to cooling, making the overall cloud feedback ''negative'' (one that slows down the warming). But if clouds become higher and thinner due to climate change, then the net cloud feedback will be ''positive'' and accelerate the warming, as clouds will be less reflective and trap more heat in the atmosphere.<ref name="Stephens2005" /> These processes have been represented in every major climate model from the 1980s onwards.<ref name="Wetherald1988">{{cite journal |author1=Wetherald, R. |author2=S. Manabe |year=1988 |title=Cloud Feedback Processes in a General Circulation Model |journal=J. Atmos. Sci. |volume=45 |issue=8 |pages=1397–1416 |bibcode=1988JAtS...45.1397W |doi=10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2 |doi-access=free}}</ref><ref name="Cess1990">{{cite journal |author=Cess, R. D. |display-authors=etal |year=1990 |title=Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models |url=http://kiwi.atmos.colostate.edu/pubs/Cessetal-1990.pdf |url-status=dead |journal=J. Geophys. Res. |volume=95 |issue=D10 |pages=16,601–16,615 |bibcode=1990JGR....9516601C |doi=10.1029/jd095id10p16601 |archive-url=https://web.archive.org/web/20180722002117/http://kiwi.atmos.colostate.edu/pubs/Cessetal-1990.pdf |archive-date=2018-07-22 |access-date=2017-10-27}}</ref><ref name="Fowler1996">{{cite journal |author1=Fowler, L.D. |author2=D.A. Randall |year=1996 |title=Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part III: Sensitivity to Modeling Assumptions |journal=J. Climate |volume=9 |issue=3 |pages=561–586 |bibcode=1996JCli....9..561F |doi=10.1175/1520-0442(1996)009<0561:LAICMI>2.0.CO;2 |doi-access=free}}</ref> Observations and [[climate model]] results now provide ''high confidence'' that the overall cloud feedback on climate change is positive.<ref name="IPCC_AR6_WG1_TS">{{Cite report |last1=Arias |first1=Paola A. |last2=Bellouin |first2=Nicolas |last3=Coppola |first3=Erika |last4=Jones |first4=Richard G. |last5=Krinner |first5=Gerhard |year=2021 |title=Technical Summary |url=https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf |journal=Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change |publisher=Cambridge University Press, Cambridge, UK and New York, NY, US |pages=35–144 |doi=10.1017/9781009157896.009 |archive-url=https://web.archive.org/web/20220721021347/https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf |archive-date=21 July 2022 }}</ref>{{rp|95}}

However, some cloud types are more difficult to observe, and so climate models have less data about them and make different estimates about their role. Thus, models can simulate cloud feedback as very positive or only weakly positive, and these disagreements are the main reason why climate models can have substantial differences in transient climate response and [[climate sensitivity]].<ref name="IPCC AR6 WG1 CH7" />{{rp|975}} In particular, a minority of the [[Coupled Model Intercomparison Project]] Phase 6 (CMIP6) models have made headlines before the publication of the [[IPCC Sixth Assessment Report]] (AR6) due to their high estimates of equilibrium [[climate sensitivity]].<ref name="NClimate2019">{{Cite journal |last=<!--Editorial, no author listed--> |date=2019-09-25 |title=The CMIP6 landscape (Editorial) |journal=Nature Climate Change |language=en |volume=9 |issue=10 |page=727 |bibcode=2019NatCC...9..727. |doi=10.1038/s41558-019-0599-1 |issn=1758-6798 |doi-access=free}}</ref><ref name="Fr242020">{{Cite web |date=2020-01-14 |title=New climate models suggest Paris goals may be out of reach |url=https://www.france24.com/en/20200114-new-climate-models-suggest-paris-goals-may-be-out-of-reach |url-status=live |archive-url=https://web.archive.org/web/20200114083228/https://www.france24.com/en/20200114-new-climate-models-suggest-paris-goals-may-be-out-of-reach |archive-date=14 January 2020 |access-date=2020-01-18 |website=France 24 |language=en}}</ref> This had occurred because they estimated cloud feedback as highly positive.<ref name="Zelinka2020">{{Cite journal |vauthors=Zelinka MD, Myers TA, McCoy DT, Po-Chedley S, Caldwell PM, Ceppi P, Klein SA, Taylor KE |date=2020 |title=Causes of Higher Climate Sensitivity in CMIP6 Models |journal=Geophysical Research Letters |language=en |volume=47 |issue=1 |page=e2019GL085782 |bibcode=2020GeoRL..4785782Z |doi=10.1029/2019GL085782 |issn=1944-8007 |doi-access=free|hdl=10044/1/76038 |hdl-access=free }}</ref><ref name="SD2020">{{cite journal |date=24 June 2020 |title=Increased warming in latest generation of climate models likely caused by clouds: New representations of clouds are making models more sensitive to carbon dioxide. |url=https://www.sciencedaily.com/releases/2020/06/200624151600.htm |url-status=live |journal=Science Daily |archive-url=https://web.archive.org/web/20200626005318/https://www.sciencedaily.com/releases/2020/06/200624151600.htm |archive-date=26 June 2020 |access-date=26 June 2020}}</ref> Those particular models were soon found to contradict both observations and [[paleoclimate]] evidence,<ref name="Zhu2020">{{cite journal |last1=Zhu |first1=Jiang |last2=Poulsen |first2=Christopher J. |last3=Otto-Bliesner |first3=Bette L. |title=High climate sensitivity in CMIP6 model not supported by paleoclimate |journal=Nature Climate Change |date=30 April 2020 |volume=10 |issue=5 |pages=378–379 |doi=10.1038/s41558-020-0764-6 |doi-access=free |bibcode=2020NatCC..10..378Z }}</ref><ref name="EricksonPhys2020">{{Cite web |last1=Erickson |first1=Jim |date=30 April 2020 |title=Some of the latest climate models provide unrealistically high projections of future warming |url=https://phys.org/news/2020-04-latest-climate-unrealistically-high-future.html |access-date=12 May 2024 |website=[[Phys.org]] |language=en |quote=But the CESM2 model projected Early Eocene land temperatures exceeding 55 degrees Celsius (131 F) in the tropics, which is much higher than the temperature tolerance of plant photosynthesis—conflicting with the fossil evidence. On average across the globe, the model projected surface temperatures at least 6 C (11 F) warmer than estimates based on geological evidence. }}</ref> and the AR6 used a more realistic estimate based on the majority of the models and this real-world evidence instead.<ref name="IPCC_AR6_WG1_TS" />{{rp|93}}<ref name="VoosenSciMag2022">{{Cite web |last1=Voosen |first1=Paul |date=4 May 2022 |title=Use of 'too hot' climate models exaggerates impacts of global warming |url=https://www.science.org/content/article/use-too-hot-climate-models-exaggerates-impacts-global-warming |access-date=12 May 2024|website=[[Science Magazine]]|language=en|quote=But for the 2019 CMIP6 round, 10 out of 55 of the models had sensitivities higher than 5°C—a stark departure. The results were also at odds with a landmark study that eschewed global modeling results and instead relied on paleoclimate and observational records to identify Earth’s climate sensitivity. It found that the value sits somewhere between 2.6°C and 3.9°C.}}</ref>

One reason why it has been more difficult to find an exact value of cloud feedbacks when compared to the others is because humans affect clouds in another major way besides the warming from greenhouse gases. Small atmospheric [[sulfate]] particles, or [[aerosol]]s, are generated due to the same sulfur-heavy [[air pollution]] which also causes [[acid rain]], but they are also very reflective, to the point their concentrations in the atmosphere cause reductions in visible sunlight known as [[global dimming]].<ref name="AGU2021">{{cite web |date=18 February 2021 |title=Aerosol pollution has caused decades of global dimming |url=https://news.agu.org/press-release/aerosol-pollution-caused-decades-of-global-dimming/ |website=[[American Geophysical Union]] |access-date=18 December 2023 |archive-url=https://web.archive.org/web/20230327143716/https://news.agu.org/press-release/aerosol-pollution-caused-decades-of-global-dimming/ |archive-date=27 March 2023 }}</ref> These particles affect the clouds in multiple ways, mostly making them more reflective. This means that changes in clouds caused by aerosols can be confused for an evidence of negative cloud feedback, and separating the two effects has been difficult.<ref name="McCoy2020">{{cite journal |last1 =McCoy |first1=Daniel T. |last2=Field |first2=Paul |last3=Gordon |first3=Hamish |last4=Elsaesser |first4=Gregory S. |last5=Grosvenor |first5=Daniel P. | date=6 April 2020 | title=Untangling causality in midlatitude aerosol–cloud adjustments | url=https://acp.copernicus.org/articles/20/4085/2020/ |journal=Atmospheric Chemistry and Physics | volume=20 |issue=7 | pages=4085–4103 |doi=10.5194/acp-20-4085-2020 |doi-access = free |bibcode=2020ACP....20.4085M }}</ref>

== Overview ==
[[File:McKim 2024 cloud formulae.png|thumb|Details of how clouds interact with shortwave and longwave radiation at different atmospheric heights<ref name="McKim2024">{{Cite journal |last1=McKim |first1=Brett |last2=Bony |first2=Sandrine |last3=Dufresne |first3=Jean-Louis |date=1 April 2024 |title=Weak anvil cloud area feedback suggested by physical and observational constraints |journal=Nature Geoscience |volume=17 |issue=5 |pages=392–397 |doi=10.1038/s41561-024-01414-4 |doi-access=free |bibcode=2024NatGe..17..392M }}</ref>]]
Clouds have two major effects on the [[Earth's energy budget]]: they reflect shortwave radiation from sunlight back to space due to their high [[albedo]], but the water vapor contained inside them also absorbs and re-emits the longwave radiation sent out by the Earth's surface as it is heated by sunlight, preventing its escape into space and retaining this heat energy for longer.<ref name="IPCC AR6 WG1 CH7" />{{rp|1022}} 

In [[meteorology]], the difference in the [[radiation budget]] caused by clouds, relative to cloud-free conditions, is described as the cloud radiative effect (CRE).<ref name="IPCC_annexVII_glossary">{{cite journal |last1=Matthews |title=Annex VII: Glossary of the Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change |date=6 July 2023 |doi=10.1017/9781009157896.022 |doi-access=free }}</ref> This is also sometimes referred to as cloud [[radiative forcing]] (CRF).<ref>{{cite web |last = NASA |title = Clouds & Radiation Fact Sheet : Feature Articles | publisher = NASA | date = 2016 | url = https://earthobservatory.nasa.gov/Features/Clouds/ | access-date = 2017-05-29}}</ref> However, since cloud changes are not normally considered an external forcing of climate, CRE is the most commonly used term. 

At the top of the atmosphere, it can be described by the following equation<ref>{{cite book |last= Hartmann |first= Dennis L. |date = 2016 |title= Global Physical Climatology |location= Amsterdam |publisher= Elsevier |isbn= 978-0123285317}}</ref>

:<math>\Delta R_{TOA} = R_{Average} - R_{Clear}</math>

The net cloud radiative effect can be decomposed into its longwave and shortwave components. This is because net radiation is absorbed solar minus the outgoing longwave radiation shown by the following equations

:<math> \Delta R_{TOA} = \Delta Q_{abs} - \Delta OLR </math>

The first term on the right is the shortwave cloud effect (''Q''<sub>abs</sub> ) and the second is the longwave effect (OLR).

The shortwave cloud effect is calculated by the following equation

:<math> \Delta Q_{abs} = (S_o/4) \cdot (1 - \alpha_{cloudy}) - (S_o/4) \cdot (1 - \alpha_{clear}) </math>

Where ''S''<sub>o</sub> is the [[solar constant]], ''∝''<sub>cloudy</sub> is the [[albedo]] with clouds and ''∝''<sub>clear</sub> is the albedo on a clear day.

The longwave effect is calculated by the next following equation

:<math> \Delta OLR = \sigma T_z^4 - F_{clear}^{up}</math>

Where σ is the [[Stefan–Boltzmann constant]], T is the temperature at the given height, and F is the upward flux in clear conditions.

Putting all of these pieces together, the final equation becomes

:<math> \Delta R_{TOA} = (S_o/4) \cdot ((1 - \alpha_{cloudy}) - (1 - \alpha_{clear})) - \sigma T_z^4 + F_{clear}^{up} </math>

[[File:Attribution of individual atmospheric component contributions to the terrestrial greenhouse effect, separated into feedback and forcing categories (NASA).png|thumb|left|Attribution of individual atmospheric component contributions to the [[greenhouse effect]], separated into feedback and forcing categories (NASA)]]

Under dry, cloud-free conditions, water vapor in atmosphere contributes 67% of the [[greenhouse effect]] on Earth. When there is enough moisture to form typical cloud cover, the greenhouse effect from "free" water vapor goes down to 50%, but water vapor which is now inside the clouds amounts to 25%, and the net greenhouse effect is at 75%.<ref>{{cite journal |last=Schmidt |first=G.A. |title=The attribution of the present-day total greenhouse effect |journal=J. Geophys. Res. |volume=115 |issue=D20 |pages=D20106 |df=dmy-all |year=2010 |bibcode=2010JGRD..11520106S |doi=10.1029/2010JD014287 |author2=R. Ruedy |author3=R.L. Miller |author4=A.A. Lacis |author-link1=Gavin Schmidt |doi-access=free}}, D20106. [http://pubs.giss.nasa.gov/abs/sc05400j.html Web page ] {{Webarchive|url=https://web.archive.org/web/20120604034848/http://pubs.giss.nasa.gov/abs/sc05400j.html|date=4 June 2012}}</ref> According to 1990 estimates, the presence of clouds reduces the [[outgoing longwave radiation]] by about 31 W/m<sup>2</sup>. However, it also increases the global [[albedo]] from 15% to 30%, and this reduces the amount of [[solar radiation]] absorbed by the Earth by about 44 W/m<sup>2</sup>. Thus, there is a net ''cooling'' of about 13 W/m<sup>2</sup>.<ref>{{cite book |last=Intergovernmental Panel on Climate Change |title=IPCC First Assessment Report.1990 |publisher=Cambridge University Press |year=1990 |location=UK |author-link=Intergovernmental Panel on Climate Change}}table 3.1</ref> If the clouds were removed with all else remaining the same, the [[Earth]] would lose this much cooling and the global temperatures would increase.<ref name="IPCC AR6 WG1 CH7" />{{rp|1022}}

[[Climate change]] increases the amount of water vapor in the atmosphere due to the [[Clausius–Clapeyron relation]], in what is known as the water-vapor feedback.<ref>{{Cite journal |last1=Held |first1=Isaac M. |last2=Soden |first2=Brian J. |date=November 2000 |title=Water vapor feedback and global warming |journal=[[Annual Review of Energy and the Environment]] |language=en |volume=25 |issue=1 |pages=441–475 |citeseerx=10.1.1.22.9397 |doi=10.1146/annurev.energy.25.1.441 |issn=1056-3466 |doi-access=free}}</ref> It also affects a range of cloud properties, such as their height, the typical distribution throughout the atmosphere, and [[cloud physics|cloud microphysics]], such as the amount of water droplets held, all of which then affect clouds' radiative forcing.<ref name="IPCC AR6 WG1 CH7" />{{rp|1023}} Differences in those properties change the role of clouds in the Earth's energy budget. The name ''cloud feedback'' refers to this relationship between climate change, cloud properties, and clouds' radiative forcing.<ref name="IPCC glossary">IPCC, 2021: [https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_AnnexVII.pdf Annex VII: Glossary] [Matthews, J.B.R., V. Möller, R. van Diemen, J.S. Fuglestvedt, V. Masson-Delmotte, C.  Méndez, S. Semenov, A. Reisinger (eds.)]. In [https://www.ipcc.ch/report/ar6/wg1/ Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change] [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2215–2256, doi:10.1017/9781009157896.022.</ref>{{rp|2224}} Clouds also affect the magnitude of internally generated [[Climate variability and change|climate variability.]]<ref>{{Cite journal |last1=Brown |first1=Patrick T. |last2=Li |first2=Wenhong |last3=Jiang |first3=Jonathan H. |last4=Su |first4=Hui |date=2015-12-07 |title=Unforced Surface Air Temperature Variability and Its Contrasting Relationship with the Anomalous TOA Energy Flux at Local and Global Spatial Scales |url=https://dukespace.lib.duke.edu/dspace/bitstream/10161/15913/1/2016_BrownLiJiangSu_JCLI.pdf |url-status=live |journal=Journal of Climate |volume=29 |issue=3 |pages=925–940 |bibcode=2016JCli...29..925B |doi=10.1175/JCLI-D-15-0384.1 |issn=0894-8755 |archive-url=https://web.archive.org/web/20180719171852/https://dukespace.lib.duke.edu/dspace/bitstream/10161/15913/1/2016_BrownLiJiangSu_JCLI.pdf |archive-date=2018-07-19 |doi-access=free}}</ref><ref>{{Cite journal |last1=Bellomo |first1=Katinka |last2=Clement |first2=Amy |last3=Mauritsen |first3=Thorsten |last4=Rädel |first4=Gaby |last5=Stevens |first5=Bjorn |date=2014-04-11 |title=Simulating the Role of Subtropical Stratocumulus Clouds in Driving Pacific Climate Variability |journal=Journal of Climate |volume=27 |issue=13 |pages=5119–5131 |bibcode=2014JCli...27.5119B |doi=10.1175/JCLI-D-13-00548.1 |issn=0894-8755 |s2cid=33019270 |hdl-access=free |hdl=11858/00-001M-0000-0014-72C1-F}}</ref>

== Representation in climate models ==
[[File:20220726_Feedbacks_affecting_global_warming_and_climate_change_-_block_diagram.svg|right|thumb|Examples of some [[Effects of climate change|effects of global warming]] that can amplify ([[positive feedback]]s) or reduce ([[negative feedback]]s) global warming<ref name="NASA_IntegratedSystem2">{{cite web |date=2016 |title=The Study of Earth as an Integrated System |url=https://climate.nasa.gov/nasa_science/science/ |url-status=live |archive-url=https://web.archive.org/web/20161102022200/https://climate.nasa.gov/nasa_science/science/ |archive-date=November 2, 2016 |website=nasa.gov |publisher=NASA}}</ref>]][[Climate model]]s have represented clouds and cloud processes for a very long time. Cloud feedback was already a standard feature in climate models designed in the 1980s.<ref name="Wetherald1988" /><ref name="Cess1990" /><ref name="Fowler1996" /> However, the physics of clouds are very complex, so models often represent various types of clouds in different ways, and even small variations between models can lead to significant changes in temperature and [[precipitation]] response.<ref name="Cess1990" /> [[Climate scientist]]s devote a lot of effort to resolving this issue. This includes the Cloud Feedback Model Intercomparison Project (CFMIP), where models simulate cloud processes under different conditions and their output is compared with the observational data. (AR6 WG1, Ch1, 223) When the [[Intergovernmental Panel on Climate Change]] had published its Sixth Assessment Report ([[AR6]]) in 2021, the [[error bar|uncertainty range]] regarding cloud feedback strength became 50% smaller since the time of the [[IPCC Fifth Assessment Report|AR5]] in 2014.<ref name="IPCC_AR6_WG1_TS" />{{rp|95}} 
[[File:McKim 2024 tropical clouds.jpg|thumb|Tropical clouds are known to have a cooling effect, but it is uncertain whether it would become stronger or weaker in the future<ref name="McKim2024" />]]
{| class="wikitable"
|+Remaining uncertainty about cloud feedbacks in [[IPCC Sixth Assessment Report]]<ref name="IPCC AR6 WG1 CH7" />{{rp|975}}
! Feedback !! Direction !! Confidence		
|-		
| High-cloud altitude feedback || Positive || High 	
|- 						
| Tropical high-cloud amount feedback || Negative || Low 	
|- 		
| Subtropical marine low-cloud feedback || Positive || High
|- 		
| Land cloud feedback || Positive || Low 	 	
|-		
| Mid-latitude cloud amount feedback  || Positive || Medium 	
|- 		
| Extratropical cloud optical depth feedback || Small negative || Medium 		
|- 		
| Arctic cloud feedback || Small positive || Low 	
|- 		
| Net cloud feedback || Positive || High 	
|}

This happened because of major improvements in the understanding of cloud behaviour over the subtropical oceans. As the result, there was ''high confidence'' that the overall cloud feedback is positive (contributes to warming).<ref name="IPCC_AR6_WG1_TS" />{{rp|95}} The AR6 value for cloud feedback is +0.42 [–0.10 to 0.94] W m–2 per every {{convert|1|C-change|F-change}} in warming. This estimate is derived from multiple lines of evidence, including both models and observations.<ref name="IPCC_AR6_WG1_TS" />{{rp|95}} The tropical high-cloud amount feedback is the main remaining area for improvement. The only way total cloud feedback may still be slightly negative is if either this feedback, or the optical depth feedback in the [[Southern Ocean]] clouds is suddenly found to be "extremely large"; the probability of that is considered to be below 10%.<ref name="IPCC AR6 WG1 CH7" />{{rp|975}} As of 2024, most recent observations from the [[CALIPSO]] satellite instead indicate that the tropical cloud feedback is very weak.<ref>{{cite journal |last1=Raghuraman |first1=Shiv Priyam |last2=Medeiros |first2=Brian |last3=Gettelman |first3=Andrew |date=30 March 2024 |title=Observational quantification of tropical high cloud changes and feedbacks |journal=Journal of Geophysical Research: Atmospheres |volume=129 |issue=7 |page=e2023JD039364 |doi=10.1029/2023JD039364 |doi-access=free |bibcode=2024JGRD..12939364R }}</ref><ref name="McKim2024" />

In spite of these improvements, clouds remain the least well-understood climate feedback, and they are the main reason why models estimate differing values for equilibrium [[climate sensitivity]] (ECS). ECS is an estimate of long-term (multi-century) warming in response to a ''doubling'' in {{CO2}}-equivalent greenhouse gas concentrations: if the future emissions are not low, it also becomes the most important factor for determining 21st century temperatures.<ref name="IPCC_AR6_WG1_TS" />{{rp|95}} In general, the current generation of gold-standard climate models, [[CMIP6]], operates with larger climate sensitivity than the previous generation, and this is largely because cloud feedback is about 20% more positive than it was in CMIP5.<ref name="IPCC_AR6_WG1_TS" />{{rp|93}}<ref name="Zelinka2020" />

However, the ''median'' cloud feedback is only slightly larger in CMIP6 than it was in CMIP5;<ref name="IPCC_AR6_WG1_TS" />{{rp|95}} the average is so much higher only because several [[Hot model|"hot" models]] have much stronger cloud feedback and higher sensitivity than the rest.<ref name="IPCC_AR6_WG1_TS" />{{rp|93}}<ref name="VoosenSciMag2022" /> Those models have a sensitivity of {{cvt|5|C|F}} and their presence had increased the median model sensitivity from {{cvt|3.2|C|F}} in CMIP5 to {{cvt|3.7|C|F}} in CMIP6.<ref name="SD2020" /> These model results had attracted considerable attention when they were first published in 2019, as they would have meant faster and more severe warming if they were accurate.<ref name="NClimate2019" /><ref name="Fr242020" /> It was soon found that the output of those "hot" models is inconsistent with both observations and [[paleoclimate]] evidence, so the consensus AR6 value for cloud feedback is smaller than the mean model output alone. The best estimate of climate sensitivity in AR6 is at {{cvt|3|C|F}}, as this is in a better agreement with observations and paleoclimate findings.<ref name="IPCC_AR6_WG1_TS" />{{rp|93}}<ref name="Zhu2020" /><ref name="EricksonPhys2020" />

== Role of aerosols ==
[[File:Bellouin_2019_aerosol_cloud_interactions.jpg|thumb|Air pollution, including from large-scale land clearing, has substantially increased the presence of aerosols in the atmosphere when compared to the preindustrial background levels. Different types of particles have different effects, and there is a variety of interactions in different atmospheric layers. Overall, they provide cooling, but complexity makes the exact strength of cooling very difficult to estimate.<ref name="Bellouin2019">{{cite journal |last1=Bellouin |first1=N. |last2=Quaas |first2=J. |last3=Gryspeerdt |first3=E. |last4=Kinne |first4=S. |last5=Stier |first5=P. |last6=Watson-Parris |first6=D. |last7=Boucher |first7=O. |last8=Carslaw |first8=K. S. |last9=Christensen |first9=M. |last10=Daniau |first10=A.-L. |last11=Dufresne |first11=J.-L. |last12=Feingold |first12=G. |last13=Fiedler |first13=S. |last14=Forster |first14=P. |last15=Gettelman |first15=A. |last16=Haywood |first16=J. M. |last17=Lohmann |first17=U. |last18=Malavelle |first18=F. |last19=Mauritsen |first19=T. |last20=McCoy |first20= D. T. |last21=Myhre |first21=G. |last22=Mülmenstädt |first22=J. |last23=Neubauer |first23=D. |last24=Possner |first24=A. |last25=Rugenstein |first25=M. |last26=Sato |first26=Y. |last27=Schulz |first27=M. |last28=Schwartz |first28=S. E. |last29=Sourdeval |first29=O. |last30=Storelvmo |first30= T. |last31=Toll |first31=V. |last32=Winker |first32=D. |last33=Stevens |first33=B. |date=1 November 2019 |title=Bounding Global Aerosol Radiative Forcing of Climate Change |journal=Reviews of Geophysics |volume=58 |issue=1 |page=e2019RG000660 |doi=10.1029/2019RG000660 |pmid=32734279 |pmc=7384191 }}</ref>]]

Atmospheric [[aerosol]]s—fine partices suspended in the air—affect cloud formation and properties, which also alters their impact on climate. While some aerosols, such as [[black carbon]] particles, make the clouds darker and thus contribute to warming,<ref>{{cite journal| title=Nature Geoscience: Global and regional climate changes due to black carbon|journal=Nature Geoscience| volume=1| issue=4| pages=221–227| doi=10.1038/ngeo156| year=2008| last1=Ramanathan| first1=V.| last2=Carmichael| first2=G.| s2cid=12455550| bibcode=2008NatGe...1..221R}}</ref> by far the strongest effect is from [[sulfate]]s, which increase the number of cloud droplets, making the clouds more reflective, and helping them cool the climate more. That is known as a ''direct'' aerosol effect; however, aerosols also have an ''indirect'' effect on [[liquid water path]], and determining it involves computationally heavy continuous calculations of evaporation and condensation within clouds. Climate models generally assume that aerosols increase liquid water path, which makes the clouds even more reflective.<ref name="McCoy2020" /> However, satellite observations taken in 2010s suggested that aerosols decreased liquid water path instead, and in 2018, this was reproduced in a model which integrated more complex cloud microphysics.<ref>{{cite journal |last1=Sato |first1=Yousuke |last2=Goto |first2=Daisuke |last3=Michibata |first3=Takuro |last4=Suzuki |first4=Kentaroh |last5=Takemura |first5=Toshihiko |last6=Tomita |first6=Hirofumi |last7=Nakajima |first7=Teruyuki |date=7 March 2018 |title=Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model |journal=Nature Communications |volume=9 |issue=1 |page=985 |doi=10.1038/s41467-018-03379-6 |pmid=29515125 |pmc=5841301 |doi-access = free |bibcode=2018NatCo...9..985S }}</ref> Yet, 2019 research found that earlier satellite observations were biased by failing to account for the thickest, most water-heavy clouds naturally raining more and shedding more particulates: very strong aerosol cooling was seen when comparing clouds of the same thickness.<ref>{{cite journal | last1 = Rosenfeld | first1 = Daniel | last2 = Zhu | first2 = Yannian | last3 = Wang | first3 = Minghuai | last4 = Zheng | first4 = Youtong | last5 = Goren | first5 = Tom | last6 = Yu | first6 = Shaocai | year = 2019 | title = Aerosol-driven droplet concentrations dominate coverage and water of oceanic low level clouds | url = https://authors.library.caltech.edu/92390/2/aav0566_Rosenfeld_SM.pdf| journal = Science | volume =  363| issue = 6427| page =  eaav0566| doi = 10.1126/science.aav0566 | pmid = 30655446 | s2cid = 58612273 | doi-access = free }}</ref>

Moreover, large-scale observations can be confounded by changes in other atmospheric factors, like humidity: i.e. it was found that while post-1980 improvements in air quality would have reduced the number of clouds over the [[East Coast of the United States]] by around 20%, this was offset by the increase in relative humidity caused by atmospheric response to [[AMOC]] slowdown.<ref name="Cao2021">{{cite journal |last1=Cao |first1=Yang |last2=Wang |first2=Minghuai |last3=Rosenfeld |first3=Daniel |last4=Zhu |first4=Yannian |last5=Liang |first5=Yuan |last6=Liu |first6=Zhoukun |last7=Bai |first7=Heming |date=10 March 2021 |title=Strong Aerosol Effects on Cloud Amount Based on Long-Term Satellite Observations Over the East Coast of the United States |journal=Geophysical Research Letters | volume=48 |issue=6 | page=e2020GL091275 |doi=10.1029/2020GL091275 |doi-access = free |bibcode=2021GeoRL..4891275C }}</ref> Similarly, while the initial research looking at sulfates from the [[2014–2015 eruption of Bárðarbunga]] found that they caused no change in liquid water path,<ref>{{Cite journal |last1=Malavelle |first1=Florent F. |last2=Haywood |first2=Jim M. |last3=Jones |first3=Andy |last4=Gettelman |first4=Andrew |last5=Clarisse |first5=Lieven |last6=Bauduin |first6=Sophie |last7=Allan |first7=Richard P. |last8=Karset |first8=Inger Helene H. |last9=Kristjánsson |first9=Jón Egill |last10=Oreopoulos |first10=Lazaros |last11=Cho |first11=Nayeong |last12=Lee |first12=Dongmin |last13=Bellouin |first13=Nicolas |last14=Boucher |first14=Olivier |last15=Grosvenor |first15=Daniel P. |last16=Carslaw |first16=Ken S. |last17=Dhomse |first17=Sandip |last18=Mann |first18=Graham W. |last19=Schmidt |first19=Anja |last20=Coe |first20=Hugh |last21=Hartley |first21=Margaret E. |last22=Dalvi |first22=Mohit |last23=Hill |first23=Adrian A. |last24=Johnson |first24=Ben T. |last25=Johnson |first25=Colin E. |last26=Knight |first26=Jeff R. |last27=O'Connor |first27=Fiona M. |last28=Partridge |first28=Daniel G. |last29=Stier |first29=Philip |last30=Myhre |first30=Gunnar |last31=Platnick |first31=Steven |last32=Stephens |first32=Graeme L. |last33=Takahashi |first33=Hanii |last34=Thordarson |first34=Thorvaldur |date=22 June 2017 |title=Strong constraints on aerosol–cloud interactions from volcanic eruptions |journal=Nature |volume=546 |issue=7659 |pages=485–491 |language=en |doi=10.1038/nature22974 |pmid=28640263 |bibcode=2017Natur.546..485M |s2cid=205257279 |hdl=10871/28042 |hdl-access=free }}</ref> it was later suggested that this finding was confounded by counteracting changes in humidity.<ref name="Cao2021"/> 
[[File:ShipTracks.jpg|thumb|left|Visible ship tracks in the Northern Pacific, on 4 March 2009]]
To avoid confounders, many observations of aerosol effects focus on [[ship tracks]], but post-2020 research found that visible ship tracks are a poor proxy for other clouds, and estimates derived from them overestimate aerosol cooling by as much as 200%.<ref>{{cite journal | last1=Glassmeier |first1=Franziska |last2=Hoffmann |first2=Fabian |last3=Johnson |first3=Jill S. |last4=Yamaguchi |first4=Takanobu |last5=Carslaw |first5=Ken S. |last6=Feingold |first6=Graham | date=29 January 2021 |title=Aerosol-cloud-climate cooling overestimated by ship-track data | journal=Science |volume =371 |issue=6528 |pages=485–489 |doi=10.1126/science.abd3980 |pmid=33510021 |doi-access = free |bibcode=2021Sci...371..485G }}</ref> At the same time, other research found that the majority of ship tracks are "invisible" to satellites, meaning that the earlier research had underestimated aerosol cooling by overlooking them.<ref>{{cite journal |last1=Manshausen |first1=Peter |last2=Watson-Parris |first2=Duncan |last3=Christensen |first3=Matthew W. |last4=Jalkanen |first4=Jukka-Pekka |last5=Stier  |first5=Philip Stier |date=7 March 2018 |title=Invisible ship tracks show large cloud sensitivity to aerosol |journal=Nature |volume=610 |issue=7930 |pages=101–106 |doi=10.1038/s41586-022-05122-0 |pmid=36198778 |pmc=9534750 |doi-access=free }}</ref> Finally, 2023 research indicates that all climate models have underestimated sulfur emissions from volcanoes which occur in the background, outside of major eruptions, and so had consequently overestimated the cooling provided by anthropogenic aerosols, especially in the Arctic climate.<ref>{{cite journal |last1=Jongebloed |first1=U. A. |last2=Schauer |first2=A. J. |last3=Cole-Dai |first3=J. |last4=Larrick |first4=C. G. |last5=Wood |first5=R. |last6=Fischer |first6=T. P. |last7=Carn |first7=S. A. |last8=Salimi |first8=S. |last9=Edouard |first9=S. R. |last10=Zhai |first10=S. |last11=Geng |first11=L. |last12=Alexander |first12=B. |title=Underestimated Passive Volcanic Sulfur Degassing Implies Overestimated Anthropogenic Aerosol Forcing | date=2 January 2023 |journal=Geophysical Research Letters | volume=50 |issue=1 |pages=e2022GL102061 |doi=10.1029/2022GL102061 |s2cid=255571342 |doi-access=free |bibcode=2023GeoRL..5002061J }}</ref>
[[File:Estimates of past and future SO2 global anthropogenic emissions.png|thumb|upright=1.25|Early 2010s estimates of past and future anthropogenic global sulfur dioxide emissions, including the [[Representative Concentration Pathway]]s. While no [[climate change scenario]] may reach Maximum Feasible Reductions (MFRs), all assume steep declines from today's levels. By 2019, sulfate emission reductions were confirmed to proceed at a very fast rate.<ref name="XuRamanathanVictor2018">{{Cite journal|last1=Xu|first1=Yangyang|last2=Ramanathan|first2=Veerabhadran|last3=Victor|first3=David G.|date=5 December 2018|title=Global warming will happen faster than we think|journal=Nature|language=en|volume=564|issue=7734|pages=30–32 |url=https://www.researchgate.net/publication/329411074 |doi=10.1038/d41586-018-07586-5|pmid=30518902|bibcode=2018Natur.564...30X|doi-access=free}}</ref>]]
Estimates of how much aerosols affect cloud cooling are very important, because the amount of sulfate aerosols in the air had undergone dramatic changes in the recent decades. First, it had increased greatly from 1950s to 1980s, largely due to the widespread burning of [[sulfur]]-heavy coal, which caused an observable reduction in visible sunlight that had been described as [[global dimming]].<ref name="AGU2021" /><ref name="Julsrud2022" /> Then, it started to decline substantially from the 1990s onwards and is expected to continue to decline in the future, due to the measures to combat [[acid rain]] and other impacts of [[air pollution]].<ref name="EPA">{{cite web | access-date=2007-03-17 | archive-date=2007-03-17 | archive-url=https://web.archive.org/web/20070317212933/http://www.epa.gov/airtrends/econ-emissions.html | url=http://www.epa.gov/airtrends/econ-emissions.html | title=Air Emissions Trends – Continued Progress Through 2005 | publisher=[[United States Environmental Protection Agency|U.S. Environmental Protection Agency]] | date=8 July 2014}}</ref> Consequently, the aerosols provided a considerable cooling effect which counteracted or "masked" some of the [[greenhouse effect]] from human emissions, and this effect had been declining as well, which contributed to acceleration of [[climate change]].<ref name="IPCC_WGI_SPM">IPCC, 2021: [https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf Summary for Policymakers]. In: [https://www.ipcc.ch/report/ar6/wg1/ Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change] [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3–32, {{doi|10.1017/9781009157896.001}}.</ref>

Climate models do account for the presence of aerosols and their recent and future decline in their projections, and typically estimate that the cooling they provide in 2020s is similar to the warming from human-added [[atmospheric methane]], meaning that simultaneous reductions in both would effectively cancel each other out.<ref name="CB2021">{{cite web|url=https://www.carbonbrief.org/explainer-will-global-warming-stop-as-soon-as-net-zero-emissions-are-reached |title=Explainer: Will global warming 'stop' as soon as net-zero emissions are reached? |author=Zeke Hausfather|publisher=[[Carbon Brief]]|date= 29 April 2021 |access-date=2023-03-23}}</ref> However, the existing uncertainty about aerosol-cloud interactions likewise introduces uncertainty into models, particularly when concerning predictions of changes in weather events over the regions with a poorer historical record of atmospheric observations.<ref name="Wang2021">{{cite journal |last1=Wang |first1=Zhili |last2=Lin |first2=Lei |last3=Xu |first3=Yangyang |last4=Che |first4=Huizheng |last5=Zhang |first5=Xiaoye |last6=Zhang |first6=Hua |last7=Dong |first7=Wenjie |last8=Wang |first8=Chense |last9=Gui |first9=Ke |last10=Xie |first10=Bing |date=12 January 2021 | title=Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models |journal=npj Climate and Atmospheric Science | volume=4 |doi=10.1029/2021JD035476 |doi-access=free |hdl=10852/97300 |hdl-access=free }}</ref><ref name="Julsrud2022">{{cite journal |last1=Julsrud |first1=I. R. |last2=Storelvmo |first2=T. |last3=Schulz |first3=M. |last4=Moseid |first4=K. O. |last5=Wild |first5=M. |date=20 October 2022 | title=Disentangling Aerosol and Cloud Effects on Dimming and Brightening in Observations and CMIP6 |journal=  Journal of Geophysical Research: Atmospheres| volume=127 |issue=21 |page=e2021JD035476 |doi=10.1029/2021JD035476 |doi-access=free |bibcode=2022JGRD..12735476J |hdl=10852/97300 |hdl-access=free }}</ref><ref name=Persad2022>{{Cite journal|last1=Persad|first1=Geeta G.|last2=Samset|first2=Bjørn H.|last3=Wilcox|first3=Laura J.|date=21 November 2022 |title=Aerosols must be included in climate risk assessments|journal=Nature|language=en|volume=611 |issue=7937 |pages=662–664 |doi=10.1038/d41586-022-03763-9 |pmid=36411334 |doi-access=free|bibcode=2022Natur.611..662P }}</ref><ref name="Ramachandran2022">{{Cite journal |last1=Ramachandran |first1=S. |last2=Rupakheti |first2=Maheswar |last3=Cherian |first3=R. |date=10 February 2022 |title=Insights into recent aerosol trends over Asia from observations and CMIP6 simulations |journal=Science of the Total Environment |volume=807 |issue=1 |page=150756 |doi=10.1016/j.scitotenv.2021.150756 |pmid=34619211 |s2cid=238474883 |doi-access=free |bibcode=2022ScTEn.80750756R }}</ref>

== Possible break-up of equatorial stratocumulus clouds ==
{{See also|Tipping points in the climate system}}
In 2019, a study employed a [[large eddy simulation]] model to estimate that equatorial [[stratocumulus cloud]]s could break up and scatter when [[carbon dioxide|{{CO2}}]] levels rise above 1,200 [[Parts per million|ppm]] (almost three times higher than the current levels, and over 4 times greater than the preindustrial levels). The study estimated that this would cause a surface warming of about {{convert|8|C-change|F-change}} globally and {{convert|10|C-change|F-change}} in the subtropics, which would be in addition to at least {{convert|4|C-change|F-change}} already caused by such {{CO2}} concentrations. In addition, stratocumulus clouds would not reform until the {{CO2}} concentrations drop to a much lower level.<ref>{{Cite journal |last1=Schneider |first1=Tapio |last2=Kaul |first2=Colleen M. |last3=Pressel |first3=Kyle G. |date=2019 |title=Possible climate transitions from breakup of stratocumulus decks under greenhouse warming |journal=Nature Geoscience |volume=12 |issue=3 |pages=163–167 |doi=10.1038/s41561-019-0310-1|bibcode=2019NatGe..12..163S |s2cid=134307699 }}</ref> It was suggested that this finding could help explain past episodes of unusually rapid warming such as [[Paleocene-Eocene Thermal Maximum]].<ref>{{cite web |date=25 February 2019 |url=https://www.quantamagazine.org/cloud-loss-could-add-8-degrees-to-global-warming-20190225/|title=A World Without Clouds|first=Natalie|last=Wolchover|website=[[Quanta Magazine]] |access-date=2 October 2022}}</ref> In 2020, further work from the same authors revealed that in their large eddy simulation, this [[Tipping_points_in_the_climate_system|tipping point]] cannot be stopped with [[solar radiation modification]]: in a hypothetical scenario where very high {{CO2}} emissions continue for a long time but are offset with extensive solar radiation modification, the break-up of stratocumulus clouds is simply delayed until {{CO2}} concentrations hit 1,700 ppm, at which point it would still cause around {{convert|5|C-change|F-change}} of unavoidable warming.<ref>{{Cite journal |last1=Schneider |first1=Tapio |last2=Kaul |first2=Colleen M. |last3=Pressel |first3=Kyle G. |date=2020 |title=Solar geoengineering may not prevent strong warming from direct effects of {{CO2}} on stratocumulus cloud cover |journal=PNAS |volume=117 |issue=48 |pages=30179–30185 |doi=10.1073/pnas.2003730117|pmid=33199624 |pmc=7720182 |bibcode=2020PNAS..11730179S |doi-access=free }}</ref>

However, because large eddy simulation models are simpler and smaller-scale than the [[general circulation model]]s used for climate projections, with limited representation of atmospheric processes like [[Subsidence (atmosphere)|subsidence]], this finding is currently considered speculative.<ref name="CB" /> Other scientists say that the model used in that study unrealistically extrapolates the behavior of small cloud areas onto all cloud decks, and that it is incapable of simulating anything other than a rapid transition, with some comparing it to "a knob with two settings".<ref>{{cite news |url=https://www.science.org/content/article/world-without-clouds-hardly-clear-climate-scientists-say |title=A world without clouds? Hardly clear, climate scientists say |date=February 26, 2019|website=Science Magazine |first=Paul |last=Voosen}}</ref> Additionally, {{CO2}} concentrations would only reach 1,200 ppm if the world follows [[Representative Concentration Pathway]] 8.5, which represents the highest possible greenhouse gas emission scenario and involves a massive expansion of [[coal]] infrastructure. In that case, 1,200 ppm would be passed shortly after 2100.<ref name="CB">{{Cite web |date=25 February 2019 |title=Extreme {{CO2}} levels could trigger clouds 'tipping point' and 8C of global warming |url=https://www.carbonbrief.org/extreme-co2-levels-could-trigger-clouds-tipping-point-and-8c-of-global-warming/ |access-date=2 October 2022 |website=[[Carbon Brief]]}}</ref>

== High clouds{{anchor|High cloud feedback}} ==
[[File:ISS-40 Thunderheads near Borneo.jpg|thumb|High clouds in the tropics]]
The '''high cloud feedback''' is defined as the change in radiative flux due to the response of high altitude clouds to warming<ref name="Ceppi-2017" />. High clouds refer to clouds with a top pressure lower than 440 hPa (i.e. cloud tops above ~6500m) and include cirrus type clouds as well as cumulonimbus<ref>{{Cite journal |last1=Ohno |first1=Tomoki |last2=Noda |first2=Akira T. |last3=Seiki |first3=Tatsuya |last4=Satoh |first4=Masaki |date=2021 |title=Importance of Pressure Changes in High Cloud Area Feedback Due to Global Warming |url=https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL093646 |journal=Geophysical Research Letters |language=en |volume=48 |issue=18 |pages=e2021GL093646 |doi=10.1029/2021GL093646 |bibcode=2021GeoRL..4893646O |issn=1944-8007|doi-access=free }}</ref>. The high cloud feedback is one part of the total cloud feedback which is an important variable in the climate system<ref name="Ceppi-2017">{{Cite journal |last1=Ceppi |first1=Paulo |last2=Brient |first2=Florent |last3=Zelinka |first3=Mark D. |last4=Hartmann |first4=Dennis L. |date=2017 |title=Cloud feedback mechanisms and their representation in global climate models |url=https://wires.onlinelibrary.wiley.com/doi/10.1002/wcc.465 |journal=WIREs Climate Change |language=en |volume=8 |issue=4 |doi=10.1002/wcc.465 |bibcode=2017WIRCC...8E.465C |issn=1757-7780}}</ref>. The cloud feedback is the reason for a large part of the uncertainty in todays [[Climate model|climate models]] and has a larger intermodel spread than any other radiative feedback.<ref name="Zelinka-2012">{{Cite journal |last1=Zelinka |first1=Mark D. |last2=Klein |first2=Stephen A. |last3=Hartmann |first3=Dennis L. |date=2012-06-01 |title=Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels |url=https://journals.ametsoc.org/view/journals/clim/25/11/jcli-d-11-00248.1.xml |journal=Journal of Climate |language=EN |volume=25 |issue=11 |pages=3715–3735 |doi=10.1175/JCLI-D-11-00248.1 |bibcode=2012JCli...25.3715Z |issn=0894-8755}}</ref> 

The cloud feedback, and therefore also the high cloud feedback, has a longwave and a shortwave part which are summed up to get the total feedback. In the current climate the CRE is positive in the longwave and negative in the shortwave regime.<ref name="Colman-2015" /> The longwave part includes the interaction of the clouds with the [[Outgoing longwave radiation|longwave radiation]] coming from the earths surface. The longwave feedback is dominated by the altitude and temperature of the cloud top, leading currently to a positive feedback.<ref name="Ceppi-2017" /><ref name="Zelinka-2010" />The shortwave CRE on the other hand include the interaction of the clouds with the [[Shortwave radiation (optics)|shortwave radiation]] coming directly from the sun. The shortwave feedback is dominated by cloud amount and the optical thickness leading currently to a weak negative shortwave feedback.<ref name="Ceppi-2017" /> Since the feedback strengths are depending on temperature, it is not clear that the longwave part will stay positive and the shortwave part negative as our climate changes.<ref name="Ceppi-2017" /> 

For high clouds the feedback is currently positive in total, as the shortwave feedback is near zero and the longwave feedback is positive.<ref name="Ceppi-2017" /> It is together with the mid-level cloud feedback a larger contributor to the total cloud feedback than low clouds.<ref name="Zelinka-2012" />

The calculation and modeling of high cloud feedback states a challenge and is an active field of research.<ref name="Ceppi-2017" />   

=== Physical Background ===
The high cloud feedback describes the change of radiation at the top of the atmosphere that is due to a change of high cloud properties.<ref name="Ceppi-2017" /> 

A negative feedback reduces the effect of a forcing back towards an equilibrium state. The shortwave part of the high cloud feedback is negative, but very close to zero.<ref name="Ceppi-2017" /> It can be influenced e.g. by changes in the reflection of [[Solar irradiance|solar radiation]] by the high cloud tops and their amount.<ref name="Ceppi-2017" /> A positive feedback amplifies the effect of a forcing. The longwave part of the high cloud feedback is positive.<ref name="Ceppi-2017" /> This is due to the increased reduction of [[outgoing longwave radiation]] with rising temperatures, triggered by the changing amount of high clouds that absorb and reflect the terrestrial radiation.<ref name="Ceppi-2017" /> The total high cloud feedback is the sum of the longwave and shortwave feedback and is positive.<ref name="Colman-2015">{{Cite journal |last=Colman |first=R. A. |date=2015-04-27 |title=Climate radiative feedbacks and adjustments at the Earth's surface |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014JD022896 |journal=Journal of Geophysical Research: Atmospheres |language=en |volume=120 |issue=8 |pages=3173–3182 |doi=10.1002/2014JD022896 |bibcode=2015JGRD..120.3173C |issn=2169-897X}}</ref>

The high cloud properties which mainly influence the high cloud feedback are the cloud area fraction, the cloud top height and the [[optical depth]].<ref name="Ceppi-2017" /> These cloud attributes, and therefore also the cloud feedback, are not spatially homogeneous.<ref name="Ceppi-2017" /> Hence the cloud feedback is mostly expressed as a global mean.<ref name="Ceppi-2017" />

The cloud feedback is quantified by measuring the difference of the [[radiative flux]] between all-sky (with clouds) and clear-sky (without clouds).<ref name="Ceppi-2017" /> It remains a challenge to model the various radiative interactions and their effects on clouds without introducing [[Bias|biases]] or unwanted dependencies.<ref name="Zelinka-2012" /> To gain insight to the connections between a feedback parameter and a cloud property, the model would have to realistically represent all the physical processes influencing the clouds.<ref name="Zelinka-2012" /> Because of the coarse resolution of most climate models, they need to rely on cloud parameterizations, which brings about large uncertainties.<ref name="Zelinka-2012" />

=== Longwave Feedback ===
The total longwave (LW) part of the high cloud feedback is positive.<ref name="Zelinka-2012" /> Contributions to the LW feedback stem from changes in cloud altitude, optical depth and cloud amount.

==== Cloud Altitude ====
The longwave feedback is dominated by the positive cloud altitude feedback<ref name="Zelinka-2010" /> which is mainly found in the tropics with the mechanisms being identical in the extra tropics.<ref name="Ceppi-2017" /> The LW radiation emitted by the high cloud tops is proportional to the temperature at the cloud top.<ref name="Ceppi-2017" /> The altitude of the high clouds changes with rising temperatures, due to the following mechanisms:<ref name="Ceppi-2017" /> Higher temperatures on the surface force the moisture to rise, which is fundamentally described by the [[Clausius–Clapeyron relation|Clausius Clapeyron]] equation.<ref name="Ceppi-2017" /><ref name="Zelinka-2010" /> The altitude at which the radiative cooling is still effective is closely tied to the humidity and rises equally.<ref name="Ceppi-2017" /><ref name="Zelinka-2010" /> The altitude, at which the [[radiative cooling]] becomes inefficient due to a lack of moisture, then determines the detrainment height of [[Atmospheric convection|deep convection]] due to the [[Conservation of mass|mass conservation]].<ref name="Ceppi-2017" /><ref name="Zelinka-2010" /> The could top height therefore strongly depends on the surface temperature.<ref name="Ceppi-2017" />

There are three theories on how the altitude and thus temperature depends on surface warming.<ref name="Ceppi-2017" /> The [[Fixed anvil temperature hypothesis|FAT]] (Fixed Anvil Temperature) hypothesis argues, that the isotherms shift upwards with [[Climate change|global warming]] and the temperature at the cloud top stays therefore constant.<ref name="Hartmann-2002">{{Cite journal |last1=Hartmann |first1=Dennis L. |last2=Larson |first2=Kristin |date=2002 |title=An important constraint on tropical cloud - climate feedback |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2002GL015835 |journal=Geophysical Research Letters |language=en |volume=29 |issue=20 |page=1951 |doi=10.1029/2002GL015835 |bibcode=2002GeoRL..29.1951H |issn=0094-8276}}</ref> This results in a positive feedback, since no more radiation is emitted while the surface temperature is rising.<ref name="Hartmann-2002" />  According to the FAT hypothesis this leads to a feedback of  0,27 W m<math>^{-2}</math> K<math>^{-1}</math><ref name="Zelinka-2010">{{Cite journal |last1=Zelinka |first1=Mark D. |last2=Hartmann |first2=Dennis L. |date=2010-08-27 |title=Why is longwave cloud feedback positive? |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2010JD013817 |journal=Journal of Geophysical Research: Atmospheres |language=en |volume=115 |issue=D16 |doi=10.1029/2010JD013817 |bibcode=2010JGRD..11516117Z |issn=0148-0227}}</ref>. The second hypothesis called PHAT (Proportionally Higher Anvil Temperature) claims a smaller cloud feedback of 0.20 W m<math>^{-2}</math> K<math>^{-1}</math><ref name="Zelinka-2010" />, due to a slight warming of the cloud tops which agrees better with observations.<ref name="Zelinka-2010" /> The static stability increases with higher surface temperatures in the upper troposphere and lets the clouds shift slightly to warmer temperatures.<ref name="Ceppi-2017" /> The third hypothesis is FAP (Fixed Anvil Pressure) which assumes a constant cloud top pressure with a warming climate, as if the cloud top does not move upwards.<ref name="Zelinka-2010" /> This results in a negative LW feedback, which does not agree with observations.<ref name="Zelinka-2010" /> It can be used to calculate the impact of the cloud height change on the LW feedback.<ref name="Zelinka-2010" /> Most models agree with the PHAT hypothesis which also agrees the most with observations.<ref name="Zelinka-2010" />

==== Optical Depth ====
The optical depth feedback is determined by the increasing optical depth of the high clouds with rising temperatures.<ref name="Stephens-1978">{{Cite journal |last=Stephens |first=G. L. |date=1978-11-01 |title=Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes |url=https://journals.ametsoc.org/view/journals/atsc/35/11/1520-0469_1978_035_2123_rpiewc_2_0_co_2.xml |journal=Journal of the Atmospheric Sciences |language=EN |volume=35 |issue=11 |pages=2123–2132 |doi=10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2 |bibcode=1978JAtS...35.2123S |issn=0022-4928|doi-access=free }}</ref> The optical depth increases the LW emission of the cloud, so that the contribution of the optical depth to the LW feedback is positive.<ref name="Stephens-1978" /> At the same time, the shortwave contribution of increasing optical depth is negative and, because it is larger than the LW  component, dominates. The overall optical depth feedback for high clouds is just below zero.<ref name="Ceppi-2017" />

==== Cloud Amount ====
The [[Cloud cover|area fraction of high clouds]] is also an important part of the LW feedback. A decrease in the area fraction would lead to a more negative feedback.<ref name="Ceppi-2017" />  Two mechanisms can lead to a decrease in the area fraction and therefore a negative feedback.<ref name="Ceppi-2017" /> The warming at the surface decreases the [[Lapse rate|moist adiabat]]  which leads to a decrease of the [[Subsidence (atmosphere)|clear sky subsidence]].<ref name="Jeevanjee-2022">{{Cite journal |last=Jeevanjee |first=Nadir |date=November 2022 |title=Three Rules for the Decrease of Tropical Convection With Global Warming |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022MS003285 |journal=Journal of Advances in Modeling Earth Systems |language=en |volume=14 |issue=11 |doi=10.1029/2022MS003285 |bibcode=2022JAMES..1403285J |issn=1942-2466}}</ref> Since the convective [[mass flux]] has to be equal to the clear sky subsidence it decreases as well and with it potentially the cloud area fraction.<ref name="Jeevanjee-2022" /> Another argument for a smaller area fraction is that the self-aggregation of clouds increases at higher temperatures.<ref name="Ceppi-2017" /> This would lead to smaller convective areas and larger dry areas which increase the radiative longwave cooling, resulting in a negative feedback.<ref name="Ceppi-2017" /> How the area fraction will change is however a topic of ongoing research and discussion.<ref name="Ceppi-2017" /> Since the area fraction of high clouds in models is sensitive, among others to [[Cloud physics|cloud micro physics]]<ref name="Ceppi-2017" />, there are also models which predict an increase in high cloud area fraction<ref name="Zelinka-2010" /> which would lead to a positive feedback.    

=== Shortwave Feedback ===
The total shortwave (SW) part of the high cloud feedback is negative.

The impact of cloud area fraction on the shortwave feedback with warming is a topic of discussion, similar to the LW feedback.<ref name="Zelinka-2012" /> The SW high cloud feedback depends on the shot cloud area fraction due to its control of SW reflection. With a larger cloud area fraction more solar radiation can be reflected.<ref name="Zelinka-2010" /> A decreasing cloud fraction would lead to a positive SW feedback.<ref name="Zelinka-2012" /> It was found that the high cloud SW feedback is anticorrelated to the [[lapse rate]] feedback (the change of the temperature profile of the atmosphere with warming) which influences the cloud coverage.<ref name="Zelinka-2010" /> Therefore the high cloud SW feedback could be computed together with the lapse rate feedback to simplify the calculations in climate models. It is important to note, that this is a topic of ongoing discussion.<ref name="Zelinka-2010" />

The impact of the cloud height and optical thickness on the SW feedback is negative. A higher optical thickness due to warming, changes fore example the cloud particle size and density which then changes the reflectivity of the cloud and therefore impacts the SW feedback.<ref name="Ceppi-2017" />

=== Challenges ===
It is difficult to detect the reason for a change in the SW and LW radiation due to cloud feedback, because there are a lot of cloud responses which could be the cause for a specific radiation feedback.<ref name="Zelinka-2012" /> Furthermore is it difficult to not count in clear sky effects<ref name="Zelinka-2012" />. There are techniques to decompose the cloud feedbacks in models and their triggers in detail by showing the cloud fraction as a function of cloud-top pressure and the optical depth of the cloud. In the GCM, which are mostly used, the main challenge is the parametrization of clouds, especially in coarse-resolution models. The characteristics of clouds need to be parametrized  in such a way, that the different feedbacks and physical interactions are as correct as possible in order to decrease the uncertainty of the models.<ref name="Zelinka-2012" />

Another challenge when dealing with (high) cloud feedbacks, is that the LW and SW part often cancel each other out, so that only a small total feedback is left.<ref name="Zelinka-2012" /> The positive and negative feedback parts are not neglectable, since they can change independent of one another with rising temperature.<ref name="Zelinka-2012" />

==See also==
*[[Cloud formation]]
*[[Earth's energy budget]]
* [[Fixed anvil temperature hypothesis]]

==References==
{{Reflist}}

{{climate change}}

[[Category:Climate forcing]]
[[Category:Cloud and fog physics]]