Cloud feedback
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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" />Template:Rp 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">Template:Cite journal</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">Template:Cite report</ref>Template:Rp
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">Template:Cite journal</ref><ref name="Cess1990">Template:Cite journal</ref><ref name="Fowler1996">Template:Cite journal</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">Template:Cite report</ref>Template:Rp
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" />Template:Rp 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">Template:Cite journal</ref><ref name="Fr242020">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> This had occurred because they estimated cloud feedback as highly positive.<ref name="Zelinka2020">Template:Cite journal</ref><ref name="SD2020">Template:Cite journal</ref> Those particular models were soon found to contradict both observations and paleoclimate evidence,<ref name="Zhu2020">Template:Cite journal</ref><ref name="EricksonPhys2020">{{#invoke:citation/CS1|citation |CitationClass=web }}</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" />Template:Rp<ref name="VoosenSciMag2022">{{#invoke:citation/CS1|citation |CitationClass=web }}</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 aerosols, 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">{{#invoke:citation/CS1|citation |CitationClass=web }}</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">Template:Cite journal</ref>
How clouds affect radiation and climate feedbackEdit
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" />Template:Rp
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">Template:Cite journal</ref> This is also sometimes referred to as cloud radiative forcing (CRF).<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</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>Template:Cite book</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 (Qabs ) 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 So is the solar constant, ∝cloudy is the albedo with clouds and ∝clear 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>
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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>Template:Cite journal, D20106. Web page Template:Webarchive</ref> According to 1990 estimates, the presence of clouds reduces the outgoing longwave radiation by about 31 W/m2. 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/m2. Thus, there is a net cooling of about 13 W/m2.<ref>Template:Cite booktable 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" />Template:Rp
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>Template:Cite journal</ref> It also affects a range of cloud properties, such as their height, the typical distribution throughout the atmosphere, and cloud microphysics, such as the amount of water droplets held, all of which then affect clouds' radiative forcing.<ref name="IPCC AR6 WG1 CH7" />Template:Rp 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: 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 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>Template:Rp Clouds also affect the magnitude of internally generated climate variability.<ref>Template:Cite journal</ref><ref>Template:Cite journal</ref>
Cloud feedback mechanismsEdit
High cloudsTemplate:AnchorEdit
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>Template:Cite journal</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">Template:Cite journal</ref> The cloud feedback is the reason for a large part of the uncertainty in todays climate models and has a larger intermodel spread than any other radiative feedback.<ref name="Zelinka-2012">Template:Cite journal</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 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 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 BackgroundEdit
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 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">Template:Cite journal</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 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 FeedbackEdit
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 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 deep convection due to the 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 FAT (Fixed Anvil Temperature) hypothesis argues, that the isotherms shift upwards with global warming and the temperature at the cloud top stays therefore constant.<ref name="Hartmann-2002">Template:Cite journal</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">Template:Cite journal</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">Template:Cite journal</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 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 moist adiabat which leads to a decrease of the clear sky subsidence.<ref name="Jeevanjee-2022">Template:Cite journal</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 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 FeedbackEdit
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" />
ChallengesEdit
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" />
Possible break-up of equatorial stratocumulus cloudsEdit
Template:See also In 2019, a study employed a large eddy simulation model to estimate that equatorial stratocumulus clouds could break up and scatter when [[carbon dioxide|Template:CO2]] levels rise above 1,200 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 Template:Convert globally and Template:Convert in the subtropics, which would be in addition to at least Template:Convert already caused by such Template:CO2 concentrations. In addition, stratocumulus clouds would not reform until the Template:CO2 concentrations drop to a much lower level.<ref>Template:Cite journal</ref> It was suggested that this finding could help explain past episodes of unusually rapid warming such as Paleocene-Eocene Thermal Maximum.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> In 2020, further work from the same authors revealed that in their large eddy simulation, this tipping point cannot be stopped with solar radiation modification: in a hypothetical scenario where very high Template: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 Template:CO2 concentrations hit 1,700 ppm, at which point it would still cause around Template:Convert of unavoidable warming.<ref>Template:Cite journal</ref>
However, because large eddy simulation models are simpler and smaller-scale than the general circulation models used for climate projections, with limited representation of atmospheric processes like subsidence, this finding is currently considered speculative.<ref name="CB">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> 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>Template:Cite news</ref> Additionally, Template: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" />
Representation in climate modelsEdit
Climate models 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 scientists 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 uncertainty range regarding cloud feedback strength became 50% smaller since the time of the AR5 in 2014.<ref name="IPCC_AR6_WG1_TS" />Template:Rp
| 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" />Template:Rp The AR6 value for cloud feedback is +0.42 [–0.10 to 0.94] W m–2 per every Template:Convert in warming. This estimate is derived from multiple lines of evidence, including both models and observations.<ref name="IPCC_AR6_WG1_TS" />Template:Rp 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" />Template:Rp As of 2024, most recent observations from the CALIPSO satellite instead indicate that the tropical cloud feedback is very weak.<ref>Template:Cite journal</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 Template: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" />Template:Rp 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" />Template:Rp<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" />Template:Rp the average is so much higher only because several "hot" models have much stronger cloud feedback and higher sensitivity than the rest.<ref name="IPCC_AR6_WG1_TS" />Template:Rp<ref name="VoosenSciMag2022" /> Those models have a sensitivity of Template:Cvt and their presence had increased the median model sensitivity from Template:Cvt in CMIP5 to Template:Cvt 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 Template:Cvt, as this is in a better agreement with observations and paleoclimate findings.<ref name="IPCC_AR6_WG1_TS" />Template:Rp<ref name="Zhu2020" /><ref name="EricksonPhys2020" />
Role of aerosol and aerosol-cloud interactionEdit
Atmospheric aerosols—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>Template:Cite journal</ref> by far the strongest effect is from sulfates, 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>Template:Cite journal</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>Template:Cite journal</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">Template:Cite journal</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>Template:Cite journal</ref> it was later suggested that this finding was confounded by counteracting changes in humidity.<ref name="Cao2021" />
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>Template:Cite journal</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>Template:Cite journal</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>Template:Cite journal</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">Template:Cite journal</ref> 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">{{#invoke:citation/CS1|citation |CitationClass=web }}</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: Summary for Policymakers. In: 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, {{#invoke:doi|main}}.</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">{{#invoke:citation/CS1|citation |CitationClass=web }}</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">Template:Cite journal</ref><ref name="Julsrud2022" /><ref name="Persad2022">Template:Cite journal</ref><ref name="Ramachandran2022">Template:Cite journal</ref> See also