Cloud feedback: Difference between revisions

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A cloud feedback is a climate change feedback where some aspects of cloud characteristics (e.g. cloud cover, composition or height) are altered due to climate change, and these changes then further affect the Earth’s energy balance.^[1]Template:Rp On their own, clouds are already an important part of the climate system, as they consist of liquid droplets and ice particles, which absorb infrared radiation and reflect visible solar radiation.^[2] 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.^[3]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). Vice versa, if they change in such a way that their warming effect increases relative to their cooling effect then the net cloud feedback, 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.^[2]

There are many mechanisms by which cloud feedbacks occur. Most substantially, evidence points to climate change causing high clouds to rise in altitude (a positive feedback), the coverage of tropical low clouds to reduce (a positive feedback) and polar low clouds to become more reflective (a negative feedback).^[4] Aside from cloud responses to human-induced warming through greenhouse gases, the interaction of clouds with aerosol particles is known to affect cloud reflectivity,^[5]^[6] and may modulate the strength of cloud feedbacks.^[7] Cloud feedback processes have been represented in every major climate model from the 1980s onwards.^[8]^[9]^[10] Observations and climate model results now provide high confidence that the overall cloud feedback on climate change is positive.^[11]Template:Rp

Cloud feedbacks are estimated using both observational data and climate models. Uncertainty in both these aspects - for example, incomplete observational data or uncertainty in the representation of processes in models mean that cloud feedback estimates differ substantially between models. 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.^[3]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 (ECS).^[12]^[13] This had occurred because they estimated cloud feedback as highly positive.^[14]^[15] Although those particular models were soon found to contradict both observations and paleoclimate evidence,^[16]^[17] it is suggested to be problematic if ruling out these 'hot' models solely based on ECS^[18] and care should be taken when weighting climate model ensembles by temperature alone.^[19]

One reason why constraining cloud feedbacks has been difficult 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.^[20] These particles affect the clouds in multiple ways, mostly making them more reflective through aerosol-cloud interactions. 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.^[21]

How clouds affect radiation and climate feedback

File:McKim 2024 cloud formulae.png

Details of how clouds interact with shortwave and longwave radiation at different atmospheric heights^[22]

Clouds have two major effects on the Earth's energy budget. Firstly, they reflect shortwave radiation from sunlight back to space due to their high albedo - a cooling effect for the Earth. Secondly, the condensed and frozen water contained inside them absorbs longwave radiation emitted by the Earth's surface. Clouds themselves also emit longwave radiation, both towards the surface and to space. The net effect is that the presence of clouds reduces the longwave radiation emitted to space, i.e. a warming effect.^[23]

In meteorology, the difference in the radiation budget caused by clouds, relative to cloud-free conditions, is described as the cloud radiative effect (CRE).^[24] This is also sometimes referred to as cloud radiative forcing (CRF).^[25] However, since cloud changes are not normally considered an external forcing of climate, CRE is the most commonly used term.

This can be described by the equation

$C R E = R_{a l l - s k y} - R_{c l e a r - s k y}$

Where CRE is cloud radiative effect (W m^-2), R_all-sky is the radiation flux (W m^-2) under all-sky conditions, and R_clear-sky is the radiation flux (W m^-2) under clear-sky conditions where there are no clouds.^[26]

Cloud feedback is one of a number of climate feedbacks. Cloud feedback is the combination of the influence of cloud variation on radiation and the response of clouds to global average temperature change. As represented by the equation

$λ_{c l o u d} = Σ \frac{\partial N}{\partial x_{c l o u d}} \frac{\partial x_{c l o u d}}{\partial T}$

where N is the net downward radiation (W m^-2),

x_{c l o u d}

is the change in cloud characteristic (e.g. cloud cover or height), and T is the global mean near-surface air temperature (K).^[27]

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

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 which is now condensed inside the clouds amounts to 25%, and the net greenhouse effect is at 75%.^[28] 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.^[29] If the clouds were removed with all else remaining the same, the Earth would lose this much cooling and the global temperatures would increase.^[3]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.^[30] 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.^[3]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.^[1]Template:Rp Clouds also affect the magnitude of internally generated climate variability.^[31]^[32]

Cloud feedback mechanisms

High cloudsScript error: No such module "anchor".

Examples of cloud feedback

File:ISS-40 Thunderheads near Borneo.jpg

High clouds in the tropics

High cloud feedback is defined as the change in radiative flux due to the response of high altitude clouds to warming.^[33] 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.^[34] The high cloud feedback is one part of the total cloud feedback which is an important variable in the climate system.^[33] 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.^[35]

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.^[36] 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.^[33]^[37] 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.^[33] 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.^[33]

For high clouds the feedback is currently positive in total, as the shortwave feedback is near zero and the longwave feedback is positive.^[33] It is together with the mid-level cloud feedback a larger contributor to the total cloud feedback than low clouds.^[35]

The calculation and modeling of high cloud feedback states a challenge and is an active field of research.^[33]

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.^[33]

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.^[33] It can be influenced e.g. by changes in the reflection of solar radiation by the high cloud tops and their amount.^[33] A positive feedback amplifies the effect of a forcing. The longwave part of the high cloud feedback is positive.^[33] 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.^[33] The total high cloud feedback is the sum of the longwave and shortwave feedback and is positive.^[36]

The high cloud properties which mainly influence the high cloud feedback are the cloud area fraction, the cloud top height and the optical depth.^[33] These cloud attributes, and therefore also the cloud feedback, are not spatially homogeneous.^[33] Hence the cloud feedback is mostly expressed as a global mean.^[33]

The cloud feedback is quantified by measuring the difference of the radiative flux between all-sky (with clouds) and clear-sky (without clouds).^[33] It remains a challenge to model the various radiative interactions and their effects on clouds without introducing biases or unwanted dependencies.^[35] 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.^[35] Because of the coarse resolution of most climate models, they need to rely on cloud parameterizations, which brings about large uncertainties.^[35]

Longwave Feedback

The total longwave (LW) part of the high cloud feedback is positive.^[35] 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^[37] which is mainly found in the tropics with the mechanisms being identical in the extra tropics.^[33] The LW radiation emitted by the high cloud tops is proportional to the temperature at the cloud top.^[33] The altitude of the high clouds changes with rising temperatures, due to the following mechanisms:^[33] Higher temperatures on the surface force the moisture to rise, which is fundamentally described by the Clausius Clapeyron equation.^[33]^[37] The altitude at which the radiative cooling is still effective is closely tied to the humidity and rises equally.^[33]^[37] 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.^[33]^[37] The could top height therefore strongly depends on the surface temperature.^[33]

There are three theories on how the altitude and thus temperature depends on surface warming.^[33] 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.^[38] This results in a positive feedback, since no more radiation is emitted while the surface temperature is rising.^[38] According to the FAT hypothesis this leads to a feedback of 0,27 W m $- 2$ K $- 1$ .^[37] The second hypothesis called PHAT (Proportionally Higher Anvil Temperature) claims a smaller cloud feedback of 0.20 W m $- 2$ K $- 1$ ,^[37] due to a slight warming of the cloud tops which agrees better with observations.^[37] The static stability increases with higher surface temperatures in the upper troposphere and lets the clouds shift slightly to warmer temperatures.^[33] 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.^[37] This results in a negative LW feedback, which does not agree with observations.^[37] It can be used to calculate the impact of the cloud height change on the LW feedback.^[37] Most models agree with the PHAT hypothesis which also agrees the most with observations.^[37]

Optical Depth

The optical depth feedback is determined by the increasing optical depth of the high clouds with rising temperatures.^[39] The optical depth increases the LW emission of the cloud, so that the contribution of the optical depth to the LW feedback is positive.^[39] 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.^[33]

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.^[33] Two mechanisms can lead to a decrease in the area fraction and therefore a negative feedback.^[33] The warming at the surface decreases the moist adiabat which leads to a decrease of the clear sky subsidence.^[40] 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.^[40] Another argument for a smaller area fraction is that the self-aggregation of clouds increases at higher temperatures.^[33] This would lead to smaller convective areas and larger dry areas which increase the radiative longwave cooling, resulting in a negative feedback.^[33] How the area fraction will change is however a topic of ongoing research and discussion.^[33] Since the area fraction of high clouds in models is sensitive, among others to cloud micro physics,^[33] there are also models which predict an increase in high cloud area fraction^[37] 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.^[35] 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.^[37] A decreasing cloud fraction would lead to a positive SW feedback.^[35] 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.^[37] 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.^[37]

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.^[33]

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.^[35] Furthermore is it difficult to not count in clear sky effects.^[35] 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.^[35]

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.^[35] The positive and negative feedback parts are not neglectable, since they can change independent of one another with rising temperature.^[35]

Possible break-up of equatorial stratocumulus clouds

Script error: No such module "Labelled list hatnote". 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.^[41] It was suggested that this finding could help explain past episodes of unusually rapid warming such as Paleocene-Eocene Thermal Maximum.^[42] 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.^[43]

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.^[44] 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".^[45] 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.^[44]

Representation in climate models

File:20220726 Feedbacks affecting global warming and climate change - block diagram.svg

Examples of some effects of global warming that can amplify (positive feedbacks) or reduce (negative feedbacks) global warming^[46]

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.^[8]^[9]^[10] 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.^[9] 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.^[11]Template:Rp

File:McKim 2024 tropical clouds.jpg

Tropical clouds are known to have a cooling effect, but it is uncertain whether it would become stronger or weaker in the future^[22]

Remaining uncertainty about cloud feedbacks in IPCC Sixth Assessment Report^[3]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).^[11]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.^[11]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%.^[3]Template:Rp As of 2024, most recent observations from the CALIPSO satellite instead indicate that the tropical cloud feedback is very weak.^[47]^[22]

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.^[11]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.^[11]Template:Rp^[14]

However, the median cloud feedback is only slightly larger in CMIP6 than it was in CMIP5;^[11]Template:Rp the average is so much higher only because several "hot" models have much stronger cloud feedback and higher sensitivity than the rest.^[11]Template:Rp^[48] 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.^[15] 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.^[12]^[13] 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.^[11]Template:Rp^[16]^[17]

Role of aerosol and aerosol-cloud interaction

File:Bellouin 2019 aerosol cloud interactions.jpg

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.^[49]

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,^[50] 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. These influences of aerosols on clouds are aerosol indirect effects, of which the famous one are the Twomey effect^[51] and the Albrecht effect^[52] through aerosols acting as cloud condensation nuclei (CCN). Less well understood indirect effects of aerosols are on the formation of ice, through variation in concentrations and types of ice nucleating particles. 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.^[21] 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.^[53] 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.^[54]

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.^[55] 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,^[56] it was later suggested that this finding was confounded by counteracting changes in humidity.^[55]

File:ShipTracks.jpg

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%.^[57] 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.^[58] 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.^[59]

File:Estimates of past and future SO2 global anthropogenic emissions.png

Early 2010s estimates of past and future anthropogenic global sulfur dioxide emissions, including the Representative Concentration Pathways. 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.^[60]

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.^[20]^[61] 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.^[62] 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.^[63]

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.^[64] 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.^[65]^[61]^[66]^[67] See also

References

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 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). Vice versa, if they change in such a way that their warming effect increases relative to their cooling effect then the net cloud feedback, 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" />
-There are many mechanisms by which cloud feedbacks occur. Most substantially, evidence points to climate change causing high clouds to rise in altitude (a positive feedback), the coverage of tropical low clouds to reduce (a positive feedback) and polar low clouds to become more reflective (a negative feedback).<ref>{{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://onlinelibrary.wiley.com/doi/abs/10.1002/wcc.465 |journal=WIREs Climate Change |language=en |volume=8 |issue=4 |pages=e465 |doi=10.1002/wcc.465 |bibcode=2017WIRCC...8E.465C |issn=1757-7799}}</ref> Aside from cloud responses to human-induced warming through greenhouse gases, the interaction of clouds with [[aerosol]] particles is known to affect cloud reflectivity<ref>{{Cite journal |last=Twomey |first=S. |date=1977 |title=The Influence of Pollution on the Shortwave Albedo of Clouds |url=https://journals.ametsoc.org/view/journals/atsc/34/7/1520-0469_1977_034_1149_tiopot_2_0_co_2.xml |journal=[[Journal of the Atmospheric Sciences]] |volume=34 |issue=7 |pages=1149–1152 |doi=10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2|bibcode=1977JAtS...34.1149T }}</ref><ref>{{Cite journal |last1=Carslaw |first1=K. S. |last2=Lee |first2=L. A. |last3=Reddington |first3=C. L. |last4=Pringle |first4=K. J. |last5=Rap |first5=A. |last6=Forster |first6=P. M. |last7=Mann |first7=G. W. |last8=Spracklen |first8=D. V. |last9=Woodhouse |first9=M. T. |last10=Regayre |first10=L. A. |last11=Pierce |first11=J. R. |date=2013 |title=Large contribution of natural aerosols to uncertainty in indirect forcing |url=https://www.nature.com/articles/nature12674 |journal=[[Nature (journal)|Nature]] |language=en |volume=503 |issue=7474 |pages=67–71 |doi=10.1038/nature12674 |pmid=24201280 |bibcode=2013Natur.503...67C |issn=1476-4687}}</ref>, and may modulate the strength of cloud feedbacks.<ref>{{Cite journal |last1=Murray |first1=Benjamin J. |last2=Carslaw |first2=Kenneth S. |last3=Field |first3=Paul R. |date=2021-01-18 |title=Opinion: Cloud-phase climate feedback and the importance of ice-nucleating particles |url=https://acp.copernicus.org/articles/21/665/2021/ |journal=[[Atmospheric Chemistry and Physics]] |language=English |volume=21 |issue=2 |pages=665–679 |doi=10.5194/acp-21-665-2021 |doi-access=free |bibcode=2021ACP....21..665M |issn=1680-7316}}</ref> Cloud feedback 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}}
+There are many mechanisms by which cloud feedbacks occur. Most substantially, evidence points to climate change causing high clouds to rise in altitude (a positive feedback), the coverage of tropical low clouds to reduce (a positive feedback) and polar low clouds to become more reflective (a negative feedback).<ref>{{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://onlinelibrary.wiley.com/doi/abs/10.1002/wcc.465 |journal=WIREs Climate Change |language=en |volume=8 |issue=4 |pages=e465 |doi=10.1002/wcc.465 |bibcode=2017WIRCC...8E.465C |issn=1757-7799}}</ref> Aside from cloud responses to human-induced warming through greenhouse gases, the interaction of clouds with [[aerosol]] particles is known to affect cloud reflectivity,<ref>{{Cite journal |last=Twomey |first=S. |date=1977 |title=The Influence of Pollution on the Shortwave Albedo of Clouds |url=https://journals.ametsoc.org/view/journals/atsc/34/7/1520-0469_1977_034_1149_tiopot_2_0_co_2.xml |journal=[[Journal of the Atmospheric Sciences]] |volume=34 |issue=7 |pages=1149–1152 |doi=10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2|bibcode=1977JAtS...34.1149T }}</ref><ref>{{Cite journal |last1=Carslaw |first1=K. S. |last2=Lee |first2=L. A. |last3=Reddington |first3=C. L. |last4=Pringle |first4=K. J. |last5=Rap |first5=A. |last6=Forster |first6=P. M. |last7=Mann |first7=G. W. |last8=Spracklen |first8=D. V. |last9=Woodhouse |first9=M. T. |last10=Regayre |first10=L. A. |last11=Pierce |first11=J. R. |date=2013 |title=Large contribution of natural aerosols to uncertainty in indirect forcing |url=https://www.nature.com/articles/nature12674 |journal=[[Nature (journal)|Nature]] |language=en |volume=503 |issue=7474 |pages=67–71 |doi=10.1038/nature12674 |pmid=24201280 |bibcode=2013Natur.503...67C |issn=1476-4687}}</ref> and may modulate the strength of cloud feedbacks.<ref>{{Cite journal |last1=Murray |first1=Benjamin J. |last2=Carslaw |first2=Kenneth S. |last3=Field |first3=Paul R. |date=2021-01-18 |title=Opinion: Cloud-phase climate feedback and the importance of ice-nucleating particles |url=https://acp.copernicus.org/articles/21/665/2021/ |journal=[[Atmospheric Chemistry and Physics]] |language=English |volume=21 |issue=2 |pages=665–679 |doi=10.5194/acp-21-665-2021 |doi-access=free |bibcode=2021ACP....21..665M |issn=1680-7316}}</ref> Cloud feedback 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}}
-Cloud feedbacks are estimated using both observational data and climate models. Uncertainty in both these aspects - for example, incomplete observational data or uncertainty in the representation of processes in models mean that cloud feedback estimates differ substantially between models. 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]] (ECS).<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> Although 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> it is suggested to be problematic if ruling out these 'hot' models solely based on ECS<ref>{{Cite journal |last1=Bloch-Johnson |first1=Jonah |last2=Rugenstein |first2=Maria |last3=Gregory |first3=Jonathan |last4=Cael |first4=B. B. |last5=Andrews |first5=Timothy |date=2022-08-23 |title=Climate impact assessments should not discount 'hot' models |url=https://www.nature.com/articles/d41586-022-02241-6 |journal=Nature |language=en |volume=608 |issue=7924 |pages=667 |doi=10.1038/d41586-022-02241-6|pmid=35999296 }}</ref> and care should be taken when weighting climate model ensembles by temperature alone<ref>{{Cite journal |last1=McDonnell |first1=Abigail |last2=Bauer |first2=Adam Michael |last3=Proistosescu |first3=Cristian |date=2024 |title=To What Extent Does Discounting 'Hot' Climate Models Improve the Predictive Skill of Climate Model Ensembles? |url=https://onlinelibrary.wiley.com/doi/abs/10.1029/2024EF004844 |journal=Earth's Future |language=en |volume=12 |issue=10 |pages=e2024EF004844 |doi=10.1029/2024EF004844 |bibcode=2024EaFut..1204844M |issn=2328-4277}}</ref>.
+Cloud feedbacks are estimated using both observational data and climate models. Uncertainty in both these aspects - for example, incomplete observational data or uncertainty in the representation of processes in models mean that cloud feedback estimates differ substantially between models. 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]] (ECS).<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> Although 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> it is suggested to be problematic if ruling out these 'hot' models solely based on ECS<ref>{{Cite journal |last1=Bloch-Johnson |first1=Jonah |last2=Rugenstein |first2=Maria |last3=Gregory |first3=Jonathan |last4=Cael |first4=B. B. |last5=Andrews |first5=Timothy |date=2022-08-23 |title=Climate impact assessments should not discount 'hot' models |url=https://www.nature.com/articles/d41586-022-02241-6 |journal=Nature |language=en |volume=608 |issue=7924 |pages=667 |doi=10.1038/d41586-022-02241-6|pmid=35999296 |url-access=subscription }}</ref> and care should be taken when weighting climate model ensembles by temperature alone.<ref>{{Cite journal |last1=McDonnell |first1=Abigail |last2=Bauer |first2=Adam Michael |last3=Proistosescu |first3=Cristian |date=2024 |title=To What Extent Does Discounting 'Hot' Climate Models Improve the Predictive Skill of Climate Model Ensembles? |url=https://onlinelibrary.wiley.com/doi/abs/10.1029/2024EF004844 |journal=Earth's Future |language=en |volume=12 |issue=10 |pages=e2024EF004844 |doi=10.1029/2024EF004844 |bibcode=2024EaFut..1204844M |issn=2328-4277|doi-access=free }}</ref>
 One reason why constraining cloud feedbacks has been difficult 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 through aerosol-cloud interactions. 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>
@@ Line 22: / Line 22: @@
 == How clouds affect radiation and climate feedback ==
 [[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|Earth's energy budget.]] Firstly, they reflect shortwave radiation from sunlight back to space due to their high [[albedo]] - a cooling effect for the Earth. Secondly, the condensed and frozen water contained inside them absorbs longwave radiation emitted by the Earth's surface. Clouds themselves also emit longwave radiation, both towards the surface and to space. The net effect is that the presence of clouds reduces the longwave radiation emitted to space, i.e. a warming effect<ref>{{Cite web |title=Cloud Radiative Effect – Geophysical Fluid Dynamics Laboratory |url=https://www.gfdl.noaa.gov/cloud-radiative-effect/ |archive-url=https://web.archive.org/web/20210508091821/https://www.gfdl.noaa.gov/cloud-radiative-effect/ |archive-date=2021-05-08 |access-date=2025-06-19 |website=www.gfdl.noaa.gov |language=en-US}}</ref>.
+Clouds have two major effects on the [[Earth's energy budget]]. Firstly, they reflect shortwave radiation from sunlight back to space due to their high [[albedo]] - a cooling effect for the Earth. Secondly, the condensed and frozen water contained inside them absorbs longwave radiation emitted by the Earth's surface. Clouds themselves also emit longwave radiation, both towards the surface and to space. The net effect is that the presence of clouds reduces the longwave radiation emitted to space, i.e. a warming effect.<ref>{{Cite web |title=Cloud Radiative Effect – Geophysical Fluid Dynamics Laboratory |url=https://www.gfdl.noaa.gov/cloud-radiative-effect/ |archive-url=https://web.archive.org/web/20210508091821/https://www.gfdl.noaa.gov/cloud-radiative-effect/ |archive-date=2021-05-08 |access-date=2025-06-19 |website=www.gfdl.noaa.gov |language=en-US}}</ref>
 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.
@@ Line 30: / Line 30: @@
 <math>CRE = R_{all-sky} - R_{clear-sky}</math>
-Where ''CRE'' is cloud radiative effect (W m<sup>-2</sup>), ''R<sub>all-sky</sub>'' is the radiation flux (W m<sup>-2</sup>) under all-sky conditions, and ''R<sub>clear-sky</sub>'' is the radiation flux (W m<sup>-2</sup>) under clear-sky conditions where there are no clouds.<ref>{{Cite journal |last1=Ramanathan |first1=V. |last2=Cess |first2=R. D. |last3=Harrison |first3=E. F. |last4=Minnis |first4=P. |last5=Barkstrom |first5=B. R. |last6=Ahmad |first6=E. |last7=Hartmann |first7=D. |date=1989-01-06 |title=Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment |url=https://www.science.org/doi/10.1126/science.243.4887.57 |journal=Science |language=en |volume=243 |issue=4887 |pages=57–63 |doi=10.1126/science.243.4887.57 |pmid=17780422 |bibcode=1989Sci...243...57R |issn=0036-8075}}</ref>
+Where ''CRE'' is cloud radiative effect (W m<sup>-2</sup>), ''R<sub>all-sky</sub>'' is the radiation flux (W m<sup>-2</sup>) under all-sky conditions, and ''R<sub>clear-sky</sub>'' is the radiation flux (W m<sup>-2</sup>) under clear-sky conditions where there are no clouds.<ref>{{Cite journal |last1=Ramanathan |first1=V. |last2=Cess |first2=R. D. |last3=Harrison |first3=E. F. |last4=Minnis |first4=P. |last5=Barkstrom |first5=B. R. |last6=Ahmad |first6=E. |last7=Hartmann |first7=D. |date=1989-01-06 |title=Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment |url=https://www.science.org/doi/10.1126/science.243.4887.57 |journal=Science |language=en |volume=243 |issue=4887 |pages=57–63 |doi=10.1126/science.243.4887.57 |pmid=17780422 |bibcode=1989Sci...243...57R |issn=0036-8075|url-access=subscription }}</ref>
 Cloud feedback is one of a number of [[climate feedbacks]]. Cloud feedback is the combination of the influence of cloud variation on radiation and the response of clouds to global average temperature change. As represented by the equation
@@ Line 58: / Line 58: @@
 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>
+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|url-access=subscription }}</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" />
@@ Line 132: / Line 132: @@
 [[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. |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 |pmc=7384191 |pmid=32734279 |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.}}</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 |last1=Ramanathan |first1=V. |last2=Carmichael |first2=G. |year=2008 |title=Nature Geoscience: Global and regional climate changes due to black carbon |journal=Nature Geoscience |volume=1 |issue=4 |pages=221–227 |bibcode=2008NatGe...1..221R |doi=10.1038/ngeo156 |s2cid=12455550}}</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. These influences of aerosols on clouds are aerosol ''indirect'' effects, of which the famous one are the [[Twomey effect]]<ref>{{Cite journal |last=Twomey |first=S. |date=July 1977 |title=The Influence of Pollution on the Shortwave Albedo of Clouds |url=http://journals.ametsoc.org/doi/10.1175/1520-0469(1977)0342.0.CO;2 |journal=Journal of the Atmospheric Sciences |language=en |volume=34 |issue=7 |pages=1149–1152 |doi=10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2 |bibcode=1977JAtS...34.1149T |issn=0022-4928}}</ref> and the [[Albrecht effect]]<ref>{{Cite journal |last=Albrecht |first=Bruce A. |date=1989-09-15 |title=Aerosols, Cloud Microphysics, and Fractional Cloudiness |url=https://www.science.org/doi/10.1126/science.245.4923.1227 |journal=Science |language=en |volume=245 |issue=4923 |pages=1227–1230 |doi=10.1126/science.245.4923.1227 |pmid=17747885 |bibcode=1989Sci...245.1227A |issn=0036-8075}}</ref> through aerosols acting as cloud condensation nuclei (CCN). Less well understood indirect effects of aerosols are on the formation of ice, through variation in concentrations and types of [[Ice nucleus|ice nucleating particles]]. 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 |bibcode=2018NatCo...9..985S |doi=10.1038/s41467-018-03379-6 |pmc=5841301 |pmid=29515125 |doi-access=free}}</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>
+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 |last1=Ramanathan |first1=V. |last2=Carmichael |first2=G. |year=2008 |title=Nature Geoscience: Global and regional climate changes due to black carbon |journal=Nature Geoscience |volume=1 |issue=4 |pages=221–227 |bibcode=2008NatGe...1..221R |doi=10.1038/ngeo156 |s2cid=12455550}}</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. These influences of aerosols on clouds are aerosol ''indirect'' effects, of which the famous one are the [[Twomey effect]]<ref>{{Cite journal |last=Twomey |first=S. |date=July 1977 |title=The Influence of Pollution on the Shortwave Albedo of Clouds |url=http://journals.ametsoc.org/doi/10.1175/1520-0469(1977)0342.0.CO;2 |journal=Journal of the Atmospheric Sciences |language=en |volume=34 |issue=7 |pages=1149–1152 |doi=10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2 |bibcode=1977JAtS...34.1149T |issn=0022-4928}}</ref> and the [[Albrecht effect]]<ref>{{Cite journal |last=Albrecht |first=Bruce A. |date=1989-09-15 |title=Aerosols, Cloud Microphysics, and Fractional Cloudiness |url=https://www.science.org/doi/10.1126/science.245.4923.1227 |journal=Science |language=en |volume=245 |issue=4923 |pages=1227–1230 |doi=10.1126/science.245.4923.1227 |pmid=17747885 |bibcode=1989Sci...245.1227A |issn=0036-8075|url-access=subscription }}</ref> through aerosols acting as cloud condensation nuclei (CCN). Less well understood indirect effects of aerosols are on the formation of ice, through variation in concentrations and types of [[Ice nucleus|ice nucleating particles]]. 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 |bibcode=2018NatCo...9..985S |doi=10.1038/s41467-018-03379-6 |pmc=5841301 |pmid=29515125 |doi-access=free}}</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 |bibcode=2021GeoRL..4891275C |doi=10.1029/2020GL091275 |doi-access=free}}</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. |date=22 June 2017 |title=Strong constraints on aerosol–cloud interactions from volcanic eruptions |journal=Nature |language=en |volume=546 |issue=7659 |pages=485–491 |bibcode=2017Natur.546..485M |doi=10.1038/nature22974 |pmid=28640263 |s2cid=205257279 |hdl-access=free |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 |hdl=10871/28042}}</ref> it was later suggested that this finding was confounded by counteracting changes in humidity.<ref name="Cao2021" />
@@ Line 148: / Line 148: @@
 {{Reflist}}
+== Further reading ==
+* {{Cite book |title=Clouds and climate: climate science's greatest challenge |date=2020 |publisher=Cambridge university press |isbn=978-1-107-06107-1 |editor-last=Siebesma |editor-first=A. Pier |location=Cambridge New York, NY |editor-last2=Stevens |editor-first2=Bjorn |editor-last3=Jakob |editor-first3=Christian |editor-last4=Bony |editor-first4=Sandrine}}
+* {{Cite journal |last1=Sherwood |first1=S. C. |last2=Webb |first2=M. J. |last3=Annan |first3=J. D. |last4=Armour |first4=K. C. |last5=Forster |first5=P. M. |last6=Hargreaves |first6=J. C. |last7=Hegerl |first7=G. |last8=Klein |first8=S. A. |last9=Marvel |first9=K. D. |last10=Rohling |first10=E. J. |last11=Watanabe |first11=M. |last12=Andrews |first12=T. |last13=Braconnot |first13=P. |last14=Bretherton |first14=C. S. |last15=Foster |first15=G. L. |date=2020 |title=An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence |journal=Reviews of Geophysics |language=en |volume=58 |issue=4 |pages=e2019RG000678 |doi=10.1029/2019RG000678 |issn=1944-9208 |pmc=7524012 |pmid=33015673|bibcode=2020RvGeo..5800678S }} Section 3 for cloud feedback types.
+* {{Cite web |title=Chapter 7: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity |url=https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7/ |access-date=2025-06-26 |website=www.ipcc.ch |language=en}}
 {{climate change}}
 [[Category:Climate forcing]]
 [[Category:Cloud and fog physics]]

Cloud feedback: Difference between revisions

Revision as of 11:58, 30 June 2025

Contents

How clouds affect radiation and climate feedback

Cloud feedback mechanisms

High cloudsScript error: No such module "anchor".

Physical Background

Longwave Feedback

Shortwave Feedback

Challenges

Possible break-up of equatorial stratocumulus clouds

Representation in climate models

Role of aerosol and aerosol-cloud interaction

References

Further reading

Navigation menu

Cloud feedback: Difference between revisions

Revision as of 11:58, 30 June 2025

How clouds affect radiation and climate feedback

Cloud feedback mechanisms

High cloudsScript error: No such module "anchor".

Physical Background

Longwave Feedback

Shortwave Feedback

Challenges

Possible break-up of equatorial stratocumulus clouds

Representation in climate models

Role of aerosol and aerosol-cloud interaction

References

Further reading

Navigation menu

Search