Jump to content

Cloud feedback

From Wikipedia, the free encyclopedia
(Redirected from Cloud radiative forcing)
During daytime, clouds scatter incoming shortwave radiation from the Sun due to their albedo, which results in substantial cooling
Water vapor in the clouds also absorbs longwave radiation from the Earth's surface and reemits it back. This effect is often weaker than the albedo cooling, but it is active day and night

Cloud feedback is a type of climate change feedback, where the overall cloud frequency, height, and the relative fraction of the different types of clouds are altered due to climate change, and these changes then affect the Earth's energy balance.[1]: 2224  On their own, clouds are already an important part of the climate system, as they consist of water vapor, which acts as a greenhouse gas and so contributes to warming; at the same time, they are bright and reflective of the Sun, which causes cooling.[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]: 1022 

If climate change causes low-level cloud cover to become more widespread, then these clouds will increase planetary albedo and contribute to cooling, making the overall cloud feedback negative (one that slows down the warming). But if clouds become higher and thinner due to climate change, then the net cloud feedback will be positive and accelerate the warming, as clouds will be less reflective and trap more heat in the atmosphere.[2] These processes have been represented in every major climate model from the 1980s onwards.[4][5][6] Observations and climate model results now provide high confidence that the overall cloud feedback on climate change is positive.[7]: 95 

However, some cloud types are more difficult to observe, and so climate models have less data about them and make different estimates about their role. Thus, models can simulate cloud feedback as very positive or only weakly positive, and these disagreements are the main reason why climate models can have substantial differences in transient climate response and climate sensitivity.[3]: 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.[8][9] This had occurred because they estimated cloud feedback as highly positive.[10][11] Those particular models were soon found to contradict both observations and paleoclimate evidence,[12][13] and the AR6 used a more realistic estimate based on the majority of the models and this real-world evidence instead.[7]: 93 [14]

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.[15] 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.[16]

Overview

[edit]
Details of how clouds interact with shortwave and longwave radiation at different atmospheric heights[17]

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.[3]: 1022 

In meteorology, the difference in the radiation budget caused by clouds, relative to cloud-free conditions, is described as the cloud radiative effect (CRE).[18] This is also sometimes referred to as cloud radiative forcing (CRF).[19] 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[20]

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

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

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

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

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

Under dry, cloud-free conditions, water vapor in atmosphere contributes 67% of the greenhouse effect on Earth. When there is enough moisture to form typical cloud cover, the greenhouse effect from "free" water vapor goes down to 50%, but water vapor which is now inside the clouds amounts to 25%, and the net greenhouse effect is at 75%.[21] 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.[22] 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]: 1022 

Climate change increases the amount of water vapor in the atmosphere due to the Clausius–Clapeyron relation, in what is known as the water-vapor feedback.[23] 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]: 1023  differences in those properties change the role of clouds in the Earth's energy budget. The name cloud feedback refers to this relationship between climate change, cloud properties, and clouds' radiative forcing.[1]: 2224  Clouds also affect the magnitude of internally generated climate variability.[24][25]

Representation in climate models

[edit]
Examples of some effects of global warming that can amplify (positive feedbacks) or reduce (negative feedbacks) global warming[26]

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.[4][5][6] 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.[5] 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.[7]: 95 

Tropical clouds are known to have a cooling effect, but it is uncertain whether it would become stronger or weaker in the future[17]
Remaining uncertainty about cloud feedbacks in IPCC Sixth Assessment Report[3]: 975 
Feedback Direction Confidence
High-cloud altitude feedback Positive High
Tropical high-cloud amount feedback Negative Low
Subtropical marine low-cloud feedback Positive High
Land cloud feedback Positive Low
Mid-latitude cloud amount feedback Positive Medium
Extratropical cloud optical depth feedback Small negative Medium
Arctic cloud feedback Small positive Low
Net cloud feedback Positive High

This happened because of major improvements in the understanding of cloud behaviour over the subtropical oceans. As the result, there was high confidence that the overall cloud feedback is positive (contributes to warming).[7]: 95  The AR6 value for cloud feedback is +0.42 [–0.10 to 0.94] W m–2 per every 1 °C (1.8 °F) in warming. This estimate is derived from multiple lines of evidence, including both models and observations.[7]: 95  The tropical high-cloud amount feedback is the main remaining area for improvement. The only way total cloud feedback may still be slightly negative is if either this feedback, or the optical depth feedback in the Southern Ocean clouds is suddenly found to be "extremely large"; the probability of that is considered to be below 10%.[3]: 975  As of 2024, most recent observations from the CALIPSO satellite instead indicate that the tropical cloud feedback is very weak.[27][17]

In spite of these improvements, clouds remain the least well-understood climate feedback, and they are the main reason why models estimate differing values for equilibrium climate sensitivity (ECS). ECS is an estimate of long-term (multi-century) warming in response to a doubling in CO2-equivalent greenhouse gas concentrations: if the future emissions are not low, it also becomes the most important factor for determining 21st century temperatures.[7]: 95  In general, the current generation of gold-standard climate models, CMIP6, operates with larger climate sensitivity then the previous generation, and this is largely because cloud feedback is about 20% more positive then it was in CMIP5.[7]: 93 [10]

However, the median cloud feedback is only slightly larger in CMIP6 than it was in CMIP5;[7]: 95  the average is so much higher only because several "hot" models have much stronger cloud feedback and higher sensitivity than the rest.[7]: 93 [14] Those models have a sensitivity of 5 °C (41 °F) and their presence had increased the median model sensitivity from 3.2 °C (37.8 °F) in CMIP5 to 3.7 °C (38.7 °F) in CMIP6.[11] 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.[8][9] 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 3 °C (37 °F), as this is in a better agreement with observations and paleoclimate findings.[7]: 93 [12][13]

Role of aerosols

[edit]
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.[28]

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,[29] 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.[16] 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.[30] 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.[31]

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.[32] 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,[33] it was later suggested that this finding was confounded by counteracting changes in humidity.[32]

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%.[34] 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.[35] 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.[36]

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.[37]

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.[15][38] 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.[39] 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.[40]

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.[41] 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.[42][38][43][44]

Possible break-up of equatorial stratocumulus clouds

[edit]
See also: Tipping points in the climate system

In 2019, a study employed a large eddy simulation model to estimate that equatorial stratocumulus clouds could break up and scatter when 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 8 °C (14 °F) globally and 10 °C (18 °F) in the subtropics, which would be in addition to at least 4 °C (7.2 °F) already caused by such CO2 concentrations. In addition, stratocumulus clouds would not reform until the CO2 concentrations drop to a much lower level.[45] It was suggested that this finding could help explain past episodes of unusually rapid warming such as Paleocene-Eocene Thermal Maximum[46] 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 CO2 emissions continue for a long time but are offset with extensive solar radiation modification, the break-up of stratocumulus clouds is simply delayed until CO2 concentrations hit 1,700 ppm, at which point it would still cause around 5 °C (9.0 °F) of unavoidable warming.[47]

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.[48] 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".[49] Additionally, CO2 concentrations would only reach 1,200 ppm if the world follows Representative Concentration Pathway 8.5, which represents the highest possible greenhouse gas emission scenario and involves a massive expansion of coal infrastructure. In that case, 1,200 ppm would be passed shortly after 2100.[48]

See also

[edit]

References

[edit]
  1. ^ a b 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.
  2. ^ a b Stephens, Graeme L. (2005-01-01). "Cloud Feedbacks in the Climate System: A Critical Review". Journal of Climate. 18 (2): 237–273. Bibcode:2005JCli...18..237S. CiteSeerX 10.1.1.130.1415. doi:10.1175/JCLI-3243.1. ISSN 0894-8755. S2CID 16122908.
  3. ^ a b c d e f g Forster, P.; Storelvmo, T.; Armour, K.; Collins, W.; Dufresne, J.-L.; Frame, D.; Lunt, D.J.; Mauritsen, T.; Watanabe, M.; Wild, M.; Zhang, H. (2021). Masson-Delmotte, V.; Zhai, P.; Pirani, A.; Connors, S. L.; Péan, C.; Berger, S.; Caud, N.; Chen, Y.; Goldfarb, L. (eds.). Chapter 7: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity (PDF). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Report). Cambridge University Press, Cambridge, UK and New York, NY, US. pp. 923–1054. doi:10.1017/9781009157896.009.
  4. ^ a b Wetherald, R.; S. Manabe (1988). "Cloud Feedback Processes in a General Circulation Model". J. Atmos. Sci. 45 (8): 1397–1416. Bibcode:1988JAtS...45.1397W. doi:10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2.
  5. ^ a b c Cess, R. D.; et al. (1990). "Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models" (PDF). J. Geophys. Res. 95 (D10): 16, 601–16, 615. Bibcode:1990JGR....9516601C. doi:10.1029/jd095id10p16601. Archived from the original (PDF) on 2018-07-22. Retrieved 2017-10-27.
  6. ^ a b Fowler, L.D.; D.A. Randall (1996). "Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part III: Sensitivity to Modeling Assumptions". J. Climate. 9 (3): 561–586. Bibcode:1996JCli....9..561F. doi:10.1175/1520-0442(1996)009<0561:LAICMI>2.0.CO;2.
  7. ^ a b c d e f g h i j Arias, Paola A.; Bellouin, Nicolas; Coppola, Erika; Jones, Richard G.; Krinner, Gerhard (2021). Technical Summary (PDF). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Report). Cambridge University Press, Cambridge, UK and New York, NY, US. pp. 35–144. doi:10.1017/9781009157896.009. Archived from the original (PDF) on 21 July 2022.
  8. ^ a b "The CMIP6 landscape (Editorial)". Nature Climate Change. 9 (10): 727. 2019-09-25. Bibcode:2019NatCC...9..727.. doi:10.1038/s41558-019-0599-1. ISSN 1758-6798.
  9. ^ a b "New climate models suggest Paris goals may be out of reach". France 24. 2020-01-14. Archived from the original on 14 January 2020. Retrieved 2020-01-18.
  10. ^ a b Zelinka MD, Myers TA, McCoy DT, Po-Chedley S, Caldwell PM, Ceppi P, Klein SA, Taylor KE (2020). "Causes of Higher Climate Sensitivity in CMIP6 Models". Geophysical Research Letters. 47 (1): e2019GL085782. Bibcode:2020GeoRL..4785782Z. doi:10.1029/2019GL085782. hdl:10044/1/76038. ISSN 1944-8007.
  11. ^ a b "Increased warming in latest generation of climate models likely caused by clouds: New representations of clouds are making models more sensitive to carbon dioxide". Science Daily. 24 June 2020. Archived from the original on 26 June 2020. Retrieved 26 June 2020.
  12. ^ a b Zhu, Jiang; Poulsen, Christopher J.; Otto-Bliesner, Bette L. (30 April 2020). "High climate sensitivity in CMIP6 model not supported by paleoclimate". Nature Climate Change. 10 (5): 378–379. Bibcode:2020NatCC..10..378Z. doi:10.1038/s41558-020-0764-6.
  13. ^ a b Erickson, Jim (30 April 2020). "Some of the latest climate models provide unrealistically high projections of future warming". Phys.org. Retrieved 12 May 2024. 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.
  14. ^ a b Voosen, Paul (4 May 2022). "Use of 'too hot' climate models exaggerates impacts of global warming". Science Magazine. Retrieved 12 May 2024. But for the 2019 CMIP6 round, 10 out of 55 of the models had sensitivities higher than 5°C—a stark departure. The results were also at odds with a landmark study that eschewed global modeling results and instead relied on paleoclimate and observational records to identify Earth's climate sensitivity. It found that the value sits somewhere between 2.6°C and 3.9°C.
  15. ^ a b "Aerosol pollution has caused decades of global dimming". American Geophysical Union. 18 February 2021. Archived from the original on 27 March 2023. Retrieved 18 December 2023.
  16. ^ a b McCoy, Daniel T.; Field, Paul; Gordon, Hamish; Elsaesser, Gregory S.; Grosvenor, Daniel P. (6 April 2020). "Untangling causality in midlatitude aerosol–cloud adjustments". Atmospheric Chemistry and Physics. 20 (7): 4085–4103. Bibcode:2020ACP....20.4085M. doi:10.5194/acp-20-4085-2020.
  17. ^ a b c McKim, Brett; Bony, Sandrine; Dufresne, Jean-Louis (1 April 2024). "Weak anvil cloud area feedback suggested by physical and observational constraints". Nature Geoscience. 17 (5): 392–397. Bibcode:2024NatGe..17..392M. doi:10.1038/s41561-024-01414-4.
  18. ^ Matthews (6 July 2023). "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". doi:10.1017/9781009157896.022. {{cite journal}}: Cite journal requires |journal= (help)
  19. ^ NASA (2016). "Clouds & Radiation Fact Sheet : Feature Articles". NASA. Retrieved 2017-05-29.
  20. ^ Hartmann, Dennis L. (2016). Global Physical Climatology. Amsterdam: Elsevier. ISBN 978-0123285317.
  21. ^ Schmidt, G.A.; R. Ruedy; R.L. Miller; A.A. Lacis (2010). "The attribution of the present-day total greenhouse effect". J. Geophys. Res. 115 (D20): D20106. Bibcode:2010JGRD..11520106S. doi:10.1029/2010JD014287., D20106. Web page Archived 4 June 2012 at the Wayback Machine
  22. ^ Intergovernmental Panel on Climate Change (1990). IPCC First Assessment Report.1990. UK: Cambridge University Press.table 3.1
  23. ^ Held, Isaac M.; Soden, Brian J. (November 2000). "Water vapor feedback and global warming". Annual Review of Energy and the Environment. 25 (1): 441–475. CiteSeerX 10.1.1.22.9397. doi:10.1146/annurev.energy.25.1.441. ISSN 1056-3466.
  24. ^ Brown, Patrick T.; Li, Wenhong; Jiang, Jonathan H.; Su, Hui (2015-12-07). "Unforced Surface Air Temperature Variability and Its Contrasting Relationship with the Anomalous TOA Energy Flux at Local and Global Spatial Scales" (PDF). Journal of Climate. 29 (3): 925–940. Bibcode:2016JCli...29..925B. doi:10.1175/JCLI-D-15-0384.1. ISSN 0894-8755. Archived (PDF) from the original on 2018-07-19.
  25. ^ Bellomo, Katinka; Clement, Amy; Mauritsen, Thorsten; Rädel, Gaby; Stevens, Bjorn (2014-04-11). "Simulating the Role of Subtropical Stratocumulus Clouds in Driving Pacific Climate Variability". Journal of Climate. 27 (13): 5119–5131. Bibcode:2014JCli...27.5119B. doi:10.1175/JCLI-D-13-00548.1. hdl:11858/00-001M-0000-0014-72C1-F. ISSN 0894-8755. S2CID 33019270.
  26. ^ "The Study of Earth as an Integrated System". nasa.gov. NASA. 2016. Archived from the original on November 2, 2016.
  27. ^ Raghuraman, Shiv Priyam; Medeiros, Brian; Gettelman, Andrew (30 March 2024). "Observational quantification of tropical high cloud changes and feedbacks". Journal of Geophysical Research: Atmospheres. 129 (7): e2023JD039364. Bibcode:2024JGRD..12939364R. doi:10.1029/2023JD039364.
  28. ^ Bellouin, N.; Quaas, J.; Gryspeerdt, E.; Kinne, S.; Stier, P.; Watson-Parris, D.; Boucher, O.; Carslaw, K. S.; Christensen, M.; Daniau, A.-L.; Dufresne, J.-L.; Feingold, G.; Fiedler, S.; Forster, P.; Gettelman, A.; Haywood, J. M.; Lohmann, U.; Malavelle, F.; Mauritsen, T.; McCoy, D. T.; Myhre, G.; Mülmenstädt, J.; Neubauer, D.; Possner, A.; Rugenstein, M.; Sato, Y.; Schulz, M.; Schwartz, S. E.; Sourdeval, O.; Storelvmo, T.; Toll, V.; Winker, D.; Stevens, B. (1 November 2019). "Bounding Global Aerosol Radiative Forcing of Climate Change". Reviews of Geophysics. 58 (1): e2019RG000660. doi:10.1029/2019RG000660. PMC 7384191. PMID 32734279.
  29. ^ Ramanathan, V.; Carmichael, G. (2008). "Nature Geoscience: Global and regional climate changes due to black carbon". Nature Geoscience. 1 (4): 221–227. Bibcode:2008NatGe...1..221R. doi:10.1038/ngeo156. S2CID 12455550.
  30. ^ Sato, Yousuke; Goto, Daisuke; Michibata, Takuro; Suzuki, Kentaroh; Takemura, Toshihiko; Tomita, Hirofumi; Nakajima, Teruyuki (7 March 2018). "Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model". Nature Communications. 9 (1): 985. Bibcode:2018NatCo...9..985S. doi:10.1038/s41467-018-03379-6. PMC 5841301. PMID 29515125.
  31. ^ Rosenfeld, Daniel; Zhu, Yannian; Wang, Minghuai; Zheng, Youtong; Goren, Tom; Yu, Shaocai (2019). "Aerosol-driven droplet concentrations dominate coverage and water of oceanic low level clouds" (PDF). Science. 363 (6427): eaav0566. doi:10.1126/science.aav0566. PMID 30655446. S2CID 58612273.
  32. ^ a b Cao, Yang; Wang, Minghuai; Rosenfeld, Daniel; Zhu, Yannian; Liang, Yuan; Liu, Zhoukun; Bai, Heming (10 March 2021). "Strong Aerosol Effects on Cloud Amount Based on Long-Term Satellite Observations Over the East Coast of the United States". Geophysical Research Letters. 48 (6): e2020GL091275. Bibcode:2021GeoRL..4891275C. doi:10.1029/2020GL091275.
  33. ^ Malavelle, Florent F.; Haywood, Jim M.; Jones, Andy; Gettelman, Andrew; Clarisse, Lieven; Bauduin, Sophie; Allan, Richard P.; Karset, Inger Helene H.; Kristjánsson, Jón Egill; Oreopoulos, Lazaros; Cho, Nayeong; Lee, Dongmin; Bellouin, Nicolas; Boucher, Olivier; Grosvenor, Daniel P.; Carslaw, Ken S.; Dhomse, Sandip; Mann, Graham W.; Schmidt, Anja; Coe, Hugh; Hartley, Margaret E.; Dalvi, Mohit; Hill, Adrian A.; Johnson, Ben T.; Johnson, Colin E.; Knight, Jeff R.; O'Connor, Fiona M.; Partridge, Daniel G.; Stier, Philip; Myhre, Gunnar; Platnick, Steven; Stephens, Graeme L.; Takahashi, Hanii; Thordarson, Thorvaldur (22 June 2017). "Strong constraints on aerosol–cloud interactions from volcanic eruptions". Nature. 546 (7659): 485–491. Bibcode:2017Natur.546..485M. doi:10.1038/nature22974. hdl:10871/28042. PMID 28640263. S2CID 205257279.
  34. ^ Glassmeier, Franziska; Hoffmann, Fabian; Johnson, Jill S.; Yamaguchi, Takanobu; Carslaw, Ken S.; Feingold, Graham (29 January 2021). "Aerosol-cloud-climate cooling overestimated by ship-track data". Science. 371 (6528): 485–489. Bibcode:2021Sci...371..485G. doi:10.1126/science.abd3980. PMID 33510021.
  35. ^ Manshausen, Peter; Watson-Parris, Duncan; Christensen, Matthew W.; Jalkanen, Jukka-Pekka; Stier, Philip Stier (7 March 2018). "Invisible ship tracks show large cloud sensitivity to aerosol". Nature. 610 (7930): 101–106. doi:10.1038/s41586-022-05122-0. PMC 9534750. PMID 36198778.
  36. ^ Jongebloed, U. A.; Schauer, A. J.; Cole-Dai, J.; Larrick, C. G.; Wood, R.; Fischer, T. P.; Carn, S. A.; Salimi, S.; Edouard, S. R.; Zhai, S.; Geng, L.; Alexander, B. (2 January 2023). "Underestimated Passive Volcanic Sulfur Degassing Implies Overestimated Anthropogenic Aerosol Forcing". Geophysical Research Letters. 50 (1): e2022GL102061. Bibcode:2023GeoRL..5002061J. doi:10.1029/2022GL102061. S2CID 255571342.
  37. ^ Xu, Yangyang; Ramanathan, Veerabhadran; Victor, David G. (5 December 2018). "Global warming will happen faster than we think". Nature. 564 (7734): 30–32. Bibcode:2018Natur.564...30X. doi:10.1038/d41586-018-07586-5. PMID 30518902.
  38. ^ a b Julsrud, I. R.; Storelvmo, T.; Schulz, M.; Moseid, K. O.; Wild, M. (20 October 2022). "Disentangling Aerosol and Cloud Effects on Dimming and Brightening in Observations and CMIP6". Journal of Geophysical Research: Atmospheres. 127 (21): e2021JD035476. Bibcode:2022JGRD..12735476J. doi:10.1029/2021JD035476. hdl:10852/97300.
  39. ^ "Air Emissions Trends – Continued Progress Through 2005". U.S. Environmental Protection Agency. 8 July 2014. Archived from the original on 2007-03-17. Retrieved 2007-03-17.
  40. ^ 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, doi:10.1017/9781009157896.001.
  41. ^ Zeke Hausfather (29 April 2021). "Explainer: Will global warming 'stop' as soon as net-zero emissions are reached?". Carbon Brief. Retrieved 2023-03-23.
  42. ^ Wang, Zhili; Lin, Lei; Xu, Yangyang; Che, Huizheng; Zhang, Xiaoye; Zhang, Hua; Dong, Wenjie; Wang, Chense; Gui, Ke; Xie, Bing (12 January 2021). "Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models". npj Climate and Atmospheric Science. 4. doi:10.1029/2021JD035476. hdl:10852/97300.
  43. ^ Persad, Geeta G.; Samset, Bjørn H.; Wilcox, Laura J. (21 November 2022). "Aerosols must be included in climate risk assessments". Nature. 611 (7937): 662–664. Bibcode:2022Natur.611..662P. doi:10.1038/d41586-022-03763-9. PMID 36411334.
  44. ^ Ramachandran, S.; Rupakheti, Maheswar; Cherian, R. (10 February 2022). "Insights into recent aerosol trends over Asia from observations and CMIP6 simulations". Science of the Total Environment. 807 (1): 150756. Bibcode:2022ScTEn.80750756R. doi:10.1016/j.scitotenv.2021.150756. PMID 34619211. S2CID 238474883.
  45. ^ Schneider, Tapio; Kaul, Colleen M.; Pressel, Kyle G. (2019). "Possible climate transitions from breakup of stratocumulus decks under greenhouse warming". Nature Geoscience. 12 (3): 163–167. Bibcode:2019NatGe..12..163S. doi:10.1038/s41561-019-0310-1. S2CID 134307699.
  46. ^ Wolchover, Natalie (25 February 2019). "A World Without Clouds". Quanta Magazine. Retrieved 2 October 2022.
  47. ^ Schneider, Tapio; Kaul, Colleen M.; Pressel, Kyle G. (2020). "Solar geoengineering may not prevent strong warming from direct effects of CO2 on stratocumulus cloud cover". PNAS. 117 (48): 30179–30185. Bibcode:2020PNAS..11730179S. doi:10.1073/pnas.2003730117. PMC 7720182. PMID 33199624.
  48. ^ a b "Extreme CO2 levels could trigger clouds 'tipping point' and 8C of global warming". Carbon Brief. 25 February 2019. Retrieved 2 October 2022.
  49. ^ Voosen, Paul (February 26, 2019). "A world without clouds? Hardly clear, climate scientists say". Science Magazine.
Retrieved from "https://wiki.riteme.site/w/index.php?title=Cloud_feedback&oldid=1262468517#Cloud_forcing"