Cloud feedback

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 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]

  1. ^ Cite error: The named reference IPCC glossary was invoked but never defined (see the help page).
  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 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. ^ 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. ^ 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. ^ 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 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. ^ "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. ^ "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. ^ 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. ^ "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. ^ 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: 378–379. doi:10.1038/s41558-020-0764-6.
  13. ^ 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. ^ 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. ^ "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. ^ 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. doi:10.5194/acp-20-4085-2020.

© MMXXIII Rich X Search. We shall prevail. All rights reserved. Rich X Search