Skip to main content

Aerosol–Cloud Interactions in the Climate System

Handbook of Air Quality and Climate Change
  • 40 Accesses

Abstract

Increased anthropogenic aerosol concentrations modify cloud micro- and macrophysical properties and precipitation, which significantly impacts the global hydrological cycle and radiation budget. Aerosol–cloud interactions (ACIs) have contributed to negative radiative forcing (i.e., cooling) since pre-industrial times, partially offsetting the warming positive radiative forcing caused by greenhouse gases. However, estimates of the magnitude of ACIs are highly uncertain because their regime-dependent behavior is poorly understood, and global climate models cannot capture complex ACIs because of their simplified treatment of clouds and precipitation. This chapter reviews the current understanding of ACIs in the climate system and prominent advances in observations, numerical modeling, and satellite simulations. Observation techniques and model parameterizations have advanced steadily, so the review focuses mainly on literature published over the past decade. For more reliable weather and climate predictions, this chapter discusses (1) how satellite observations can constrain ACIs, (2) where model–observation discrepancies arise, and (3) what can be done to improve model parameterizations, thus reducing ACI uncertainties at fundamental process levels. Challenges in constraining uncertain processes with multi-platform observations and process modeling are also considered.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. Albrecht BA (1989) Aerosols, cloud microphysics, and fractional cloudiness. Science 245:1227–1230

    Article  Google Scholar 

  2. Andrews T, Gregory JM, Webb MJ (2015) The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J Clim 28:1630–1648. https://doi.org/10.1175/JCLI-D-14-00545.1

    Article  Google Scholar 

  3. Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne JL, Klein SA, Zhang Y, Marchand R, Haynes JM, Pincus R, John VO (2011) COSP: satellite simulation software for model assessment. Bull Am Meteorol Soc 92:1023–1043. https://doi.org/10.1175/2011BAMS2856.1

    Article  Google Scholar 

  4. Boucher O, Randall D, Artaxo P, Bretherton C, Feingold G, Forster P, Kerminen VM, Kondo Y, Liao H, Lohmann U, Rasch P, Satheesh SK, Sherwood S, Stevens B, Zhang XY (2013) Clouds and aerosols. Cambridge University Press, Cambridge, UK, pp 571–657. https://doi.org/10.1017/CBO9781107415324.016

    Book  Google Scholar 

  5. Carslaw KS, Lee LA, Reddington CL, Pringle KJ, Rap A, Forster PM, Mann GW, Spracklen DV, Woodhouse MT, Regayre LA, Pierce JR (2013) Large contribution of natural aerosols to uncertainty in indirect forcing. Nature 503(7474):67–71. https://doi.org/10.1038/nature12674

    Article  Google Scholar 

  6. Cesana G, Chepfer H (2013) Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP. J Geophys Res Atmos 118:7922–7937. https://doi.org/10.1002/jgrd.50376

    Article  Google Scholar 

  7. Chen YC, Christensen MW, Stephens GL, Seinfeld JH (2014) Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds. Nat Geosci 7:643–646. https://doi.org/10.1038/ngeo2214

    Article  Google Scholar 

  8. Christensen MW, Suzuki K, Zambri B, Stephens GL (2014) Ship track observations of a reduced shortwave aerosol indirect effect in mixed-phase clouds. Geophys Res Lett 6970–6977. https://doi.org/10.1002/2014GL061184

  9. Dadashazar H, Wang Z, Crosbie E, Brunke M, Zeng X, Jonsson H, Woods RK, Flagan RC, Seinfeld JH, Sorooshian A (2017) Relationships between giant sea salt particles and clouds inferred from aircraft physicochemical data. J Geophys Res Atmos 3421–3434. https://doi.org/10.1002/2016JD026019

  10. Dal Gesso S, van der Dussen JJ, Siebesma AP, de Roode SR, Boutle IA, Kamae Y, Roehrig R, Vial J (2015) A single-column model intercomparison on the stratocumulus representation in present-day and future climate. J Adv Model Earth Syst 7:617–647. https://doi.org/10.1002/2014MS000377

    Article  Google Scholar 

  11. Douglas A, L’Ecuyer T (2020) Quantifying cloud adjustments and the radiative forcing due to aerosol-cloud interactions in satellite observations of warm marine clouds. Atmos Chem Phys Discuss 1–28. https://doi.org/10.5194/acp-2020-36

  12. Eguchi K, Uno I, Yumimoto K, Takemura T, Nakajima TY, Uematsu M, Liu Z (2011) Modulation of cloud droplets and radiation over the North Pacific by sulfate aerosol erupted from Mount Kilauea. Sci Online Lett Atmos 7:77–80. https://doi.org/10.2151/sola.2011-020

    Article  Google Scholar 

  13. Eliasson S, Buehler SA, Milz M, Eriksson P, John VO (2011) Assessing observed and modelled spatial distributions of ice water path using satellite data. Atmos Chem Phys 11:375–391. https://doi.org/10.5194/acp-11-375-2011

    Article  Google Scholar 

  14. Fan J, Zhang Y, Yang Y, Comstock JM, Feng Z, Gao W, Mei F, Rosenfeld D, Li Z, Giangrande SE, Wang J, Machado LA, Braga RC, Martin ST, Artaxo P, Barbosa HM, Gomes HB, Pohlker C, Pohlker ML, Poschl U, De Souza RA (2018) Substantial convection and precipitation enhancements by ultrafine aerosol particles. Science 359:411–418. https://doi.org/10.1126/science.aan8461

    Article  Google Scholar 

  15. Gettelman A (2015) Putting the clouds back in aerosol–cloud interactions. Atmos Chem Phys 15:12397–12411. https://doi.org/10.5194/acp-15-12397-2015

    Article  Google Scholar 

  16. Gettelman A, Morrison H, Santos S, Bogenschutz P, Caldwell PM (2015) Advanced two-moment bulk microphysics for global models. Part II: global model solutions and aerosol–cloud interactions. J Clim 28:1288–1307. https://doi.org/10.1175/JCLI-D-14-00103.1

    Article  Google Scholar 

  17. Ghan S, Wang M, Zhang S, Ferrachat S, Gettelman A, Griesfeller J, Kipling Z, Lohmann U, Morrison H, Neubauer D, Partridge DG, Stier P, Takemura T, Wang H, Zhang K (2016) Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability. Proc Natl Acad Sci USA 113:5804–5811. https://doi.org/10.1073/pnas.1514036113

    Article  Google Scholar 

  18. Ghan SJ, Easter RC (1992) Computationally efficient approximations to stratiform cloud microphysics parameterization. Mon Weather Rev 120:1572–1582

    Article  Google Scholar 

  19. Golaz JC, Horowitz L, Levy H (2013) Cloud tuning in a coupled climate model: impact on 20th century warming. Geophys Res Lett 40:2246–2251. https://doi.org/10.1002/grl.50232

    Article  Google Scholar 

  20. Gryspeerdt E, Goren T, Sourdeval O, Quaas J, Mulmenstadt J, Dipu S, Unglaub C, Gettelman A, Christensen M (2019) Constraining the aerosol influence on cloud liquid water path. Atmos Chem Phys 19:5331–5347. https://doi.org/10.5194/acp-19-5531-2019

    Article  Google Scholar 

  21. Heyn I, Block K, Mulmenstadt J, Gryspeerdt E, Kuhne P, Salzmann M, Quaas J (2017) Assessment of simulated aerosol effective radiative forcings in the terrestrial spectrum. Geophys Res Lett 44:1001–1007. https://doi.org/10.1002/2016GL071975

    Article  Google Scholar 

  22. Hoose C, Kristjánsson JE, Iversen T, Kirkevåg A, Seland Ø, Gettelman A (2009) Constraining cloud droplet number concentration in GCMs suppresses the aerosol indirect effect. Geophys Res Lett 36(12):1–5. https://doi.org/10.1029/2009GL038568

    Article  Google Scholar 

  23. Hourdin F, Mauritsen T, Gettelman A, Golaz JC, Balaji V, Duan Q, Folini D, Ji D, Klocke D, Qian Y, Rauser F, Rio C, Tomassini L, Watanabe M, Williamson D (2017) The art and science of climate model tuning. Bull Am Meteorol Soc 589–602. https://doi.org/10.1175/BAMS-D-15-00135.1

  24. Illingworth AJ, Barker HW, Beljaars A, Ceccaldi M, Chepfer H, Clerbaux N, Cole J, Delanoë J, Domenech C, Donovan DP, Fukuda S, Hirakata M, Hogan RJ, Huenerbein A, Kollias P, Kubota T, Nakajima T, Nakajima TY, Nishizawa T, Ohno Y, Okamoto H, Oki R, Sato K, Satoh M, Shephard MW, Velázquez-Blázquez A, Wandinger U, Wehr T, Van Zadelhoff GJ (2015) The earthcare satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation. Bull Am Meteorol Soc 96:1311–1332. https://doi.org/10.1175/BAMS-D-12-00227.1

    Article  Google Scholar 

  25. Jiang JH, Su H, Huang L, Wang Y, Massie S, Zhao B, Omar A, Wang Z (2018) Contrasting effects on deep convective clouds by different types of aerosols. Nat Commun 9. https://doi.org/10.1038/s41467-018-06280-4

  26. Jing X, Suzuki K, Michibata T (2019) The key role of warm rain parameterization in determining the aerosol indirect effect in a global climate model. J Clim 32:4409–4430. https://doi.org/10.1175/JCLI-D-18-0789.1

    Article  Google Scholar 

  27. Kay JE, L’Ecuyer T, Pendergrass A, Chepfer H, Guzman R, Yettella V (2018) Scale-aware and definition-aware evaluation of modeled near-surface precipitation frequency using CloudSat observations. J Geophys Res Atmos 123. https://doi.org/10.1002/2017JD028213

  28. Khairoutdinov M, Kogan Y (2000) A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon Weather Rev 128:229–243

    Article  Google Scholar 

  29. Lebo ZJ, Feingold G (2014) On the relationship between responses in cloud water and precipitation to changes in aerosol. Atmos Chem Phys 14(21):11817–11831. https://doi.org/10.5194/acp-14-11817-2014

    Article  Google Scholar 

  30. Lebsock M, Morrison H, Gettelman A (2013) Microphysical implications of cloud-precipitation covariance derived from satellite remote sensing. J Geophys Res Atmos 118:6521–6533. https://doi.org/10.1002/jgrd.50347

    Article  Google Scholar 

  31. Li JF, Xu K, Jiang JH, Lee W, Wang L, Yu J, Stephens G, Fetzer E, Wang Y (2020) An overview of CMIP5 and CMIP6 simulated cloud ice, radiation fields, surface wind stress, sea surface temperatures, and precipitation over tropical and subtropical oceans. J Geophys Res Atmos 125:e2020JD032848. https://doi.org/10.1029/2020jd032848

    Article  Google Scholar 

  32. Li JLF, Waliser DE, Chen WT, Guan B, Kubar T, Stephens G, Ma HY, Deng M, Donner L, Seman C, Horowitz L (2012) An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data. J Geophys Res Atmos 117. https://doi.org/10.1029/2012JD017640

  33. Lohmann U (2017) Anthropogenic aerosol influences on mixed-phase clouds. Curr Clim Chang Rep 3:32–44. https://doi.org/10.1007/s40641-017-0059-9

    Article  Google Scholar 

  34. Ma PL, Rasch PJ, Chepfer H, Winker DM, Ghan SJ (2018) Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations. Nat Commun 9:2640. https://doi.org/10.1038/s41467-018-05028-4

    Article  Google Scholar 

  35. Malavelle FF, Haywood JM, Jones A, Gettelman A, Clarisse L, Bauduin S, Allan RP, Karset IHH, Kristjansson JE, Oreopoulos L, Cho N, Lee D, Bellouin N, Boucher O, Grosvenor DP, Carslaw KS, Dhomse S, Mann GW, Schmidt A, Coe H, Hartley ME, Dalvi M, Hill AA, Johnson BT, Johnson CE, Knight JR, O’Connor FM, Partridge DG, Stier P, Myhre G, Platnick S, Stephens GL, Takahashi H, Thordarson T (2017) Strong constraints on aerosol–cloud interactions from volcanic eruptions. Nature 546:485–491. https://doi.org/10.1038/nature22974

    Article  Google Scholar 

  36. Masunaga H, Matsui T, Tao WK, Hou AY, Kummerow CD, Nakajima T, Bauer P, Olson WS, Sekiguchi M, Nakajima TY (2010) Satellite data simulator unit a multisensor, multispectral satellite simulator package. Bull Am Meteorol Soc 91:1625–1632. https://doi.org/10.1175/2010BAMS2809.1

    Article  Google Scholar 

  37. McCoy D, Field P, Gordon H, Elsaesser G, Grosvenor D (2020) Untangling causality in midlatitude aerosol-cloud adjustments. Atmos Chem Phys 20:4085–4103. https://doi.org/10.5194/acp-20-4085-2020

    Article  Google Scholar 

  38. Meehl GA, Senior CA, Eyring V, Flato G, Lamarque JF, Stouffer RJ, Taylor KE, Schlund M (2020) Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science. Advances 6:eaba1981. https://doi.org/10.1126/sciadv.aba1981

    Article  Google Scholar 

  39. Michibata T, Suzuki K (2020) Reconciling compensating errors between precipitation constraints and the energy budget in a climate model. Geophys Res Lett 47:e2020GL088340. https://doi.org/10.1029/2020GL088340

    Article  Google Scholar 

  40. Michibata T, Suzuki K, Sato Y, Takemura T (2016) The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-train retrievals. Atmos Chem Phys 16:15413–15424. https://doi.org/10.5194/acp-16-15413-2016

    Article  Google Scholar 

  41. Michibata T, Suzuki K, Sekiguchi M, Takemura T (2019a) Prognostic precipitation in the MIROC6-SPRINTARS GCM: description and evaluation against satellite observations. J Adv Model Earth Syst 11:839–860. https://doi.org/10.1029/2018MS001596

    Article  Google Scholar 

  42. Michibata T, Suzuki K, Ogura T, Jing X (2019b) Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation. Geosci Model Dev 12:4297–4307. https://doi.org/10.5194/gmd-12-4297-2019

    Article  Google Scholar 

  43. Michibata T, Suzuki K, Takemura T (2020) Snow-induced buffering in aerosol-cloud interactions. Atmos Chem Phys 20:13771–13780. https://doi.org/10.5194/acp-20-13771-2020

    Article  Google Scholar 

  44. Morrison H, Lier-Walqui M, Fridlind AM, Grabowski WW, Harrington JY, Hoose C, Korolev A, Kumjian MR, Milbrandt JA, Pawlowska H, Posselt DJ, Prat OP, Reimel KJ, Shima S, Diedenhoven B, Xue L (2020) Confronting the challenge of modeling cloud and precipitation microphysics. J Adv Model Earth Syst 12:e2019MS001689. https://doi.org/10.1029/2019MS001689

    Article  Google Scholar 

  45. Mulmenstädt J, Nam C, Salzmann M, Kretzschmar J, Ecuyer TSL, Lohmann U, Pl M, Myhre G, Neubauer D, Stier P, Suzuki K, Wang M, Quaas J (2020) Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes. Sci Adv 6:eaaz6433. https://doi.org/10.1126/sciadv.aaz6433

    Article  Google Scholar 

  46. Nam C, Bony S, Dufresne JL, Chepfer H (2012) The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophys Res Lett 39:L21801. https://doi.org/10.1029/2012GL053421

    Article  Google Scholar 

  47. Okamoto H, Sato K, Borovoi A, Ishimoto H, Masuda K, Konoshonkin A, Kustova N (2020) Wavelength dependence of ice cloud backscatter properties for space-borne polarization lidar applications. Opt Express 28:29178. https://doi.org/10.1364/oe.400510

    Article  Google Scholar 

  48. Ovchinnikov M, Ackerman AS, Avramov A, Cheng A, Fan J, Fridlind AM, Ghan S, Harrington J, Hoose C, Korolev A, McFarquhar GM, Morrison H, Paukert M, Savre J, Shipw BJ, Sulia K (2014) Intercomparison of large-eddy simulations of Arctic mixed-phase clouds: importance of ice size distribution assumptions. J Adv Model Earth Syst 6:223–248. https://doi.org/10.1002/2013MS000282

    Article  Google Scholar 

  49. Rasp S, Pritchard MS, Gentine P (2018) Deep learning to represent subgrid processes in climate models. Proc Natl Acad Sci USA 115:9684–9689. https://doi.org/10.1073/pnas.1810286115

    Article  Google Scholar 

  50. Redemann J, Wood R, Zuidema P, Doherty SJ, Luna B, LeBlanc SE, Diamond MS, Shinozuka Y, Chang IY, Ueyama R, Pfister L, Ryoo JM, Dobracki AN, da Silva AM, Longo KM, Kacenelenbogen MS, Flynn CJ, Pistone K, Knox NM, Piketh SJ, Haywood JM, Formenti P, Mallet M, Stier P, Ackerman AS, Bauer SE, Fridlind AM, Carmichael GR, Saide PE, Ferrada GA, Howell SG, Freitag S, Cairns B, Holben BN, Knobelspiesse KD, Tanelli S, L’Ecuyer TS, Dzambo AM, Sy OO, McFarquhar GM, Poellot MR, Gupta S, O’Brien JR, Nenes A, Kacarab M, Wong JP, Small-Griswold JD, Thornhill KL, Noone D, Podolske JR, Sebastian Schmidt K, Pilewskie P, Chen H, Cochrane SP, Sedlacek AJ, Lang TJ, Stith E, Segal-Rozenhaimer M, Ferrare RA, Burton SP, Hostetler CA, Diner DJ, Seidel FC, Platnick SE, Myers JS, Meyer KG, Spangenberg DA, Maring H, Gao L (2021) An overview of the ORACLES (ObseRvations of aerosols above CLouds and their intEractionS) project: aerosol-cloud-radiation interactions in the Southeast Atlantic basin. Atmos Chem Phys 21:1507–1563. https://doi.org/10.5194/acp-21-1507-2021

    Article  Google Scholar 

  51. Regayre LA, Johnson JS, Yoshioka M, Pringle KJ, Sexton DM, Booth BB, Lee LA, Bellouin N, Carslaw KS (2018) Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF. Atmos Chem Phys 18:9975–10006. https://doi.org/10.5194/acp-18-9975-2018

    Article  Google Scholar 

  52. Rosenfeld D, Zhu Y, Minghuai W, Zheng Y, Goren T, Yu S (2019) Aerosol-driven droplet concentrations dominate converge and water of oceanic low level clouds. Science 363:599. https://doi.org/10.1126/science.aav0566

    Article  Google Scholar 

  53. Sant V, Posselt R, Lohmann U (2015) Prognostic precipitation with three liquid water classes in the ECHAM5–HAM GCM. Atmos Chem Phys 15:8717–8738. https://doi.org/10.5194/acp-15-8717-2015

    Article  Google Scholar 

  54. Seinfeld JH, Pandis SN (2016) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, New York

    Google Scholar 

  55. Seinfeld JH, Bretherton C, Carslaw KS, Coe H, DeMott PJ, Dunlea EJ, Feingold G, Ghan S, Guenther AB, Kahn R, Kraucunas I, Kreidenweis SM, Molina MJ, Nenes A, Penner JE, Prather KA, Ramanathan V, Ramaswamy V, Rasch PJ, Ravishankara AR, Rosenfeld D, Stephens G, Wood R (2016) Improving our fundamental understanding of the role of aerosol–cloud interactions in the climate system. Proc Natl Acad Sci USA 113(21):5781–5790. https://doi.org/10.1073/pnas.1514043113

    Article  Google Scholar 

  56. Shima S, Sato Y, Hashimoto A, Misumi R (2020) Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, −2.2.1, and −2.2.2. Geosci Model Dev 13:4107–4157. https://doi.org/10.5194/gmd-13-4107-2020

    Article  Google Scholar 

  57. Sorooshian A, Feingold G, Lebsock MD, Jiang H, Stephens GL (2009) On the precipitation susceptibility of clouds to aerosol perturbations. Geophys Res Lett 36(13):L13803. https://doi.org/10.1029/2009GL038993

    Article  Google Scholar 

  58. Stephens G, Winker D, Pelon J, Trepte C, Vane D, Yuhas C, L’Ecuyer T, Lebsock M (2018) CloudSat and CALIPSO within the A-train: ten years of actively observing the Earth system. Bull Am Meteorol Soc 569–581. https://doi.org/10.1175/BAMS-D-16-0324.1

  59. Stephens GL, Haynes JM (2007) Near global observations of the warm rain coalescence process. Geophys Res Lett 34(20):L20805. https://doi.org/10.1029/2007GL030259

    Article  Google Scholar 

  60. Stevens B, Feingold G (2009) Untangling aerosol effects on clouds and precipitation in a buffered system. Nature 461(7264):607–613. https://doi.org/10.1038/nature08281

    Article  Google Scholar 

  61. Suzuki K, Nakajima TY, Stephens GL (2010) Particle growth and drop collection efficiency of warm clouds as inferred from joint CloudSat and MODIS observations. J Atmos Sci 67:3019–3032. https://doi.org/10.1175/2010JAS3463.1

    Article  Google Scholar 

  62. Suzuki K, Golaz JC, Stephens GL (2013) Evaluating cloud tuning in a climate model with satellite observations. Geophys Res Lett 40. https://doi.org/10.1002/grl.50874

  63. Suzuki K, Stephens G, Bodas-Salcedo A, Wang M, Golaz JC, Yokohata T, Koshiro T (2015) Evaluation of the warm rain formation process in global models with satellite observations. J Atmos Sci 72:3996–4014. https://doi.org/10.1175/JAS-D-14-0265.1

    Article  Google Scholar 

  64. Swales DJ, Pincus R, Bodas-Salcedo A (2018) The cloud feedback model Intercomparison project observational simulator package: version 2. Geosci Model Dev 11:77–81. https://doi.org/10.5194/gmd-11-77-2018

    Article  Google Scholar 

  65. Terai CR, Pritchard MS, Blossey P, Bretherton CS (2020) The impact of resolving subkilometer processes on aerosol-cloud interactions of low-levels clouds in global model simulations. J Adv Model Earth Syst 12:e2020MS002274. https://doi.org/10.1029/2020ms002274

    Article  Google Scholar 

  66. Tsushima Y, Brient F, Klein SA, Konsta D, Nam CC, Qu X, Williams KD, Sherwood SC, Suzuki K, Zelinka MD (2017) The Cloud Feedback Model Intercomparison Project (CFMIP) diagnostic codes catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models. Geosci Model Dev 10:4285–4305. https://doi.org/10.5194/gmd-10-4285-2017

    Article  Google Scholar 

  67. Twomey S (1977) The influence of pollution on the shortwave albedo of clouds. J Atmos Sci 34:1149–1152

    Article  Google Scholar 

  68. Wang M, Ghan S, Liu X, L’Ecuyer TS, Zhang K, Morrison H, Ovchinnikov M, Easter R, Marchand R, Chand D, Qian Y, Penner JE (2012) Constraining cloud lifetime effects of aerosols using A-train satellite observations. Geophys Res Lett 39(15):L15709. https://doi.org/10.1029/2012GL052204

    Article  Google Scholar 

  69. Wood R, Kubar TL, Hartmann DL (2009) Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part II: heuristic models of rain formation. J Atmos Sci 66(10):2973–2990. https://doi.org/10.1175/2009JAS3072.1

    Article  Google Scholar 

  70. Zeng S, Riedi J, Trepte CR, Winker DM, Hu YX (2014) Study of global cloud droplet number concentration with A-train satellites. Atmos Chem Phys 14:7125–7134. https://doi.org/10.5194/acp-14-7125-2014

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Japan Society for the Promotion of Science KAKENHI (grant nos. JP19K14795, and JP19H05669); the Integrated Research Program for Advancing Climate Models (TOUGOU) from the Ministry of Education, Culture, Sports, Science and Technology (grant no. JPMXD0717935457); the Environment Research and Technology Development Fund (grant nos. JPMEERF20202R03, and JPMEERF21S12004) of the Environmental Restoration and Conservation Agency of Japan; and the JST FOREST Program (grant no. JPMJFR206Y).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Takuro Michibata .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Michibata, T. (2022). Aerosol–Cloud Interactions in the Climate System. In: Akimoto, H., Tanimoto, H. (eds) Handbook of Air Quality and Climate Change. Springer, Singapore. https://doi.org/10.1007/978-981-15-2527-8_35-2

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2527-8_35-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2527-8

  • Online ISBN: 978-981-15-2527-8

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Aerosol–Cloud Interactions in the Climate System
    Published:
    06 July 2022

    DOI: https://doi.org/10.1007/978-981-15-2527-8_35-3

  2. Aerosol–Cloud Interactions in the Climate System
    Published:
    17 June 2022

    DOI: https://doi.org/10.1007/978-981-15-2527-8_35-2

  3. Original

    Aerosol–Cloud Interactions in the Climate System
    Published:
    07 April 2022

    DOI: https://doi.org/10.1007/978-981-15-2527-8_35-1