Weak average liquid-cloud-water response to anthropogenic aerosols

An Author Correction to this article was published on 03 January 2020

This article has been updated

Abstract

The cooling of the Earth’s climate through the effects of anthropogenic aerosols on clouds offsets an unknown fraction of greenhouse gas warming. An increase in the amount of water inside liquid-phase clouds induced by aerosols, through the suppression of rain formation, has been postulated to lead to substantial cooling, which would imply that the Earth’s surface temperature is highly sensitive to anthropogenic forcing. Here we provide direct observational evidence that, instead of a strong increase, aerosols cause a relatively weak average decrease in the amount of water in liquid-phase clouds compared with unpolluted clouds. Measurements of polluted clouds downwind of various anthropogenic sources—such as oil refineries, smelters, coal-fired power plants, cities, wildfires and ships—reveal that aerosol-induced cloud-water increases, caused by suppressed rain formation, and decreases, caused by enhanced evaporation of cloud water, partially cancel each other out. We estimate that the observed decrease in cloud water offsets 29% of the global climate-cooling effect caused by aerosol-induced increases in the concentration of cloud droplets. These findings invalidate the hypothesis that increases in cloud water cause a substantial climate cooling effect and translate into reduced uncertainty in projections of future climate.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Examples of pollution tracks in clouds.
Fig. 2: Locations of analysed pollution tracks.
Fig. 3: Bidirectional LWP responses in tracks with different heights.
Fig. 4: Meteorological dependence of LWP responses.
Fig. 5: Frequency distributions for polluted and unpolluted cloud properties over land and ocean.
Fig. 6: Estimated radiative forcing.

Data availability

The MODIS cloud products MYD06_L2 from Aqua and MOD06_L2 from Terra used in this study are available from the Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), https://ladsweb.nascom.nasa.gov/. ERA-Interim data are available from the European Centre for Medium-range Weather Forecasts (ECMWF) archive, https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim/. The hand-logged centre line coordinates of various types of polluted cloud track and their corresponding MODIS time stamps are available from the University of Reading Research Data Archive, https://researchdata.reading.ac.uk/208/ or https://doi.org/10.17864/1947.208.

Code availability

Python code for detection of polluted cloud pixels in satellite images is available at GitHub, https://github.com/VelleToll/polluted_cloud_tracks/, or https://doi.org/10.5281/zenodo.2669447.

Change history

  • 03 January 2020

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Boucher, O. et al. Clouds and aerosols. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  2. 2.

    Stevens, B., Sherwood, S., Bony, S. & Webb, M. Prospects for narrowing bounds on Earth’s equilibrium climate sensitivity. Earths Futur. 4, 512–522 (2016).

    ADS  Google Scholar 

  3. 3.

    Andreae, M., Jones, C. & Cox, P. Strong present-day aerosol cooling implies a hot future. Nature 435, 1187–1190 (2005).

    ADS  PubMed  CAS  Google Scholar 

  4. 4.

    Millar, R. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017); correction 11, 454–455 (2018).

    ADS  CAS  Google Scholar 

  5. 5.

    Twomey, S. Pollution and the planetary albedo. Atmos. Environ. 8, 1251–1256 (1974).

    ADS  Google Scholar 

  6. 6.

    Breon, F. Aerosol effect on cloud droplet size monitored from satellite. Science 295, 834–838 (2002).

    ADS  PubMed  CAS  Google Scholar 

  7. 7.

    Feingold, G., Eberhard, W., Veron, D. & Previdi, M. First measurements of the Twomey indirect effect using ground-based remote sensors. Geophys. Res. Lett. 30, 1287 (2003).

    ADS  Google Scholar 

  8. 8.

    McCoy, D. et al. Natural aerosols explain seasonal and spatial patterns of Southern Ocean cloud albedo. Sci. Adv. 1, e1500157 (2015).

    ADS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Storelvmo, T. Aerosol effects on climate via mixed-phase and ice clouds. Annu. Rev. Earth Planet. Sci. 45, 199–222 (2017).

    ADS  CAS  Google Scholar 

  10. 10.

    Lohmann, U. et al. Total aerosol effect: radiative forcing or radiative flux perturbation? Atmos. Chem. Phys. 10, 3235–3246 (2010).

    ADS  CAS  Google Scholar 

  11. 11.

    Stevens, B. & Feingold, G. Untangling aerosol effects on clouds and precipitation in a buffered system. Nature 461, 607–613 (2009).

    ADS  PubMed  CAS  Google Scholar 

  12. 12.

    Wang, M. et al. Constraining cloud lifetime effects of aerosols using A-Train satellite observations. Geophys. Res. Lett. 39, L15709 (2012).

    ADS  Google Scholar 

  13. 13.

    Suzuki, K., Stephens, G. & Lebsock, M. Aerosol effect on the warm rain formation process: satellite observations and modeling. J. Geophys. Res. D 118, 170–184 (2013).

    ADS  CAS  Google Scholar 

  14. 14.

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

    ADS  PubMed  CAS  Google Scholar 

  15. 15.

    Ackerman, A., Kirkpatrick, M., Stevens, D. & Toon, O. The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature 432, 1014–1017 (2004).

    ADS  PubMed  CAS  Google Scholar 

  16. 16.

    Bretherton, C., Blossey, P. & Uchida, J. Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo. Geophys. Res. Lett. 34, L03813 (2007).

    ADS  Google Scholar 

  17. 17.

    Wood, R. Cancellation of aerosol indirect effects in marine stratocumulus through cloud thinning. J. Atmos. Sci. 64, 2657–2669 (2007).

    ADS  Google Scholar 

  18. 18.

    Small, J., Chuang, P., Feingold, G. & Jiang, H. Can aerosol decrease cloud lifetime? Geophys. Res. Lett. 36, L16806 (2009).

    ADS  Google Scholar 

  19. 19.

    Chen, Y., Christensen, M., Stephens, G. & Seinfeld, J. Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds. Nat. Geosci. 7, 643–646 (2014).

    ADS  CAS  Google Scholar 

  20. 20.

    Lebsock, M., Stephens, G. & Kummerow, C. Multisensor satellite observations of aerosol effects on warm clouds. J. Geophys. Res. 113, D15205 (2008).

    ADS  Google Scholar 

  21. 21.

    Mauger, G. & Norris, J. Meteorological bias in satellite estimates of aerosol-cloud relationships. Geophys. Res. Lett. 34, L16824 (2007); correction 35, L07815 (2008).

    ADS  Google Scholar 

  22. 22.

    Christensen, M. et al. Unveiling aerosol–cloud interactions – Part 1: cloud contamination in satellite products enhances the aerosol indirect forcing estimate. Atmos. Chem. Phys. 17, 13151–13164 (2017).

    ADS  CAS  Google Scholar 

  23. 23.

    Neubauer, D., Christensen, M., Poulsen, C. & Lohmann, U. Unveiling aerosol–cloud interactions – Part 2: minimising the effects of aerosol swelling and wet scavenging in ECHAM6–HAM2 for comparison to satellite data. Atmos. Chem. Phys. 17, 13165–13185 (2017).

    ADS  CAS  Google Scholar 

  24. 24.

    Grosvenor, D. et al. Remote sensing of droplet number concentration in warm clouds: a review of the current state of knowledge and perspectives. Rev. Geophys. 56, 409–453 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Rosenfeld, D. et al. Aerosol-driven droplet concentrations dominate coverage and water of oceanic low level clouds. Science 363, eaav0566 (2019); correction 364, eaay4194 (2019)

    PubMed  CAS  Google Scholar 

  26. 26.

    Gryspeerdt, E. et al. Constraining the aerosol influence on cloud liquid water path. Atmos. Chem. Phys. Discuss. 19, 5331–5347 (2018).

    ADS  Google Scholar 

  27. 27.

    Toll, V., Christensen, M., Gassó, S. & Bellouin, N. Volcano and ship tracks indicate excessive aerosol-induced cloud water increases in a climate model. Geophys. Res. Lett. 44, 12492–12500 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Malavelle, F. et al. Strong constraints on aerosol–cloud interactions from volcanic eruptions. Nature 546, 485–491 (2017); erratum 551, 256 (2017).

    ADS  PubMed  CAS  Google Scholar 

  29. 29.

    Christensen, M. & Stephens, G. Microphysical and macrophysical responses of marine stratocumulus polluted by underlying ships: 2. Impacts of haze on precipitating clouds. J. Geophys. Res. 117, D11203 (2012).

    ADS  Google Scholar 

  30. 30.

    Platnick, S. et al. The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens. 55, 502–525 (2017).

    ADS  PubMed  Google Scholar 

  31. 31.

    Durkee, P. et al. Composite ship track characteristics. J. Atmos. Sci. 57, 2542–2553 (2000).

    ADS  Google Scholar 

  32. 32.

    Mülmenstädt, J. & Feingold, G. The radiative forcing of aerosol–cloud interactions in liquid clouds: wrestling and embracing uncertainty. Curr. Clim. Change Rep. 4, 23–40 (2018).

    Google Scholar 

  33. 33.

    King, M., Platnick, S., Menzel, W., Ackerman, S. & Hubanks, P. Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites. IEEE Trans. Geosci. Remote Sens. 51, 3826–3852 (2013).

    ADS  Google Scholar 

  34. 34.

    Rosenfeld, D. Suppression of rain and snow by urban and industrial air pollution. Science 287, 1793–1796 (2000).

    ADS  PubMed  CAS  Google Scholar 

  35. 35.

    Christensen, M., Chen, Y. & Stephens, G. Aerosol indirect effect dictated by liquid clouds. J. Geophys. Res. 121, 14636–14650 (2016).

    Google Scholar 

  36. 36.

    Wood, R. Stratocumulus clouds. Mon. Weath. Rev. 140, 2373–2423 (2012).

    ADS  Google Scholar 

  37. 37.

    Greenwald, T. A. A 2-year comparison of AMSR-E and MODIS cloud liquid water path observations. Geophys. Res. Lett. 36, L20805 (2009).

    ADS  MathSciNet  Google Scholar 

  38. 38.

    Possner, A., Wang, H., Wood, R., Caldeira, K. & Ackerman, T. The efficacy of aerosol–cloud radiative perturbations from near-surface emissions in deep open-cell stratocumuli. Atmos. Chem. Phys. 18, 17475–17488 (2018).

    ADS  CAS  Google Scholar 

  39. 39.

    Hobbs, P. et al. Emissions from ships with respect to their effects on clouds. J. Atmos. Sci. 57, 2570–2590 (2000).

    ADS  Google Scholar 

  40. 40.

    Huang, X. et al. Effects of aerosol–radiation interaction on precipitation during biomass-burning season in East China. Atmos. Chem. Phys. 16, 10063–10082 (2016).

    ADS  CAS  Google Scholar 

  41. 41.

    Gordon, H. et al. Large simulated radiative effects of smoke in the south-east Atlantic. Atmos. Chem. Phys. 18, 15261–15289 (2018).

    ADS  CAS  Google Scholar 

  42. 42.

    Tao, W. et al. Role of atmospheric aerosol concentration on deep convective precipitation: cloud-resolving model simulations. J. Geophys. Res. 112, D24S18 (2007).

    ADS  Google Scholar 

  43. 43.

    Yuan, T., Remer, L., Pickering, K. & Yu, H. Observational evidence of aerosol enhancement of lightning activity and convective invigoration. Geophys. Res. Lett. 38, L04701 (2011).

    ADS  Google Scholar 

  44. 44.

    Penner, J., Zhou, C., Garnier, A. & Mitchell, D. Anthropogenic aerosol indirect effects in cirrus clouds. J. Geophys. Res. 123, 11652–11677 (2018).

    Google Scholar 

  45. 45.

    Ghan, S. et al. Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability. Proc. Natl Acad. Sci. USA 113, 5804–5811 (2016); correction 113, E3049 (2016).

    ADS  PubMed  CAS  Google Scholar 

  46. 46.

    Chen, Y. et al. Occurrence of lower cloud albedo in ship tracks. Atmos. Chem. Phys. 12, 8223–8235 (2012).

    ADS  CAS  Google Scholar 

  47. 47.

    Coakley, J., Bernstein, R. & Durkee, P. Effect of ship-stack effluents on cloud reflectivity. Science 237, 1020–1022 (1987).

    ADS  PubMed  Google Scholar 

  48. 48.

    Fioletov, V. E. et al. A global catalogue of large SO2 sources and emissions derived from the Ozone Monitoring Instrument. Atmos. Chem. Phys. 16, 11497–11519 (2016).

    ADS  CAS  Google Scholar 

  49. 49.

    Grosvenor, D. & Wood, R. The effect of solar zenith angle on MODIS cloud optical and microphysical retrievals within marine liquid water clouds. Atmos. Chem. Phys. 14, 7291–7321 (2014).

    ADS  CAS  Google Scholar 

  50. 50.

    Segrin, M., Coakley, J. & Tahnk, W. MODIS observations of ship tracks in summertime stratus off the west coast of the United States. J. Atmos. Sci. 64, 4330–4345 (2007).

    ADS  Google Scholar 

  51. 51.

    Carn, S., Fioletov, V., McLinden, C., Li, C. & Krotkov, N. A decade of global volcanic SO2 emissions measured from space. Sci. Rep. 7, 44095 (2017).

    ADS  PubMed  PubMed Central  CAS  Google Scholar 

  52. 52.

    Quaas, J., Boucher, O., Bellouin, N. & Kinne, S. Satellite-based estimate of the direct and indirect aerosol climate forcing. J. Geophys. Res. 113, D05204 (2008).

    ADS  Google Scholar 

  53. 53.

    Kato, S. et al. Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances. J. Clim. 26, 2719–2740 (2013).

    ADS  Google Scholar 

  54. 54.

    Brenguier, J. et al. Radiative properties of boundary-layer clouds: droplet effective radius versus number concentration. J. Atmos. Sci. 57, 803–821 (2000).

    ADS  Google Scholar 

  55. 55.

    Quaas, J., Boucher, O. & Lohmann, U. Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data. Atmos. Chem. Phys. 6, 947–955 (2006).

    ADS  CAS  Google Scholar 

  56. 56.

    Bellouin, N., Quaas, J., Morcrette, J. & Boucher, O. Estimates of aerosol radiative forcing from the MACC re-analysis. Atmos. Chem. Phys. 13, 2045–2062 (2013).

    ADS  CAS  Google Scholar 

  57. 57.

    Stephens, G., Gabriel, P. & Partain, P. Parameterization of atmospheric radiative rransfer. Part I: validity of simple models. J. Atmos. Sci. 58, 3391–3409 (2001).

    ADS  Google Scholar 

  58. 58.

    Ackerman, A. et al. Effects of aerosols on cloud albedo: evaluation of Twomey’s parameterization of cloud susceptibility using measurements of ship tracks. J. Atmos. Sci. 57, 2684–2695 (2000).

    ADS  Google Scholar 

  59. 59.

    Platnick, S. & Twomey, S. Determining the susceptibility of cloud albedo to changes in droplet concentration with the Advanced Very-High-Resolution Radiometer. J. Appl. Meteorol. 33, 334–347 (1994).

    ADS  Google Scholar 

  60. 60.

    Christensen, M. & Stephens, G. Microphysical and macrophysical responses of marine stratocumulus polluted by underlying ships: evidence of cloud deepening. J. Geophys. Res. 116, D03201 (2011).

    ADS  Google Scholar 

  61. 61.

    Dee, D. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. Royal Meteorol. Soc. 137, 553–597 (2011).

    ADS  Google Scholar 

  62. 62.

    Rosenfeld, D., Wang, H. & Rasch, P. The roles of cloud drop effective radius and LWP in determining rain properties in marine stratocumulus. Geophys. Res. Lett. 39, L13801 (2012).

    ADS  Google Scholar 

  63. 63.

    Winker, D. et al. The CALIPSO Mission: a global 3D view of aerosols and clouds. Bull. Am. Meteorol. Soc. 91, 1211–1230 (2010).

    ADS  Google Scholar 

Download references

Acknowledgements

This study was funded by the University of Reading, with support from the CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016) project, funded by the UK Natural Environment Research Council under grant agreement NE/L013479/1. V.T. acknowledges support from the Estonian Research Council personal research funding grant PSG202. We thank G. Myhre for comments on this work.

Author information

Affiliations

Authors

Contributions

V.T. and N.B. designed the study. V.T. analysed the observations. M.C. contributed ship track observations. J.Q. contributed methods and input data for radiative forcing calculations. V.T. and N.B. wrote the manuscript with contributions from M.C. and J.Q.

Corresponding author

Correspondence to Velle Toll.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature thanks Brian Toon, Anna Possner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Classification of MODIS cloud pixels as polluted or unpolluted.

a, b, In both small-scale pollution tracks (a) and large-industry tracks (b), polluted pixels are defined by their higher NIR reflectance compared to the nearby unpolluted clouds. Polluted pixels are shown as yellow upward-pointing triangles and unpolluted control pixels are shown as blue downward-pointing triangles. The pixels represented by grey crosses are not included in the analyses.

Extended Data Fig. 2 Further examples of pollution tracks.

Brighter polluted clouds in grey/white colours are distinguishable from nearby unpolluted clouds in yellow/brown colours. a, Large-industry track caused by oil production in Kazakhstan on 20 November 2012. Night lights of the oil production industry are shown in white. b, Pollution tracks originating from the oil refining industry in the Nenets region, Russia, on 8 August 2006. c, Two volcano tracks in trade-wind cumulus clouds above Vanuatu on 6 July 2016. The red arrows point towards the volcano tracks. d, A volcano track in stratiform clouds above the Kuril Islands on 23 April 2016. Note that the map scales are different in each panel.

Extended Data Fig. 3 Frequency distributions of meteorological conditions in various types of track.

a, LWP. b, Lower tropospheric stability (LTS). c, Re. d, Above-cloud relative humidity (RH) for unpolluted clouds adjacent to the pollution tracks studied. The pollution tracks were sampled under a wide range of meteorological conditions. St (or Cu) land (or ocean), stratiform (or trade-wind cumulus) clouds sampled over land (or ocean).

Extended Data Fig. 4 Meteorological conditions sampled over land and ocean.

a, b, Joint histogram for CTH and COD in unpolluted clouds adjacent to the land-based (a) and ocean-based (b) pollution tracks embedded in stratiform clouds. Note that the colour bar is nonlinear.

Extended Data Fig. 5 Frequency distributions of increases and decreases in LWP depending on the properties of unpolluted clouds.

ad, Frequency distributions of increases and decreases in LWP depending on CTH (a); relative humidity above clouds (b); LWP (c); and Re (d).

Extended Data Fig. 6 Susceptibility of cloud albedo to CDNC perturbations in continental and ocean-based stratiform clouds.

The solid black line (forced through the origin) shows a least-squares fit to cloud albedo (A) susceptibility in the studied pollution tracks. The dashed black line shows the slope of the one-third contribution expected from just the Twomey effect, assuming a constant LWP. Fractional changes in the LWP for individual tracks are given in colour. a, For land-based industry and fire tracks the slope of the fitted solid black line is 0.27, implying that the relative increase in cloud albedo is less than that expected from only the Twomey effect. b, For ocean-based volcano and ship tracks the slope of the fitted solid black line is 0.32, implying that the relative increase in cloud albedo is very similar to that expected from only the Twomey effect.

Extended Data Table 1 Comparisons between polluted and unpolluted cloud properties for various types of pollution track
Extended Data Table 2 Average characteristics of various types of track
Extended Data Table 3 Approximate SO2 emission rates for various emission sources
Extended Data Table 4 LWP sensitivity to CDNC perturbation depending on Re

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Toll, V., Christensen, M., Quaas, J. et al. Weak average liquid-cloud-water response to anthropogenic aerosols. Nature 572, 51–55 (2019). https://doi.org/10.1038/s41586-019-1423-9

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing