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Weak average liquid-cloud-water response to anthropogenic aerosols

Nature volume 572, pages 51–55 (2019)Cite this article

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

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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.

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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.

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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.

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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

Authors and Affiliations

  1. Department of Meteorology, University of Reading, Reading, UK

    Velle Toll & Nicolas Bellouin

  2. Institute of Physics, University of Tartu, Tartu, Estonia

    Velle Toll

  3. Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK

    Matthew Christensen

  4. Institute for Meteorology, Universität Leipzig, Leipzig, Germany

    Johannes Quaas

Authors
  1. Velle Toll
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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.

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Correspondence to Velle Toll.

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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.

a–d, 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
Full size table
Extended Data Table 2 Average characteristics of various types of track
Full size table
Extended Data Table 3 Approximate SO2 emission rates for various emission sources
Full size table
Extended Data Table 4 LWP sensitivity to CDNC perturbation depending on Re
Full size table

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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

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