Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Oct;610(7930):101-106.
doi: 10.1038/s41586-022-05122-0. Epub 2022 Oct 5.

Invisible ship tracks show large cloud sensitivity to aerosol

Peter Manshausen  1 , Duncan Watson-Parris  2 , Matthew W Christensen  3 , Jukka-Pekka Jalkanen  4 , Philip Stier  2
Affiliations

Invisible ship tracks show large cloud sensitivity to aerosol

Peter Manshausen et al. Nature. 2022 Oct.

Abstract

Cloud reflectivity is sensitive to atmospheric aerosol concentrations because aerosols provide the condensation nuclei on which water condenses1. Increased aerosol concentrations due to human activity affect droplet number concentration, liquid water and cloud fraction2, but these changes are subject to large uncertainties3. Ship tracks, long lines of polluted clouds that are visible in satellite images, are one of the main tools for quantifying aerosol-cloud interactions4. However, only a small fraction of the clouds polluted by shipping show ship tracks5,6. Here we show that even when no ship tracks are visible in satellite images, aerosol emissions change cloud properties substantially. We develop a new method to quantify the effect of shipping on all clouds, showing a cloud droplet number increase and a more positive liquid water response when there are no visible tracks. We directly detect shipping-induced cloud property changes in the trade cumulus regions of the Atlantic, which are known to display almost no visible tracks. Our results indicate that previous studies of ship tracks were suffering from selection biases by focusing only on visible tracks from satellite imagery. The strong liquid water path response we find translates to a larger aerosol cooling effect on the climate, potentially masking a higher climate sensitivity than observed temperature trends would otherwise suggest.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. A view of the Chilean Sc deck from the MODIS satellite instrument.
The colour scale indicates cloud droplet effective radius. a,b, Grey boxes show the location of emissions (a) and where they are advected to with the wind (b). There are visible ship tracks in darker colours, most of which are well collocated to advected ship emissions. MODIS Aqua on 6 August 2018. c, The same region but for a day without visible ship tracks, even though there are ships. d, Advected emission locations and in addition the counterfactual retrieval boxes to either side of them (yellow boxes). MODIS Aqua on 14 September 2019, selected ships. Map of the retrieval location from cartopy.
Fig. 2
Fig. 2. There are cloud property changes even when there are no visible tracks.
a,b, Comparing Nd (a) and LWP (b) in the Chilean Sc between those days when ship tracks are visible (blue) and those when none are visible (orange) in the region. Plotted are ratios of hourly means of in-track and out-of-track properties, so that a value larger than 0% means an enhancement in the track. Shaded areas show standard errors of the means. LWP anomalies are given in 2 h means rather than 1 h because of noisier data. Retrievals from the MODIS-cloud product on Aqua and Terra, all for 2014–2019, 25% of days with visible tracks. Also, there are measurable responses to ship emissions everywhere.c, A plot of a comparison of in-track and out-of-track properties in four regions: the Chilean (Chil., light blue) and Angolan/Namibian (Ango., blue) Sc, and the Cu around the Azores (Azor., navy) and Cabo Verde (C.Ver., green). The regions are shown in different colours representing the ship track location data of around six days. d–f, Plots showing relative Nd (d), LWP (e) and albedo (f) anomalies. Shaded areas show standard errors of the means. Retrievals from the MODIS-cloud product on Aqua and Terra, all for 2014–2019. Map of the different regions from cartopy. Source data
Fig. 3
Fig. 3. More droplets in Sc, more LWP in Cu.
a,b, Heatmap of relative Nd (a) and LWP anomaly (b), that is, mean of in-track properties divided by mean of out-of-track properties. Nd responses are strongest in the Sc regions, but LWP responses are strongest in the trade Cu. Retrievals from MODIS-cloud product on Aqua and Terra, all for 2014–2019. Map from cartopy. c, The possible mechanism for a more positive LWP response in weak-inversion regions. Previous ship track studies focused on regions with stable conditions and relatively high background Nd (top). There, cloud depth cannot increase because of the strong inversion topping the cloud. Invisible tracks occur more often in unstable, low background Nd clouds (bottom). When more CCN are provided by a ship, these clouds can deepen. As liquid water content (LWC) increases linearly with height, LWP, its integral, increases quadratically. Source data
Extended Data Fig. 1
Extended Data Fig. 1. Time dependence of resulting sensitivity.
The time-resolved LWP sensitivity βL in the Chilean Sc on those days when ship tracks are visible (blue) and those when none are visible (orange) in the region. The data is the same as that presented in Fig. 2 (MODIS 2014-2019), with the error propagated from the standard error of LWP.
Extended Data Fig. 2
Extended Data Fig. 2. Visible and invisible tracks differ in their meteorological background.
Meteorological conditions of days that show any ship tracks and no ship tracks for the Chilean Sc in 2019. Plotted are density histograms in arbitrary units. Data is from ERA5, evaluated at the advected shipping emission locations and from the MODIS-cloud product on Aqua and Terra for Nd.
Extended Data Fig. 3
Extended Data Fig. 3. The responses to shipping emissions are correlated to stability and background Nd.
Plotted is a heatmap of inversion strength and background droplet number. Data is from ERA5 for EIS, evaluated at the advected shipping emission locations. The Nd data is from the MODIS retrieval to the sides of the emission locations. Maps from cartopy.
Extended Data Fig. 4
Extended Data Fig. 4. Stronger LWP changes may be due to cloud deepening, but data is noisy.
Heatmap of cloud top temperature (CTT) anomalies. Note that this heatmap is of absolute rather than relative differences like in Fig. 3, and that the color bar is reversed. Retrievals from MODIS-cloud product on Aqua and Terra, all of 2014-2019. Map from cartopy.
Extended Data Fig. 5
Extended Data Fig. 5. Changes to Nd and albedo can be detected in a small region after only one and four months, respectively.
Shown is the fraction of significant anomalies for albedo and Nd as a function of the number of days studied. This is for the Chilean Sc described above. For a given number of days n, 20 samples of n days are drawn from the total observation period and anomalies are calculated. This is to emulate a random draw of observation days. Anomalies are taken to be significant if they are larger than two standard errors, equivalent to a Student’s t-test at 95% confidence. We assume that we are able to make a confident detection if in 95% of draws, so 19 out of 20, we observe significant anomalies. Source data
Extended Data Fig. 6
Extended Data Fig. 6. The resulting forcing from LWP adjustments is concentrated in low-stability, high cloud fraction regions.
Shown are spatial patterns of quantities related to radiative forcing. a) Liquid cloud retrieval fractions from MODIS 2014-2019 are multiplied with b) LWP sensitivities calculated from ship track observations, extrapolated globally according to EIS, as well as the downwelling shortwave radiation from ERA5 and the estimate for anthropogenic Nd changes from Bellouin et al. This gives an estimate c) of radiative forcing from the adjustment of LWP to anthropogenic Nd changes (plotted is the case of higher cloud fraction and anthropogenic Nd enhancement). Maps from cartopy. Source data
Extended Data Fig. 7
Extended Data Fig. 7. Comparing Nd and LWP anomalies under different stability conditions, as measured by EIS.
Plotted are ratios of hourly means of in-track and out-of track properties, so that a value larger than 0% means an enhancement in the track. Shaded areas show standard errors of the means. LWP anomalies are given in 2h means rather than 1 h because of noisier data. The boundaries of EIS bins are given in Extended Data Table 1. Retrievals from MODIS-cloud product on Aqua and Terra, all of 2014-2019.
See this image and copyright information in PMC

References

    1. Twomey, S. The Influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci.34, 1149–1152 (1977).
    1. Albrecht B. Fractional cloudiness. Science. 1989;245:1227–1230. doi: 10.1126/science.245.4923.1227. - DOI - PubMed
    1. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).
    1. Christensen, M. et al. Opportunistic experiments to constrain aerosol effective radiative forcing. Atmos. Chem. Phys.22, 641–674 (2022). - PMC - PubMed
    1. Schreier, M. Mannstein, H. Eyring, V. & Bovensmann, H. Global ship track distribution and radiative forcing from 1 year of AATSR data. Geophys. Res. Lett.10.1029/2007GL030664 (2007)

Publication types

LinkOut - more resources