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Shipping Emissions Have a Larger Impact on Climate Than Previously Thought

Container ship at sea
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Findings from two separate research papers analysing ship tracks, one led by Dr Duncan Watson-Parris and the other by Peter Manshausen, both from the Department of Physics, indicate that air pollution has a larger effect on climate than previously thought. Analysing ship tracks is a unique way to observe and quantify the effect of human aerosol emissions on the climate – not just those from shipping – and both papers indicate that changes to emission levels, and so their cooling effect, must be taken into account when tackling climate change.


Ship emissions can occur in remote ocean environments and so provide unique opportunities to study the effects of aerosol in isolation of other human-induced factors. Dr Watson-Parris led work looking at visible ship tracks – lines of brighter clouds that were discovered in some of the first satellite images in the 1960s – while Peter Manshausen focused his efforts on analysing invisible tracks, to evaluate the impact of emissions on clouds and so, in turn, on climate. To date, measuring the impact of aerosol emissions on clouds has been notoriously challenging and these two novel approaches pave the way for further analysis to help inform policy.


Human aerosol emissions have a cooling effect on the planet because they can make clouds brighter by providing extra condensation nuclei on which cloud droplets form. Brighter clouds reflect more of the sunlight that strikes them, deflecting it from the earth's surface. However, it is currently unclear how large this cooling effect is, particularly if the cloud brightness change cannot be seen in satellite images. This could be when the emissions are diffuse, such as from a city’s traffic, or when there are winds that disperse them. The cooling effect offsets some of the warming effect of greenhouse gasses, and provides the largest uncertainty in human perturbations to the climate system.


Visible ship tracks


Visible ship tracks have long provided an opportunity to quantify these effects however, until now, the process has been time-consuming and limited; as it relied on manual analysis of individual satellite images, it could only be done either over a short period of time or over a small geographical area. To tackle the problem, Dr Watson-Parris and co-authors, including Professor Philip Stier, turned to machine learning. They developed a novel algorithm to automate the detection of ship tracks and as a result, over the last two years, they have gathered and analysed some 250TB of satellite data which uncovered more than 1 million ship tracks from the last 20 years.


This extensive database confirmed previous assessments of the seasons and locations most conducive to ship-tracks forming; with most tracks being found in the stratocumulus cloud decks in the East Pacific and East Atlantic. What was very striking however was the change in the number of ship tracks which occurred in 2020 when the International Maritime Organization introduced strict new fuel regulations to reduce the air pollution caused by global shipping. The team found a uniform 25% reduction in the number of tracks almost immediately. They do not see any similar change in other large-scale cloud properties, showing the value of this technique, and machine learning more generally, for tracking the effect of regulatory change on the climate.


Studying invisible ship tracks


Due to different types of clouds and variable meteorological conditions, only a fraction of ships leave visible tracks that are picked up by satellites however – the majority leave no immediately visible trace. In complementary work therefore, Peter Manshausen and colleagues – including Dr Watson-Parris and Professor Stier – set about filling in the gaps and analysing the invisible tracks. Did no track mean that the majority of ships’ aerosol emissions did not affect clouds?


Rather than searching for visible tracks in the clouds like Dr Watson-Parris’ work, the group used a database of ship routes containing the locations of almost all ships at a given time. Using historical weather observations, they then simulated where all these ships’ emissions were carried by the wind and entered the cloud. Studying these locations in satellite data allowed them to measure the number of droplets and the amount of water in the polluted and unpolluted clouds. Importantly, this method does not depend on there being a visible track in the clouds.


The group found that averaging over enough ship-polluted clouds did indeed show a clear effect on cloud properties. This is the first time that the cloud effects of individual ships could be quantified when the ships do not form tracks. Surprisingly, the effect of the aerosol emissions is different when tracks are not visible: the number of droplets increases less, while the amount of water increases more than in visible tracks. Importantly, the same may be true for aerosol emissions more generally – clouds may react more strongly to air pollution than previously thought, getting brighter and having a stronger cooling effect. This further implies that when air pollution is reduced, as it must be for health reasons, we may risk even more severe global heating.


‘These techniques show the value of combining novel data science approaches with the huge amount of earth observational data now available,’ explains Dr Duncan Watson-Parris. Professor Stier continues: ‘This allows us to transform the analysis of climate processes in earth observations from case studies to global monitoring, providing entirely new observational constraints on our understanding of the climate system and future climate models.’


References: Manshausen P, Watson-Parris D, Christensen MW, Jalkanen JP, Stier P. Invisible ship tracks show large cloud sensitivity to aerosol. Nature. 2022;610(7930):101-106. doi:10.1038/s41586-022-05122-0


Watson-Parris D, Christensen MW, Laurenson A, Clewley D, Gryspeerdt E, Stier P. Shipping regulations lead to large reduction in cloud perturbations. PNAS. 2022;119(41):e2206885119. doi:10.1073/pnas.2206885119


This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.


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