Findings from two separate research papers analysing ship tracks, one led by Dr Duncan Watson-Parris and the other by Peter Manshausen, both from University of Oxford’s Department of Physics, indicate that air pollution has a larger effect on climate than previously thought.
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.
However, 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, adding that “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.”
EXPLORE MORE AT mr Manshausen’S REPORT
EXPLORE MORE AT dr Watson-Parris’S REPORT
