Quantifying and Reducing aviation Contrail radiative forcing (QR-CODE)

IPCC stresses that limiting warming to 1.5 oC requires "reaching net zero CO2 emission globally around 2050". Aviation is one of the most important economic sectors, and is expected to steadily grow by 4-5% per year. If aviation emissions growth is unmitigated, it could contribute 4-15% of emission budget in 2050 for a 2 oC target. UK has committed to Jet-Zero by decarbonizing aviation until 2050, however, aviation's climate warming and uncertainty are both dominated (> 50%) by contrail cirrus. Therefore, it is vital to "quantify and reduce aviation contrail radiative forcing (QR-CODE)". This will enable the design of mitigation strategies via trade-offs: reduce large non-CO2 warming but with a subtle increase in CO2 emissions, such as flights diversion to avoid contrails.



Contrails, or condensation trails, are cirrus clouds created by aircraft when flying through cold and humid regions. Fresh contrails are line-shaped and usually short-lived in dry ambient air; but under humid and cold conditions, contrails can persist for up to tens of hours and spread out as contrail cirrus (CC) covering up to thousands of km2. These contrail and CC can reflect shortwave sunlight back to space (cooling), but also trap longwave terrestrial radiation as CO2 does (warming). The net forcing of aviation cirrus (including contrail and CC) has been assessed to be the largest component of aviation-induced warming forcing but also with the largest uncertainty. One key challenge is the lack of observational evidence to constrain and improve aviation-induced cirrus prediction in numerical models, particularly because CC often merges with natural cirrus making it indistinguishable neither to quantify the associated radiative forcing.



Recent developments mean that the lack of constraints are now changing, it is timely and ripe to overcome the challenge and achieve QR-CODE ambitions. The developments include: 1) The COVID global lockdown grounded more than 80% flights, which provides unprecedented large-scale natural experiments for deriving aviation fingerprints on cirrus. 2) The availability of 20+ years continuous observations of cirrus clouds from satellites enables advance application of machine-learning to develop natural experiments for disentangling aviation fingerprints. 3) The recent advance in computer vision enables automatic detection of line-shaped young contrails from satellite images during 2001-2022, which was almost an impossible task using manual techniques. Google led a recent innovation in applying computer vision in successful detecting line-shaped contrails from satellite images (similar to ship-tracks in liquid-clouds). Another our recent innovation in applying machine-learning to develop natural experiments has demonstrated its fidelity in unambiguously quantifying aerosol fingerprints on different types of natural clouds and the associated radiative forcing. These natural experiments use long-term satellite observations-based machine-learning to predict how clouds would look if they were unperturbed under the same meteorology, and therefore enabling discerning the fingerprints of large perturbations (including aviation during COVID) on cirrus (similar to liquid-clouds impacted by plumes, e.g., ship emission to marine boundary-layer clouds).

QR-CODE will further develop and apply the above two modern innovations to aviation line-shaped contrails and CC to generate the first ever large ensemble of observation-based constraints for developing aviation cirrus predictors (Theme 1.3). This will allow us to improve aviation cirrus prediction and quantify its climate effects (Theme 1.1), hence enabling the optimization and implement of trade-off mitigation strategies via contrail avoidance through the Met Office Civil Aviation Authority to support the UK Jet Zero strategy (Themes 2.1-2.3).