Kevin McCloskey
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Observing long-lived longwave contrail forcing
Aarón Sonabend
Joe Ng
Atmospheric Measurement Techniques (2026)
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Contrail microphysical simulations and climate simulations have indicated that contrail cirrus cause a substantial fraction of aviation’s climate impact. While the approximations and parameter selections in these simulations have been well-validated over the past two decades, the heat trapping of contrails has not been observed using satellite data beyond a few hours. This is because contrails lose their linear shape after a few hours, making them difficult to distinguish from natural cirrus clouds. Here we provide satellite-driven analysis of long-lived heat trapping by contrails over North and South America. We aggregate a dataset of GOES-16 estimated outgoing longwave radiation and advected trace density of flight paths, and apply causal inference to discern the effect of contrails while controlling for radiative and cloud confounders. As a means of validation, we also generate synthetic datasets with known ground truth, and confirm that applying the causal inference method is able to recover the synthetic ground truth. Since this method yields an estimate which has some differences from both “instantaneous radiative forcing” (iRF) and “effective radiative forcing” (ERF) estimates which have been reported in the literature so far, we introduce the new term “observational radiative forcing, 12 hours” (oRF12). Our analysis estimates the longwave oRF12 from contrails over the Americas averaged 47.9 gigajoules per flight kilometer (95% CI: 31 to 52 GJ/km) during April 2019 to April 2020.
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Efficacy of Scalable Airline-led Contrail Avoidance
Thomas Dean
Tristan Abbott
Jill Blickstein
Alejandra Martín Frías
Mark Galyen
Rebecca Grenham
Paul Hodgson
Alan Pechman
Tyler Robarge
Dinesh Sanekommu
Aarón Sonabend
Marc E.J. Stettler
Raimund Zopp
Journal of Environmentally Compatible Air Transport System (JECATS) (2026) (to appear)
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Contrails account for a large portion of aviation's contribution to anthropogenic climate change. Navigational contrail avoidance is a promising solution to mitigate the warming caused by contrails. Prior trials testing navigational contrail avoidance have relied on bespoke integrations of contrail forecasts into airline operations. Here, we use a randomized control trial to test the feasibility of dispatcher-led contrail avoidance integrated into standard flight planning operations using a workflow which scales to an airline's entire network. Using satellite imagery and an automated flight-contrail attribution algorithm, we observed an 11.6% reduction in contrail formation rate for the 1232 flights marked as eligible for contrail avoidance (intent-to-treat) relative to the flights in the control group (p = 0.0109). In the 112 flights which flew contrail avoidance as planned (per-protocol flights), we observed a 62.0% lower contrail formation rate relative to the flights in the control group (p < 0.001). No statistically significant difference in fuel usage was observed between the two groups.
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Improving reanalysis weather for contrail validation by incorporating satellite observations
Susanne Rohs
Roger Teoh
Journal of Environmentally Compatible Air Transport System (JECATS) (2026)
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Aviation-induced condensation trails (contrails) contribute significantly to anthropogenic radiative forcing. While navigational contrail avoidance has been proposed as a strategy to mitigate this climate impact, the operational viability of such maneuvers relies on the ability to verify their efficacy. Current verification methodologies often employ contrail models driven by reanalysis weather data; however, these assessments are limited by the variable fidelity of the underlying meteorological datasets. In this work, we address this uncertainty by leveraging satellite observations to refine reanalysis estimates for specific contrail events. We demonstrate that this approach significantly improves the agreement between reanalysis data and in-situ measurements obtained from the IAGOS program, thereby offering a more robust framework for evaluating avoidance strategies.
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Observational constraints on net radiative forcing confirm aviation contrail warming
Aarón Sonabend
Joe Yue-Hei Ng
arXiv (2026)
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Contrail cirrus represents a critical component of aviation’s non-CO2 climate impact, but its net radiative forcing, the balance between longwave warming and shortwave cooling, remains poorly constrained by direct observations. As a result, current assessments rely almost exclusively on microphysical models such as CoCiP and global climate simulations. Existing empirical estimates are largely restricted to young, linear tracks, because satellite detection masks have a poor recall of contrails once they spread and merge with natural cirrus, leaving a structural gap in our understanding of long-lived, non-linear contrail cirrus. To address this we use a causal framework that isolates the net radiative contrail effect of flight traffic over the Americas. Building on recent progress that quantified the longwave warming contrail effect using advected flight paths as a proxy for contrails, we expand this continuous treatment approach to capture the highly skewed shortwave cooling impact, delivering a 12-hour lifespan net observational radiative forcing. Our analysis reveals a statistically significant net warming energy forcing of 33.7 (95% CI: 20.8, 47.8) GJ/km flown from April 2019 to April 2020, providing a large-scale empirical quantification of long contrail lifespan impact of the same order as, though somewhat larger than, previous bottom-up simulation estimates. This observational benchmark offers an independent line of evidence on the sign and magnitude of the climate impact of contrails.
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Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Vincent Rudolf Meijer
Rémi Chevallier
Allie Duncan
Kyle McConnaughay
Atmospheric Measurement Techniques, 18 (2025), pp. 3495-3532
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Condensation trail (contrail) cirrus clouds cause a substantial fraction of aviation's climate impact. One proposed method for the mitigation of this impact involves modifying flight paths to avoid particular regions of the atmosphere that are conducive to the formation of persistent contrails, which can transform into contrail cirrus. Determining the success of such avoidance maneuvers can be achieved by ascertaining which flight formed each nearby contrail observed in satellite imagery. The same process can be used to assess the skill of contrail forecast models. The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities, and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but it lacks an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail detections with predetermined contrail-to-flight attributions that can be used to evaluate – or “benchmark” – and improve such attribution algorithms. The resulting performance metrics can be employed to understand the implications of using these observational data in downstream tasks, such as forecast model evaluation and the analysis of contrail avoidance trials, although the metrics do not directly quantify real-world performance. We also introduce a novel, highly scalable contrail-to-flight attribution algorithm that leverages the characteristic compounding of error induced by simulating contrail advection using numerical weather models. The benchmark shows an improvement of approximately 25 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.
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A scalable system to measure contrail formation on a per-flight basis
Erica Brand
Sebastian Eastham
Carl Elkin
Thomas Dean
Zebediah Engberg
Ulrike Hager
Ian Langmore
Joe Ng
Dinesh Sanekommu
Marc Shapiro
Environmental Research Communications (2024)
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In this work we describe a scalable, automated system to determine from satellite data whether a given flight has made a persistent contrail.
The system works by comparing flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a `flight matching' algorithm and use it to label each flight segment as a `match' or `non-match'. We perform this analysis on 1.6 million flight segments and compare these labels to existing contrail prediction methods based on weather forecast data. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We find that current contrail prediction models fail to correctly predict whether we will match a contrail in many cases.
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Feasibility test of per-flight contrail avoidance in commercial aviation
Dinesh Sanekommu
Zebediah Engberg
Ulrike Hager
John P Dudley
Aarón Sonabend
Joe Ng
Carl Elkin
Sixing Chen
Noman Ali
Marc Shapiro
Frank Opel
Rachel Soh
Erica Brand
Ole Schütt
Marco Jany
Thomas Dean
Nature Communications Engineering (2024) (to appear)
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Contrails, formed by aircraft engines, are a major source of anthropogenic climate change. Contrail avoidance, a promising climate change mitigation strategy, has been shown to be feasible in simulations but not yet in practice. We conducted a feasibility randomized controlled trial of contrail avoidance in commercial aviation at the per-flight level. Predictions for regions prone to contrail formation came from a physics-based simulation model and a machine learning model. Participating pilots made flight-altitude adjustments based on contrail formation predictions for flights assigned to the treatment arm. We manually verified results using satellite-based imagery and found a statistically significant reduction in contrails in the treatment group (p = 0.0316), with 63.6% fewer contrails observed than in the control group. This study demonstrates that per-flight contrail avoidance is feasible in commercial aviation and suggests it could lead to a significant reduction in the climate impact of aviation.
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The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
Marc Shapiro
Zebediah Engberg
Marc E.J. Stettler
Roger Teoh
Ulrich Schumann
Susanne Rohs
Erica Brand
Environmental Research Communications, 6 (2024), pp. 095015
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Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain. In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight segments with high contrail energy forcing. We find that skill is greater than climatological predictions alone, even accounting for uncertainty in weather fields and model parameters. We estimate the uncertainty due to humidity by using the ensemble ERA5 weather reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity measurements taken at cruising altitude. We take CoCiP energy forcing estimates calculated using one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying segments with large positive proxy energy forcing. We further estimate the uncertainty due to model parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from uncertainty distributions consistent with the literature. When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost and fuel impact of contrail avoidance.
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Discussing ContrailBench, a method for comparing contrail forecasts
In a separate session I will demonstrate how to use the attached colab to compute these metrics on our public contrail detection dataset. The colab currently also uses a dataset from Vincent Meijer at TU Delft. If we doesn't give permission to use that dataset I'll remove that part
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Contrail Detection on GOES-16 ABI with the OpenContrails Dataset
Joe Ng
Jian Cui
Vincent Rudolf Meijer
Erica Brand
IEEE Transactions on Geoscience and Remote Sensing (2023)
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Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are a substantial contributor to aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this article, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage at gs://goes_contrails_dataset .
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