Marc Shapiro’s research while affiliated with Breakthrough and other places

The global annual mean contrail climate forcing may exceed that of aviation's cumulative CO2 emissions. As only 2 %–3 % of all flights are likely responsible for 80 % of the global annual contrail energy forcing (EFcontrail), re-routing these flights could reduce the occurrence of strongly warming contrails. Here, we develop a contrail forecasting tool that produces global maps of persistent contrail formation and their EFcontrail formatted to align with standard weather and turbulence forecasts for integration into existing flight planning and air traffic management workflows. This is achieved by extending the existing trajectory-based contrail cirrus prediction model (CoCiP), which simulates contrails formed along flight paths, to a grid-based approach that initializes an infinitesimal contrail segment at each point in a 4D spatiotemporal grid and tracks them until their end of life. Outputs are provided for N aircraft-engine groups, with groupings based on similarities in aircraft mass and engine particle number emissions: N=7 results in a 3 % mean error between the trajectory- and grid-based CoCiP, while N=3 facilitates operational simplicity but increases the mean error to 13 %. We use the grid-based CoCiP to simulate contrails globally using 2019 meteorology and compare its forecast patterns with those from previous studies. Two approaches are proposed to apply these forecasts for contrail mitigation: (i) monetizing EFcontrail and including it as an additional cost parameter within a flight trajectory optimizer or (ii) constructing polygons to avoid airspace volumes with strongly warming contrails. We also demonstrate a probabilistic formulation of the grid-based CoCiP by running it with ensemble meteorology and excluding grid cells with significant uncertainties in the simulated EFcontrail. This study establishes a working standard for incorporating contrail mitigation into flight management protocols and demonstrates how forecasting uncertainty can be incorporated to minimize unintended consequences associated with increased CO2 emissions from re-routes.

Observations of contrails are vital for improving our understanding of the contrail formation and life cycle, informing models, and assessing mitigation strategies. Here, we developed a methodology that utilises ground-based cameras for tracking and analysing young contrails (< 35 min) formed under clear-sky conditions, comparing these observations against reanalysis meteorology and simulations from the contrail cirrus prediction model (CoCiP) with actual flight trajectories. Our observations consist of 14 h of video footage recorded over 5 different days in Central London, capturing 1582 flight waypoints from 281 flights. The simulation correctly predicted contrail formation and absence for around 75 % of these waypoints, with incorrect contrail predictions occurring at warmer temperatures than those with true-positive predictions (7.8 K vs. 12.8 K below the Schmidt–Appleman criterion threshold temperature). When evaluating contrails with observed lifetimes of at least 2 min, the simulation's correct prediction rate for contrail formation increases to over 85 %. Among all waypoints with contrail observations, 78 % of short-lived contrails (observed lifetimes < 2 min) formed under ice-subsaturated conditions, whereas 75 % of persistent contrails (observed lifetimes > 10 min) formed under ice-supersaturated conditions. On average, the simulated contrail geometric width was around 100 m smaller than the observed (visible) width over its observed lifetime, with the mean underestimation reaching up to 280 m within the first 5 min. Discrepancies between the observed and simulated contrail formation, lifetime, and width can be associated with uncertainties in reanalysis meteorology due to known model limitations and sub-grid-scale variabilities, contrail model simplifications, uncertainties in aircraft performance estimates, and observational challenges, among other possible factors. Overall, this study demonstrates the potential of ground-based cameras to create essential observational and benchmark datasets for validating and improving existing weather and contrail models.

Contrails, formed by aircraft engines, are a major component of aviation’s impact on anthropogenic climate change. Contrail avoidance is a potential option to mitigate this warming effect, however, uncertainties surrounding operational constraints and accurate formation prediction make it unclear whether it is feasible. Here we address this gap with a feasibility test through a 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 altitude adjustments based on contrail formation predictions for flights assigned to the treatment group. Using satellite-based imagery we observed 64% fewer contrails in these flights relative to the control group flights, a statistically significant reduction (p = 0.0331). Our targeted per-flight intervention allowed the airline to track their expected vs actual fuel usage, we found that there is a 2% increase in fuel per adjusted flight. This study demonstrates that per-flight detectable contrail avoidance is feasible in commercial aviation.

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.

Black carbon emitted in the aircraft exhaust plume has the potential to seed cirrus clouds, as well as modify optical properties of existing clouds. Optical differences between natural- and aviation-induced cirrus are not well characterized or understood. This study combines datasets containing advected aircraft locations with two sources of LIDAR observations. We find that ice clouds that correspond to the locations of aircraft exhaust plumes show higher depolarization ratios (mean increase of 3.36% [95% CI: 3.19% to 3.54%]). This increase in depolarization occurs without a proportional increase in backscatter, but with a large increase in extinction (mean increase from 5.58e-5 [95% CI: 3.70e-5 to 7.50e-5] to 1.78e-4 [95% CI: 1.38e-4 to 2.17e-4]). Using linear optical scattering theory, we show that these changes are well explained by the inclusion of black carbon within the ice crystals. No suitable explanation has previously been offered to explain this measured increase in depolarization.

The global annual mean contrail net radiative forcing may exceed that of aviation’s cumulative CO2 emissions by at least two-fold. As only around 2–3 % of all flights are likely responsible for 80 % of the global annual contrail climate forcing, re-routing these flights could reduce the formation of strongly warming contrails. Here, we develop a contrail forecasting model that produces global predictions of persistent contrail formation and their associated climate forcing. This model builds on the methods of the existing contrail cirrus prediction model (CoCiP) to efficiently evaluate infinitesimal contrail segments initialized at each point in a regular 4D spatiotemporal grid until their end-of-life. Outputs are reported in a concise meteorology data format that integrates with existing flight planning and air traffic management workflows. This “grid-based” CoCiP is used to conduct a global contrail simulation for 2019 to compare with previous work and analyze spatial trends related to strongly warming/cooling contrails. We explore two approaches for integrating contrail forecasts into existing flight planning and air traffic management systems: (i) using contrail forcing as an additional cost parameter within a flight trajectory optimizer; or (ii) constructing polygons of airspace volumes with strongly-warming contrails to avoid. We demonstrate a probabilistic formulation of the grid-based model by running a Monte Carlo simulation with ensemble meteorology to mask grid cells with significant uncertainties in the simulated contrail climate forcing. This study establishes a working standard for incorporating contrail mitigation within existing flight planning and management workflows and demonstrates how forecasting uncertainty can be incorporated to minimize unintended consequences associated with increased CO2 emissions of avoidance.

Observations of contrail are vital for improving understanding of contrail formation and lifecycle, informing models, and assessing contrail mitigation strategies. Ground-based cameras offer a cost-effective means to observe the formation and evolution of young contrails and can be used to assess the accuracy of existing models. Here, we develop a methodology to track and analyse contrails from ground-based cameras, comparing these observations against simulations from the contrail cirrus prediction model (CoCiP) with actual flight trajectories. The ground-based contrail observations consist of 14 h of video footage recorded on five different days over Central London, capturing a total of 1,619 flight waypoints from 283 unique flights. Our results suggest that the best agreement between the observed and simulated contrail formation occurs at around 35,000–40,000 feet and at temperatures at least 10 K below the Schmidt-Appleman Criterion threshold temperature (TSAC). Conversely, the largest discrepancies occurred when contrails are formed below 30,000 feet and at temperatures within 2.5 K of TSAC. On average, the simulated contrail width is 17.5 % smaller than the observed geometric width. This discrepancy could be caused by the underestimation of sub-grid scale wind shear and turbulent mixing in the simulation, and model representation of the contrail cross-sectional shape. Overall, these findings demonstrate the capability of ground-based cameras to inform weather and contrail model development when combined with flight telemetry.

The current best-estimate of the global annual mean radiative forcing (RF) attributable to contrail cirrus is thought to be 3 times larger than the RF from aviation's cumulative CO2 emissions. Here, we simulate the global contrail RF for 2019–2021 using reanalysis weather data and improved engine emission estimates along actual flight trajectories derived from Automatic Dependent Surveillance–Broadcast telemetry. Our 2019 global annual mean contrail net RF (62.1 mW m-2) is 44 % lower than current best estimates for 2018 (111 [33, 189] mW m-2, 95 % confidence interval). Regionally, the contrail net RF is largest over Europe (876 mW m-2) and the USA (414 mW m-2), while the RF values over East Asia (64 mW m-2) and China (62 mW m-2) are close to the global average, because fewer flights in these regions form persistent contrails resulting from lower cruise altitudes and limited ice supersaturated regions in the subtropics due to the Hadley Circulation. Globally, COVID-19 reduced the flight distance flown and contrail net RF in 2020 (-43 % and -56 %, respectively, relative to 2019) and 2021 (-31 % and -49 %, respectively) with significant regional variations. Around 14 % of all flights in 2019 formed a contrail with a net warming effect, yet only 2 % of all flights caused 80 % of the annual contrail energy forcing. The spatiotemporal patterns of the most strongly warming and cooling contrail segments can be attributed to flight scheduling, engine particle number emissions, tropopause height, and background radiation fields. Our contrail RF estimates are most sensitive to corrections applied to the global humidity fields, followed by assumptions on the engine particle number emissions, and are least sensitive to radiative heating effects on the contrail plume and contrail–contrail overlapping. Using this sensitivity analysis, we estimate that the 2019 global contrail net RF could range between 34.8 and 74.8 mW m-2.

Aircraft condensation trails, also known as contrails, contribute a substantial portion of aviation’s overall climate footprint. Contrail impacts can be reduced through smart flight planning that avoids contrail-forming regions of the atmosphere. While previous studies have explored the operational impacts of contrail avoidance in simulated environments, this paper aims to characterize the feasibility and cost of contrail avoidance precisely within a commercial flight planning system. This study leverages the commercial Flightkeys 5D (FK5D) algorithm, developed by Flightkeys GmbH, with a prototypical contrail forecast model based on the Contrail Cirrus Prediction (CoCiP) model to simulate contrail avoidance on 49,411 flights during the first two weeks of June 2023, and 35,429 flights during the first two weeks of January 2024. The utilization of a commercial flight planning system enables high-accuracy estimates of additional cost and fuel investments by operators to achieve estimated reductions in contrail-energy forcing and overall flight Global Warming Potential (GWP). The results show that navigational contrail avoidance will require minimal additional cost (0.08%) and fuel (0.11%) investments to achieve notable reductions in contrail climate forcing (-73.0%). This simulation provides evidence that contrail mitigation entails very low operational risks, even regarding fuel. This study aims to serve as an incentive for operators and air traffic controllers to initiate contrail mitigation testing as soon as possible and begin reducing aviation’s non-CO2 emissions.

Persistent contrails make up a large fraction of aviation’s contribution to global warming. We describe a scalable, automated detection and matching (ADM) system to determine from satellite data whether a flight has made a persistent contrail. The ADM system compares 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. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We assess the agreement between our labels and available prediction models based on weather forecasts. Shifting air traffic to avoid regions of contrail formation has been proposed as a possible mitigation with the potential for very low Persistent contrails are a major part of aviation’s contribution to climate change, and it may be possible to prevent them. This paper describes a method of assessing whether a flight made a contrail that can be scaled to cover all global air traffic.