Efficacy of Scalable Airline-led Contrail Avoidance

Abstract

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 that scales to an airline's entire network. We validated the efficacy of this intervention using satellite imagery and an automated flight-contrail attribution algorithm. Using this system, 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 = 0.011). In the 112 flights that 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.

Figures (8)

• Figure 1: Eastbound flight routes considered for this trial. The displayed routes are great circle paths between each city pair and do not necessarily reflect the actual paths that were flown. A full list of city pairs included in the trial and their applicable time periods is provided in \ref{['tab:route_assignments']}.
• Figure 2: Histogram of fuel/flight distance for each flight in the control and treatment groups. Two peaks correspond to the two different aircraft types in the trial: Boeing 777 and Boeing 787. In panel \ref{['fig:fuel_unadjusted']}, the groups have different flight counts per peak; this imbalance is mostly removed in panel \ref{['fig:fuel_adjusted']} after adjusting for aircraft type. Values in brackets represent the 95% confidence interval.
• Figure 3: Bootstrap mean distributions of expected contrail distance for two potential choices of counterfactual plan (minimum cost plan and most recent plan before takeoff) compared against the flown path for flights in the L1 group where the contrail avoidance plan was not released ("L1 not L2" flights). The minimum cost counterfactual plan matches better to the flown values.
• Figure 4: Comparison of the ML formation probability forecast vs. observed match distance per flight distance for the control and 3 treatment groups in the trial. The value of the bar is the mean match distance per flight distance for the group, with error bars showing the standard deviation. For each group, the forecast is well calibrated to the observations.
• Figure 5: Example of a transformation from geodetic altitude to radiative path length. This simplified example considers a contrail that is over the Equator at 40$^{\circ}$ longitude away from the nadir of the geostationary satellite. Its geodetic altitude is 10 km, which is translated into a radiative path length of 14.47 km using the triangle formed by the satellite, contrail, and center of the Earth and the purple triangle formed by the contrail, the point directly underneath it on the surface, and the point on the surface that the satellite is viewing for the pixel in which the contrail appears. Side lengths and angles other than the 40 degrees longitude, Earth radius, and geostationary orbit distance are derived via trigonometric identities.