Contrail cirrus clouds persisting in ice-supersaturated air 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. Ascertaining which flight formed each observed contrail can be used to assess and improve contrail forecast models, as well as study the effectiveness of performing contrail avoidance. 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 lack an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail observations with predetermined contrail-to-flight attributions which can be used to evaluate – or "benchmark" – and improve such attribution algorithms. The resulting performance metrics can be used to understand the implications of using this observational data in downstream tasks such as forecast model evaluation and analysis of contrail avoidance trials. 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 about 30 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.

Contrails are potentially the largest contributor to aviation-attributable climate change, but estimates of their coverage are highly uncertain. No study has provided observation-based continental-scale estimates of the diurnal, seasonal, and regional variability in contrail coverage. We present contrail coverage estimates for the years 2018, 2019 and 2020 for the contiguous United States, derived by developing and applying a deep learning algorithm to over 100 000 satellite images. We estimate that contrails covered an area the size of Massachusetts and Connecticut combined in the years 2018 and 2019. Comparing 2019 and 2020, we quantify a 35.8% reduction in distance flown above 8 km altitude and an associated reduction in contrail coverage of 22.3%. We also find that the diurnal pattern in contrail coverage aligns with that of flight traffic, but that the amount of contrail coverage per distance flown decreases in the afternoon.

Model-based estimates of aviation’s climate impacts have found that contrails contribute 36%–81% of aviation’s instantaneous radiative forcing. These estimates depend on the accuracy of meteorological data provided by reanalyses like ECMWF Reanalysis 5th Generation (ERA5) and Modern Era Retrospective analysis for Research and Applications V2 (MERRA-2). Using data from 793 044 radiosondes, we find persistent contrails forming at cruise altitudes in 30° N–60° S are overestimated by factors of 2.0 and 3.5 for ERA5 and MERRA-2, respectively. Seasonal and inter-annual trends are well-reproduced by both models (R2 = 0.79 and 0.74). We also find a contrail lifetime metric is overestimated by 17% in ERA5 and 45% in MERRA-2. Finally, the reanalyses incorrectly identify individual regions that could form persistent contrails 87% and 52% of the time, respectively. These results suggest that contrail models currently overestimate the number and lifetime of persistent contrails. Additional observations are needed for future models in order to provide locally accurate estimates of contrails or to support mitigation strategies.