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.

Aircraft induced cirrus clouds are estimated to account for 57% of aviation’s current-day climate impact, but this value is highly uncertain with the fidelity and biases in meteorological data being significant contributing factors. Our work aims to address this uncertainty and to provide empirical evaluation of multiple contrail modeling approaches. First, we use a dataset of contrail cross sections observed from the CALIOP orbital LIDAR that were attributed to specific flights to calibrate parameterizations for the initial widths and depths of contrails. We then examine the effect of systematic biases in wind, humidity and temperature, analyzing which modifications to the data provide the best agreement between a simulated contrail (using the APCEMM contrail model) and observations. Finally, we evaluate the degree of accuracy of the calibrated APCEMM model across a larger dataset and compare the results to those from the widely-used CoCiP model.

Contrail avoidance promises to be a near-term solution for mitigating part of aviation’s climate impact [1]. Atmospheric regions that allow for contrails to form and persist have been shown to be horizontally wide but vertically thin [2], motivating the idea that small vertical deviations are sufficient for avoiding the most impactful contrails [1]. Nonetheless, the concept of contrail avoidance relies on skillful forecasts of the regions where contrails will form and persist. Recent comparisons of NWP data and humidity measurements and contrail observations show that the prediction of contrail persistence is problematic [3,4]. Since simulation studies that have previously investigated contrail avoidance have assumed the prediction of these regions to be correct [1], real-world contrail avoidance strategies may be less effective than thought previously [4]. There is thus a need to both understand and improve the performance of prediction methods that could be utilized for contrail avoidance.

Previous work has [5] has resulted in a dataset of over 3000 contrail cross-sections found in CALIOP LIDAR data, obtained by collocating contrails detected using GOES-16 imagery [6]. We have now developed an algorithm that finds the location where an aircraft’s exhaust plume intersects CALIOP data. This allows us to estimate which contrail cross-section corresponds to which flight, as well as estimate which flights did not form a persistent contrail. The resulting dataset is used for the evaluation of existing forecast methods that rely on numerical weather prediction data, as well as a nowcasting algorithm that relies on contrail detections and altitude estimates from GOES-16 data [5,6].

This new forecast evaluation dataset and method can be used to better understand the limitations of existing approaches and enable the development of improved techniques for persistent contrail prediction.

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.

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.