Probability of Successful Avoidance of Persistent Contrails

Contrails are estimated to have one of the largest contributions to aviation’s effective radiative forcing [1]. Fortunately, potential operational and technical measures have been identified to mitigate contrail induced warming. A promising mitigation strategy is to avoid persistent contrail formation through optimised flight trajectories. However, employing such a strategy is currently hampered by the large uncertainties involved in predictions for persistent contrail formation. This uncertainty introduces a risk of unsuccessful or unnecessary detours, resulting in needless extra emissions and fuel burn. Policymakers need to know the magnitude of this risk to carry out mitigation strategies effectively. Yet, estimates of persistent contrail formation are often provided in a deterministic manner, lacking quantifications of uncertainty. Furthermore, earlier studies that do quantify uncertainty often introduce an assumed or simplified uncertainty or propagate a limited scope of uncertainty sources (e.g., [2]). Therefore, instead of simple binary outputs (persistent/non-persistent), this work constructs an approach to quantify the probability of persistent contrail formation for flight waypoints, regarding a wider scope of realistically quantified uncertainty sources than done before.

To quantify the uncertainty of meteorological parameters, we employ the method of Bayesian Model Averaging (BMA) [3]. Using BMA, modelled weather data from ECMWF Reanalysis v5 (ERA5) is calibrated with data from the IAGOS measurement campaign [4]. The calibration process overcomes the bias and under dispersiveness of ERA5 and constructs probability distributions for humidity, temperature and wind. Relevant uncertainties related to the aircraft and its performance are quantified using the variance of these parameters among different estimates of their value.

The obtained uncertainties are propagated to obtain the probability that the condition for contrail formation, the Schmidt-Appleman criterion, is satisfied for waypoints along a flight. Further in the contrail development, we assess the chance that a formed contrail survives the wake downwash phase, to quantify the likeliness of contrail persistence. Moreover, we produce a probabilistic result for potential contrail cirrus coverage (PCC), a parameter representing the fractional area of a grid box in which contrail cirrus can persist once they have been formed. The approach is applied to real flights over the North Atlantic in 2019, from the perspective of an airliner when planning flights for persistent contrail avoidance. We intend to verify the hypothesis that the presented approach reduces the risk of failed avoidance of persistent contrail formation with respect to an approach using binary estimates of contrail persistence.

[1] Lee, D.S. et al. (2021) ‘The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018’, Atmospheric Environment, 244, p. 117834.

[2] Platt, J.C. et al. (2024) ‘The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing’, Environmental Research Communications, 6(9), p. 095015.

[3] Raftery, Adrian E., et al. (2005) ‘Using Bayesian model averaging to calibrate forecast ensembles’, Monthly weather review, 133.5, p. 1155-1174.

[4] Petzold, A. et al. (2015) ‘Global-scale atmosphere monitoring by in-service aircraft – current achievements and future prospects of the European Research Infrastructure IAGOS’, Tellus B: Chemical and Physical Meteorology, 67(1), p. 28452.