Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0

Abstract

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.