Preprints
https://doi.org/10.5194/egusphere-2024-1361
https://doi.org/10.5194/egusphere-2024-1361
20 Jun 2024
 | 20 Jun 2024
Status: this preprint is open for discussion.

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

Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro

Abstract. The global annual mean contrail net radiative forcing may exceed that of aviation’s cumulative CO2 emissions by at least two-fold. As only around 2–3 % of all flights are likely responsible for 80 % of the global annual contrail climate forcing, re-routing these flights could reduce the formation of strongly warming contrails. Here, we develop a contrail forecasting model that produces global predictions of persistent contrail formation and their associated climate forcing. This model builds on the methods of the existing contrail cirrus prediction model (CoCiP) to efficiently evaluate infinitesimal contrail segments initialized at each point in a regular 4D spatiotemporal grid until their end-of-life. Outputs are reported in a concise meteorology data format that integrates with existing flight planning and air traffic management workflows. This “grid-based” CoCiP is used to conduct a global contrail simulation for 2019 to compare with previous work and analyze spatial trends related to strongly warming/cooling contrails. We explore two approaches for integrating contrail forecasts into existing flight planning and air traffic management systems: (i) using contrail forcing as an additional cost parameter within a flight trajectory optimizer; or (ii) constructing polygons of airspace volumes with strongly-warming contrails to avoid. We demonstrate a probabilistic formulation of the grid-based model by running a Monte Carlo simulation with ensemble meteorology to mask grid cells with significant uncertainties in the simulated contrail climate forcing. This study establishes a working standard for incorporating contrail mitigation within existing flight planning and management workflows and demonstrates how forecasting uncertainty can be incorporated to minimize unintended consequences associated with increased CO2 emissions of avoidance.

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Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro

Status: open (until 15 Aug 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on egusphere-2024-1361', Volker Grewe, 12 Jul 2024 reply
  • RC1: 'Comment on egusphere-2024-1361', Anonymous Referee #1, 18 Jul 2024 reply
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro

Model code and software

pycontrails: Python library for modeling aviation climate impacts Marc L. Shapiro, Zeb Engberg, Roger Teoh, Marc E. J. Stettler, and Thomas Dean https://zenodo.org/records/11263606

Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro

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Short summary
While some atmospheric regions produce strongly warming contrails, other regions may produce neutral or cooling contrails. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail mitigation.