Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
<p>The global annual mean contrail climate forcing may exceed that of aviation's cumulative CO<span class="inline-formula"><sub>2</sub></span> emissions. As only 2 %–3 % of all flights are likely responsible for 80 % of the global annual contrail energy f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-01-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/253/2025/gmd-18-253-2025.pdf |
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| Summary: | <p>The global annual mean contrail climate forcing may exceed that of aviation's cumulative CO<span class="inline-formula"><sub>2</sub></span> emissions. As only 2 %–3 % of all flights are likely responsible for 80 % of the global annual contrail energy forcing (EF<span class="inline-formula"><sub>contrail</sub></span>), 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 EF<span class="inline-formula"><sub>contrail</sub></span> 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 <span class="inline-formula"><i>N</i></span> aircraft-engine groups, with groupings based on similarities in aircraft mass and engine particle number emissions: <span class="inline-formula"><i>N</i>=7</span> results in a 3 % mean error between the trajectory- and grid-based CoCiP, while <span class="inline-formula"><i>N</i>=3</span> 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 EF<span class="inline-formula"><sub>contrail</sub></span> 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 EF<span class="inline-formula"><sub>contrail</sub></span>. 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 CO<span class="inline-formula"><sub>2</sub></span> emissions from re-routes.</p> |
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| ISSN: | 1991-959X 1991-9603 |