The DLR CO2-equivalent estimator FlightClim v1.0
An easy-to-use estimation of per flight CO2 and non-CO2 climate effects
Hannes Bruder (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Robin N. Thor (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Malte Niklaß (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Katrin Dahlmann (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Roland Eichinger (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Florian Linke (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Volker Grewe (Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Aerospace Engineering)
Sigrun Matthes (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Simon Unterstrasser (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
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Abstract
As aviation's contribution to anthropogenic climate change is increasing, the sector aims at reducing its climate effect in accordance with international agreements. The strong and variable non-CO2 effects are complex, making reliable climate effect quantification a necessary first step. To support this, we develop the easy-To-use first-order climate effect estimator for single flights FlightClim v1.0. The tool estimates the flight-specific climate effect with a simplified calculation model, without requiring detailed information on exact routing, amount of fuel burn, or weather conditions. For this purpose, we first analyze a global flight dataset containing detailed trajectories, associated flight emissions, and climate responses. Similar flights are grouped into clusters, and regression formulas are derived to estimate the Average Temperature Response over 100 years (ATR100) for CO2 and non-CO2 effects. To prevent abrupt changes at cluster boundaries, we apply linear smoothing as postprocessing. Second, we compare a Multiple and a Symbolic Regression approach, where choice of method depends on the specific application as they differ in effort and complexity. The two approaches offer similar estimation quality, which shows that the errors are based on the database, the regression parameters as well as the regression error metric and the physical processes rather than on too easy regression models. Both methods are designed for climate footprint assessments due to their simplicity though not suitable for policy measures. Emission trading or monitoring and reporting systems instead require detailed weather and route data to incentivize operational non-CO2 mitigation. Compared to previous studies, our approach relies on a globally representative and considerably larger dataset covering more aircraft types, including most commercial airliners. In addition it improves precision through smoothed clustering and a dedicated parameterization of aircraft size influence on the contrail effects. The resulting climate effect functions are embedded into the Excel-based tool FlightClim v1.0, which implements the formulas of the Multiple Regression approach due to slight qualitative advantages. Requiring only aircraft size and origin-destination airports as input, FlightClim estimates climate effect for CO2, H2O, NOx emissions and contrail-induced cloudiness. It includes per seat allocation and supports different climate metrics.
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