Supporting dataset for "Evaluating the ozone and climate effects of supersonic aircraft fleet design choices using machine learning"
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This dataset is a supporting dataset for "Evaluating the ozone and climate effects of supersonic aircraft fleet design choices using machine learning", accepted for publication in Environmental Data Science. This dataset contains all data necessary to reproduce the results of the publication. In this work the GEOS-Chem atmospheric chemistry transport model is used to evaluate the effects of 60 supersonic fleet adoption scenarios on a 2050 atmosphere based on the SSP 3.7 scenario. These scenarios are selected from a wider range of scenarios that is generated using Latin Hypercube Sampling, covering a design space that spans conventional, low-sulfur, and hydrogen fuels alongside a range of cruise altitudes and fuel consumption. The full range of generated emission inventories is provided in emission_inventories.zip with supporting information, such as the specifications of the different inventories, being provided in emission_inventories.xslx . The inventories themselves are provided as netcdf files with their unique identifier (e.g. inv_1.nc4) encoded to the input format expected by the GEOS-Chem model. Data describing the change in atmospheric states is provided in repositories …
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