Supporting dataset and code for the PhD dissertation "Characterizing the transport patterns and climate effects of aviation emissions using a novel Lagrangian tagging method"

This repository supports Chapter 6 of the PhD dissertation titled <strong><em>"Characterizing the transport patterns and climate effects of aviation emissions using a novel Lagrangian tagging method"</em></strong><em>.</em> It contains six simulations conducted with the newly developed <strong>AIRTRAC v2.0</strong> submodel, integrated within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model framework.<br>Two of these simulations comprise the <strong>Lagrangian dataset</strong> used to track the production and transport of sulfate aerosols (<strong>SO₄</strong> in the soluble Aitken and accumulation modes) resulting from aviation emissions of <strong>SO₂ and H₂SO₄</strong>. These species are emitted at points following the SO₂ mass flux distributions from the 2015 CMIP6 aviation emissions inventory and injected at a pressure altitude of approximately 240 hPa. The magnitude of total emitted SO₂ corresponds to the global total from one day of aviation activity.<br>The remaining four simulations are part of a <strong>perturbation experiment</strong> designed to validate the AIRTRAC v2.0 submodel. For each period, a pair of simulations was conducted: one with aviation SO₂ and H₂SO₄ emissions and one without. The simulation without emissions serves as a reference case.<br>All simulations span two 90-day periods: <strong>January 1–March 31, 2015</strong> and <strong>July 1–September 30, 2015</strong>.<br>The repository also includes the Python code for the<strong> EP_selector tool</strong>, which was developed to automatically define a set of pulse emission points that closely align with sustained emissions from any given emissions inventory used in simulations. <br>For further details, please consult the README file and the dissertation.