Abstract
Context. The largest satellite of Neptune, Triton, is a likely Kuiper Belt object captured by the planet. It has a tenuous nitrogen atmosphere, similar to that of Pluto, and it may be an ocean world. The Neptunian system has only been visited once: by Voyager 2 in 1989. Over the past few years, the demand for a new mission to the ice giants and their systems has risen. Thus, a theoretical basis upon which to prepare for such a mission is needed. Aims. We aim to develop a photochemical model of Triton’s atmosphere with an up-to-date chemical scheme, as previous photochemical models date back to the post-flyby years. This purpose is to achieve a better understanding of the mechanisms governing Triton’s atmospheric chemistry and to highlight the critical parameters that have a significant impact on the atmospheric composition. We also study the model uncertainties to find what chemical studies are necessary to improve the modeling of Triton’s atmosphere. Methods. We used a model of Titan’s atmosphere and tailored it to Triton’s conditions. We first used Titan’s chemical scheme before updating it to better model Triton’s atmospheric conditions. Once the nominal results were obtained, we studied the model uncertainties with a Monte Carlo procedure, considering the reaction rates as random variables. Finally, we performed global sensitivity analyses to identify the reactions responsible for model uncertainties. Results. With the nominal results, we determined the composition of Triton’s atmosphere and studied the production and loss processes for the main atmospheric species. We highlighted key chemical reactions that are most important for the overall chemistry. We also identified some key parameters that have a significant impact on the results. The uncertainties are high for most of the main atmospheric species since the atmospheric temperature is very low. We identified key uncertainty reactions that have the greatest impact on the result uncertainties. These reactions must be studied as a priority in order to improve the significance of our results by finding ways of lowering these uncertainties.
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