By the same authors

Re-approaching global iodine emissions: A novel parameterisation for sea-surface iodide concentrations using a machine learning approach

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DatePublished - 14 Dec 2017
Original languageEnglish

Abstract

Halogens (Cl, Br, I) in the troposphere have been shown to play a profound role in determining the concentrations of ozone and OH. Iodine, which is essentially oceanic in source, exerts its largest impacts on composition in both the marine boundary layer, and in the upper troposphere. This chemistry has only recently been implemented into global models and significant uncertainties remain, particularly regarding the magnitude of iodine emissions. Iodine emissions are dominated by the inorganic oxidation of iodide in the sea surface by ozone, which leads to release of gaseous inorganic iodine (HOI, I2). Critical for calculation of these fluxes is the sea-surface concentration of iodide, which is poorly constrained by observations.
Previous parameterizations for sea-surface iodide concentration have focused on simple regressive relationships with sea surface temperature and another single oceanographic variables. This leads to differences in iodine fluxes of approximately a factor of two, and leads to substantial differences in the modelled impact of iodine on atmospheric composition.

Here we use an expanded dataset of oceanic iodide observations, which incorporates new data that has been targeted at areas with poor coverage previously. A novel approach of multivariate machine learning techniques is applied to this expanded dataset to generate a model that yields improved estimates of the global sea surface iodide distribution. We then use a global chemical transport model (GEOS-Chem) to explore the impact of this new parameterisation on the atmospheric budget of iodine and its impact on tropospheric composition.

    Research areas

  • halogens, Machine Learning, iodide, iodine, emissions, Inventory

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