A machine-learning-based global sea-surface iodide distribution

Abstract

Abstract. Iodide in the sea-surface plays an important role in the Earth system. It modulates the oxidising capacity of the troposphere and provides iodine to terrestrial ecosystems. However, our understanding of its distribution is limited due to a paucity of observations. Previous efforts to generate global distributions have generally fitted sea-surface iodide observations to relatively simple functions using proxies for iodide such as nitrate and sea-surface temperature. This approach fails to account for coastal influences and variation in the bio-geochemical environment. Here we use a machine learning regression approach (random forest regression) to generate a high-resolution (0.125∘×0.125∘, ∼12.5km×12.5km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (1967–2018) that has a 45 % larger sample size than has been used previously as the dependent variable and co-located ancillary parameters (temperature, nitrate, phosphate, salinity, shortwave radiation, topographic depth, mixed layer depth, and chlorophyll a) from global climatologies as the independent variables. We investigate the regression models generated using different combinations of ancillary parameters and select the 10 best-performing models to be included in an ensemble prediction. We then use this ensemble of models, combined with global fields of the ancillary parameters, to predict new high-resolution monthly global sea-surface iodide fields representing the present day. Sea-surface temperature is the most important variable in all 10 models. We estimate a global average sea-surface iodide concentration of 106 nM (with an uncertainty of ∼20 %), which is within the range of previous estimates (60–130 nM). Similar to previous work, higher concentrations are predicted for the tropics than for the extra-tropics. Unlike the previous parameterisations, higher concentrations are also predicted for shallow areas such as coastal regions and the South China Sea. Compared to previous work, the new parameterisation better captures observed variability. The iodide concentrations calculated here are significantly higher (40 % on a global basis) than the commonly used MacDonald et al. (2014) parameterisation, with implications for our understanding of iodine in the atmosphere. We envisage these fields could be used to represent present-day sea-surface iodide concentrations, in applications such as climate and air-quality modelling. The global iodide dataset is made freely available to the community (https://doi.org/10/gfv5v3, Sherwen et al., 2019), and as new observations are made, we will update the global dataset through a “living data” model.

Influences of oceanic ozone deposition on tropospheric photochemistry

“…This considers the physical and chemical controls of ozone loss in the sea surface. In contrast to Luhar et al (2018), our work has used a variable surface micro-layer depth and the higher ocean iodide concentrations from Sherwen et al (2019). The new scheme results in a halving of the global mean ozone deposition https://doi.org/10.5194/acp-2019-1043 Preprint.…”

“…the temperature dependent k (M −1 s −1 ) for the aqueous phase reactions between ozone and iodide fromMagi et al (1997) ocean iodide concentration distribution [I − ] (M) is taken from the most recent global climatology(Sherwen et al, 2019).…”

“…Sherwen et al (2019) ocean iodide distributions, whereas they useMacDonald et al (2014) Sherwen et al (2019). is based on a recent collation of sea surface iodide observations(Chance et al, 2019) which are interpolated using a machine learning approach.…”

“…is based on a recent collation of sea surface iodide observations(Chance et al, 2019) which are interpolated using a machine learning approach. MacDonald et al(2014)is based on a more restrictive observational dataset and uses a simple temperature based parameterization Sherwen et al (2019). calculates a global average sea-surface [I − ] of 105.8 ± 45.6 nM which is a significant increase from the global mean of 58.9 ± 34.9 nM found byMacDonald et al (2014).…”

Estimation of Reactive Inorganic Iodine Fluxes in the Indian and Southern Ocean Marine Boundary Layer

“…Existing global parameterisations discussed in this study follow three different methods for SSI estimation. The first is a linear regression approach against biogeochemical and oceanographic variables (Chance et al, 2014), the second uses an exponential relationship with sea surface temperature as a proxy for SSI (MacDonald et al, 2014), and the third is a recent machine-learning-based model (Sherwen et al, 2019a) that predicts monthly global SSI fields for the present-day. Where such approaches are based on large scale relationships, they may not properly capture smaller scale, regional differences in SSI (as observed for Chance et al, 2014;MacDonald et al, 2014) or underestimate surface iodide concentration (in case of Sherwen et al, 2019).Furthermore, there are large differences in predicted iodide concentrations between these parametrisations in some regions (refer Sect.…”