Global sea-surface iodide observations, 1967–2018

Chance, Rosie; Tinel, Liselotte; Sherwen, Tomás; Baker, Alex R.; Bell, Thomas G.; Brindle, John; Campos, M. L. A. M.; Croot, Peter; Ducklow, Hugh W.; He, Peng; Hopkins, Frances E.; Hoogakker, Babette A A; Hughes, Claire; Jickells, Timothy D.; Loades, David; Macaya, Dharma Andrea Reyes; Mahajan, Anoop S.; Malin, Gill; Phillips, Daniel P.; Roberts, Ieuan J.; Roy, Rajdeep; Sarkar, Amit; Sinha, Alok; Song, Xiuxian; Winkelbauer, Helge Arne; Wuttig, Kathrin; Yang, Mingxi; Zhou, Ping; Carpenter, Lucy J.

doi:10.1038/s41597-019-0288-y

Cited by 40 publications

(81 citation statements)

References 76 publications

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“…Chance et al (2019b) provides a compilation of the available 1342 sea-surface (< 20 m depth) iodide observations. The dataset is available from the British Oceanographic Data Centre (BODC, Chance et al (2019a); DOI:https://doi.org/10/czhx).…”

Section: Input Datasetsmentioning

confidence: 99%

“…It includes 45 % more data points, and has greater spatial coverage, than the previous compilation of 925 observations (Chance et al, 2014). Observations are categorised in Chance et al (2019b) as "coastal" or "non-coastal", according to the designation of their static Longhurst biogeochemical province (Longhurst, 1998). We adopt the same categorisation here.…”

Section: Input Datasetsmentioning

confidence: 99%

“…A reason for gaps in our understanding is that the observational dataset of iodide and iodate remains relatively sparse (Chance et al, 2014(Chance et al, , 2019b. Despite this paucity in observations, iodine's role in the Earth system has driven multidisciplinary interest in the distribution of iodine compounds in seawater from a number of different research communities, including paleoceanography (Lu et al, 2016(Lu et al, , 2018Zhou et al, 2015), atmospheric composition (Saiz-Lopez et al, 2014;Sherwen et al, 2016a), and air-quality prediction (Sarwar et al, 2015;Luhar et al, 2017Luhar et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we use a recently expanded compilation of sea-surface iodide observations (Chance et al, 2019b) to build a new seasurface iodide parameterisation using a data-driven machine learning approach. We choose to use the Random Forest Regressor (RFR) algorithm (Breiman, 2001;Pedregosa et al, 2011), which is relatively simple and produces results that are also easy to understand.…”

Section: Introductionmentioning

confidence: 99%

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A machine learning based global sea-surface iodide distribution

Sherwen¹,

Chance²,

Tinel³

et al. 2019

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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 of sea-surface temperature (Chance et al., 2014; MacDonald et al., 2014). 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° x 0.125°, ∼ 12.5 km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (Chance et al., 2019b) that is 45 % larger than has been used previously (Chance et al., 2014) 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 ten 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 a new high resolution global sea-surface iodide field. Sea-surface temperature is the most important variable in all of the top ten 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. The global iodide dataset is made freely available to the community (DOI: https://doi.org/10/gfv5v3) and as new observations are made, we will update the global dataset through a "living data" model.

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Section: Input Datasetsmentioning

confidence: 99%

Section: Input Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A machine learning based global sea-surface iodide distribution

Sherwen¹,

Chance²,

Tinel³

et al. 2019

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“…Wesely (1989) gives a value of r c = 2000 s m −1 for ozone in all water types, and this is used in most atmospheric chemistry models (Hardacre et al, 2015;Luhar et al, 2017Luhar et al, , 2018. This chemical loss of ozone is the limiting factor for ozone deposition (95 % of the sum of the resistances is the value of r c ; Chang et al, 2004) and so yields an almost constant (0.05 cm s −1 ) overall deposition velocity, with only small variation due to meteorological variation in r a and r b . However, observations of ozone deposition show significant variability.…”

Section: Introductionmentioning

confidence: 99%

Influences of oceanic ozone deposition on tropospheric photochemistry

et al. 2020

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Abstract. The deposition of ozone to seawater is an important ozone sink. Despite constituting as much as a third of the total ozone deposition, it receives significantly less attention than the deposition to terrestrial ecosystems. Models have typically calculated the deposition rate based on a resistance-in-series model with a uniform waterside resistance. This leads to models having an essentially uniform deposition velocity of approximately 0.05 cm s−1 to seawater, which is significantly higher than the limited observational dataset. Following from Luhar et al. (2018) we include a representation of the oceanic deposition of ozone in the GEOS-Chem model of atmospheric chemistry and transport based on its reaction with sea-surface iodide. The updated scheme halves the calculated annual area-weighted mean deposition velocity to water from 0.0464 cm s−1 (25th and 75th percentiles of 0.0461 cm s−1 and 0.0471 cm s−1 respectively) to 0.0231 cm s−1 (25th and 75th percentiles of 0.0121 cm s−1 and 0.0303 cm s−1 respectively). The calculated ozone deposition velocity varies from 0.009 cm s−1 in polar waters to 0.040 cm s−1 at the tropics. This improves comparisons to observations. The variability is driven mainly by the temperature-dependent rate constant for the reaction between iodide and ozone, the temperature dependence of the solubility, and variations in the ocean iodide concentration. The calculated annual deposition flux of ozone to the ocean is reduced from 222 to 122 Tg yr−1, and overall deposition of ozone to all surface types reduces from 862 to 758 Tg yr−1. Tropospheric ozone burdens and global mean OH increase from 324 to 328 Tg, and from 1.17×106 to 1.18×106 molec.cm-3, respectively. A total of 34 % of surface grid boxes experience a 10 % or greater increase in ozone concentration. Comparisons between observations of surface ozone and the model are improved with the new parameterization notably around the Southern Ocean. Process-level representation of oceanic deposition of ozone thus appears essential for representing the concentration of surface ozone over the planet.

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A Global Model for Iodine Speciation in the Upper Ocean

Hepach

Wadley

Stevens

et al. 2020

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An ocean iodine cycling model is presented, which predicts upper ocean iodine speciation. The model comprises a three-layer advective and diffusive ocean circulation model of the upper ocean and an iodine cycling model embedded within this circulation. The two primary reservoirs of iodine are represented, iodide and iodate. Iodate is reduced to iodide in the mixed layer in association with primary production, linked by an iodine to carbon (I:C) ratio. A satisfactory model fit with observations cannot be obtained with a globally constant I:C ratio, and the best fit is obtained when the I:C ratio is dependent on sea surface temperature, increasing at low temperatures. Comparisons with observed iodide distributions show that the best model fit is obtained when oxidation of iodide back to iodate is associated with mixed layer nitrification. Sensitivity tests, where model parameters and processes are perturbed, reveal that primary productivity, mixed layer depth, oxidation, advection, surface freshwater flux, and the I:C ratio all have a role in determining surface iodide concentrations, and the timescale of iodide in the mixed layer is sufficiently long for nonlocal processes to be important. Comparisons of the modeled iodide surface field with parameterizations by other authors show good agreement in regions where observations exist but significant differences in regions without observations. This raises the question of whether the existing parameterizations are capturing the full range of processes involved in determining surface iodide and shows the urgent need for observations in regions where there are currently none.Plain Language Summary Iodine in the ocean is important because small emissions of iodine species to the atmosphere have a significant impact on ozone and air quality. Iodine is converted between two chemical forms by phytoplankton and bacteria, but only one chemical form (iodide) results in atmospheric emissions. We have developed a model that predicts the amount of each type of iodine in the global oceans. We find that this distribution has a more complex structure than that suggested by the limited number of observations, with the ocean circulation playing an important role. The model improves our understanding of both ocean iodine cycling and the resultant impacts on ozone distribution and air quality and also shows that biological and chemical changes to the oceans due to increased atmospheric greenhouse gas concentrations are likely to result in significant changes in ocean iodine, with implications for atmospheric air quality and global elemental cycles.

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Global sea-surface iodide observations, 1967–2018

Cited by 40 publications

References 76 publications

A machine learning based global sea-surface iodide distribution

A machine learning based global sea-surface iodide distribution

Influences of oceanic ozone deposition on tropospheric photochemistry

A Global Model for Iodine Speciation in the Upper Ocean

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