This paper presents an updated estimation of the bottom-up global surface seawater dimethyl sulfide (DMS) climatology. This update, called DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, L11 (Lana et al., 2011), that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, creating a database of 873 539 observations leading to an ∼ 18-fold increase in raw data as compared to the last estimation. The second is significant improvements in data handling, processing, and filtering, to avoid biases due to different observation frequencies which result from different measurement techniques. Thirdly, we incorporate the dynamic seasonal changes observed in the geographic boundaries of the ocean biogeochemical provinces. The fourth change involves the refinement of the interpolation algorithm used to fill in the missing data. Lastly, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) helps to reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration to be ∼ 2.26 nM (2.39 nM without a sea-ice mask), i.e., about 4 % lower than the previous bottom-up L11 climatology. However, significant regional differences of more than 100 % as compared to L11 are observed. The global sea-to-air flux of DMS is estimated at ∼ 27.1 TgS yr⁻¹, which is about 4 % lower than L11, although, like the DMS distribution, large regional differences were observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions that were under-sampled in the past also show large differences between revisions of the climatology. Finally, DMS-Rev3 reduces the previously observed patchiness in high productivity regions. The new climatology, along with the algorithm, can be found in the online repository: https://doi.org/10.17632/hyn62spny2.1 (Mahajan, 2021).

Observations of halogen oxides, ozone, meteorological parameters, and physical and biogeochemical water column measurements were made in the Indian Ocean and its marine boundary layer as a part of the Second International Indian Ocean Expedition (IIOE‐2). The expedition took place on board the oceanographic research vessel Sagar Nidhi during 4–22 December 2015 from Goa, India, to Port Louis, Mauritius. Observations of mixed layer depth, averaged temperature, salinity, and nitrate concentrations were used to calculate predicted iodide concentrations in the seawater. The inorganic iodine ocean‐atmosphere flux (hypoiodous acid [HOI] and molecular iodine [I₂]) was computed using the predicted iodide concentrations, measured atmospheric ozone, and wind speed. Iodine oxide (IO) mixing ratios peaked at 0.47 ± 0.29 pptv (parts per trillion by volume) in the remote open ocean environment. The estimated iodide concentrations and HOI and I₂ fluxes peaked at 200/500 nM, 410/680 nmol·m⁻²·day⁻¹, and 20/80 nmol·m⁻²·day⁻¹, respectively, depending on the parameterization used. The calculated fluxes for HOI and I₂ were higher closer to the Indian subcontinent; however, atmospheric IO was only observed above the detection limit in the remote open ocean environment. We use NO₂ observations to show that titration of IO by NO₂ is the main reason for this result. These observations show that inorganic iodine fluxes and atmospheric IO show similar trends in the Indian Ocean marine boundary layer, but the impact of inorganic iodine emissions on iodine chemistry is buffered in elevated NO_x environments, even though the estimated oceanic iodine fluxes are higher.

Dimethyl sulfide (DMS) contributes to cloud condensation nuclei (CCN) formation in the marine environment. DMS is ventilated from the ocean to the atmosphere, and, in most models, this flux is calculated using seawater DMS concentrations and a sea–air flux parameterization. Here, climatological seawater DMS concentrations from interpolation and parameterization techniques are passed through seven flux parameterizations to estimate the DMS flux. The seasonal means of calculated fluxes are compared to identify differences in absolute values and spatial distributions, which show large differences depending on the flux parameterization used. In situ flux observations were used to validate the estimated fluxes from all seven parameterizations. Even though we see a correlation between the estimated and observation values, all methods underestimate the fluxes in the higher range (>20 µmol m⁻² d⁻¹) and overestimate the fluxes in the lower range (<20 µmol m⁻² d⁻¹). The estimated uncertainty in DMS fluxes is driven by the uncertainty in seawater DMS concentrations in some regions but by the choice of flux parameterization in others. We show that the resultant flux is, hence, highly sensitive to both and suggest that there needs to be an improvement in the estimation methods of global seawater DMS concentration and sea–air fluxes for accurately modeling the effect of DMS on the atmosphere.

Dimethyl sulfide (DMS) is a naturally emitted trace gas that can affect the Earth's radiative budget by changing cloud albedo. Most atmospheric models that represent aerosol processes depend on regional or global distributions of seawater DMS concentrations and sea–air flux parameterizations to estimate its emissions. In this study, we analyse the differences between three estimations of seawater DMS, one of which is an observation-based interpolation method following Hulswar et al. (2022) (hereafter referred to as H22) and two of which are proxy-based parameterization methods following Galí et al. (2018) (hereafter referred to as G18) and Wang et al. (2020a) (hereafter referred to as W20). The interpolation-based method depends on the distribution of observations and the methods used to fill data between observations, while the parameterization-based methods rely on establishing a relationship between DMS and environmental parameters such as chlorophyll a, mixed-layer depth, nutrients, sea surface temperature, etc., which can then be used to predict DMS concentrations. On average, the interpolation-based methods show higher DMS values compared to the parameterization-based methods. Even though the interpolation method shows higher values than the parameterization-based methods, it fails to capture mesoscale variability. The regression-based parameterization method (G18) shows the lowest values compared to other estimations, especially in the Southern Ocean, which is the high-DMS region in austral summer. The parameterization-based methods suggest positive long-term trends in seawater DMS (3.82±0.79 % per decade for G18 and 2.13±0.32 % per decade for W20). Since large differences, often more than 100 %, are observed between the different estimations of seawater DMS, the derived sea–air fluxes and, hence, the impact of DMS on the radiative budget are sensitive to the estimate used.