Preprints
https://doi.org/10.5194/essd-2023-352
https://doi.org/10.5194/essd-2023-352
06 Dec 2023
 | 06 Dec 2023
Status: a revised version of this preprint is currently under review for the journal ESSD.

IPB-MSA&SO4: a daily 0.25° resolution dataset of In-situ Produced Biogenic Methanesulfonic Acid and Sulfate over the North Atlantic during 1998–2022 based on machine learning

Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi

Abstract. Accurate long-term marine-derived biogenic sulfur aerosol concentrations at high spatial and temporal resolutions are critical for a wide range of studies including climatology, trend analysis, model evaluation, accurate investigation of their contribution to aerosol burden, or to elucidate their radiative impacts and to provide boundary conditions for regional models. By applying machine learning algorithms, we constructed the first, publicly available, daily gridded dataset of in-situ produced biogenic methanesulfonic acid (MSA) and sulfate (SO4) concentrations covering the North Atlantic Ocean. The dataset is of high spatial resolution of 0.25° × 0.25°, spanning 25 years (1998–2022), far exceeding what observations alone could achieve both space- and time-wise. The machine learning models were generated by combining in-situ observations of sulfur aerosol data at Mace Head research station, west coast of Ireland, and from NAAMES cruises in the NW Atlantic, combined with the constructed sea-to-air dimethylsulfide flux (FDMS) and ECMWF-ERA5 reanalysis datasets. To determine the optimal method for regression, we employed four machine learning model types: support vector machines, ensemble, Gaussian process, and artificial neural networks. A comparison of the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) revealed that the Gaussian process regression (GPR) was the most effective algorithm, outperforming the other models in simulating the biogenic MSA and SO4 concentrations. For predicting daily MSA (SO4), GPR displayed the highest R2 value of 0.86 (0.72) and the lowest MAE of 0.014 (0.10) µg m–3. The GPR partial dependence analysis suggests that the relationships between predictors and MSA and SO4 concentrations are complex rather than linear. Using the GPR algorithm, we produced a high-resolution daily dataset of In-situ Produced Biogenic MSA and SO4 sea-level concentrations over the North Atlantic, which we named IPB-MSA&SO4. The obtained IPB-MSA&SO4 data allowed us to analyze the spatiotemporal patterns of MSA, SO4, and the ratio between them (MSA:SO4). A comparison with the existing CAMS-EAC4 reanalysis suggests that our high-resolution dataset reproduces with high accuracy the spatial and temporal patterns of the biogenic sulfur aerosol concentration and has high consistency with independent measurements in the Atlantic Ocean. The IPB-MSA&SO4 is publicly available at https://doi.org/10.17632/j8bzd5dvpx.1 (Mansour et al., 2023b).

Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-352', Anonymous Referee #1, 07 Jan 2024
  • RC2: 'Comment on essd-2023-352', Anonymous Referee #2, 01 Feb 2024
Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi

Data sets

IPB-MSA&SO4: In-situ Produced Biogenic Methanesulfonic Acid and Sulfate over the North Atlantic Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn Russell, Marco Paglione, Colin O'Dowd, and Matteo Rinaldi https://doi.org/10.17632/j8bzd5dvpx.1

Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi

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Short summary
We propose a machine learning predictive algorithm to model unprecedented high-resolution and long-term datasets of in-situ produced biogenic methanesulfonic acid and sulfate concentrations in the North Atlantic Ocean. The improved parameterizations of biogenic sulfur aerosols at regional scales are essential for determining their radiative forcing, which could help further understand oceanic sulfur-aerosol-cloud interactions and aim at reducing uncertainties in climate models.
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