IPB-MSA&SO 4 : 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

• Published in: Earth System Science Data
• Main Authors: K. Mansour, S. Decesari, D. Ceburnis, J. Ovadnevaite, L. M. Russell, M. Paglione, L. Poulain, S. Huang, C. O'Dowd, M. Rinaldi
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; North Atlantic
• Online Access: https://doi.org/10.5194/essd-16-2717-2024; https://doaj.org/article/b564d99c43d947e989ad34c01af45e4c

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, and model evaluation; this information is also imperative for the accurate investigation of the contribution of marine-derived biogenic sulfur aerosol concentrations to the aerosol burden, for the elucidation of 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 non-sea-salt sulfate (nss-SO 4 = <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="681c12b7ebc0d0a218afd389f448be6c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-16-2717-2024-ie00001.svg" width="8pt" height="14pt" src="essd-16-2717-2024-ie00001.png"/></svg:svg> ) concentrations covering the North Atlantic. The dataset is of high spatial resolution (0.25° × 0.25°) and spans 25 years (1998–2022), far exceeding what observations alone could achieve both spatially and temporally. The machine learning models were generated by combining in situ observations of sulfur aerosol data from Mace Head Atmospheric Research Station, located on the west coast of Ireland, and from the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) cruises in the northwestern Atlantic with the constructed sea-to-air dimethylsulfide flux ( F DMS ) and ECMWF ERA5 reanalysis datasets. To determine the optimal method for regression, we employed five machine learning model types: support vector machines, decision tree, regression ensemble, Gaussian process regression, and artificial neural networks. A comparison of the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination ( R 2 ) revealed that Gaussian process regression (GPR) was the most ...