Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 6
Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2717-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2717-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 6
Data description paper
 | 
12 Jun 2024
Data description paper |  | 12 Jun 2024

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

Viewed

Total article views: 2,885 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,203 593 89 2,885 227 104 127
  • HTML: 2,203
  • PDF: 593
  • XML: 89
  • Total: 2,885
  • Supplement: 227
  • BibTeX: 104
  • EndNote: 127
Views and downloads (calculated since 06 Dec 2023)
Cumulative views and downloads (calculated since 06 Dec 2023)

Viewed (geographical distribution)

Total article views: 2,885 (including HTML, PDF, and XML) Thereof 2,782 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
We propose and evaluate machine learning predictive algorithms to model freshly formed biogenic methanesulfonic acid and sulfate concentrations. The long-term constructed dataset covers the North Atlantic at an unprecedented resolution. The improved parameterization of biogenic sulfur aerosols at regional scales is essential for determining their radiative forcing, which could help further understand marine-aerosol–cloud interactions and reduce uncertainties in climate models
Read more
Share
Altmetrics
Final-revised paper
Preprint