Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean

Mansour, Karam; Rinaldi, Matteo; Preißler, Jana; Decesari, Stefano; Ovadnevaitė, Jurgita; Čeburnis, Darius; Paglione, Marco; Facchini, M. C.; O’Dowd, Colin

doi:10.1029/2021jd036355

Cited by 7 publications

(2 citation statements)

References 114 publications

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“…The dataset includes daily MSA and nss-SO = 4 concentrations at a 0.25°× 0.25°spatial resolution over the North Atlantic from January 1998 to December 2022. The data are publicly available in NetCDF format as daily files on the Mendeley Data online repository at https://doi.org/10.17632/ j8bzd5dvpx.1 (Mansour et al, 2023b).…”

Section: Data Availabilitymentioning

confidence: 99%

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

Mansour,

Decesari,

Ceburnis

et al. 2024

Earth Syst. Sci. Data

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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, 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-SO4=) 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 (FDMS) 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 (R2) revealed that Gaussian process regression (GPR) was the most effective algorithm, outperforming the other models with respect to simulating the biogenic MSA and nss-SO4= concentrations. For predicting daily MSA (nss-SO4=), GPR displayed the highest R2 value of 0.86 (0.72) and the lowest MAE of 0.014 (0.10) µg m−3. GPR partial dependence analysis suggests that the relationships between predictors and MSA and nss-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 nss-SO4= sea-level concentrations over the North Atlantic, which we named “In-situ Produced Biogenic Methanesulfonic Acid and Sulfate over the North Atlantic” (IPB-MSA&SO4). The obtained IPB-MSA&SO4 data allowed us to analyze the spatiotemporal patterns of MSA and nss-SO4= as well as the ratio between them (MSA:nss-SO4=). A comparison with the existing Copernicus Atmosphere Monitoring Service ECMWF Atmospheric Composition Reanalysis 4 (CAMS-EAC4) reanalysis suggested that our high-resolution dataset reproduces the spatial and temporal patterns of the biogenic sulfur aerosol concentration with high accuracy and has high consistency with independent measurements in the Atlantic Ocean. IPB-MSA&SO4 is publicly available at https://doi.org/10.17632/j8bzd5dvpx.1 (Mansour et al., 2023b).

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Section: Data Availabilitymentioning

confidence: 99%

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

Mansour,

Decesari,

Ceburnis

et al. 2024

Earth Syst. Sci. Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dataset includes daily MSA and SO4 concentrations at 0.25° × 0.25° spatial resolution over the North Atlantic Ocean from January 1998 to December 2022. The datasets are publicly available in NetCDF format as daily files on the Mendeley online repository at https://doi.org/10.17632/j8bzd5dvpx.1 (Mansour et al, 2023b).…”

Section: Data Availability 550mentioning

confidence: 99%

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

Mansour,

Decesari,

Ceburnis

et al. 2023

Preprint

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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).

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Sensitivity of cloud microphysics to aerosol is highly associated with cloud water content: Implications for indirect radiative forcing

Wang,

Jia,

Zhang

et al. 2024

Atmospheric Research

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Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean

Cited by 7 publications

References 114 publications

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

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

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

Sensitivity of cloud microphysics to aerosol is highly associated with cloud water content: Implications for indirect radiative forcing

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Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean

Cited by 7 publications

References 114 publications

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

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

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

Sensitivity of cloud microphysics to aerosol is highly associated with cloud water content: Implications for indirect radiative forcing

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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

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

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