Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations

Jakob Boyd Pernov, William H. Aeberhard, Michele Volpi, Eliza Harris, Benjamin Hohermuth, Sakiko Ishino, Ragnhild B. Skeie, Stephan Henne, Ulas Im, Patricia K. Quinn, Lucia M. Upchurch, and Julia Schmale

Supplement

https://doi.org/10.5194/acp-25-6497-2025-supplement

Data sets

ERA5 data Climate Data Store https://cds.climate.copernicus.eu/

CAMS data Atmosphere Data Store https://ads.atmosphere.copernicus.eu/

CAMS global emission inventories Copernicus Atmosphere Monitoring Service https://doi.org/10.24381/1D158BEC

Long-term Air Chemistry Monitoring Experiment-Submicron ERDDAP https://data.pmel.noaa.gov/pmel/erddap/tabledap/station_barrow_submicron_chemistry.html

EBAS Data EBAS home https://ebas.nilu.no/

Chlorophyll a Data GlobColour https://hermes.acri.fr/

Model code and software

ArcticNap/MSAmodeling Michele Volpi et al. https://gitlab.renkulab.io/arcticnap/msamodeling

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Short summary

Particulate methanesulfonic acid (MSA_p) is vital for the Arctic climate system. Numerical models struggle to reproduce the MSA_p seasonal cycle. We evaluate three numerical models and one reanalysis product’s ability to simulate MSA_p. We develop data-driven models for MSA_p at four Arctic stations. The data-driven models outperform the numerical models and reanalysis product and identified precursor source-, chemical-processing-, and removal-related features as being important for modeling MSA_p.