Articles | Volume 12, issue 9
https://doi.org/10.5194/gmd-12-3863-2019
https://doi.org/10.5194/gmd-12-3863-2019
Development and technical paper
 | 
03 Sep 2019
Development and technical paper |  | 03 Sep 2019

Improved tropospheric and stratospheric sulfur cycle in the aerosol–chemistry–climate model SOCOL-AERv2

Aryeh Feinberg, Timofei Sukhodolov, Bei-Ping Luo, Eugene Rozanov, Lenny H. E. Winkel, Thomas Peter, and Andrea Stenke

Related authors

Influences of sources and weather dynamics on atmospheric deposition of Se species and other trace elements
Esther S. Breuninger, Julie Tolu, Iris Thurnherr, Franziska Aemisegger, Aryeh Feinberg, Sylvain Bouchet, Jeroen E. Sonke, Véronique Pont, Heini Wernli, and Lenny H. E. Winkel
EGUsphere, https://doi.org/10.5194/egusphere-2023-1135,https://doi.org/10.5194/egusphere-2023-1135, 2023
Short summary
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
Geosci. Model Dev., 16, 2037–2054, https://doi.org/10.5194/gmd-16-2037-2023,https://doi.org/10.5194/gmd-16-2037-2023, 2023
Short summary
Atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0: description and evaluation
Timofei Sukhodolov, Tatiana Egorova, Andrea Stenke, William T. Ball, Christina Brodowsky, Gabriel Chiodo, Aryeh Feinberg, Marina Friedel, Arseniy Karagodin-Doyennel, Thomas Peter, Jan Sedlacek, Sandro Vattioni, and Eugene Rozanov
Geosci. Model Dev., 14, 5525–5560, https://doi.org/10.5194/gmd-14-5525-2021,https://doi.org/10.5194/gmd-14-5525-2021, 2021
Short summary
Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis
Aryeh Feinberg, Moustapha Maliki, Andrea Stenke, Bruno Sudret, Thomas Peter, and Lenny H. E. Winkel
Atmos. Chem. Phys., 20, 1363–1390, https://doi.org/10.5194/acp-20-1363-2020,https://doi.org/10.5194/acp-20-1363-2020, 2020
Short summary
Exploring accumulation-mode H2SO4 versus SO2 stratospheric sulfate geoengineering in a sectional aerosol–chemistry–climate model
Sandro Vattioni, Debra Weisenstein, David Keith, Aryeh Feinberg, Thomas Peter, and Andrea Stenke
Atmos. Chem. Phys., 19, 4877–4897, https://doi.org/10.5194/acp-19-4877-2019,https://doi.org/10.5194/acp-19-4877-2019, 2019
Short summary

Related subject area

Atmospheric sciences
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024,https://doi.org/10.5194/gmd-17-1469-2024, 2024
Short summary
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024,https://doi.org/10.5194/gmd-17-1271-2024, 2024
Short summary
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024,https://doi.org/10.5194/gmd-17-1111-2024, 2024
Short summary
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024,https://doi.org/10.5194/gmd-17-1091-2024, 2024
Short summary
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024,https://doi.org/10.5194/gmd-17-815-2024, 2024
Short summary

Cited articles

Allen, R. J. and Landuyt, W.: The vertical distribution of black carbon in CMIP5 models: Comparison to observations and the importance of convective transport, J. Geophys. Res.-Atmos., 119, 4808–4835, https://doi.org/10.1002/2014jd021595, 2014. a
Andres, R. and Kasgnoc, A.: A time-averaged inventory of subaerial volcanic sulfur emissions, J. Geophys. Res., 103, 25251–25261, https://doi.org/10.1029/98jd02091, 1998. a
Arfeuille, F., Luo, B. P., Heckendorn, P., Weisenstein, D., Sheng, J. X., Rozanov, E., Schraner, M., Brönnimann, S., Thomason, L. W., and Peter, T.: Modeling the stratospheric warming following the Mt. Pinatubo eruption: uncertainties in aerosol extinctions, Atmos. Chem. Phys., 13, 11221–11234, https://doi.org/10.5194/acp-13-11221-2013, 2013. a
Ayers, G., Gillett, R., and Gras, J.: On the vapor pressure of sulfuric acid, Geophys. Res. Lett., 7, 433–436, https://doi.org/10.1029/GL007i006p00433, 1980. a
Baran, A. and Foot, J.: New application of the operational sounder HIRS in determining a climatology of sulphuric acid aerosol from the Pinatubo eruption, J. Geophys. Res., 99, 25673–25679, https://doi.org/10.1029/94jd02044, 1994. a
Download
Short summary
We have improved several aspects of atmospheric sulfur cycling in SOCOL-AER, an aerosol–chemistry–climate model. The newly implemented features in SOCOL-AERv2 include interactive deposition schemes, improved sulfur mass conservation, and expanded tropospheric chemistry. SOCOL-AERv2 shows better agreement with stratospheric aerosol observations and sulfur deposition networks compared to SOCOL-AERv1. SOCOL-AERv2 can be used to study impacts of sulfate aerosol on climate, chemistry, and ecosystems.