Articles | Volume 21, issue 23
https://doi.org/10.5194/acp-21-17243-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-17243-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Nov 2021
Research article |
| 29 Nov 2021
| 29 Nov 2021
Reduced effective radiative forcing from cloud–aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth system model
Sara Marie Blichner
CORRESPONDING AUTHOR
Department of Geosciences and Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
Moa Kristina Sporre
Department of Physics, Lund University, Lund, Sweden
Terje Koren Berntsen
Department of Geosciences and Centre for Biogeochemistry in the Anthropocene, University of Oslo, Oslo, Norway
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We study the short- and long-term stratospheric impact of smoke from the massive Australian wildfires in Dec 2019–Jan 2020 using four satellite sensors. Smoke entered the stratosphere rapidly via transport by firestorms, as well as weeks after the fires. The smoke particle properties evolved over time together with rapidly decreasing stratospheric aerosol load, suggesting photolytic loss of organics in the smoke particles. The depletion rate was estimated to a half-life (e folding) of 10 (14) d.
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Large amounts of wildfire smoke reached the stratosphere in 2017. The literature on stratospheric aerosol is mainly based on horizontally viewing sensors that saturate in dense smoke. Using also a vertically viewing sensor with orders of magnitude shorter path in the smoke, we show that the horizontally viewing sensors miss a dramatic exponential decline of the aerosol load with a half-life of 10 d, where 80 %–90 % of smoke is lost. We attribute the decline to photolytic loss of organic aerosol.
Oscar S. Sandvik, Johan Friberg, Moa K. Sporre, and Bengt G. Martinsson
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A method to form SO2 profiles in the stratosphere with high vertical resolution following volcanic eruptions is introduced. The method combines space-based high-resolution vertical aerosol profiles and SO2 measurements the first 2 weeks after an eruption with air mass trajectory analyses. The SO2 is located at higher altitude than in most previous studies. The detailed resolution of the SO2 profile is unprecedented compared to other methods.
Stefanie Falk, Ane V. Vollsnes, Aud B. Eriksen, Lisa Emberson, Connie O'Neill, Frode Stordal, and Terje Koren Berntsen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-260, https://doi.org/10.5194/bg-2021-260, 2021
Revised manuscript not accepted
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Subarctic vegetation is threatened by climate change and ozone. We assess essential climate variables in 2018/19. 2018 was warmer and brighter than usual in Spring with forest fires and elevated ozone in summer. Visible damage was observed on plant species in 2018. We find that generic parameterizations used in modeling ozone dose do not suffice. We propose a method to acclimate these parameterizations and find an ozone-induced biomass loss of 2.5 to 17.4 % (up to 6 % larger than default).
Stefanie Falk, Ane V. Vollsnes, Aud B. Eriksen, Frode Stordal, and Terje Koren Berntsen
Atmos. Chem. Phys., 21, 15647–15661, https://doi.org/10.5194/acp-21-15647-2021, https://doi.org/10.5194/acp-21-15647-2021, 2021
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We evaluate regional and global models for ozone modeling and damage risk mapping of vegetation over subarctic Europe. Our analysis suggests that low-resolution global models do not reproduce the observed ozone seasonal cycle at ground level, underestimating ozone by 30–50 %. High-resolution regional models capture the seasonal cycle well, still underestimating ozone by up to 20 %. Our proposed gap-filling method for site observations shows a 76 % accuracy compared to the regional model (80 %).
Sara M. Blichner, Moa K. Sporre, Risto Makkonen, and Terje K. Berntsen
Geosci. Model Dev., 14, 3335–3359, https://doi.org/10.5194/gmd-14-3335-2021, https://doi.org/10.5194/gmd-14-3335-2021, 2021
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Aerosol–cloud interactions are the largest contributor to climate forcing uncertainty. In this study we combine two common approaches to aerosol representation in global models: a sectional scheme, which is closer to first principals, for the smallest particles forming in the atmosphere and a log-modal scheme, which is faster, for the larger particles. With this approach, we improve the aerosol representation compared to observations, while only increasing the computational cost by 15 %.
Peter Horvath, Hui Tang, Rune Halvorsen, Frode Stordal, Lena Merete Tallaksen, Terje Koren Berntsen, and Anders Bryn
Biogeosciences, 18, 95–112, https://doi.org/10.5194/bg-18-95-2021, https://doi.org/10.5194/bg-18-95-2021, 2021
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We evaluated the performance of three methods for representing vegetation cover. Remote sensing provided the best match to a reference dataset, closely followed by distribution modelling (DM), whereas the dynamic global vegetation model (DGVM) in CLM4.5BGCDV deviated strongly from the reference. Sensitivity tests show that use of threshold values for predictors identified by DM may improve DGVM performance. The results highlight the potential of using DM in the development of DGVMs.
Marianne T. Lund, Borgar Aamaas, Camilla W. Stjern, Zbigniew Klimont, Terje K. Berntsen, and Bjørn H. Samset
Earth Syst. Dynam., 11, 977–993, https://doi.org/10.5194/esd-11-977-2020, https://doi.org/10.5194/esd-11-977-2020, 2020
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Achieving the Paris Agreement temperature goals requires both near-zero levels of long-lived greenhouse gases and deep cuts in emissions of short-lived climate forcers (SLCFs). Here we quantify the near- and long-term global temperature impacts of emissions of individual SLCFs and CO2 from 7 economic sectors in 13 regions in order to provide the detailed knowledge needed to design efficient mitigation strategies at the sectoral and regional levels.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.:
A Parameterization of Aerosol Activation: 2. Multiple Aerosol Types,
J. Geophys. Res.-Atmos.,
105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. a, b
Andrade, M., Zaratti, F., Forno, R., Gutiérrez, R., Moreno, I., Velarde, F., Ávila, F., Roca, M., Sánchez, M. F., Laj, P., Jaffrezo, J. L., Ginot, P., Sellegri, K., Ramonet, M., Laurent, O., Weinhold, K., Wiedensohler, A., Krejci, R., Bonasoni, P., Cristofanelli, P., Whiteman, D., Vimeux, F., Dommergue, A., and Magand, O.:
Puesta En Marcha de Una Nueva Estación de Monitoreo Climático En Los Andes Centrales de Bolivia: La Estación Gaw/Chacaltaya,
Revista Boliviana de Física,
26, 06–15, 2015. a
Andreae, M. O., Acevedo, O. C., Araùjo, A., Artaxo, P., Barbosa, C. G. G., Barbosa, H. M. J., Brito, J., Carbone, S., Chi, X., Cintra, B. B. L., da Silva, N. F., Dias, N. L., Dias-Júnior, C. Q., Ditas, F., Ditz, R., Godoi, A. F. L., Godoi, R. H. M., Heimann, M., Hoffmann, T., Kesselmeier, J., Könemann, T., Krüger, M. L., Lavric, J. V., Manzi, A. O., Lopes, A. P., Martins, D. L., Mikhailov, E. F., Moran-Zuloaga, D., Nelson, B. W., Nölscher, A. C., Santos Nogueira, D., Piedade, M. T. F., Pöhlker, C., Pöschl, U., Quesada, C. A., Rizzo, L. V., Ro, C.-U., Ruckteschler, N., Sá, L. D. A., de Oliveira Sá, M., Sales, C. B., dos Santos, R. M. N., Saturno, J., Schöngart, J., Sörgel, M., de Souza, C. M., de Souza, R. A. F., Su, H., Targhetta, N., Tóta, J., Trebs, I., Trumbore, S., van Eijck, A., Walter, D., Wang, Z., Weber, B., Williams, J., Winderlich, J., Wittmann, F., Wolff, S., and Yáñez-Serrano, A. M.: The Amazon Tall Tower Observatory (ATTO): overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols, Atmos. Chem. Phys., 15, 10723–10776, https://doi.org/10.5194/acp-15-10723-2015, 2015. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.:
Bounding Global Aerosol Radiative Forcing of Climate Change,
Rev. Geophys.,
58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a, b
Blichner, S. M.:
Model Output for “Reduced Effective Radiative Forcing from Cloud-Aerosol Interactions (ERFaci) with Improved Treatment of Early Aerosol Growth in an Earth System Model”, Norstore [data set],
https://doi.org/10.11582/2021.00087, 2021a. a
Blichner, S. M.:
Sarambl/OAS-ERF: Publication Release,
Zenodo [code],
https://doi.org/10.5281/zenodo.5559027, 2021b. a
Blichner, S. M.:
Sarambl/OAS-Code-Setup: Pre-Publication Release,
Zenodo [code],
https://doi.org/10.5281/zenodo.4265057, 2020. a
Blichner, S. M., Sporre, M. K., Makkonen, R., and Berntsen, T. K.: Implementing a sectional scheme for early aerosol growth from new particle formation in the Norwegian Earth System Model v2: comparison to observations and climate impacts, Geosci. Model Dev., 14, 3335–3359, https://doi.org/10.5194/gmd-14-3335-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.:
Higher-Order Turbulence Closure and Its Impact on Climate Simulations in the Community Atmosphere Model,
J. Climate,
26, 9655–9676, https://doi.org/10.1175/JCLI-D-13-00075.1, 2013. a
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018. a, b
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X.:
Clouds and Aerosols,
in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.,
Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 571–658, 2013. a
Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A., Forster, P. M., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., and Pierce, J. R.:
Large Contribution of Natural Aerosols to Uncertainty in Indirect Forcing,
Nature,
503, 67–71, https://doi.org/10.1038/nature12674, 2013. a, b, c, d, e, f
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.:
The Community Earth System Model Version 2 (CESM2),
J. Adv. Model. Earth Sy.,
12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a, b
Dentener, F., Kinne, S., Bond, T., Boucher, O., Cofala, J., Generoso, S., Ginoux, P., Gong, S., Hoelzemann, J. J., Ito, A., Marelli, L., Penner, J. E., Putaud, J.-P., Textor, C., Schulz, M., van der Werf, G. R., and Wilson, J.: Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom, Atmos. Chem. Phys., 6, 4321–4344, https://doi.org/10.5194/acp-6-4321-2006, 2006. a
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010. a
Forster, P. M., Richardson, T., Maycock, A. C., Smith, C. J., Samset, B. H., Myhre, G., Andrews, T., Pincus, R., and Schulz, M.:
Recommendations for Diagnosing Effective Radiative Forcing from Climate Models for CMIP6,
J. Geophys. Res.-Atmos.,
121, 12460–12475, https://doi.org/10.1002/2016JD025320, 2016. a, b
Gettelman, A. and Morrison, H.:
Advanced Two-Moment Bulk Microphysics for Global Models. Part I: Off-Line Tests and Comparison with Other Schemes,
J. Climate,
28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015. a
Ghan, S. J.: Technical Note: Estimating aerosol effects on cloud radiative forcing, Atmos. Chem. Phys., 13, 9971–9974, https://doi.org/10.5194/acp-13-9971-2013, 2013. a, b, c, d
Gordon, H., Sengupta, K., Rap, A., Duplissy, J., Frege, C., Williamson, C., Heinritzi, M., Simon, M., Yan, C., Almeida, J., Tröstl, J., Nieminen, T., Ortega, I. K., Wagner, R., Dunne, E. M., Adamov, A., Amorim, A., Bernhammer, A.-K., Bianchi, F., Breitenlechner, M., Brilke, S., Chen, X., Craven, J. S., Dias, A., Ehrhart, S., Fischer, L., Flagan, R. C., Franchin, A., Fuchs, C., Guida, R., Hakala, J., Hoyle, C. R., Jokinen, T., Junninen, H., Kangasluoma, J., Kim, J., Kirkby, J., Krapf, M., Kürten, A., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S., Molteni, U., Monks, S. A., Onnela, A., Peräkylä, O., Piel, F., Petäjä, T., Praplan, A. P., Pringle, K. J., Richards, N. A. D., Rissanen, M. P., Rondo, L., Sarnela, N., Schobesberger, S., Scott, C. E., Seinfeld, J. H., Sharma, S., Sipilä, M., Steiner, G., Stozhkov, Y., Stratmann, F., Tomé, A., Virtanen, A., Vogel, A. L., Wagner, A. C., Wagner, P. E., Weingartner, E., Wimmer, D., Winkler, P. M., Ye, P., Zhang, X., Hansel, A., Dommen, J., Donahue, N. M., Worsnop, D. R., Baltensperger, U., Kulmala, M., Curtius, J., and Carslaw, K. S.:
Reduced Anthropogenic Aerosol Radiative Forcing Caused by Biogenic New Particle Formation,
P. Natl. Acad. Sci. USA,
113, 12053–12058, https://doi.org/10.1073/pnas.1602360113, 2016. a, b
Gordon, H., Kirkby, J., Baltensperger, U., Bianchi, F., Breitenlechner, M., Curtius, J., Dias, A., Dommen, J., Donahue, N. M., Dunne, E. M., Duplissy, J., Ehrhart, S., Flagan, R. C., Frege, C., Fuchs, C., Hansel, A., Hoyle, C. R., Kulmala, M., Kürten, A., Lehtipalo, K., Makhmutov, V., Molteni, U., Rissanen, M. P., Stozkhov, Y., Tröstl, J., Tsagkogeorgas, G., Wagner, R., Williamson, C., Wimmer, D., Winkler, P. M., Yan, C., and Carslaw, K. S.:
Causes and Importance of New Particle Formation in the Present-Day and Preindustrial Atmospheres: CAUSES AND ROLE OF NEW PARTICLE FORMATION,
J. Geophys. Res.-Atmos.,
122, 8739–8760, https://doi.org/10.1002/2017JD026844, 2017. a
Hansen, J., Sato, M., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A., Russell, G., Aleinov, I., Bauer, M., Bauer, S., Bell, N., Cairns, B., Canuto, V., Chandler, M., Cheng, Y., Genio, A. D., Faluvegi, G., Fleming, E., Friend, A., Hall, T., Jackman, C., Kelley, M., Kiang, N., Koch, D., Lean, J., Lerner, J., Lo, K., Menon, S., Miller, R., Minnis, P., Novakov, T., Oinas, V., Perlwitz, J., Perlwitz, J., Rind, D., Romanou, A., Shindell, D., Stone, P., Sun, S., Tausnev, N., Thresher, D., Wielicki, B., Wong, T., Yao, M., and Zhang, S.:
Efficacy of Climate Forcings,
J. Geophys. Res.-Atmos.,
110, D18104, https://doi.org/10.1029/2005JD005776, 2005. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a
Iversen, T., Bentsen, M., Bethke, I., Debernard, J. B., Kirkevåg, A., Seland, Ø., Drange, H., Kristjansson, J. E., Medhaug, I., Sand, M., and Seierstad, I. A.: The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections, Geosci. Model Dev., 6, 389–415, https://doi.org/10.5194/gmd-6-389-2013, 2013. a, b
Jacobson, M. Z.:
Development and Application of a New Air Pollution Modeling System — Part III. Aerosol–Phase Simulations,
Atmos. Environ.,
31, 587–608, https://doi.org/10.1016/S1352-2310(96)00201-4, 1997. a
Jacobson, M. Z.:
Fundamentals of Atmospheric Modeling, second edn.,
Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781139165389, 2005. a, b
Karset, I. H. H., Berntsen, T. K., Storelvmo, T., Alterskjær, K., Grini, A., Olivié, D., Kirkevåg, A., Seland, Ø., Iversen, T., and Schulz, M.: Strong impacts on aerosol indirect effects from historical oxidant changes, Atmos. Chem. Phys., 18, 7669–7690, https://doi.org/10.5194/acp-18-7669-2018, 2018. a, b
Kerminen, V.-M. and Kulmala, M.:
Analytical Formulae Connecting the “Real” and the “Apparent” Nucleation Rate and the Nuclei Number Concentration for Atmospheric Nucleation Events,
J. Aerosol Sci.,
33, 609–622, https://doi.org/10.1016/S0021-8502(01)00194-X, 2002. a, b
Kerminen, V.-M., Chen, X., Vakkari, V., Petäjä, T., Kulmala, M., and Bianchi, F.:
Atmospheric New Particle Formation and Growth: Review of Field Observations,
Environ. Res. Lett.,
13, 103003, https://doi.org/10.1088/1748-9326/aadf3c, 2018. a, b, c, d
Kirkevåg, A., Iversen, T., Seland, Ø., Hoose, C., Kristjánsson, J. E., Struthers, H., Ekman, A. M. L., Ghan, S., Griesfeller, J., Nilsson, E. D., and Schulz, M.: Aerosol–climate interactions in the Norwegian Earth System Model – NorESM1-M, Geosci. Model Dev., 6, 207–244, https://doi.org/10.5194/gmd-6-207-2013, 2013. a, b
Kirkevåg, A., Grini, A., Olivié, D., Seland, Ø., Alterskjær, K., Hummel, M., Karset, I. H. H., Lewinschal, A., Liu, X., Makkonen, R., Bethke, I., Griesfeller, J., Schulz, M., and Iversen, T.: A production-tagged aerosol module for Earth system models, OsloAero5.3 – extensions and updates for CAM5.3-Oslo, Geosci. Model Dev., 11, 3945–3982, https://doi.org/10.5194/gmd-11-3945-2018, 2018. a, b
Kooperman, G. J., Pritchard, M. S., Ghan, S. J., Wang, M., Somerville, R. C. J., and Russell, L. M.:
Constraining the Influence of Natural Variability to Improve Estimates of Global Aerosol Indirect Effects in a Nudged Version of the Community Atmosphere Model 5,
J. Geophys. Res.-Atmos.,
117, D23204, https://doi.org/10.1029/2012JD018588, 2012. a
Laboratory of Atmospheric Physics LFA/IF-USP: Particle size distribution measurements at the ATTO tower site, LFA [data set], available at: http://ftp.lfa.if.usp.br/ftp/public/LFA_Processed_Data/T0a_ATTO/Level3/SMPS_2014toNov2020_ATTO_60m_InstTower/, last access: 15 August 2021. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V., and Zeng, X.:
The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty,
J. Adv. Model. Earth Sy.,
11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a, b
Lee, S.-H., Gordon, H., Yu, H., Lehtipalo, K., Haley, R., Li, Y., and Zhang, R.:
New Particle Formation in the Atmosphere: From Molecular Clusters to Global Climate,
J. Geophys. Res.-Atmos.,
124, 7098–7146, https://doi.org/10.1029/2018JD029356, 2019. a, b
Lee, Y. H., Pierce, J. R., and Adams, P. J.: Representation of nucleation mode microphysics in a global aerosol model with sectional microphysics, Geosci. Model Dev., 6, 1221–1232, https://doi.org/10.5194/gmd-6-1221-2013, 2013. a, b, c
Lehtinen, K. E. J., Dal Maso, M., Kulmala, M., and Kerminen, V.-M.:
Estimating Nucleation Rates from Apparent Particle Formation Rates and Vice Versa: Revised Formulation of the Kerminen–Kulmala Equation,
J. Aerosol Sci.,
38, 988–994, https://doi.org/10.1016/j.jaerosci.2007.06.009, 2007. a, b, c, d, e, f, g, h
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.:
Anthropogenic and Natural Radiative Forcing,
in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.,
Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 659–740, 2013. a, b
Andrade, M. and Zaratti, F.: Ground based in situ observations of particle_number_size_distribution at Mount Chacaltaya (BO0001R) using smps, EBAS [data set], available at:
http://ebas-data.nilu.no/DataSets.aspx?nations=BO068BOL&components=particle_number_size_distribution&matrices=aerosol&fromDate=1970-01-01&toDate=2021-12-31, last access: 15 August 2021. a
Olenius, T. and Riipinen, I.:
Molecular-Resolution Simulations of New Particle Formation: Evaluation of Common Assumptions Made in Describing Nucleation in Aerosol Dynamics Models,
Aerosol Sci. Tech.,
51, 397–408, https://doi.org/10.1080/02786826.2016.1262530, 2017. a, b, c
Paasonen, P., Nieminen, T., Asmi, E., Manninen, H. E., Petäjä, T., Plass-Dülmer, C., Flentje, H., Birmili, W., Wiedensohler, A., Hõrrak, U., Metzger, A., Hamed, A., Laaksonen, A., Facchini, M. C., Kerminen, V.-M., and Kulmala, M.: On the roles of sulphuric acid and low-volatility organic vapours in the initial steps of atmospheric new particle formation, Atmos. Chem. Phys., 10, 11223–11242, https://doi.org/10.5194/acp-10-11223-2010, 2010. a, b, c
Riccobono, F., Schobesberger, S., Scott, C. E., Dommen, J., Ortega, I. K., Rondo, L., Almeida, J., Amorim, A., Bianchi, F., Breitenlechner, M., David, A., Downard, A., Dunne, E. M., Duplissy, J., Ehrhart, S., Flagan, R. C., Franchin, A., Hansel, A., Junninen, H., Kajos, M., Keskinen, H., Kupc, A., Kürten, A., Kvashin, A. N., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S., Nieminen, T., Onnela, A., Petäjä, T., Praplan, A. P., Santos, F. D., Schallhart, S., Seinfeld, J. H., Sipilä, M., Spracklen, D. V., Stozhkov, Y., Stratmann, F., Tomé, A., Tsagkogeorgas, G., Vaattovaara, P., Viisanen, Y., Vrtala, A., Wagner, P. E., Weingartner, E., Wex, H., Wimmer, D., Carslaw, K. S., Curtius, J., Donahue, N. M., Kirkby, J., Kulmala, M., Worsnop, D. R., and Baltensperger, U.:
Oxidation Products of Biogenic Emissions Contribute to Nucleation of Atmospheric Particles,
Science,
344, 717–721, https://doi.org/10.1126/science.1243527, 2014. a, b
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020a. a, b, c, d, e, f, g
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Gao, S., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moree, A., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NorESM2 source code as used for CMIP6 simulations (2.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.3760870, 2020b. a
Semeniuk, K. and Dastoor, A.:
Current State of Aerosol Nucleation Parameterizations for Air-Quality and Climate Modeling,
Atmos. Environ.,
179, 77–106, https://doi.org/10.1016/j.atmosenv.2018.01.039, 2018. a, b
Shrivastava, M., Cappa, C. D., Fan, J., Goldstein, A. H., Guenther, A. B., Jimenez, J. L., Kuang, C., Laskin, A., Martin, S. T., Ng, N. L., Petaja, T., Pierce, J. R., Rasch, P. J., Roldin, P., Seinfeld, J. H., Shilling, J., Smith, J. N., Thornton, J. A., Volkamer, R., Wang, J., Worsnop, D. R., Zaveri, R. A., Zelenyuk, A., and Zhang, Q.:
Recent Advances in Understanding Secondary Organic Aerosol: Implications for Global Climate Forcing,
Rev. Geophys.,
55, 2016RG000540, https://doi.org/10.1002/2016RG000540, 2017. a
Smith, C. J., Kramer, R. J., Myhre, G., Alterskjær, K., Collins, W., Sima, A., Boucher, O., Dufresne, J.-L., Nabat, P., Michou, M., Yukimoto, S., Cole, J., Paynter, D., Shiogama, H., O'Connor, F. M., Robertson, E., Wiltshire, A., Andrews, T., Hannay, C., Miller, R., Nazarenko, L., Kirkevåg, A., Olivié, D., Fiedler, S., Lewinschal, A., Mackallah, C., Dix, M., Pincus, R., and Forster, P. M.: Effective radiative forcing and adjustments in CMIP6 models, Atmos. Chem. Phys., 20, 9591–9618, https://doi.org/10.5194/acp-20-9591-2020, 2020. a
Sporre, M. K., Blichner, S. M., Schrödner, R., Karset, I. H. H., Berntsen, T. K., van Noije, T., Bergman, T., O'Donnell, D., and Makkonen, R.: Large difference in aerosol radiative effects from BVOC-SOA treatment in three Earth system models, Atmos. Chem. Phys., 20, 8953–8973, https://doi.org/10.5194/acp-20-8953-2020, 2020. a, b
Sullivan, R. C., Crippa, P., Matsui, H., Leung, L. R., Zhao, C., Thota, A., and Pryor, S. C.:
New Particle Formation Leads to Cloud Dimming,
npj Climate and Atmospheric Science,
1, 1–9, https://doi.org/10.1038/s41612-018-0019-7, 2018. a
Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P. J., Artaxo, P., Bahadur, R., Balkanski, Y., Bauer, S. E., Bellouin, N., Benedetti, A., Bergman, T., Berntsen, T. K., Beukes, J. P., Bian, H., Carslaw, K. S., Chin, M., Curci, G., Diehl, T., Easter, R. C., Ghan, S. J., Gong, S. L., Hodzic, A., Hoyle, C. R., Iversen, T., Jathar, S., Jimenez, J. L., Kaiser, J. W., Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y. H., Lin, G., Liu, X., Luo, G., Ma, X., Mann, G. W., Mihalopoulos, N., Morcrette, J.-J., Müller, J.-F., Myhre, G., Myriokefalitakis, S., Ng, N. L., O'Donnell, D., Penner, J. E., Pozzoli, L., Pringle, K. J., Russell, L. M., Schulz, M., Sciare, J., Seland, Ø., Shindell, D. T., Sillman, S., Skeie, R. B., Spracklen, D., Stavrakou, T., Steenrod, S. D., Takemura, T., Tiitta, P., Tilmes, S., Tost, H., van Noije, T., van Zyl, P. G., von Salzen, K., Yu, F., Wang, Z., Wang, Z., Zaveri, R. A., Zhang, H., Zhang, K., Zhang, Q., and Zhang, X.: The AeroCom evaluation and intercomparison of organic aerosol in global models, Atmos. Chem. Phys., 14, 10845–10895, https://doi.org/10.5194/acp-14-10845-2014, 2014. a
Twomey, S.:
The Nuclei of Natural Cloud Formation Part II: The Supersaturation in Natural Clouds and the Variation of Cloud Droplet Concentration,
Geofisica pura e applicata,
43, 243–249, https://doi.org/10.1007/BF01993560, 1959. a
Twomey, S.:
Aerosols, Clouds and Radiation,
Atmos. Environ. A-Gen.,
25, 2435–2442, https://doi.org/10.1016/0960-1686(91)90159-5, 1991. a, b
van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geosci. Model Dev., 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017. a
Vehkamäki, H., Kulmala, M., Napari, I., Lehtinen, K. E. J., Timmreck, C., Noppel, M., and Laaksonen, A.:
An Improved Parameterization for Sulfuric Acid–Water Nucleation Rates for Tropospheric and Stratospheric Conditions,
J. Geophys. Res.-Atmos.,
107, 4622, https://doi.org/10.1029/2002JD002184, 2002. a
Wofsy, S. C. and ATom Science Team: ATom: Aircraft Flight Track and Navigational Data, ORNL DAAC [data set], Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1613,
2018. a
Wofsy, S., Afshar, S., Allen, H., Apel, E., Asher, E., Barletta, B., Bent, J., Bian, H., Biggs, B., Blake, D., Blake, N., Bourgeois, I., Brock, C., Brune, W., Budney, J., Bui, T., Butler, A., Campuzano-Jost, P., Chang, C., Chin, M., Commane, R., Correa, G., Crounse, J., Cullis, P. D., Daube, B., Day, D., Dean-Day, J. M., Dibb, J., DiGangi, J., Diskin, G., Dollner, M., Elkins, J., Erdesz, F., Fiore, A., Flynn, C., Froyd, K., Gesler, D., Hall, S., Hanisco, T., Hannun, R., Hills, A., Hintsa, E., Hoffman, A., Hornbrook, R., Huey, L., Hughes, S., Jimenez, J., Johnson, B., Katich, J., Keeling, R., Kim, M., Kupc, A., Lait, L., Lamarque, J.-F., Liu, J., McKain, K., Mclaughlin, R., Meinardi, S., Miller, D., Montzka, S., Moore, F., Morgan, E., Murphy, D., Murray, L., Nault, B., Neuman, J., Newman, P., Nicely, J., Pan, X., Paplawsky, W., Peischl, J., Prather, M., Price, D., Ray, E., Reeves, J., Richardson, M., Rollins, A., Rosenlof, K., Ryerson, T., Scheuer, E., Schill, G., Schroder, J., Schwarz, J., St.Clair, J., Steenrod, S., Stephens, B., Strode, S., Sweeney, C., Tanner, D., Teng, A., Thames, A., Thompson, C., Ullmann, K., Veres, P., Vieznor, N., Wagner, N., Watt, A., Weber, R., Weinzierl, B., Wennberg, P., Williamson, C., Wilson, J., Wolfe, G., Woods, C., and Zeng, L.:
Atmospheric Tomography Mission (ATom)ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols, p. 0 MB, https://doi.org/10.3334/ORNLDAAC/1581, 2018.
a, b
Zhang, G. J. and McFarlane, N. A.:
Sensitivity of Climate Simulations to the Parameterization of Cumulus Convection in the Canadian Climate Centre General Circulation Model,
Atmos. Ocean,
33, 407–446, https://doi.org/10.1080/07055900.1995.9649539, 1995. a
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014. a
Short summary
In this study we quantify how a new way of modeling the formation of new particles in the atmosphere affects the estimated cooling from aerosol–cloud interactions since pre-industrial times. Our improved scheme merges two common approaches to aerosol modeling: a sectional scheme for treating early growth and the pre-existing modal scheme in NorESM. We find that the cooling from aerosol–cloud interactions since pre-industrial times is reduced by 10 % when the new scheme is used.
In this study we quantify how a new way of modeling the formation of new particles in the...
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