Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint
We use the Global Modelling Initiative (GMI) chemical transport model with a cloud droplet parameterisation adjoint to quantify the sensitivity of cloud droplet number concentration to uncertainties in predicting CCN concentrations. Published CCN closure uncertainties for six different sets of simpl...
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ftinfoscience:oai:infoscience.epfl.ch:257426 2024-02-27T08:38:21+00:00 Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint Moore, R. H. Karydis, V. A. Capps, S. L. Lathem, T. L. Nenes, Athanasios 2018-10-15T13:27:46Z http://infoscience.epfl.ch/record/257426 https://doi.org/10.5194/acp-13-4235-2013 https://infoscience.epfl.ch/record/257426/files/13-4235-2013-acp-13-4235-2013.pdf unknown http://infoscience.epfl.ch/record/257426 doi:10.5194/acp-13-4235-2013 https://infoscience.epfl.ch/record/257426/files/13-4235-2013-acp-13-4235-2013.pdf http://infoscience.epfl.ch/record/257426 Text 2018 ftinfoscience https://doi.org/10.5194/acp-13-4235-2013 2024-01-29T01:30:06Z We use the Global Modelling Initiative (GMI) chemical transport model with a cloud droplet parameterisation adjoint to quantify the sensitivity of cloud droplet number concentration to uncertainties in predicting CCN concentrations. Published CCN closure uncertainties for six different sets of simplifying compositional and mixing state assumptions are used as proxies for modelled CCN uncertainty arising from application of those scenarios. It is found that cloud droplet number concentrations (Nd) are fairly insensitive to the number concentration (Na) of aerosol which act as CCN over the continents (∂ lnNd/∂ lnNa ̃ 10-30 %), but the sensitivities exceed 70% in pristine regions such as the Alaskan Arctic and remote oceans. This means that CCN concentration uncertainties of 4-71% translate into only 1- 23% uncertainty in cloud droplet number, on average. Since most of the anthropogenic indirect forcing is concentrated over the continents, this work shows that the application of Köhler theory and attendant simplifying assumptions in models is not a major source of uncertainty in predicting cloud droplet number or anthropogenic aerosol indirect forcing for the liquid, stratiform clouds simulated in these models. However, it does highlight the sensitivity of some remote areas to pollution brought into the region via long-range transport (e.g., biomass burning) or from seasonal biogenic sources (e.g., phytoplankton as a source of dimethylsulfide in the southern oceans). Since these transient processes are not captured well by the climatological emissions inventories employed by current large-scale models, the uncertainties in aerosol-cloud interactions during these events could be much larger than those uncovered here. This finding motivates additional measurements in these pristine regions, for which few observations exist, to quantify the impact (and associated uncertainty) of transient aerosol processes on cloud properties. © Author(s) 2013. Text Arctic Phytoplankton EPFL Infoscience (Ecole Polytechnique Fédérale Lausanne) Arctic Atmospheric Chemistry and Physics 13 8 4235 4251 |
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EPFL Infoscience (Ecole Polytechnique Fédérale Lausanne) |
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We use the Global Modelling Initiative (GMI) chemical transport model with a cloud droplet parameterisation adjoint to quantify the sensitivity of cloud droplet number concentration to uncertainties in predicting CCN concentrations. Published CCN closure uncertainties for six different sets of simplifying compositional and mixing state assumptions are used as proxies for modelled CCN uncertainty arising from application of those scenarios. It is found that cloud droplet number concentrations (Nd) are fairly insensitive to the number concentration (Na) of aerosol which act as CCN over the continents (∂ lnNd/∂ lnNa ̃ 10-30 %), but the sensitivities exceed 70% in pristine regions such as the Alaskan Arctic and remote oceans. This means that CCN concentration uncertainties of 4-71% translate into only 1- 23% uncertainty in cloud droplet number, on average. Since most of the anthropogenic indirect forcing is concentrated over the continents, this work shows that the application of Köhler theory and attendant simplifying assumptions in models is not a major source of uncertainty in predicting cloud droplet number or anthropogenic aerosol indirect forcing for the liquid, stratiform clouds simulated in these models. However, it does highlight the sensitivity of some remote areas to pollution brought into the region via long-range transport (e.g., biomass burning) or from seasonal biogenic sources (e.g., phytoplankton as a source of dimethylsulfide in the southern oceans). Since these transient processes are not captured well by the climatological emissions inventories employed by current large-scale models, the uncertainties in aerosol-cloud interactions during these events could be much larger than those uncovered here. This finding motivates additional measurements in these pristine regions, for which few observations exist, to quantify the impact (and associated uncertainty) of transient aerosol processes on cloud properties. © Author(s) 2013. |
format |
Text |
author |
Moore, R. H. Karydis, V. A. Capps, S. L. Lathem, T. L. Nenes, Athanasios |
spellingShingle |
Moore, R. H. Karydis, V. A. Capps, S. L. Lathem, T. L. Nenes, Athanasios Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint |
author_facet |
Moore, R. H. Karydis, V. A. Capps, S. L. Lathem, T. L. Nenes, Athanasios |
author_sort |
Moore, R. H. |
title |
Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint |
title_short |
Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint |
title_full |
Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint |
title_fullStr |
Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint |
title_full_unstemmed |
Droplet number uncertainties associated with CCN: An assessment using observations and a global model adjoint |
title_sort |
droplet number uncertainties associated with ccn: an assessment using observations and a global model adjoint |
publishDate |
2018 |
url |
http://infoscience.epfl.ch/record/257426 https://doi.org/10.5194/acp-13-4235-2013 https://infoscience.epfl.ch/record/257426/files/13-4235-2013-acp-13-4235-2013.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Phytoplankton |
genre_facet |
Arctic Phytoplankton |
op_source |
http://infoscience.epfl.ch/record/257426 |
op_relation |
http://infoscience.epfl.ch/record/257426 doi:10.5194/acp-13-4235-2013 https://infoscience.epfl.ch/record/257426/files/13-4235-2013-acp-13-4235-2013.pdf |
op_doi |
https://doi.org/10.5194/acp-13-4235-2013 |
container_title |
Atmospheric Chemistry and Physics |
container_volume |
13 |
container_issue |
8 |
container_start_page |
4235 |
op_container_end_page |
4251 |
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1792045257115828224 |