What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?

Aerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuc...

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Main Authors: Schmale, J., Henning, S., Decesari, S., Henzing, B., Keskinen, H., Paramonov, M., Sellegri, K., Ovadnevaite, J., Pöhlker, M., Brito, J., Bougiatioti, A., Kristensson, A., Kalivitis, N., Stavroulas, I., Carbone, S., Jefferson, A., Park, M., Schlag, P., Iwamoto, Y., Aalto, P., Äijälä, M., Bukowiecki, N., Ehn, M., Frank, G., Fröhlich, R., Frumau, A., Herrmann, E., Herrmann, H., Holzinger, R., Kos, G., Kulmala, M., Mihalopoulos, N., Nenes, A., O'Dowd, C., Petäjä, T., Picard, D., Pöhlker, C., Pöschl, U., Poulain, L., Prévôt, A., Swietlicki, E., Andreae, M., Artaxo, P., Wiedensohler, A., Ogren, J., Matsuki, A., Yum, S., Stratmann, F., Baltensperger, U., Gysel, M.
Format: Report
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/11858/00-001M-0000-002E-16EF-2
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spelling ftpubman:oai:pure.mpg.de:item_2492308 2023-08-20T04:05:32+02:00 What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories? Schmale, J. Henning, S. Decesari, S. Henzing, B. Keskinen, H. Paramonov, M. Sellegri, K. Ovadnevaite, J. Pöhlker, M. Brito, J. Bougiatioti, A. Kristensson, A. Kalivitis, N. Stavroulas, I. Carbone, S. Jefferson, A. Park, M. Schlag, P. Iwamoto, Y. Aalto, P. Äijälä, M. Bukowiecki, N. Ehn, M. Frank, G. Fröhlich, R. Frumau, A. Herrmann, E. Herrmann, H. Holzinger, R. Kos, G. Kulmala, M. Mihalopoulos, N. Nenes, A. O'Dowd, C. Petäjä, T. Picard, D. Pöhlker, C. Pöschl, U. Poulain, L. Prévôt, A. Swietlicki, E. Andreae, M. Artaxo, P. Wiedensohler, A. Ogren, J. Matsuki, A. Yum, S. Stratmann, F. Baltensperger, U. Gysel, M. 2017 http://hdl.handle.net/11858/00-001M-0000-002E-16EF-2 eng eng info:eu-repo/semantics/altIdentifier/doi/10.5194/acp-2017-798 http://hdl.handle.net/11858/00-001M-0000-002E-16EF-2 Atmospheric Chemistry and Physics Discussions info:eu-repo/semantics/workingPaper 2017 ftpubman https://doi.org/10.5194/acp-2017-798 2023-08-01T22:03:05Z Aerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Numerous observations of CCN number concentration exist, and many closure studies have been performed to predict CCN number concentrations based on particle number size distributions, chemical composition, and the κ-Köhler theory. Most of these studies provide details for short time periods or focus on special environmental conditions. These observations, however, cannot address questions of large-scale temporal and spatial CCN variability. Here we analyze long-term observations of CCN number concentrations, particle number size distributions and chemical composition from twelve sites on three continents. Eight of these stations are part of the European Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS). We group the observatories into categories according to their official classification: coastal background (Barrow, Alaska; Mace Head, Ireland; Finokalia, Crete; Noto Peninsula, Japan), rural background (Melpitz, Germany; Cabauw, the Netherlands; Vavihill, Sweden), alpine sites (Puy de Dôme, France; Jungfraujoch, Switzerland), remote forest sites (ATTO, Brazil; SMEAR, Finland) and the urban environment (Seoul, South Korea). Expectedly, CCN characteristics are highly variable across regions. However, they also vary within categories, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behavior, most continental stations exhibit very similar relative activation ratios across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the activation ratios spread more widely across the SS spectrum. Several stations show strong seasonal cycles of CCN number concentrations and ... Report Barrow Alaska Max Planck Society: MPG.PuRe Mace ENVELOPE(155.883,155.883,-81.417,-81.417) Noto ENVELOPE(-60.811,-60.811,-62.471,-62.471)
institution Open Polar
collection Max Planck Society: MPG.PuRe
op_collection_id ftpubman
language English
description Aerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Numerous observations of CCN number concentration exist, and many closure studies have been performed to predict CCN number concentrations based on particle number size distributions, chemical composition, and the κ-Köhler theory. Most of these studies provide details for short time periods or focus on special environmental conditions. These observations, however, cannot address questions of large-scale temporal and spatial CCN variability. Here we analyze long-term observations of CCN number concentrations, particle number size distributions and chemical composition from twelve sites on three continents. Eight of these stations are part of the European Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS). We group the observatories into categories according to their official classification: coastal background (Barrow, Alaska; Mace Head, Ireland; Finokalia, Crete; Noto Peninsula, Japan), rural background (Melpitz, Germany; Cabauw, the Netherlands; Vavihill, Sweden), alpine sites (Puy de Dôme, France; Jungfraujoch, Switzerland), remote forest sites (ATTO, Brazil; SMEAR, Finland) and the urban environment (Seoul, South Korea). Expectedly, CCN characteristics are highly variable across regions. However, they also vary within categories, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behavior, most continental stations exhibit very similar relative activation ratios across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the activation ratios spread more widely across the SS spectrum. Several stations show strong seasonal cycles of CCN number concentrations and ...
format Report
author Schmale, J.
Henning, S.
Decesari, S.
Henzing, B.
Keskinen, H.
Paramonov, M.
Sellegri, K.
Ovadnevaite, J.
Pöhlker, M.
Brito, J.
Bougiatioti, A.
Kristensson, A.
Kalivitis, N.
Stavroulas, I.
Carbone, S.
Jefferson, A.
Park, M.
Schlag, P.
Iwamoto, Y.
Aalto, P.
Äijälä, M.
Bukowiecki, N.
Ehn, M.
Frank, G.
Fröhlich, R.
Frumau, A.
Herrmann, E.
Herrmann, H.
Holzinger, R.
Kos, G.
Kulmala, M.
Mihalopoulos, N.
Nenes, A.
O'Dowd, C.
Petäjä, T.
Picard, D.
Pöhlker, C.
Pöschl, U.
Poulain, L.
Prévôt, A.
Swietlicki, E.
Andreae, M.
Artaxo, P.
Wiedensohler, A.
Ogren, J.
Matsuki, A.
Yum, S.
Stratmann, F.
Baltensperger, U.
Gysel, M.
spellingShingle Schmale, J.
Henning, S.
Decesari, S.
Henzing, B.
Keskinen, H.
Paramonov, M.
Sellegri, K.
Ovadnevaite, J.
Pöhlker, M.
Brito, J.
Bougiatioti, A.
Kristensson, A.
Kalivitis, N.
Stavroulas, I.
Carbone, S.
Jefferson, A.
Park, M.
Schlag, P.
Iwamoto, Y.
Aalto, P.
Äijälä, M.
Bukowiecki, N.
Ehn, M.
Frank, G.
Fröhlich, R.
Frumau, A.
Herrmann, E.
Herrmann, H.
Holzinger, R.
Kos, G.
Kulmala, M.
Mihalopoulos, N.
Nenes, A.
O'Dowd, C.
Petäjä, T.
Picard, D.
Pöhlker, C.
Pöschl, U.
Poulain, L.
Prévôt, A.
Swietlicki, E.
Andreae, M.
Artaxo, P.
Wiedensohler, A.
Ogren, J.
Matsuki, A.
Yum, S.
Stratmann, F.
Baltensperger, U.
Gysel, M.
What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
author_facet Schmale, J.
Henning, S.
Decesari, S.
Henzing, B.
Keskinen, H.
Paramonov, M.
Sellegri, K.
Ovadnevaite, J.
Pöhlker, M.
Brito, J.
Bougiatioti, A.
Kristensson, A.
Kalivitis, N.
Stavroulas, I.
Carbone, S.
Jefferson, A.
Park, M.
Schlag, P.
Iwamoto, Y.
Aalto, P.
Äijälä, M.
Bukowiecki, N.
Ehn, M.
Frank, G.
Fröhlich, R.
Frumau, A.
Herrmann, E.
Herrmann, H.
Holzinger, R.
Kos, G.
Kulmala, M.
Mihalopoulos, N.
Nenes, A.
O'Dowd, C.
Petäjä, T.
Picard, D.
Pöhlker, C.
Pöschl, U.
Poulain, L.
Prévôt, A.
Swietlicki, E.
Andreae, M.
Artaxo, P.
Wiedensohler, A.
Ogren, J.
Matsuki, A.
Yum, S.
Stratmann, F.
Baltensperger, U.
Gysel, M.
author_sort Schmale, J.
title What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
title_short What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
title_full What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
title_fullStr What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
title_full_unstemmed What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
title_sort what do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?
publishDate 2017
url http://hdl.handle.net/11858/00-001M-0000-002E-16EF-2
long_lat ENVELOPE(155.883,155.883,-81.417,-81.417)
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Noto
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op_source Atmospheric Chemistry and Physics Discussions
op_relation info:eu-repo/semantics/altIdentifier/doi/10.5194/acp-2017-798
http://hdl.handle.net/11858/00-001M-0000-002E-16EF-2
op_doi https://doi.org/10.5194/acp-2017-798
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