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Title: Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation

Abstract

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 model shave been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements(aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan.The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal andshort-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where dataare available, of –24% and –35% for particles with dry diameters <50 and <120 nm, as well as –36% and –34% for CCNat supersaturations of 0.2% and 1.0%, respectively. Yet, they seem to behave differently for particles activating at very low supersaturations (<0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N 3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN 0.2) compared to that for N 3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated o naverage by the models by 40% during winter and 20% in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB –13% and –22% for updraft velocities 0.3 and 0.6m s –1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration( N d / N a ) and to updraft velocity ( N d / w ). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities N d / N a and N d / w ; models may be predisposed to be too“aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well.This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [4]; ORCiD logo [5];  [6]; ORCiD logo [7];  [5];  [8];  [9]; ORCiD logo [10]; ORCiD logo [11];  [12];  [13];  [14]; ORCiD logo [11]; ORCiD logo [10]; ORCiD logo [15]; ORCiD logo [16] more »; ORCiD logo [17]; ORCiD logo [4];  [18]; ORCiD logo [16]; ORCiD logo [17]; ORCiD logo [18];  [5]; ORCiD logo [19];  [20]; ORCiD logo [15];  [1];  [21];  [7]; ORCiD logo [22];  [23];  [1];  [24]; ORCiD logo [21];  [12] « less
  1. Univ. of Crete, Heraklion (Greece)
  2. Ecole Polytechnique Federale Lausanne (Switzlerland); Foundation for Research & Technology-Hellas, Crete (Greece)
  3. NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States); Columbia Univ., New York, NY (United States)
  4. Royal Netherlands Meteorological Inst. (KNMI), De Bilt (Netherlands)
  5. Univ. of Leeds (United Kingdom)
  6. Norwegian Meteorological Inst., Oslo (Norway); Univ. of Oslo (Norway); Centre National de Recherches Météorologiques, Toulouse (France); Univ. of Helsinki (Finland)
  7. Cornell Univ., Ithaca, NY (United States)
  8. Max Planck Society, Mainz (Germany); Forschungszentrum Julich (Germany)
  9. Norwegian Meteorological Inst., Oslo (Norway)
  10. Colorado State Univ., Fort Collins, CO (United States)
  11. Univ. of Zurich (Switzerland)
  12. State Univ. of New York (SUNY), Albany, NY (United States)
  13. Finnish Meteorological Inst., Helsinki (Finland); Univ. of Helsinki (Finland)
  14. Nagoya Univ. (Japan)
  15. Paul Scherrer Inst. (PSI), Villigen (Switzerland)
  16. Univ. of Oxford (United Kingdom)
  17. Columbia Univ., New York, NY (United States); NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)
  18. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  19. Univ. of Bremen (Germany)
  20. Italian National Research Council, Bologna (Italy). Inst. of Atmospheric Sciences and Climate (ISAC)
  21. Univ. of Wyoming, Laramie, WY (United States)
  22. National Observatory of Athens, Penteli (Greece)
  23. Lund Univ. (Sweden)
  24. Max Planck Society, Mainz (Germany)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); European Research Council (ERC)
OSTI Identifier:
1548281
Report Number(s):
PNNL-SA-143854
Journal ID: ISSN 1680-7324
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Volume: 19; Journal Issue: 13; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Fanourgakis, George S., Kanakidou, Maria, Nenes, Athanasios, Bauer, Susanne E., Bergman, Tommi, Carslaw, Ken S., Grini, Alf, Hamilton, Douglas S., Johnson, Jill S., Karydis, Vlassis A., Kirkevåg, Alf, Kodros, John K., Lohmann, Ulrike, Luo, Gan, Makkonen, Risto, Matsui, Hitoshi, Neubauer, David, Pierce, Jeffrey R., Schmale, Julia, Stier, Philip, Tsigaridis, Kostas, van Noije, Twan, Wang, Hailong, Watson-Parris, Duncan, Westervelt, Daniel M., Yang, Yang, Yoshioka, Masaru, Daskalakis, Nikos, Decesari, Stefano, Gysel-Beer, Martin, Kalivitis, Nikos, Liu, Xiaohong, Mahowald, Natalie M., Myriokefalitakis, Stelios, Schrödner, Roland, Sfakianaki, Maria, Tsimpidi, Alexandra P., Wu, Mingxuan, and Yu, Fangqun. Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation. United States: N. p., 2019. Web. doi:10.5194/acp-19-8591-2019.
Fanourgakis, George S., Kanakidou, Maria, Nenes, Athanasios, Bauer, Susanne E., Bergman, Tommi, Carslaw, Ken S., Grini, Alf, Hamilton, Douglas S., Johnson, Jill S., Karydis, Vlassis A., Kirkevåg, Alf, Kodros, John K., Lohmann, Ulrike, Luo, Gan, Makkonen, Risto, Matsui, Hitoshi, Neubauer, David, Pierce, Jeffrey R., Schmale, Julia, Stier, Philip, Tsigaridis, Kostas, van Noije, Twan, Wang, Hailong, Watson-Parris, Duncan, Westervelt, Daniel M., Yang, Yang, Yoshioka, Masaru, Daskalakis, Nikos, Decesari, Stefano, Gysel-Beer, Martin, Kalivitis, Nikos, Liu, Xiaohong, Mahowald, Natalie M., Myriokefalitakis, Stelios, Schrödner, Roland, Sfakianaki, Maria, Tsimpidi, Alexandra P., Wu, Mingxuan, & Yu, Fangqun. Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation. United States. doi:10.5194/acp-19-8591-2019.
Fanourgakis, George S., Kanakidou, Maria, Nenes, Athanasios, Bauer, Susanne E., Bergman, Tommi, Carslaw, Ken S., Grini, Alf, Hamilton, Douglas S., Johnson, Jill S., Karydis, Vlassis A., Kirkevåg, Alf, Kodros, John K., Lohmann, Ulrike, Luo, Gan, Makkonen, Risto, Matsui, Hitoshi, Neubauer, David, Pierce, Jeffrey R., Schmale, Julia, Stier, Philip, Tsigaridis, Kostas, van Noije, Twan, Wang, Hailong, Watson-Parris, Duncan, Westervelt, Daniel M., Yang, Yang, Yoshioka, Masaru, Daskalakis, Nikos, Decesari, Stefano, Gysel-Beer, Martin, Kalivitis, Nikos, Liu, Xiaohong, Mahowald, Natalie M., Myriokefalitakis, Stelios, Schrödner, Roland, Sfakianaki, Maria, Tsimpidi, Alexandra P., Wu, Mingxuan, and Yu, Fangqun. Mon . "Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation". United States. doi:10.5194/acp-19-8591-2019. https://www.osti.gov/servlets/purl/1548281.
@article{osti_1548281,
title = {Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation},
author = {Fanourgakis, George S. and Kanakidou, Maria and Nenes, Athanasios and Bauer, Susanne E. and Bergman, Tommi and Carslaw, Ken S. and Grini, Alf and Hamilton, Douglas S. and Johnson, Jill S. and Karydis, Vlassis A. and Kirkevåg, Alf and Kodros, John K. and Lohmann, Ulrike and Luo, Gan and Makkonen, Risto and Matsui, Hitoshi and Neubauer, David and Pierce, Jeffrey R. and Schmale, Julia and Stier, Philip and Tsigaridis, Kostas and van Noije, Twan and Wang, Hailong and Watson-Parris, Duncan and Westervelt, Daniel M. and Yang, Yang and Yoshioka, Masaru and Daskalakis, Nikos and Decesari, Stefano and Gysel-Beer, Martin and Kalivitis, Nikos and Liu, Xiaohong and Mahowald, Natalie M. and Myriokefalitakis, Stelios and Schrödner, Roland and Sfakianaki, Maria and Tsimpidi, Alexandra P. and Wu, Mingxuan and Yu, Fangqun},
abstractNote = {A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 model shave been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements(aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan.The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal andshort-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where dataare available, of –24% and –35% for particles with dry diameters <50 and <120 nm, as well as –36% and –34% for CCNat supersaturations of 0.2% and 1.0%, respectively. Yet, they seem to behave differently for particles activating at very low supersaturations (<0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated o naverage by the models by 40% during winter and 20% in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB –13% and –22% for updraft velocities 0.3 and 0.6m s–1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration(∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too“aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well.This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.},
doi = {10.5194/acp-19-8591-2019},
journal = {Atmospheric Chemistry and Physics (Online)},
issn = {1680-7324},
number = 13,
volume = 19,
place = {United States},
year = {2019},
month = {7}
}

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journal, January 2015

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  • Geoscientific Model Development, Vol. 8, Issue 3
  • DOI: 10.5194/gmd-8-631-2015

Development of a global aerosol model using a two-dimensional sectional method: 1. Model design: 2-D SECTIONAL GLOBAL AEROSOL MODEL 1
journal, August 2017

  • Matsui, H.
  • Journal of Advances in Modeling Earth Systems, Vol. 9, Issue 4
  • DOI: 10.1002/2017MS000936

Gas/aerosol partitioning 2. Global modeling results
journal, January 2002


The influence of chemical composition and mixing state of Los Angeles urban aerosol on CCN number and cloud properties
journal, January 2008

  • Cubison, M. J.; Ervens, B.; Feingold, G.
  • Atmospheric Chemistry and Physics, Vol. 8, Issue 18
  • DOI: 10.5194/acp-8-5649-2008

Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom
journal, January 2006

  • Dentener, F.; Kinne, S.; Bond, T.
  • Atmospheric Chemistry and Physics, Vol. 6, Issue 12
  • DOI: 10.5194/acp-6-4321-2006

Gas/aerosol partitioning: 1. A computationally efficient model
journal, January 2002


Size Matters More Than Chemistry for Cloud-Nucleating Ability of Aerosol Particles
journal, June 2006


Quantification of the volatility of secondary organic compounds in ultrafine particles during nucleation events
journal, January 2011

  • Pierce, J. R.; Riipinen, I.; Kulmala, M.
  • Atmospheric Chemistry and Physics, Vol. 11, Issue 17
  • DOI: 10.5194/acp-11-9019-2011

Biomass-burning impact on CCN number, hygroscopicity and cloud formation during summertime in the eastern Mediterranean
journal, January 2016

  • Bougiatioti, Aikaterini; Bezantakos, Spiros; Stavroulas, Iasonas
  • Atmospheric Chemistry and Physics, Vol. 16, Issue 11
  • DOI: 10.5194/acp-16-7389-2016

Cloud condensation nuclei prediction error from application of Köhler theory: Importance for the aerosol indirect effect
journal, January 2007

  • Sotiropoulou, Rafaella-Eleni P.; Nenes, Athanasios; Adams, Peter J.
  • Journal of Geophysical Research, Vol. 112, Issue D12
  • DOI: 10.1029/2006JD007834

Investigation of global particulate nitrate from the AeroCom phase III experiment
journal, January 2017

  • Bian, Huisheng; Chin, Mian; Hauglustaine, Didier A.
  • Atmospheric Chemistry and Physics, Vol. 17, Issue 21
  • DOI: 10.5194/acp-17-12911-2017

Atmospheric nucleation: highlights of the EUCAARI project and future directions
journal, January 2010

  • Kerminen, V. -M.; Petäjä, T.; Manninen, H. E.
  • Atmospheric Chemistry and Physics, Vol. 10, Issue 22
  • DOI: 10.5194/acp-10-10829-2010

Regional new particle formation as modulators of cloud condensation nuclei and cloud droplet number in the eastern Mediterranean
journal, January 2019

  • Kalkavouras, Panayiotis; Bougiatioti, Aikaterini; Kalivitis, Nikos
  • Atmospheric Chemistry and Physics, Vol. 19, Issue 9
  • DOI: 10.5194/acp-19-6185-2019

Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories
journal, January 2018

  • Schmale, Julia; Henning, Silvia; Decesari, Stefano
  • Atmospheric Chemistry and Physics, Vol. 18, Issue 4
  • DOI: 10.5194/acp-18-2853-2018

Development of two-moment cloud microphysics for liquid and ice within the NASA Goddard Earth Observing System Model (GEOS-5)
journal, January 2014

  • Barahona, D.; Molod, A.; Bacmeister, J.
  • Geoscientific Model Development, Vol. 7, Issue 4
  • DOI: 10.5194/gmd-7-1733-2014

Droplet activation parameterization: the population-splitting concept revisited
journal, January 2014


The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei
journal, January 2013

  • Lee, L. A.; Pringle, K. J.; Reddington, C. L.
  • Atmospheric Chemistry and Physics, Vol. 13, Issue 17
  • DOI: 10.5194/acp-13-8879-2013

Sources, sinks, and transatlantic transport of North African dust aerosol: A multimodel analysis and comparison with remote sensing data
journal, May 2014

  • Kim, Dongchul; Chin, Mian; Yu, Hongbin
  • Journal of Geophysical Research: Atmospheres, Vol. 119, Issue 10
  • DOI: 10.1002/2013JD021099

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model
journal, January 2014


Submicron NE Atlantic marine aerosol chemical composition and abundance: Seasonal trends and air mass categorization: Seasonal Trends of Marine Aerosol
journal, October 2014

  • Ovadnevaite, Jurgita; Ceburnis, Darius; Leinert, Stephan
  • Journal of Geophysical Research: Atmospheres, Vol. 119, Issue 20
  • DOI: 10.1002/2013JD021330

Source attribution of aerosol size distributions and model evaluation using Whistler Mountain measurements and GEOS-Chem-TOMAS simulations
journal, January 2016

  • D&apos;Andrea, S. D.; Ng, J. Y.; Kodros, J. K.
  • Atmospheric Chemistry and Physics, Vol. 16, Issue 1
  • DOI: 10.5194/acp-16-383-2016

Exploring the vertical profile of atmospheric organic aerosol: comparing 17 aircraft field campaigns with a global model
journal, January 2011


A parameterization of aerosol activation: 2. Multiple aerosol types
journal, March 2000

  • Abdul-Razzak, Hayder; Ghan, Steven J.
  • Journal of Geophysical Research: Atmospheres, Vol. 105, Issue D5
  • DOI: 10.1029/1999JD901161

The nucleus in and the growth of hygroscopic droplets
journal, January 1936


Parameterization of cloud droplet formation in global climate models
journal, January 2003


Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system
journal, May 2016

  • Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 21
  • DOI: 10.1073/pnas.1514043113

On the formation and growth of atmospheric nanoparticles
journal, November 2008


Cloud condensation nuclei measurements in the marine boundary layer of the Eastern Mediterranean: CCN closure and droplet growth kinetics
journal, January 2009

  • Bougiatioti, A.; Fountoukis, C.; Kalivitis, N.
  • Atmospheric Chemistry and Physics, Vol. 9, Issue 18
  • DOI: 10.5194/acp-9-7053-2009

A 17 month climatology of the cloud condensation nuclei number concentration at the high alpine site Jungfraujoch
journal, January 2011

  • Jurányi, Z.; Gysel, M.; Weingartner, E.
  • Journal of Geophysical Research, Vol. 116, Issue D10
  • DOI: 10.1029/2010JD015199

Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint
journal, January 2013

  • Moore, R. H.; Karydis, V. A.; Capps, S. L.
  • Atmospheric Chemistry and Physics, Vol. 13, Issue 8
  • DOI: 10.5194/acp-13-4235-2013

The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing
journal, January 2018

  • Johnson, Jill S.; Regayre, Leighton A.; Yoshioka, Masaru
  • Atmospheric Chemistry and Physics, Vol. 18, Issue 17
  • DOI: 10.5194/acp-18-13031-2018

Predicting global aerosol size distributions in general circulation models
journal, January 2002


Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges
journal, November 2016

  • Fan, Jiwen; Wang, Yuan; Rosenfeld, Daniel
  • Journal of the Atmospheric Sciences, Vol. 73, Issue 11
  • DOI: 10.1175/JAS-D-16-0037.1

Boreal forests, aerosols and the impacts on clouds and climate
journal, September 2008

  • Spracklen, Dominick V.; Bonn, Boris; Carslaw, Kenneth S.
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 366, Issue 1885
  • DOI: 10.1098/rsta.2008.0201

The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 1: Aerosol evaluation
journal, January 2019

  • Tegen, Ina; Neubauer, David; Ferrachat, Sylvaine
  • Geoscientific Model Development, Vol. 12, Issue 4
  • DOI: 10.5194/gmd-12-1643-2019

Parameterization of cloud droplet formation in large-scale models: Including effects of entrainment
journal, January 2007

  • Barahona, Donifan; Nenes, Athanasios
  • Journal of Geophysical Research, Vol. 112, Issue D16
  • DOI: 10.1029/2007JD008473