Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization
<p>The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (<span class="inline-formula"><i>N</i><sub>CCN</sub></span>) data are scar...
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Copernicus Publications
2019-12-01
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Series: | Atmospheric Chemistry and Physics |
Online Access: | https://www.atmos-chem-phys.net/19/15483/2019/acp-19-15483-2019.pdf |
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record_format |
Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Y. Shen Y. Shen A. Virkkula A. Virkkula A. Virkkula A. Ding K. Luoma H. Keskinen H. Keskinen P. P. Aalto X. Chi X. Qi W. Nie X. Huang T. Petäjä T. Petäjä M. Kulmala V.-M. Kerminen |
spellingShingle |
Y. Shen Y. Shen A. Virkkula A. Virkkula A. Virkkula A. Ding K. Luoma H. Keskinen H. Keskinen P. P. Aalto X. Chi X. Qi W. Nie X. Huang T. Petäjä T. Petäjä M. Kulmala V.-M. Kerminen Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization Atmospheric Chemistry and Physics |
author_facet |
Y. Shen Y. Shen A. Virkkula A. Virkkula A. Virkkula A. Ding K. Luoma H. Keskinen H. Keskinen P. P. Aalto X. Chi X. Qi W. Nie X. Huang T. Petäjä T. Petäjä M. Kulmala V.-M. Kerminen |
author_sort |
Y. Shen |
title |
Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization |
title_short |
Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization |
title_full |
Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization |
title_fullStr |
Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization |
title_full_unstemmed |
Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization |
title_sort |
estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization |
publisher |
Copernicus Publications |
series |
Atmospheric Chemistry and Physics |
issn |
1680-7316 1680-7324 |
publishDate |
2019-12-01 |
description |
<p>The concentration of cloud condensation nuclei (CCN) is an essential
parameter affecting aerosol–cloud interactions within warm clouds. Long-term
CCN number concentration (<span class="inline-formula"><i>N</i><sub>CCN</sub></span>) data are scarce; there are a lot more
data on aerosol optical properties (AOPs). It is therefore valuable to
derive parameterizations for estimating <span class="inline-formula"><i>N</i><sub>CCN</sub></span> from AOP measurements. Such
parameterizations have already been made, and in the present work a new parameterization
is presented. The relationships between <span class="inline-formula"><i>N</i><sub>CCN</sub></span>, AOPs, and size
distributions were investigated based on in situ measurement data from six
stations in very different environments around the world. The relationships
were used for deriving a parameterization that depends on the scattering
Ångström exponent (SAE), backscatter fraction (BSF), and total
scattering coefficient (<span class="inline-formula"><i>σ</i><sub>sp</sub></span>) of PM<span class="inline-formula"><sub>10</sub></span> particles. The analysis
first showed that the dependence of <span class="inline-formula"><i>N</i><sub>CCN</sub></span> on supersaturation (SS) can be
described by a logarithmic fit in the range SS <span class="inline-formula"><1.1</span> %, without
any theoretical reasoning. The relationship between <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs was
parameterized as <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi>N</mi><mi mathvariant="normal">CCN</mi></msub><mo>≈</mo><mo>(</mo><mo>(</mo><mn mathvariant="normal">286</mn><mo>±</mo><mn mathvariant="normal">46</mn><mo>)</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="93pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="1db18230eee7b12e7ed258c4199f0c7d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-15483-2019-ie00001.svg" width="93pt" height="13pt" src="acp-19-15483-2019-ie00001.png"/></svg:svg></span></span>SAE ln(SS/(<span class="inline-formula">0.093±0.006</span>))(BSF <span class="inline-formula">−</span> BSF<span class="inline-formula"><sub>min</sub></span>) <span class="inline-formula">+</span> (<span class="inline-formula">5.2±3.3</span>))<span class="inline-formula"><i>σ</i><sub>sp</sub></span>, where BSF<span class="inline-formula"><sub>min</sub></span> is the minimum BSF, in practice
the 1st percentile of BSF data at a site to be analyzed. At the lowest
supersaturations of each site (SS <span class="inline-formula">≈0.1</span> %), the average bias,
defined as the ratio of the AOP-derived and measured <span class="inline-formula"><i>N</i><sub>CCN</sub></span>, varied from
<span class="inline-formula">∼0.7</span> to <span class="inline-formula">∼1.9</span> at most sites except at a
Himalayan site where the bias was <span class="inline-formula">>4</span>. At SS <span class="inline-formula">>0.4</span> % the average bias ranged from <span class="inline-formula">∼0.7</span> to <span class="inline-formula">∼1.3</span> at most sites. For the marine-aerosol-dominated site Ascension Island the bias was
higher, <span class="inline-formula">∼1.4</span>–1.9. In other words, at SS <span class="inline-formula">>0.4</span> % <span class="inline-formula"><i>N</i><sub>CCN</sub></span> was estimated with an average uncertainty of approximately
30 % by using nephelometer data. The biases were mainly due to the biases
in the parameterization related to the scattering Ångström exponent
(SAE). The squared correlation coefficients between the AOP-derived and
measured <span class="inline-formula"><i>N</i><sub>CCN</sub></span> varied from <span class="inline-formula">∼0.5</span> to <span class="inline-formula">∼0.8</span>. To
study the physical explanation of the relationships between <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and
AOPs, lognormal unimodal particle size distributions were generated and
<span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs were calculated. The simulation showed that the
relationships of <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs are affected by the geometric mean
diameter and width of the size distribution and the activation diameter. The
relationships of <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs were similar to those of the observed
ones.</p> |
url |
https://www.atmos-chem-phys.net/19/15483/2019/acp-19-15483-2019.pdf |
work_keys_str_mv |
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spelling |
doaj-5f3b1713ffee4d5e899243fd5582a8112020-11-25T01:53:41ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242019-12-0119154831550210.5194/acp-19-15483-2019Estimating cloud concentration nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterizationY. Shen0Y. Shen1A. Virkkula2A. Virkkula3A. Virkkula4A. Ding5K. Luoma6H. Keskinen7H. Keskinen8P. P. Aalto9X. Chi10X. Qi11W. Nie12X. Huang13T. Petäjä14T. Petäjä15M. Kulmala16V.-M. Kerminen17Joint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandAtmospheric Composition Research, Finnish Meteorological Institute, 00101 Helsinki, FinlandJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandHyytiälä Forestry Field Station, Hyytiäläntie 124, Korkeakoski FI 35500, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaJoint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, 00014 University of Helsinki, Helsinki, Finland<p>The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (<span class="inline-formula"><i>N</i><sub>CCN</sub></span>) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating <span class="inline-formula"><i>N</i><sub>CCN</sub></span> from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between <span class="inline-formula"><i>N</i><sub>CCN</sub></span>, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (<span class="inline-formula"><i>σ</i><sub>sp</sub></span>) of PM<span class="inline-formula"><sub>10</sub></span> particles. The analysis first showed that the dependence of <span class="inline-formula"><i>N</i><sub>CCN</sub></span> on supersaturation (SS) can be described by a logarithmic fit in the range SS <span class="inline-formula"><1.1</span> %, without any theoretical reasoning. The relationship between <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs was parameterized as <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi>N</mi><mi mathvariant="normal">CCN</mi></msub><mo>≈</mo><mo>(</mo><mo>(</mo><mn mathvariant="normal">286</mn><mo>±</mo><mn mathvariant="normal">46</mn><mo>)</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="93pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="1db18230eee7b12e7ed258c4199f0c7d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-15483-2019-ie00001.svg" width="93pt" height="13pt" src="acp-19-15483-2019-ie00001.png"/></svg:svg></span></span>SAE ln(SS/(<span class="inline-formula">0.093±0.006</span>))(BSF <span class="inline-formula">−</span> BSF<span class="inline-formula"><sub>min</sub></span>) <span class="inline-formula">+</span> (<span class="inline-formula">5.2±3.3</span>))<span class="inline-formula"><i>σ</i><sub>sp</sub></span>, where BSF<span class="inline-formula"><sub>min</sub></span> is the minimum BSF, in practice the 1st percentile of BSF data at a site to be analyzed. At the lowest supersaturations of each site (SS <span class="inline-formula">≈0.1</span> %), the average bias, defined as the ratio of the AOP-derived and measured <span class="inline-formula"><i>N</i><sub>CCN</sub></span>, varied from <span class="inline-formula">∼0.7</span> to <span class="inline-formula">∼1.9</span> at most sites except at a Himalayan site where the bias was <span class="inline-formula">>4</span>. At SS <span class="inline-formula">>0.4</span> % the average bias ranged from <span class="inline-formula">∼0.7</span> to <span class="inline-formula">∼1.3</span> at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, <span class="inline-formula">∼1.4</span>–1.9. In other words, at SS <span class="inline-formula">>0.4</span> % <span class="inline-formula"><i>N</i><sub>CCN</sub></span> was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Ångström exponent (SAE). The squared correlation coefficients between the AOP-derived and measured <span class="inline-formula"><i>N</i><sub>CCN</sub></span> varied from <span class="inline-formula">∼0.5</span> to <span class="inline-formula">∼0.8</span>. To study the physical explanation of the relationships between <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs, lognormal unimodal particle size distributions were generated and <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs were calculated. The simulation showed that the relationships of <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of <span class="inline-formula"><i>N</i><sub>CCN</sub></span> and AOPs were similar to those of the observed ones.</p>https://www.atmos-chem-phys.net/19/15483/2019/acp-19-15483-2019.pdf |