Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations
<p>Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of...
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Copernicus Publications
2020-06-01
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Series: | Atmospheric Chemistry and Physics |
Online Access: | https://www.atmos-chem-phys.net/20/7167/2020/acp-20-7167-2020.pdf |
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author | D. Painemal D. Painemal F.-L. Chang F.-L. Chang R. Ferrare S. Burton Z. Li Z. Li W. L. Smith Jr. P. Minnis P. Minnis Y. Feng M. Clayton M. Clayton |
author_facet | D. Painemal D. Painemal F.-L. Chang F.-L. Chang R. Ferrare S. Burton Z. Li Z. Li W. L. Smith Jr. P. Minnis P. Minnis Y. Feng M. Clayton M. Clayton |
author_sort | D. Painemal |
collection | DOAJ |
description | <p>Satellite quantification of aerosol effects on clouds relies on
aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud
condensation nuclei (CCN). However, the lack of error characterization of
satellite-based results hampers their use for the evaluation and improvement
of global climate models. We show that the use of AOD for assessing
aerosol–cloud interactions (ACIs) is inadequate over vast oceanic areas in
the subtropics. Instead, we postulate that a more physical approach that
consists of matching vertically resolved aerosol data from the Cloud-Aerosol
Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at
the cloud-layer height with Moderate Resolution Imaging
Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in
satellite-based ACI estimates. Combined aerosol extinction coefficients
(<span class="inline-formula"><i>σ</i>)</span> below cloud top (<span class="inline-formula"><i>σ</i><sub>BC</sub>)</span> from the Cloud-Aerosol Lidar
with Orthogonal Polarization (CALIOP) and cloud droplet number
concentrations (<span class="inline-formula"><i>N</i><sub>d</sub></span>) from MODIS Aqua yield high correlations across a
broad range of <span class="inline-formula"><i>σ</i><sub>BC</sub></span> values, with <span class="inline-formula"><i>σ</i><sub>BC</sub></span> quartile
correlations <span class="inline-formula">≥0.78</span>. In contrast, CALIOP-based AOD yields correlations
with MODIS <span class="inline-formula"><i>N</i><sub>d</sub></span> of 0.54–0.62 for the two lower AOD quartiles. Moreover,
<span class="inline-formula"><i>σ</i><sub>BC</sub></span> explains 41 % of the spatial variance in MODIS <span class="inline-formula"><i>N</i><sub>d</sub></span>,
whereas AOD only explains 17 %, primarily caused by the lack of spatial
covariability in the eastern Pacific. Compared with <span class="inline-formula"><i>σ</i><sub>BC</sub></span>,
near-surface <span class="inline-formula"><i>σ</i></span> weakly correlates in space with MODIS <span class="inline-formula"><i>N</i><sub>d</sub></span>,
accounting for a 16 % variance. It is concluded that the linear regression
calculated from ln(<span class="inline-formula"><i>N</i><sub>d</sub>)</span>–ln(<span class="inline-formula"><i>σ</i><sub>BC</sub>)</span> (the standard method for
quantifying ACIs) is more physically meaningful than that derived from the
<span class="inline-formula"><i>N</i><sub>d</sub></span>–AOD pair.</p> |
first_indexed | 2024-12-22T02:40:34Z |
format | Article |
id | doaj.art-d00181abde45489ebf0182a66611d226 |
institution | Directory Open Access Journal |
issn | 1680-7316 1680-7324 |
language | English |
last_indexed | 2024-12-22T02:40:34Z |
publishDate | 2020-06-01 |
publisher | Copernicus Publications |
record_format | Article |
series | Atmospheric Chemistry and Physics |
spelling | doaj.art-d00181abde45489ebf0182a66611d2262022-12-21T18:41:40ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242020-06-01207167717710.5194/acp-20-7167-2020Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observationsD. Painemal0D. Painemal1F.-L. Chang2F.-L. Chang3R. Ferrare4S. Burton5Z. Li6Z. Li7W. L. Smith Jr.8P. Minnis9P. Minnis10Y. Feng11M. Clayton12M. Clayton13Science Systems and Applications Inc., Hampton, Virginia 23666, USANASA Langley Research Center, Hampton, Virginia 23691 USAScience Systems and Applications Inc., Hampton, Virginia 23666, USANASA Langley Research Center, Hampton, Virginia 23691 USANASA Langley Research Center, Hampton, Virginia 23691 USANASA Langley Research Center, Hampton, Virginia 23691 USAScience Systems and Applications Inc., Hampton, Virginia 23666, USANASA Langley Research Center, Hampton, Virginia 23691 USANASA Langley Research Center, Hampton, Virginia 23691 USAScience Systems and Applications Inc., Hampton, Virginia 23666, USANASA Langley Research Center, Hampton, Virginia 23691 USAArgonne National Laboratory, Lemont, Illinois 60439, USAScience Systems and Applications Inc., Hampton, Virginia 23666, USANASA Langley Research Center, Hampton, Virginia 23691 USA<p>Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol–cloud interactions (ACIs) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (<span class="inline-formula"><i>σ</i>)</span> below cloud top (<span class="inline-formula"><i>σ</i><sub>BC</sub>)</span> from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (<span class="inline-formula"><i>N</i><sub>d</sub></span>) from MODIS Aqua yield high correlations across a broad range of <span class="inline-formula"><i>σ</i><sub>BC</sub></span> values, with <span class="inline-formula"><i>σ</i><sub>BC</sub></span> quartile correlations <span class="inline-formula">≥0.78</span>. In contrast, CALIOP-based AOD yields correlations with MODIS <span class="inline-formula"><i>N</i><sub>d</sub></span> of 0.54–0.62 for the two lower AOD quartiles. Moreover, <span class="inline-formula"><i>σ</i><sub>BC</sub></span> explains 41 % of the spatial variance in MODIS <span class="inline-formula"><i>N</i><sub>d</sub></span>, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with <span class="inline-formula"><i>σ</i><sub>BC</sub></span>, near-surface <span class="inline-formula"><i>σ</i></span> weakly correlates in space with MODIS <span class="inline-formula"><i>N</i><sub>d</sub></span>, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(<span class="inline-formula"><i>N</i><sub>d</sub>)</span>–ln(<span class="inline-formula"><i>σ</i><sub>BC</sub>)</span> (the standard method for quantifying ACIs) is more physically meaningful than that derived from the <span class="inline-formula"><i>N</i><sub>d</sub></span>–AOD pair.</p>https://www.atmos-chem-phys.net/20/7167/2020/acp-20-7167-2020.pdf |
spellingShingle | D. Painemal D. Painemal F.-L. Chang F.-L. Chang R. Ferrare S. Burton Z. Li Z. Li W. L. Smith Jr. P. Minnis P. Minnis Y. Feng M. Clayton M. Clayton Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations Atmospheric Chemistry and Physics |
title | Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations |
title_full | Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations |
title_fullStr | Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations |
title_full_unstemmed | Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations |
title_short | Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations |
title_sort | reducing uncertainties in satellite estimates of aerosol cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations |
url | https://www.atmos-chem-phys.net/20/7167/2020/acp-20-7167-2020.pdf |
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