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Climatological‐Scale Analysis of Intensive and Semi‐intensive Aerosol Parameters Derived From AERONET Retrievals Over the Arctic

DOI:10.1029/2019JD031569
Authors:
Y. Abo El-Fetouh
Y. Abo El-Fetouh
Y. Abo El-Fetouh
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N. T. O'Neill
N. T. O'Neill
N. T. O'Neill
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K. Ranjbar
K. Ranjbar
K. Ranjbar
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Sareh Hesaraki
Sareh Hesaraki
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Abstract and Figures

We investigated the climatological‐scale, monthly binned, seasonal variation of AERONET/Dubovik retrievals across six stations in the North American and European Arctic (multiyear sampling periods ranging from 8 to 17 years). A robust, spring‐to‐summer (StoS) increase in the radius of the peak of the fine mode (FM) component of the particle size distribution (PSD) was observed for five of the six stations. The FM aerosol optical depth (AOD) and the FM effective radius at the individual stations showed, respectively, a negligible to moderate StoS decrease and a significant increase. This was interpreted as a trade‐off between the waning influence of smaller FM Arctic haze aerosols and the increasing influence of large FM smoke particles. A springtime, pan‐Arctic PSD peak in the 1.3 μm coarse mode (CM) bin was attributed to Asian dust. It was suggested that the increase in amplitude of a second (4–7 μm) CM peak from July to August at the low‐elevation coastal sites was influenced by wind‐induced sea salt. The CM AOD went through a StoS decrease attributed to the decreasing amplitude of the 1.3 μm peak. A significant StoS CM effective radius increase was ascribed to the decreasing influence of the 1.3 μm peak. StoS FM fraction increases were largely due to the decrease of the CM AOD (decreasing influence of springtime Asian dust). This extensive and intensive climatology of remotely sensed, bimodal properties will, we believe, provide an important reference for future measurements and modeling of Arctic aerosols.
Monthly AOD (arithmetic) averages for different aerosol species as generated by the GEOS‐Chem model at a wavelength of 550 nm (Hesaraki et al. (2017)). The native resolution of the simulations was 6 hr. The monthly averaged AODs were themselves arithmetically averaged across five Arctic stations: OPAL and PEARL, Resolute Bay, Barrow, and Thule over the 2009–2012 period. (a) FM AOD seasonal variation, (b) CM AOD seasonal variation. Acronyms: OC = organic carbon, BC = black carbon, SS = sea salt, SO4 = sulfates.
Monthly AOD (arithmetic) averages for different aerosol species as generated by the GEOS‐Chem model at a wavelength of 550 nm (Hesaraki et al. (2017)). The native resolution of the simulations was 6 hr. The monthly averaged AODs were themselves arithmetically averaged across five Arctic stations: OPAL and PEARL, Resolute Bay, Barrow, and Thule over the 2009–2012 period. (a) FM AOD seasonal variation, (b) CM AOD seasonal variation. Acronyms: OC = organic carbon, BC = black carbon, SS = sea salt, SO4 = sulfates.
… 
A map showing the research sites whose data were employed in this study. The five Arctic stations covering the North American Arctic and the single station representing the eastern part of the Euro‐Asian Arctic (Hornsund) are shown in red. Four southern stations, shown in gray, were considered for comparative purposes.
A map showing the research sites whose data were employed in this study. The five Arctic stations covering the North American Arctic and the single station representing the eastern part of the Euro‐Asian Arctic (Hornsund) are shown in red. Four southern stations, shown in gray, were considered for comparative purposes.
… 
Monthly (arithmetic) averaged volume PSDs across the MYSP for each of the six Arctic stations as a function of particle radius (μm). The curves are colored with respect to months; blue/green represent the spring months, orange/red the summer months, and the purple/bright pink curves the fall months. The total number of retrievals for each month over the whole MYSP are indicated in the legend (in parentheses). Both the PSD axes and the radius axes are logarithmic: The left, center, and right bin radius coordinates for the 22 PSD bins whose center point PSD values are seen on each graph are defined in Table S1 in the supporting information. The gray arrows indicate the position of the 1.3‐μm springtime peak, while the dashed vertical lines show the range of movement of the fine mode peak between spring (pink dashed line) and summer (brown line). In two cases (May and June at the western most station of Hornsund) the springtime peak appears at 1.71 μm.
Monthly (arithmetic) averaged volume PSDs across the MYSP for each of the six Arctic stations as a function of particle radius (μm). The curves are colored with respect to months; blue/green represent the spring months, orange/red the summer months, and the purple/bright pink curves the fall months. The total number of retrievals for each month over the whole MYSP are indicated in the legend (in parentheses). Both the PSD axes and the radius axes are logarithmic: The left, center, and right bin radius coordinates for the 22 PSD bins whose center point PSD values are seen on each graph are defined in Table S1 in the supporting information. The gray arrows indicate the position of the 1.3‐μm springtime peak, while the dashed vertical lines show the range of movement of the fine mode peak between spring (pink dashed line) and summer (brown line). In two cases (May and June at the western most station of Hornsund) the springtime peak appears at 1.71 μm.
… 
(left‐hand graphs) Histograms of τa, τc, and τf in log (base 10) space for the total data ensemble (all stations encompassed by all their respective MYSPs). (right‐hand graphs) Linear histograms of reff,a, reff,c, and reff,f for the same data ensemble as the left‐hand graphs. The first, second and third rows show histograms of, respectively; τa and reff,a, τc and reff,c, and τf and reff,f. Light and dark color shades indicate, respectively, spring (April and May) and summer (June to September). It is important to note that the spring and summer histograms are stacked and not superimposed histograms. N represents the total number of observations. The average and standard deviation (which in the case of the log(τf) histogram is σ(logτf) = μf) are also listed together with, if any, the number of retrievals that are located beyond or below the chosen x axis limits. The appropriate fashion for reporting the geometric statistics is 〈log(τf)〉 ± σ (log τx) or hence τg,x × μx±1. Site‐specific figures analogous to this ensemble figure are presented in the supporting information.
(left‐hand graphs) Histograms of τa, τc, and τf in log (base 10) space for the total data ensemble (all stations encompassed by all their respective MYSPs). (right‐hand graphs) Linear histograms of reff,a, reff,c, and reff,f for the same data ensemble as the left‐hand graphs. The first, second and third rows show histograms of, respectively; τa and reff,a, τc and reff,c, and τf and reff,f. Light and dark color shades indicate, respectively, spring (April and May) and summer (June to September). It is important to note that the spring and summer histograms are stacked and not superimposed histograms. N represents the total number of observations. The average and standard deviation (which in the case of the log(τf) histogram is σ(logτf) = μf) are also listed together with, if any, the number of retrievals that are located beyond or below the chosen x axis limits. The appropriate fashion for reporting the geometric statistics is 〈log(τf)〉 ± σ (log τx) or hence τg,x × μx±1. Site‐specific figures analogous to this ensemble figure are presented in the supporting information.
… 
(a) The four plots on the left are the 〈τa〉 comparisons between (extinction) AODs of Tomasi et al. (2015) (550 nm, Version 2, Level 2.0), and our 550 nm averages as defined in section 3.4 (green squares and solid black circles, respectively). AOD arithmetic averages were used only here, and for comparative purposes. The gray error bars show the ±σ(τa) extension about the black 〈τa〉 circles while the pink and red curves represent 〈τf〉 and τf,g,respectively. All monthly averages, except those of Tomasi, were calculated from individual retrievals (see sections 3.2 and 3.3 for details on the retrieval processing employed in this study). Tomasi's monthly averages were calculated from daily averaged data. The original climatologically averaged, Tomasi AODs were reported at 500 nm: We employed their climatologically averaged Angstrom exponents to estimate the slightly lower values of climatologically averaged AODs at our wavelength standard of 550 nm (reduction by < ~0.01). Panel (b) shows the analogous plots after applying a threshold filter of τf ≥ 0.4 to remove extreme fine mode events.
+5
(a) The four plots on the left are the 〈τa〉 comparisons between (extinction) AODs of Tomasi et al. (2015) (550 nm, Version 2, Level 2.0), and our 550 nm averages as defined in section 3.4 (green squares and solid black circles, respectively). AOD arithmetic averages were used only here, and for comparative purposes. The gray error bars show the ±σ(τa) extension about the black 〈τa〉 circles while the pink and red curves represent 〈τf〉 and τf,g,respectively. All monthly averages, except those of Tomasi, were calculated from individual retrievals (see sections 3.2 and 3.3 for details on the retrieval processing employed in this study). Tomasi's monthly averages were calculated from daily averaged data. The original climatologically averaged, Tomasi AODs were reported at 500 nm: We employed their climatologically averaged Angstrom exponents to estimate the slightly lower values of climatologically averaged AODs at our wavelength standard of 550 nm (reduction by < ~0.01). Panel (b) shows the analogous plots after applying a threshold filter of τf ≥ 0.4 to remove extreme fine mode events.
… 
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Climatological‐Scale Analysis of Intensive and
Semi‐intensive Aerosol Parameters Derived
From AERONET Retrievals
Over the Arctic
Y. AboEl‐Fetouh
1
, N. T. O'Neill
1
, K. Ranjbar
1
, S. Hesaraki
1
, I. Abboud
2
, and P. S. Sobolewski
3
1
CARTEL, Université de Sherbrooke, Sherbrooke, Quebec, Canada,
2
Air Quality Research Division, Environment and
Climate Change Canada (ECCC), Toronto, Ontario, Canada,
3
Institute of Geophysics Polish Academy of Sciences,
Warsaw, Poland
Abstract We investigated the climatological‐scale, monthly binned, seasonal variation of
AERONET/Dubovik retrievals across six stations in the North American and European Arctic (multiyear
sampling periods ranging from 8 to 17 years). A robust, spring‐to‐summer (StoS) increase in the radius of the
peak of the fine mode (FM) component of the particle size distribution (PSD) was observed for five of the
six stations. The FM aerosol optical depth (AOD) and the FM effective radius at the individual stations
showed, respectively, a negligible to moderate StoS decrease and a significant increase. This was interpreted
as a trade‐off between the waning influence of smaller FM Arctic haze aerosols and the increasing
influence of large FM smoke particles. A springtime, pan‐Arctic PSD peak in the 1.3 μm coarse mode (CM)
bin was attributed to Asian dust. It was suggested that the increase in amplitude of a second (4–7μm)
CM peak from July to August at the low‐elevation coastal sites was influenced by wind‐induced sea salt. The
CM AOD went through a StoS decrease attributed to the decreasing amplitude of the 1.3 μm peak. A
significant StoS CM effective radius increase was ascribed to the decreasing influence of the 1.3 μm peak.
StoS FM fraction increases were largely due to the decrease of the CM AOD (decreasing influence of
springtime Asian dust). This extensive and intensive climatology of remotely sensed, bimodal properties
will, we believe, provide an important reference for future measurements and modeling of Arctic aerosols.
1. Introduction
The dynamics of the Arctic environment has a direct impact on global climate and weather, sea level rise,
and in turn commerce (AMAP, 2017). Its impact, therefore, extends much beyond the Arctic Circle.
According to AMAP (2017), the Arctic's average temperature has risen twice that of the global average in
the past five decades. This phenomenon is known as Arctic amplification. This rise in temperature is mainly
attributed to the heating influence induced by absorption of thermal radiation by greenhouse gases (GHGs)
(AMAP, 2017). At the same time, it is well established that the greatest uncertainty in the radiative forcing
budget is attributed to aerosols via the direct and indirect effect (Boucher et al., 2013). A study by Najafi
et al. (2015) sheds light on the additional impact made by aerosols. In the models used to study the influences
of GHGs and aerosols on Arctic temperature, they state that 60% of the GHG‐induced warming has been off-
set by the combined response to other anthropogenic forcings and that these forcings were mostly domi-
nated by aerosols (Najafiet al., 2015). It is generally accepted that aerosols mask a fraction of the
warming effect caused by increasing GHGs (Boucher, 2015). The uneven distribution of aerosols in the
atmosphere results in both warming and cooling of the climate system in a way that impacts the weather
(Boucher et al., 2013).
A variety of aerosols, from both anthropogenic and natural sources, can be found in the Arctic atmosphere.
In general, they are transported from lower latitudes along isentropic pathways to the middle or upper Arctic
troposphere. They can also originate locally from erosive and water‐surface interactions over the land and
ocean (see Tomasi et al., 2015 for a detailed overview of the sources of Arctic aerosols). There are two main
formation mechanisms for aerosols: primary aerosols that are injected directly into the atmosphere and sec-
ondary aerosols formed from gas‐to‐particle conversion processes (Seinfeld & Pandis, 2006). The formation
mechanisms result in different types of aerosols having different microphysical properties. In general, the
©2020. American Geophysical Union.
All Rights Reserved.
RESEARCH ARTICLE
10.1029/2019JD031569
Key Points:
•Spring to summer increase in fine
mode (FM) particle size; likely
associated with waning Arctic haze
and waxing smoke presence
•Springtime, coarse mode (CM)
feature, of small CM particle radius
in the particle size distribution;
likely associated with Asian dust
•Behavior of FM and CM (intensive,
semi‐intensive and extensive)
properties were largely a function of
the first two observations
Supporting Information:
•Supporting Information S1
•Table S1
Correspondence to:
Y. AboEl‐Fetouh,
yasmin.ahmed.samy@usherbrooke.ca
Citation:
AboEl‐Fetouh, Y., O'Neill, N. T.,
Ranjbar, K., Hesaraki, S., Abboud, I., &
Sobolewski, P. S. (2020).
Climatological‐scale analysis of
intensive and semi‐intensive aerosol
parameters derived from AERONET
retrievals over the Arctic. Journal of
Geophysical Research: Atmospheres,
125, e2019JD031569. https://doi.org/
10.1029/2019JD031569
Received 28 AUG 2019
Accepted 8 APR 2020
Accepted article online 26 APR 2020
Author Contributions:
Conceptualization: Y. AboEl‐Fetouh
Data curation: Y. AboEl‐Fetouh
Formal analysis: Y. AboEl‐Fetouh, N.
T. O'Neill
Funding acquisition: N. T. O'Neill
Investigation: Y. AboEl‐Fetouh, N. T.
O'Neill
Methodology: Y. AboEl‐Fetouh, N. T.
O'Neill
Resources: S. Hesaraki, I. Abboud, P.
S. Sobolewski
Software: Y. AboEl‐Fetouh
Supervision: N. T. O'Neill
Validation: N. T. O'Neill, K. Ranjbar
Visualization: Y. AboEl‐Fetouh, N. T.
O'Neill
Writing ‐original draft: Y.
AboEl‐Fetouh, N. T. O'Neill
(continued)
ABOEL‐FETOUH ET AL. 1of19
... As an important component, atmospheric aerosols play a crucial role in the Earth-atmosphere system (Garrett and Zhao, 2006;Ghan and Easter, 2006;Nabat et al., 2015;Wei et al., 2021;Xue et al., 2020). Aerosols have a variety of effects on Earth's climate, including the significant direct effect (Rap et al., 2013;Xing et al., 2017), indirect effect (Albrecht, 1989;Liu et al., 2019Liu et al., , 2020aRighi et al., 2011;Twomey, 1977;Zhao and Garrett, 2015) and semi-direct effect (Amiri-Farahani et al., 2017;Johnson, 2005;Koren et al., 2004). Meanwhile, different aerosol types often have different physical, chemical and optical properties, and the balance between cooling and warming depends to some extent on aerosol characteristics (Boucher et al., 2013). ...
... As an important component, atmospheric aerosols play a crucial role in the Earth-atmosphere system (Garrett and Zhao, 2006;Ghan and Easter, 2006;Nabat et al., 2015;Wei et al., 2021;Xue et al., 2020). Aerosols have a variety of effects on Earth's climate, including the significant direct effect (Rap et al., 2013;Xing et al., 2017), indirect effect (Albrecht, 1989;Liu et al., 2019Liu et al., , 2020aRighi et al., 2011;Twomey, 1977;Zhao and Garrett, 2015) and semi-direct effect (Amiri-Farahani et al., 2017;Johnson, 2005;Koren et al., 2004). Meanwhile, different aerosol types often have different physical, chemical and optical properties, and the balance between cooling and warming depends to some extent on aerosol characteristics (Boucher et al., 2013). ...
... In spring, meanwhile, the proportion of dust and polluted dust increases significantly in the Arctic, which is due to the transported dust from Asian desert sources (Barrie, 1995). Similar results for the seasonal variation in aerosol type over the Arctic were also simulated by the GEOS-Chem model (AboEl-Fetouh et al., 2020). Differently from the Arctic, clean marine aerosol was the dominant aerosol type in the Antarctic, especially in summer, accounting for about 61.2 %. ...
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... With respect to the coarse-mode, the peak monthly-averaged AOD of 0.04 is associated with the month of April and is thought 10 to be due to dust particles. Landis et al. (2017) demonstrated through systematic chemical analysis of summertime CM aerosol from ground-based measurements that a majority of dust is due to local surface mining activities where heavy machinery (trucks and excavators) lift significant amounts of dust into the air, although long-range transport of Asian dust may be more prominent in the spring (e.g., McKendry et al., 2007, AboEl-Fetouh et al., 2020. AboEl-Fetouh et al., 2020 argued, for example, that a springtime coarse mode event was continental in scale (including the North American and European Arctic) 15 ...
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The sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types. SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (ε c) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (ε f). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius ("in-flection point") across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarch-ing dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a "linearly inverse" fashion (slope and intercept of approximately 1 − ε c and ε c) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of ε c over ε f in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis.
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Seasonal comparisons of GEOS-chem-TOMAS (GCT) simulations with AERONET-inversion retrievals over sites in the North American and European Arctic
Article
GEOS-Chem TOMAS (GCT) simulations of AERONET-inversion products during 2015 were compared with AERONET-inversion products from the multi-year climatology of AboEl-Fetouh et al. (2020) (AeF) and for year 2015 acquired over 5 stations in the North American and European Arctic. The GCT simulations of particle size distributions (PSD) did not capture a spring to summer radius increase of the fine mode (FM) peak observed by AeF but did capture AeF's springtime coarse mode (CM) peak (small-sized CM peak with a radius ∼ 1.3 μm) and a weak late summer/fall increase in the amplitude of that peak. The lack of a spring to summer FM radius increase was likely due to the large GCT cell size (4° × 5°) and associated difficulties in the modelling of coagulation-induced smoke particle size. Conversely, the GCT simulation of the small-sized CM peak indicated a successful capture of the springtime influx of Asian dust. The fall increase of that GCT peak was associated with an increase of a larger (4–7 μm) PSD mode that AeF suggested was due to local dust. GCT captured the seasonal (climatological-scale) FM AOD trend, the decreasing CM AOD trend, and the increasing trend of the FM fraction. The GCT CM AOD also showed a fall increase that was coherent with the increase of the simulated small-sized CM peak and with a lesser rate of decrease of the AeF CM AOD. Large GCT deviations from the AERONET retrievals were attributed to an extreme July 2015 forest fire event.
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Airborne and ground-based measurements of aerosol optical depth of freshly emitted anthropogenic plumes in the Athabasca Oil Sands Region
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In this work we report the airborne aerosol optical depth (AOD) from measurements within freshly emitted anthropogenic plumes arising from mining and processing operations in the Athabasca Oil Sands Region (AOSR) in the context of ground-based AERONET climatological daily averaged AODs at Fort McMurray (Alberta, Canada). During two flights on 9 and 18 June 2018, the NASA airborne 4STAR (Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research) Sun photometer registered high fine-mode (FM
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How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth
Article
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Many applications of geophysical data – whether from surface observations, satellite retrievals, or model simulations – rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal (e.g. daily and monthly) resolution than the highest available from the technique. Almost all of these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for normally distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to log-normal, for which a geometric mean and standard deviation would be more appropriate. This study presents a method of assessing whether a given sample of data is more consistent with an underlying normal or log-normal distribution, using the Shapiro–Wilk test, and tests AOD frequency distributions on spatial scales of 1∘ and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse, and MISR and MODIS represent different satellite retrieval techniques and sampling. As a model simulation, G5NR is spatiotemporally complete. As timescales increase from days to months to seasons, data become increasingly more consistent with log-normal than normal distributions, and the differences between arithmetic- and geometric-mean AOD become larger, with geometric mean becoming systematically smaller. Assuming normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is small enough (
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How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth
Article
Full-text available
Many applications of geophysical data – whether from surface observations, satellite retrievals, or model simulations – rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal (e.g. daily, monthly) resolution than the highest available from the technique. Almost all these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for Normally-distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to Lognormal, for which geometric mean and standard deviation would be more appropriate. This study presents a method to assess whether a given sample of data are more consistent with an underlying Normal or Lognormal distribution, using the Shapiro-Wilk test, and tests AOD frequency distributions on spatial scales of 1° and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse; MISR and MODIS represent different satellite retrieval techniques and sampling; as a model simulation, G5NR is spatiotemporally complete. As time scales increase from days to months to seasons, data become increasingly more consistent with Lognormal than Normal distributions, and the differences between arithmetic and geometric mean AOD become larger, with geometric mean becoming systematically smaller. Assuming Normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is sufficiently small (
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Aerosol optical, microphysical, chemical and radiative properties of high aerosol load cases over the Arctic based on AERONET measurements
Article
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  • Jun 2018
Columnar mass concentrations of aerosol components over the Arctic are estimated using microphysical parameters derived from direct sun extinction and sky radiance measurements of Aerosol Robotic Network. Aerosol optical, microphysical, chemical and radiative properties show that Arctic aerosols are dominated by fine mode particles, especially for high aerosol load cases. The average aerosol optical depth (AOD) of the selected Arctic sites in the sampling period is approximately 0.08, with 75% composed of fine mode particles. The fine mode fraction mostly exceeds 0.9 when AOD greater than 0.4. The ammonium sulfate-like component (AS) contributes about 68% of total dry aerosol mass for high-AOD events. The estimated compositions and back trajectories show that the transported aerosol particles from biomass burning events have large amounts of black carbon (BC) and brown carbon, while those from pollution events are characterised by large AS fractions. The instantaneous radiative forcing at the top-of-atmosphere is higher for the more absorbing components, and varies greatly with surface albedo and solar zenith angle. A regression model of columnar composition and radiative forcing within the atmosphere (RFATM) for Arctic aerosol is established, showing that BC dominates a positive RFATM with a high warming efficiency.
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AEROCAN, the Canadian sub-network of AERONET: Aerosol monitoring and air quality applications
Article
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  • Aug 2017
Previous studies have demonstrated the utility of AERONET (Aerosol Robotic Network) aerosol optical depth (AOD) data for monitoring the spatial variability of particulate matter (PM) in relatively polluted regions of the globe. AEROCAN, a Canadian sub-network of AERONET, was established 20 years ago and currently consists of twenty sites across the country. In this study, we examine whether the AEROCAN sunphotometer data provide evidence of anthropogenic contributions to ambient particulate matter concentrations in relatively clean Canadian locations. The similar weekly cycle of AOD and PM2.5 over Toronto provides insight into the impact of local pollution on observed AODs. High temporal correlations (up to r = 0.78) between daily mean AOD (or its fine-mode component) and PM2.5 are found at southern Ontario AEROCAN sites during May–August, implying that the variability in the aerosol load resides primarily in the boundary layer and that sunphotometers capture day-to-day PM2.5 variations at moderately polluted sites. The sensitivity of AEROCAN AOD data to anthropogenic surface-level aerosol enhancements is demonstrated using boundary-layer wind information for sites near sources of aerosol or its precursors. An advantage of AEROCAN relative to the Canadian in-situ National Air Pollution Surveillance (NAPS) network is the ability to detect free tropospheric aerosol enhancements, which can be large in the case of lofted forest fire smoke or desert dust. These aerosol plumes eventually descend to the surface, sometimes in populated areas, exacerbating air quality. In cases of large AOD (≥0.4), AEROCAN data are also useful in characterizing the aerosol type. The AEROCAN network includes three sites in the high Arctic, a region not sampled by the NAPS PM2.5 monitoring network. These polar sites show the importance of long-range transport and meteorology in the Arctic haze phenomenon. Also, AEROCAN sunphotometers are, by design and due to regular maintenance, the most valuable monitors available for long term aerosol trends. Using a variety of data analysis techniques and timescales, the usefulness of this ground-based remote-sensing sub-network for providing information relevant to air quality is demonstrated.
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Aerosol optical depth over the Arctic: a comparison of ECHAM-HAM and TM5 with ground-based, satellite and reanalysis data
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We compare ground-based measurements of aerosol optical depth and Ångström parameter at six Arctic stations in the period 2001–2006 with the results from two global aerosol dynamics and transport models, ECHAM-HAM and TM5. Satellite measurements from MODIS and the MACC reanalysis product are used to examine the spatial distribution and the seasonality of these parameters and to compare them with model results. We find that both models provide a good reproduction of the Ångström parameter but significantly underestimate the observed AOD values. We also explore the effects of changes in emissions, model resolution and the parametrization of wet scavenging.
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High Latitude Dust in the Earth System: High Latitude Dust in the Earth System
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  • Jun 2016
Natural dust is often associated with hot, subtropical deserts, but significant dust events have been reported from cold, high latitudes. This review synthesizes current understanding of high-latitude (≥50°N and ≥40°S) dust source geography and dynamics and provides a prospectus for future research on the topic. Although the fundamental processes controlling aeolian dust emissions in high latitudes are essentially the same as in temperate regions, there are additional processes specific to or enhanced in cold regions. These include low temperatures, humidity, strong winds, permafrost and niveo-aeolian processes all of which can affect the efficiency of dust emission and distribution of sediments. Dust deposition at high latitudes can provide nutrients to the marine system, specifically by contributing iron to high-nutrient, low-chlorophyll oceans; it also affects ice albedo and melt rates. There have been no attempts to quantify systematically the expanse, characteristics, or dynamics of high-latitude dust sources. To address this, we identify and compare the main sources and drivers of dust emissions in the Northern (Alaska, Canada, Greenland, and Iceland) and Southern (Antarctica, New Zealand, and Patagonia) Hemispheres. The scarcity of year-round observations and limitations of satellite remote sensing data at high latitudes are discussed. It is estimated that under contemporary conditions high-latitude sources cover >500,000km2 and contribute at least 80-100Tgyr-1 of dust to the Earth system (~5% of the global dust budget); both are projected to increase under future climate change scenarios.
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Processes controlling the seasonal cycle of Arctic aerosol number and size distributions
Article
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Measurements at high-Arctic sites show a strong seasonal cycle in aerosol number and size. The number of aerosols with diameters larger than 20 nm exhibits a maximum in late spring associated with a dominant accumulation mode (0.1 to 1 μm in diameter), and a second maximum in the summer associated with a dominant Aitken mode (10 to 100 nm in diameter). Seasonal-mean aerosol effective diameter ranges from about 180 nm in summer to 260 nm in winter. This study interprets these seasonal cycles with the GEOS-Chem-TOMAS global aerosol microphysics model. We find improved agreement with in-situ measurements of aerosol size at both Alert, Nunavut, and Mt. Zeppelin, Svalbard following model developments that: (1) increase the efficiency of wet scavenging in the Arctic summer and (2) represent coagulation between interstitial aerosols and aerosols activated to form cloud droplets. Our simulations indicate that the dominant summertime Aitken mode is associated with increased efficiency of wet removal, which limits the number of larger aerosols and promotes local new-particle formation. We also find an important role of interstitial coagulation in clouds in the Arctic, which limits the number of Aitken-mode aerosols in the non-summer seasons when direct wet removal of these aerosols is inefficient. Total aerosol number reaches a minimum in October at both Alert and Mt. Zeppelin. Our simulations indicate that this October minimum can be explained by diminishing local new-particle formation, limited transport of pollution from lower latitudes, and efficient wet removal. We recommend that the key processes of aerosol wet removal, interstitial coagulation and new-particle formation be carefully considered in size-resolved aerosol simulations of the Arctic. Uncertainties about these processes, which strongly control the seasonal cycle of aerosol number and size, limit confidence in estimates of aerosol radiative effects on the Arctic climate.
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Comparisons of a Chemical Transport Model with a Four-Year (April to September) Analysis of Fine- and Coarse-Mode Aerosol Optical Depth Retrievals Over the Canadian Arctic
Article
  • Aug 2017
We compared April to September retrievals of total, fine-mode (sub-micron), and coarse-mode (super-micron) aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) with simulations from a global three-dimensional chemical transport model, the Goddard Earth Observing System (GEOS-Chem), across five Arctic stations and a four-year sampling period. It was determined that the AOD histograms of both the retrievals and the simulations were better represented by a lognormal distribution and that the successful simulation of this empirical feature as well as its consequences (including a better model versus retrieval coefficient of determination in log-log AOD space) represented a general indicator of model evaluation success. Seasonal (monthly averaged) AOD retrievals were sensitive to the way in which the averaging was performed; this was ascribed to the presence of highly variable fine-mode smoke in the western Arctic. The retrieved and modelled station-by-station fine-mode AOD averages showed a peak in April/May that decreased over the summer, while the model underestimated the fine-mode AOD by an average of about 0.004 (∼6%). Both the retrievals and simulations showed seasonal coarse-mode AOD variations with a peak in April/May that was attributed to Asian and/or Saharan dust. The model's success in capturing such weak seasonal events helps to confirm the relevance of the separation of the fine and coarse modes and the general validity of model estimates in the Arctic.
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