ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-3699-2017Wintertime enhancements of sea salt aerosol in polar regions consistent with a sea ice source from blowing snowHuangJiayueJaegléLyattjaegle@uw.eduDepartment of Atmospheric Sciences, University of Washington, Seattle,
Washington, USALyatt Jaeglé (jaegle@uw.edu)16March2017175369937122November201615November201622February201728February2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/3699/2017/acp-17-3699-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/3699/2017/acp-17-3699-2017.pdf
Sea salt aerosols (SSA) are generated via air bubbles bursting at the ocean
surface as well as by wind mobilization of saline snow and frost flowers over
sea-ice-covered areas. The relative magnitude of these sources remains poorly
constrained over polar regions, affecting our ability to predict their impact
on halogen chemistry, cloud formation, and climate. We implement a blowing
snow and a frost flower emission scheme in the GEOS-Chem global chemical
transport model, which we validate against multiyear (2001–2008) in situ
observations of SSA mass concentrations at three sites in the Arctic, two
sites in coastal Antarctica, and from the 2008 ICEALOT cruise in the Arctic. A simulation including only
open ocean emissions underestimates SSA mass concentrations by factors of
2–10 during winter–spring for all ground-based and ship-based observations.
When blowing snow emissions are added, the model is able to reproduce
observed wintertime SSA concentrations, with the model bias decreasing from a
range of -80 to -34 % for the open ocean simulation to -2 to
+9 % for the simulation with blowing snow emissions. We find that the
frost flower parameterization cannot fully explain the high wintertime
concentrations and displays a seasonal cycle decreasing too rapidly in early
spring. Furthermore, the high day-to-day variability of observed SSA is
better reproduced by the blowing snow parameterization. Over the Arctic
(> 60∘ N) (Antarctic, > 60∘ S), we
calculate that submicron SSA emissions from blowing snow account for
1.0 Tg yr-1 (2.5 Tg yr-1), while frost flower emissions lead to
0.21 Tg yr-1 (0.25 Tg yr-1) compared to 0.78 Tg yr-1
(1.0 Tg yr-1) from the open ocean. Blowing snow emissions are largest
in regions where persistent strong winds occur over sea ice (east of
Greenland, over the central Arctic, Beaufort Sea, and the Ross and Weddell
seas). In contrast, frost flower emissions are largest where cold air
temperatures and open leads are co-located (over the Canadian Arctic
Archipelago, coastal regions of Siberia, and off the Ross and Ronne ice
shelves). Overall, in situ observations of mass concentrations of SSA suggest
that blowing snow is likely to be the dominant SSA source during winter, with
frost flowers playing a much smaller role.
Introduction
Breaking waves over the open ocean are recognized as the main mechanism for
the global production of sea salt aerosol (SSA) (Lewis and Schwartz, 2004;
de Leeuw et al., 2011, and references therein). Observations of SSA in polar
regions, however, exhibit several characteristics that are not consistent
with this canonical open ocean source. Indeed, submicron or total SSA mass
concentrations at Arctic (Sirois and Barrie, 1999; Quinn et al., 2002) and
Antarctic sites (Wagenbach et al., 1998; Weller et al., 2008; Jourdain et
al., 2008; Udisti et al., 2012) often exhibit a maximum during local winter,
when polar ocean waters are mostly covered by sea ice. Furthermore, the
ionic composition of SSA observed at polar sites during winter shows a
systematic depletion of the sulfate-to-sodium mass ratio relative to bulk
sea water (Wagenbach et al., 1998; Rankin et al., 2000; Jourdain et al.,
2008; Hara et al., 2012; Jacobi et al., 2012; Seguin et al., 2014). Finally,
Arctic and Antarctic ice core records display factors of 2.5–4 increase in
SSA deposition fluxes during glacial periods relative to warmer interglacial
period (Wolff et al., 2006; Fischer et al., 2007; Abram et al., 2013).
To explain these seasonal and glacial–interglacial variations, frost flowers
have been proposed as a new source of SSA (Wagenbach et al., 1998; Rankin et
al., 2000, 2002; Wolff et al., 2003; Shaw et al., 2010). They are highly
saline ice crystals that can rapidly form on freshly freezing sea ice
(Martin et al., 1995; Domine et al., 2005; Roscoe et al., 2011). Frost
flowers wick up brine from the sea ice and can be lofted in the atmosphere
by surface winds to become SSA (Rankin et al., 2000; Domine et al., 2004; Xu
et al., 2013, and references therein). The seasonality of frost flower
formation and their sulfate-to-sodium ratios are similar to those of
observed SSA in polar regions (Rankin et al., 2002; Rankin and Wolff, 2003;
Wolff et al., 2003; Alvarez-Aviles et al., 2008; Beaudon and Moore, 2009;
Seguin et al., 2014). Field observations have cast some doubt on the role of
frost flowers as SSA, noting that frost flowers are rigid and difficult to
break (Domine et al., 2005; Alvarez-Avilez et al., 2008). In particular,
Obbard et al. (2009) observed no mechanical breakage of frost flowers in
winds up to 6 m s-1 over the Hudson Bay. Furthermore, laboratory
experiments performed by Roscoe et al. (2011) demonstrated that no aerosol
were produced when frost flowers were exposed to winds speeds up to 12 m s-1.
Their result is consistent with electron microscope imaging by
Yang et al. (2017), which shows that evaporating frost flowers form a
cohesive chunk of salt that is unlikely to be a source of SSA.
Another hypothesis is that blowing snow, often observed over sea-ice-covered
regions (Nishimura and Nemoto, 2005; Savelyev et al., 2006), could act as a
direct source of SSA (Simpson et al., 2007a; Yang et al., 2008). The snow
over sea ice becomes salty by upward migration of brine from the sea ice to
the snow surface, incorporation of frost flowers, and SSA deposition
from the adjacent open ocean (Domine et al., 2004). The first two of these
mechanisms lead to depletion of the sulfate-to-sodium ratio relative to bulk
sea water as mirabilite (Na2SO4 ⚫ 10H2O)
precipitates from brine at temperatures below -8 ∘C during sea ice
and frost flower formation (Alvarez-Aviles et al., 2008). Once lifted by
wind, these salty snow particles can produce SSA via sublimation (Yang et
al., 2008).
Many questions remain on the formation, composition, occurrence, and
mobility of frost flowers and salty blowing snow. Most studies examining
these two sources have focused on their potential role as an indirect source
of gas-phase bromine resulting in ozone depletion events during late winter
and early spring, with conflicting results as to which source would be most
important. Kaleschke et al. (2004) developed a one-dimensional thermodynamic
model to calculate frost flower coverage. They found that more than 66 %
of forward trajectories from areas with high frost flower coverage
intercepted regions with enhanced BrO over the Arctic and Antarctic during
polar sunrise. Similarly, Jones et al. (2006) showed that ozone depletion
events observed at Halley station in Antarctica were associated with air
masses with recent contact with newly forming sea ice. However, using
back trajectories to examine the origin of enhanced BrO abundance measured at
Barrow, Alaska, Simpson et al. (2007a) found that saline snow and ice on
first-year sea ice was a more likely source of bromine than frost flowers.
Yang et al. (2010) implemented a blowing snow bromine source in the p-TOMCAT
chemical transport model. With this blowing snow source they were able to
successfully simulate bromine explosion events retrieved from the Global
Ozone Monitoring Experiment (GOME).
To our knowledge, the only modeling studies examining blowing snow and frost
flowers as direct sources of SSA are the work of Xu et al. (2013, 2016),
Levine et al. (2014), and Legrand et al. (2016). Xu et al. (2013) developed
an empirical frost flower formulation in the WRF-Chem model. They found that
adding frost flower emissions doubled the surface concentrations of Na+
at Barrow, in better agreement with observations. However, their study was
limited to 2 days during winter 2009. Their work was expanded in Xu et al. (2016),
where the same frost flower emission scheme was implemented in the
Community Earth System Model (CESM) for the year 2000 and compared to SSA
observations at Barrow and Alert. They found that the frost flower
simulation led to improved agreement Barrow but overestimated observations
at Alert by 150 %. Levine et al. (2014) found that the local winter peak
in Na+ mass concentrations at polar sites was attributable to blowing
snow, but they were not able to constrain the relative sources of open ocean
and blowing snow because rates of SSA wet deposition were tuned regionally
in the p-TOMCAT model. Using the p-TOMCAT model, Legrand et al. (2016)
report that 50–70 % of wintertime Na+ concentrations at two East
Antarctic sites were due to sea salt emissions from blowing snow. However,
the model overestimated observed Na+ concentrations by factors of 2–3
and was not able to reproduce the observed seasonal cycle.
In this study, we implement a blowing snow and a frost flower
parameterization in the GEOS-Chem global chemical transport model. We
evaluate the ability of these two sources to reproduce multiyear
(2001–2008) in situ measurements of Na+ mass concentrations at three
Arctic sites (Barrow, Alaska; Alert, Canada; Zeppelin, Svalbard) and two
coastal Antarctic sites (Neumayer and Dumont d'Urville), as well as Na+
measurements obtained during the International Chemistry Experiment in the
Arctic LOwer Troposphere (ICEALOT) cruise during spring 2008. We then
examine the relative contributions of open ocean, blowing snow and frost
flower sources to the distribution of SSA over polar regions.
Model simulations and observationsThe GEOS-Chem chemical transport model
We use the GEOS-Chem global 3-D chemical transport model (Bey et al., 2001)
driven by the Modern-Era Retrospective Analysis for Research and
Applications (MERRA; Rienecker et al., 2011) meteorological fields. The
MERRA fields have a native horizontal resolution of 1/2∘
latitude by 2/3∘ longitude with 72 vertical levels. We
regrid these fields to a 2∘× 2.5∘
horizontal resolution and vertical levels above 80 hPa are merged to retain
47 vertical levels in total for computational expediency. The temporal
resolution of MERRA data is 3 h except for surface variables and mixing
depths, which have a 1 h resolution. The sea ice concentration boundary
conditions in MERRA are derived from the weekly product of Reynolds et al. (2002),
which is based on Special Sensor Microwave Imager (SSMI) instruments
on Defense Meteorological Satellite Program (DMSP) satellites. The weekly
products are linearly interpolated in time to each model time step. In this
study we use GEOS-Chem v10-01 (http://www.geos-chem.org).
The GEOS-Chem open ocean SSA simulation is described in Jaeglé et al. (2011).
Ocean emissions are based on the wind-speed-dependent source
function of Gong (2003) and Monahan et al. (1986), with an empirical
dependence on sea surface temperature (SST) derived by Jaeglé et al. (2011)
via comparisons to open ocean cruise observations of coarse-mode SSA
mass concentrations. This SST dependence leads to a decrease in SSA
emissions of a factor of 2.6 as SST decreases from 25 to
5 ∘C, consistent with the factors of 2–3 measured in
laboratory experiments for particles with a radius greater than 0.5 µm
(Bowyer, 1984, 1990; Woolf et al., 1987; Mårtensson et al., 2003;
Sellegri et al., 2006). Most of the in situ observations of SSA mass
concentrations used to derive the polynomial SST dependence (Fig. 6 in
Jaeglé et al., 2011: f(SST) = 0.3 + 0.1 × SST - 0.0076 × SST2+ 0.00021 × SST3) were for
SST > 5 ∘C. In this work, we find that the SST
dependence results in a factor of 2 overestimate of summertime SSA
observations at coastal polar sites with SST ranging from
-2 to 5 ∘C. This indicates that the
suppression at cold SST might not be strong enough. We thus modify the
expression derived in Jaeglé et al. (2011) to impose f(SST) = 0.25 for
SST < 5 ∘C (see Fig. S1 in the Supplement). This is
consistent with the laboratory study of Mårtensson et al. (2003), who
report a 50–60 % decrease in aerosol production (for r>0.1µm)
when seawater temperature decreased from 5 to
-2 ∘C. We note that another potential explanation for the
summertime overestimate in SSA mass concentrations is inefficient wet
removal from low-intensity summer precipitation in GEOS-Chem (Croft et al.,
2016).
Within GEOS-Chem, we assume that open ocean emissions occur only in
grid boxes covered by more than 50 % water. We thus neglect emissions from
bubble bursting in leads within sea ice. This is based on observations that
the small fetch of leads results in SSA production which is an order of
magnitude lower compared to open ocean (Nilsson et al., 2001). Including SSA
emissions over leads results in a 1 % increase in SSA emissions over polar
regions (> 60∘). Even if we were to assume that
leads were as efficient as the open ocean in producing SSA, this would only
result in a 10 % increase in SSA emissions.
Dry deposition of SSA over land accounting for particles growth under high
humidity conditions follows the size-segregated scheme described in Zhang et
al. (2001). The dry deposition velocity over the ocean is calculated based
on the Slinn and Slinn (1980) deposition model for natural waters. Over snow
and ice surfaces, Fisher et al. (2011) implemented a dry deposition velocity
of 0.03 cm s-1 based on the measurements of Nilsson and Rannik (2001).
The wet deposition scheme includes convective updraft scavenging, rainout
and washout from precipitation (Liu et al., 2001), and snow
scavenging (Wang et al., 2011). For this work, we track SSA mass in two size
bins, accumulation mode (rdry=0.01–0.5 µm) and coarse mode
(rdry=0.5–4 µm), except in the comparison to in situ mass
concentrations of SSA for which we use rdry=0.01–0.3 µm and
rdry=0.3–3 µm (see Sect. 2.3). In the rest of the
paper we will refer to the accumulation- and coarse-mode SSA as
submicron and supermicron SSA based on their diameters.
The GEOS-Chem SSA simulation was evaluated by Jaeglé et al. (2011)
against in situ measurements of SSA from six open ocean cruises (mean
normalized bias of +33 %) and 15 ground-based stations (mean normalized
bias of -5 %) as well as aerosol optical depth (AOD) from MODIS and
AERONET. Detailed comparisons of GEOS-Chem black carbon and organic aerosol
(Wang et al., 2011) as well as sulfate and ammonium aerosol (Fisher et al.,
2011) to ground-based and aircraft observations over the Arctic during
winter and spring suggest that transport and removal processes are
reasonably captured by the model.
Implementation of blowing snow and frost flower parameterizations in
GEOS-Chem
We implement SSA emissions from blowing snow following the parameterization
of Yang et al. (2008, 2010), with a few modifications. The SSA production
from blowing snow is a function of relative humidity, temperature, age of
snow, snow salinity, and wind speed. The wind needs to be strong enough
(> about 7 m s-1) to saltate and suspend snow particles from
the sea ice surface. The size distribution of suspended blowing snow
particles follows a two-parameter gamma distribution (Yang et al., 2008, and
references therein). We set a uniform surface snow salinity of 0.1 psu
(practical salinity unit) over Arctic sea ice based on observations of
surface snow salinity (Mundy et al., 2005; Krnavek et al., 2012). This is an
order of magnitude lower than the salinity assumed in Yang et al. (2008,
2010). Their salinity was based on bulk snow measurements (Massom et al.,
2001), which overestimate surface snow salinity because of the rapid
decreases of salinity with height above the ice surface. For example, Mundy
et al. (2005) found that springtime snowpack over first-year sea ice in the
central Canadian Arctic displayed a salinity of 7.6±3.3 psu in the
bottom snow layers and decreased to 0.26 ± 0.37 psu and
0.11 ± 0.25 psu in the middle and surface snow layers. This profile is consistent
with a salinity source from the underlying sea ice and little influence from
atmospheric deposition. Toom-Sauntry and Barrie (2002) find that freshly
fallen snow itself tends to have low salinity (< 0.01 psu). For
simplicity, we assume the same salinity for surface snow on first-year and
multiyear sea ice, although we recognize that in reality the surface snow
may be less salty on multiyear sea ice due to less efficient upward
transport of brine. Indeed, Krnavek et al. (2012) reported that the ion
concentrations of surface snow sampled in the Alaskan Arctic display large
variability depending on sea ice type: 0.01 psu for snow on multiyear sea
ice, 0.1 psu for snow on thick first-year sea ice, and 0.8 psu for snow on
thin first-year sea ice. Snowpack on Antarctic sea ice is thicker than over
Arctic sea ice. Indeed, Antarctica is surrounded by the Southern Ocean, which
brings moisture, while the Arctic is surrounded by land with wintertime
precipitation that is 3 times lower than over Antarctic sea ice (Huffman et
al., 2001). As the salinity of snow decreases with snowpack thickness, we
assume that the salinity of snow on Antarctic sea ice is 0.03 psu, a factor
of 3 lower than over the Arctic following Yang et al. (2010).
Based on the time between precipitation events in the MERRA fields, we
estimate a mean snow age of 3 days for the Arctic and 1.5 days for the
Antarctic. In comparison, Yang et al. (2008) used a globally uniform snow
age of 3 days, while Levine et al. (2014) assumed 5 days. Our younger snow
age over Antarctic sea ice (1.5 days) increases the ease of lifting snow
particles and SSA production increases by 40 % compared to a 3-day snow
age. We also assume that five SSA particles are produced per snowflake. This
fractionation can occur when snowflakes are broken by strong winds and
abraded into smaller particles with round corners (Mellor, 1965) or when
SSA particles experience cracking during sublimation under low relative
humidity as observed in the laboratory experiments of Wise et al. (2012).
The number of particles produced per snowflake (N) does not change the total
SSA production substantially, but it influences the size distribution of SSA
particles produced from these lofted snow particles. We choose the value of
N=5 based on wintertime observations of supermicron and submicron SSA at
Barrow (see Fig. S2 in the Supplement). This leads to a doubling of
submicron SSA production compared to the assumption of N=1 in Yang et al. (2008).
Our simulation yields blowing snow SSA emissions with 38 % of SSA
mass in the submicron range (0.01–0.5 µm) and 62 % in the
supermicron range (0.5–4 µm) for the Arctic. As we assume a lower
salinity in the Antarctic, more of the blowing snow emissions are in the
submicron range (60 %) in that region. Overall, our modifications to the
Yang et al. (2008, 2010) parameterization lead to SSA emissions from blowing
snow that are an order of magnitude lower than in Yang et al. (2010).
We implement a frost flower SSA source in GEOS-Chem following the
parameterization of Xu et al. (2013, 2016), which is based on the potential
frost flower (PFF) coverage derived by Kaleschke et al. (2004). Frost
flowers are formed on very young sea ice once ambient air temperatures are
cold enough (< -20 ∘C). Below that threshold, PFF
increases rapidly as temperature decreases. We set a limit of 10 cm for the
thickness of newly formed sea ice beyond which we assume that frost flower
crystals can no longer be formed. Based on the thermodynamic model of
Kaleschke et al. (2004), it takes 1–2 days for sea ice to reach a thickness
of 10 cm for air temperatures of -40 to -20 ∘C. Following
Xu et al. (2013), we assume that SSA from frost flowers have a lognormal
size distribution with a geometric mean diameter of 0.15 µm and a
geometric standard deviation of 1.9, hence all frost flower emissions occur
in the submicron range (rdry≤0.5µm). We use the same
scaling factor of 1×106 m2 s-1 as Xu et al. (2013)
for our frost flower source. We also conducted a sensitivity simulation
(Fig. S3), in which we assumed that frost flowers can only form under mild
wind speed conditions (< 5 m s-1), as strong winds inhibit
frost flower formation and bury existing frost flowers with snow (Perovich
and Richeter-Menge, 1994; Rankin et al., 2000).
We conduct three simulations. Our standard simulation (STD) includes only
the open ocean source of SSA. In a second simulation (STD-SNOW), we add the
blowing snow source to the STD simulation. A third simulation (STD-FF) adds
frost flower emissions to the STD simulation.
Monthly mean mass concentrations of SSA at Arctic (a Barrow,
b Alert, c Zeppelin) and Antarctic sites (d Neumayer, e Dumont d'Urville).
All observations and model results are for 2001–2008 except at Neumayer
(2001–2007). Note that the seasonal cycles are centered over local winter.
The observed mean concentrations are indicated with filled black circles,
while the lines are for the GEOS-Chem simulations (STD: black line;
STD-SNOW: red line; STD-FF: green line). The black vertical lines and shaded
areas correspond to the standard deviations of monthly means for
observations and model simulations. For each individual panel, the legend
lists mean concentrations and standard deviations, as well as the normalized
mean bias
(NMB =(model‾/obs‾-1)×100).
In situ observations
We use in situ observations of Na+ mass concentrations from five polar
sites: Barrow, Alaska (71.3∘ N, 156.6∘ W;
11 m a.s.l.; Quinn et al., 2002); Alert, Nunavut, Canada
(82.5∘ N, 62.5∘ W; 210 m a.s.l.; WMO/GAW,
2003); Zeppelin Mountain, Svalbard, Norway (78.9∘ N,
11.9∘ E; 475 m a.s.l.; WMO/GAW, 2003); and stations Neumayer
(70.7∘ S, 8.3∘ W; 42 m a.s.l.; Weller et al., 2008) and
Dumont d'Urville (66.7∘ S, 140∘ E; 43 m a.s.l.; Legrand et al., 2012).
These observations are available for
2001–2008 (except for Neumayer station, 2001–2007). At Barrow Na+ mass
concentrations are available for both submicron and supermicron aerosol,
while all the other sites measure total mass concentrations. The Na+
mass concentrations are determined by ion chromatography with uncertainties
of 5–11 % (0.01 µgm-3 in absolute uncertainty). The aerosol
sampling frequency ranges from daily (Zeppelin, Dumont d'Urville, submicron
at Barrow) to weekly (Alert, Neumayer, Barrow supermicron). In winter
months, the coastlines near these sites are mostly covered by sea ice.
We also use the submicron Na+ mass concentrations measured aboard the
R/V Knorr during the ICEALOT cruise in March–April 2008
(http://saga.pmel.noaa.gov/Field/icealot). The research cruise took place
over the North Atlantic Ocean and the ice-free Arctic Ocean
(41–81∘ N).
(a) Daily variations in submicron SSA mass concentrations at
Barrow for 1 January to 30 June 2001. Observations are shown with filled
black circles, while the GEOS-Chem simulations are indicated with lines
(STD: black; STD-SNOW: red; and STD-FF: green). (b) MERRA 10 m wind speed
(u10m) at Barrow. The red line indicates the wind speed threshold for
blowing snow events calculated with the local MERRA 2 m temperatures. Shaded
gray areas indicate time periods when u10m exceeds the blowing snow
wind threshold.
For comparison between the GEOS-Chem model and the observations, we convert
observed Na+ mass concentrations to SSA mass concentrations using a
factor of 3.256 based on the mass ratio of Na+ in seawater (Riley and
Chester, 1971). For frost flowers, this ratio is 3.237 (Rankin et al.,
2000). Krnavek et al. (2012) find a ratio of 3.24–3.278 for snow on first
year sea ice. As this ratio varies by less than 0.5 % for these different
SSA sources, we use a constant factor of 3.256.
The reported aerodynamic cutoff diameters of the measurements are 1
and 10 µm at Barrow and during ICEALOT (Quinn et al., 2002) and
7–10 µm at the other sites (Wagenbach et al., 1998; WMO/GAW, 2003; Weller et
al., 2008). In order to compare to model simulations, we need to convert
these aerodynamic diameters to dry geometric radii. This conversion depends
on aerosol density, relative humidity during sampling, and whether the
particle is spherical (Seinfeld and Pandis, 2006). For example, a 10 µm
aerodynamic diameter could correspond to a dry geometric radius of 2.3 µm
(80 % RH, 1.2 gcm-3 pure NaCl solution, a factor of 2 growth
between dry and 80 % RH), 3 µm (dry cubical NaCl particle,
2.2 g cm-3;
Lewis and Schwartz, 2004), or 3.8 µm (30 % RH, ammonium
sulfate and sea salt aerosol, 1.7 gcm-3; Quinn et al., 1996). Thus for
comparison to observations we choose the mid-range estimate and conduct a
simulation with two size bins: rdry=0.01–0.3 µm and
rdry=0.3–3 µm.
Model evaluation with ground-based and ship-based in situ
observations
Observations at the three Arctic sites display enhanced SSA mass
concentrations of 1–3 µgm-3 during the cold part of the year from
November to April (Fig. 1a–c). In contrast, when the sea ice retreats during
summer and late fall, SSA concentrations are much lower (< 0.5 µgm-3
at Alert and Zeppelin). This seasonality is opposite to what is
expected from an open ocean source. Indeed, we find that the STD simulation
fails to capture the high wintertime concentrations at all three Arctic
sites but reproduces the summer/late-fall observations reasonably well.
During winter at Barrow and Alert, the STD simulation predicts very low SSA
concentrations (< 0.1 µgm-3), while at Zeppelin, which is
closer to the open Atlantic Ocean, STD mass concentrations reach 0.5–1 µgm-3.
At Neumayer station (Fig. 1d), SSA observations show a broad maximum of
1–1.5 µgm-3 during the cold months (March–September). The
seasonality is opposite at Dumont d'Urville (Fig. 1e), with a summertime
maximum of 2.9 µgm-3, as it is exposed to a longer open ocean
season compared to Neumayer (Wagenbach et al., 1998). Between March and
November, SSA concentrations remain fairly constant around 0.8 µgm-3.
The summertime maximum at Dumont d'Urville is captured by the STD
simulation, confirming the open ocean source. However, the STD model
predicts SSA concentrations < 0.3 µgm-3 during cold
months, factors of 3–10 lower than observations at both Antarctic sites.
The addition of a blowing snow source in GEOS-Chem (STD-SNOW) results in
improved agreement with observations. The normalized mean bias
(NMB =(model‾/obs‾-1)×100)
decreases significantly at all five sites: Barrow (STD: -64 %,
STD-SNOW: +9 %), Alert (STD: -85 %; STD-SNOW: +25 %), Zeppelin
(STD: -34 %; STD-SNOW: +12 %), Neumayer (STD: -63 %; STD-SNOW:
-2 %), and Dumont D'Urville (STD: -40 %; STD-SNOW: +12 %). The STD-SNOW
simulation captures the observed SSA seasonal cycle reasonably well, with
modeled wintertime SSA mass concentrations increasing to 1–2 µgm-3.
Overall, the frost flower simulation (STD-FF) displays a large geographical
variability, with little influence at Dumont d'Urville and Zeppelin, but
much larger influence at Barrow, Neumayer, and especially at Alert, where
modeled SSA concentrations reach 1 µgm-3. Indeed, the STD-FF
simulation predicts very large SSA emissions over the Canadian Arctic
Archipelago (see Sect. 4 for a detailed discussion and maps of spatial
distributions). The NMB in the STD-FF simulation ranges from -49 %
(Neumayer) to -27 % (Zeppelin), not displaying as large an improvement as
the STD-SNOW simulation. Furthermore, the seasonal cycle of frost flower SSA
concentrations decreases too rapidly during early spring compared to
observations at Alert, Barrow, and Neumayer.
(a) Time series of submicron SSA mass concentrations during the
ICEALOT cruise between 19 March and 24 April 2008. Observations are shown as
filled black circles with horizontal bars indicating the sampling period.
The GEOS-Chem simulations are indicated with lines (STD: black; STD-SNOW:
red; and STD-FF: green). The 15–19 April period discussed in the text is
indicated by the gray shading. The bottom panels show the spatial
distribution of mean surface SSA mass concentrations for the 15–19 April
period. SSA mass concentrations due to open ocean emissions are shown in
panel (b) while those due to blowing snow are shown in panel (c). The ship
track is indicated with the black line and dots in panels (b–d). The larger
circles near Svalbard correspond to the location of the ship on 15–19 April,
and they are color-coded based on observed SSA mass concentrations (same
color scale are the model). Panel (d) displays the MERRA sea ice extent.
We examine in more detail the daily variability in submicron SSA at Barrow
for January–July 2001 (Fig. 2a). Between January and late April, the
observations show large day-to-day variations with concentrations ranging
from < 0.5 µgm-3 to 2–4 µgm-3. These SSA
enhancements last for 1–7 days. We find that the timing and magnitude of
these events are often reproduced by the blowing snow simulation
(observations: 0.98 ± 0.9 µgm-3; STD-SNOW: 0.94 ± 0.8 µgm-3)
and are driven in part by variability in wind speed (Fig. 2b, gray shaded areas).
Some events are not associated with high local winds
(29 January–5 February; 24–28 March) and are due to transport from nearby
regions. For example, the high levels of SSA submicron concentrations seen
in the blowing snow simulation on 29 January–5 February are associated
with wind-blown snow coming from sea ice in the northern Beaufort Sea. In
contrast, the frost flower simulation fails to capture the variability and
magnitude of observed SSA events (STD-FF: 0.28 ± 0.29 µgm-3).
An examination of weekly SSA mass concentrations at Alert and
daily concentrations at Dumont d'Urville for 2001 yields similar conclusions
(see Fig. S4 in the Supplement).
Figure 3a shows submicron SSA mass concentrations measured aboard the R/V
Knorr during the ICEALOT experiment in March–April 2008. The first
part of the cruise took place over the North Atlantic, where the largest
enhancements in SSA mass concentrations (1–2 µgm-3 on 25–26
and 29 March) were due to open ocean SSA emissions and are reproduced by the
STD simulation. As the R/V Knorr traveled towards the Norwegian,
Barents, and Greenland seas (3–20 April), the STD simulation predicts very
low SSA concentrations (< 0.2 µgm-3) and can no longer
reproduce the observed concentrations (0.5–2 µgm-3). The STD-FF
simulation produces enhancements that are too weak, but the STD-SNOW
simulation captures some of these enhancements, in particular on 6–7 April
(the R/V Knorr was along the Norwegian coast) and 15–19 April (near
the coast of Svalbard). During both periods, Gilman et al. (2010) report
concurrent decreases in observed O3 and in the acetylene-to-benzene
ratio, indicative of destruction of surface O3 by Br and oxidation of
acetylene by both Br and Cl. Figure 3c shows that based on our STD-SNOW
simulation, a major blowing snow event developed on 15–19 April over the
central Arctic, poleward of 80∘ N. At that time the R/V
Knorr was positioned within a few kilometers off the sea ice edge
and the observed O3 decreased from 43 to 1.5 ppbv (Gilman et al.,
2010). The STD-SNOW simulation predicts an increase in SSA concentrations of
up to 1.5 µgm-3 (Fig. 3a, shaded gray area), reproducing the
timing and magnitude of the observed enhancement.
Seasonality of submicron SSA emissions in 2005 from open ocean,
blowing snow, and frost flowers over (a) the Arctic and (e) the Antarctic
for latitudes poleward of 60∘. Also shown are spatial distributions of
wintertime submicron SSA emissions over the Arctic (b–d) and the Antarctic (f–h).
Filled diamonds in panels (b) and (f) correspond to the locations of
Barrow (1), Alert (2), Zeppelin (3), Neumayer (4), and Dumont d'Urville (5).
Overall, we find that the blowing snow source can explain the large
wintertime enhancements in observed SSA mass concentrations over both the
Arctic and Antarctic regions. Furthermore, the STD-SNOW simulation captures
the episodic nature of the observed enhancements. The frost flower source
reproduces some of the observed enhancements over the Arctic but is not
able to match the high SSA concentrations over coastal Antarctica and does
not have a strong enough day-to-day variability. It is possible that both
blowing snow and frost flower emissions act together. However, when we add
the contributions from both sources, we find that modeled SSA mass
concentrations are a factor of 2–3 too high compared to observations at
Barrow and Alert (Fig. S5). In particular, the frost flower simulation leads
to a peak in SSA in February at Barrow, which is not observed. Our
simulations thus suggest that the dominant influence is from blowing snow.
Arctic (> 60∘ N) and Antarctic
(> 60∘ S) SSA budgets for the open ocean, blowing snow,
and frost flower sources for the year 2005.
Table 1 summarizes the annual SSA budgets over the Arctic and Antarctic as
calculated in GEOS-Chem for the year 2005 poleward of 60∘
latitude (see Table S1 in the Supplement for the global budgets). We find
that annual SSA emissions vary by 10–30 % for 2004–2008, but the overall
seasonality and spatial distribution of emissions are similar from year to
year. The total (0.01–4 µm) blowing snow source is 2.6 Tg yr-1 for
the Arctic and 4.2 Tg yr-1 for Antarctica. We find that the larger
blowing snow source over Antarctica, despite the lower snow salinity, is a
result of faster winds over Antarctic sea ice. Furthermore, the younger age
of snow assumed over Antarctic sea ice (1.5 days compared to 3 days over
Arctic sea ice) contributes to 30 % of the difference in blowing snow
emissions between the Arctic and Antarctic. The frost flower emissions are
slightly stronger over the Antarctic (0.25 Tg yr-1) than the Arctic
(0.21 Tg yr-1) due to strong katabatic winds over the Antarctic. The
open ocean accounts for 30 Tg yr-1 over the Arctic and 40 Tg yr-1
over Antarctica. Examining submicron SSA (rdry=0.01–0.5 µm),
we see that this is the size range where blowing snow (Arctic: 1.0 Tg yr-1;
Antarctic: 2.5 Tg yr-1) and frost flower (0.21 Tg yr-1;
0.25 Tg yr-1) emissions have their largest impact relative to the open
ocean (0.78 Tg yr-1; 1.0 Tg yr-1). This difference in size
distributions is related to the different physical mechanisms for SSA
emissions from open ocean emissions (breaking waves and bubble bursting)
compared to blowing snow (saltation of fallen snow and suspension) or frost
flowers (saltation of broken frost flower crystals and suspension). The
original crystalline form of snow particles/frost flower fragments is
expected to be shattered by repeated impact with the ground and other
particles during saltation. Sublimation of ice from these particles leads to
relatively small SSA compared to bubble bursting in the open ocean. In the
following sections we focus on the seasonality and spatial distribution of
submicron SSA emissions (Figs. 4 and 5).
Surface mass concentrations of wintertime submicron SSA. Zonal
mean concentrations are shown over the (a) Arctic and (e) Antarctic for the
open ocean (black line), blowing snow (red line), and frost flowers (green
line). The panels on the right side show the spatial distributions of
wintertime surface submicron SSA mass concentrations over the
Arctic (b–d)
and the Antarctic (f–h) for each source type. Filled diamonds in
panels (b) and (f) correspond to the locations of the ground stations (see Fig. 4).
Arctic
Figure 4a shows the seasonal evolution of our three SSA sources over the
Arctic (> 60∘ N). SSA emissions from the open
ocean maximize in September–October as a result of strong winds combined
with minimum sea ice extent. During winter months, SSA emissions from the
open ocean are largest over the ice-free North Atlantic Ocean, extending
towards the Barents Sea (Fig. 4b). SSA emissions from blowing snow reach
their maximum in December–April (Fig. 4a) with the largest emissions
occurring over sea-ice-covered regions with the strongest winds (Fig. 4c):
east of Greenland, over the central Arctic, and the Beaufort Sea. The modeled
blowing snow SSA surface mass concentrations reach 2–3.5 µgm-3
over these regions (Fig. 5c). We find that atmospheric transport leads to
inland incursions of blowing snow SSA over northern Canada, Alaska, and
Siberia (Fig. 5c).
Frost flower emissions maximize in December–March and are 2–4 times smaller
than blowing snow emissions during these months (Fig. 4a). We find that
frost flower emissions are highly localized with the strongest emissions
over the Canadian Arctic Archipelago (Fig. 4d), where surface concentrations
of SSA reach 2–3 µgm-3 (Fig. 5d), explaining the large influence
we noted at Alert and Barrow (Fig. 1). Weaker emissions occur over coastal
Siberia and in leads located within the central Arctic sea ice. In our
simulation, the location of frost flower emissions largely depends on the
simultaneous occurrence of very cold air temperatures (< -20 ∘C)
and open leads. Other regions in the Arctic have cold
temperatures during winter, but they are mostly covered by sea ice with
limited open leads areas. Our emissions from frost flowers over the Arctic
(0.21 Tg yr-1) are consistent with the accumulation mode emissions reported
by Xu et al. (2016) (0.24 Tg for November–February; their Table 2). We find a geographic distribution similar to Xu et al. (2016).
We note that the mean lifetime of both blowing snow and frost flower
submicron SSA is 6–7 days in the Arctic, nearly twice as long as open ocean
SSA (Table 1). Open ocean SSA form over lower latitude warmer regions, while
sea ice SSA emissions occur at higher latitudes under much colder
conditions, with less efficient removal processes in mixed-phased and ice
clouds. The current parameterization in GEOS-Chem assumes that in-cloud
scavenging of SSA does not occur in cold clouds (T<258 K) (Wang et
al., 2011), and thus wintertime sea-ice-generated SSA are only removed by
below-cloud scavenging (which is slow for accumulation-mode aerosols) and
dry deposition. Recent laboratory studies have shown that SSA could act as
ice nuclei by deposition freezing (Wise et al., 2012) and immersion freezing
(DeMott et al., 2016) and might thus undergo in-cloud scavenging in mixed
and ice clouds. This process is not currently included in GEOS-Chem.
Antarctic
In the southern hemispheric polar regions, open ocean SSA emissions display a
weak seasonal cycle due to persistent strong winds over the Southern Ocean
(Fig. 4e). During austral winter, emissions from the open ocean are
strongest at ∼ 50∘ S leading to modeled
surface SSA concentrations of 1–3 µgm-3 (Figs. 4f and 5f). Blowing
snow emissions maximize in June–October (Fig. 4a) and are strongest over the
sea ice of the Ross and Amundsen seas because of the strong katabatic winds
flowing off the Antarctic Plateau as well as strong winds in the Indian
Ocean sector (Fig. 4g). In these regions, modeled submicron SSA
concentrations from salty snow reach 1–3 µgm-3, explaining the
increase of 1–2 µgm-3 seen at Neumayer and Dumont d'Urville
(Figs. 5g and 1d–e). The model predicts that frost flower emissions are
concentrated near the Ross, Ronne, and Amery ice shelves and along coastlines
(Fig. 4h), accounting for 1–2 µgm-3 surface submicron SSA over
these regions (Fig. 5h). Neumayer thus receives influence from frost flowers
formed off the Ronne ice shelf (Figs. 5h and 1d), while Dumont d'Urville has
a weaker influence from frost flowers forming along the local coastline
(< 0.1 µgm-3).
Spatially, we find that the locations of blowing snow and frost flower
emissions are complementary to each other due to the different requirements
of sea state (sea ice compared to open leads).
Discussion and conclusions
In this work, we implement two new SSA emission schemes in the GEOS-Chem
chemical transport model: a blowing snow parameterization following the work
of Yang et al. (2008, 2010) and a frost flower parameterization based on Xu
et al. (2013) and Kaleschke et al. (2004). We find that the GEOS-Chem
simulation with open ocean emissions fails to capture the elevated SSA mass
concentrations observed at five coastal stations in the Arctic and Antarctic
during winter (2001–2008) and during the ICEALOT research cruise in
March–April 2008. When blowing snow emissions are added, the model is able
to reproduce the wintertime observed SSA levels as well as their large
day-to-day variability driven by wind speed. We find that the frost flower
parameterization cannot fully explain the high wintertime concentrations and
displays a seasonal cycle decreasing too rapidly in early spring.
Furthermore, our frost flower simulation cannot reproduce the large daily
variability of observed SSA.
Over the Arctic, we estimate that annual blowing snow emissions of submicron
SSA are 1.0 Tg yr-1, compared to 0.78 Tg yr-1 from the open ocean.
Over the Antarctic, these emissions are 2.5 Tg yr-1 for blowing snow
and 1.0 Tg yr-1 for the open ocean. Blowing snow emissions are mostly
controlled by wind speed and are thus larger over the Antarctic due to the
strong katabatic winds off the Antarctic Plateau and strong westerlies over
the Southern Ocean. Frost flower SSA emissions are 0.21 Tg yr-1 over
the Arctic (0.25 Tg yr-1 for the Antarctic) and depend on the
co-location of cold air temperatures and open leads.
The parameterizations for blowing snow and frost flowers have several
intrinsic assumptions, such as the salinity of snow and the scaling factor
for frost flowers, which will affect the relative magnitudes of these two
sources in polar regions. The geographic distribution, seasonal cycle, and
daily variability of these sources, however, are controlled by sea ice
extent and meteorological parameters (winds and temperature). In this study,
we showed that the temporal and geographical variability of SSA observations
at five polar sites is more consistent with blowing snow than with frost
flowers. Based on this comparison, we conclude that blowing snow is likely
to be the dominant source of SSA in polar winter, although frost flowers
cannot be entirely ruled out. In particular, they may contribute indirectly
to SSA emissions by salinating wind-blown snow (Obbard et al., 2009).
These polar sources of SSA are subject to substantial uncertainties due to
the limited observations available. One key uncertainty in our simulations
is snow salinity. Indeed, SSA emissions from blowing snow have a near-linear
dependence on the salinity of snow. Thus a doubling of the assumed salinity
would lead to a doubling in SSA emissions from blowing snow. Furthermore, we
assume a uniform salinity of snow over both first-year and multiyear sea
ice. This likely overestimates the contribution of blowing snow SSA over the
western Arctic, which is dominated by multiyear sea ice. More extensive
observations of surface snow salinity at multiple locations over both
first-year and multiyear sea ice can help further refine these assumptions.
Sampling of SSA size distributions during blowing snow events can help
determine the number of particles per snowflake, which we determined
empirically in this study. This number will not affect to total SSA
emissions, but it will change the relative importance of submicron and
supermicron SSA emissions. There is insufficient knowledge on frost flower
occurrence, growth, and mobilization by winds. In particular, the role
of favorable wind conditions, as well as the ice thickness for frost flower
to grow, is highly uncertain, and thus the predicted locations of frost
flower emissions in our simulation are also uncertain.
Reducing these remaining uncertainties would help constrain how sea ice
emissions of SSA affect the chemistry of the polar atmosphere by acting as a
source of halogens, leading to ozone and mercury depletion events (Barrie et
al., 1988; Fan and Jacob, 1992; Simpson et al., 2007b; Schroeder et al.,
1998; Steffen et al., 2008). Improved process-based understanding of these
emissions would also lead to better constraints on the potential climatic
impact of wintertime SSA on clouds, in particular mixed-phase and ice clouds,
which have a strong influence on downward longwave radiative forcing. Indeed,
recent studies have shown the role of SSA as ice nuclei (Wise et al., 2012;
DeMott et al., 2016). Thus over the Arctic and Antarctic regions, where the
abundance of other ice nuclei such as dust or black carbon are low, SSA from
local sea ice sources could influence the formation, radiative forcing, and
precipitation of mixed-phase and ice clouds.
Data at Barrow can be accessed on the NOAA PMEL website
(https://saga.pmel.noaa.gov/data/stations) (Quinn et al., 2002). For
Alert, data access is through the Environment Canada website
(http://www.ec.gc.ca/donneesnatchem-natchemdata/). Data at Zeppelin are
available from the European Monitoring and Evaluation Programme website
(http://ebas.nilu.no/) (WMO/GAW, 2003). Data at Neumayer are available
from Pangaea (10.1594/PANGAEA.691456) (Weller et al., 2008). Data at
Dumont D'Urville are available from CESOA
(http://www-lgge.obs.ujf-grenoble.fr/CESOA/spip.php?rubrique2) (Legrand
et al., 2012). ICEALOT cruise observations are available from NOAA PMEL
(https://saga.pmel.noaa.gov/Field/icealot).
The Supplement related to this article is available online at doi:10.5194/acp-17-3699-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by funding from the NASA Atmospheric Composition
Modeling and Analysis Program under award NNX15AE32G. The authors wish to
thank the NOAA Pacific Marine Environmental Laboratory (PMEL) Atmospheric
chemistry group for providing the in situ aerosol observations at Barrow and
during the ICEALOT field campaign. We also thank Environment Canada for
providing the in situ observations at Alert, the French observation service
CESOA (http://www-lgge.obs.ujf-grenoble.fr/CESOA/spip.php?rubrique3) for the
Dumont D'Urville observations, and the Norwegian Institute for Air Research
(NILU) for the Zeppelin Mountain observations. The authors would like to
acknowledge useful discussions with Maurizio Di Pierro, Steve Warren,
Cecilia Bitz, and Becky Alexander.
Edited by: A. Jones
Reviewed by: three anonymous referees
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