Reactive halogens play a prominent role in the atmospheric chemistry of the
Arctic during springtime. Field measurements and modeling studies suggest
that halogens are emitted into the atmosphere from snowpack and reactions on wind-blown snow-sourced aerosols. The relative importance of snowpack and
blowing snow sources is still debated, both at local scales and regionally
throughout the Arctic. To understand the implications of these halogen sources on a pan-Arctic scale, we simulate Arctic reactive bromine chemistry in the
atmospheric chemical transport model GEOS-Chem. Two mechanisms are included:
(1) a blowing snow sea salt aerosol formation mechanism and (2) a snowpack
mechanism assuming uniform molecular bromine production from all snow
surfaces. We compare simulations including neither mechanism, each mechanism
individually, and both mechanisms to examine conditions where one process
may dominate or the mechanisms may interact. We compare the models using
these mechanisms to observations of bromine monoxide (BrO) derived from
multiple-axis differential optical absorption spectroscopy (MAX-DOAS)
instruments on O-Buoy platforms on the sea ice and at a coastal site in
Utqiaġvik, Alaska, during spring 2015. Model estimations of hourly and monthly average BrO are improved by assuming a constant yield of 0.1 %
molecular bromine from all snowpack surfaces on ozone deposition. The
blowing snow aerosol mechanism increases modeled BrO by providing more
bromide-rich aerosol surface area for reactive bromine recycling. The
snowpack mechanism led to increased model BrO across the Arctic Ocean with
maximum production in coastal regions, whereas the blowing snow aerosol
mechanism increases BrO in specific areas due to high surface wind speeds.
Our uniform snowpack source has a greater impact on BrO mixing ratios than
the blowing snow source. Model results best replicate several features of
BrO observations during spring 2015 when using both mechanisms in
conjunction, adding evidence that these mechanisms are both active during
the Arctic spring. Extending our transport model throughout the entire year leads to predictions of enhanced fall BrO that are not supported by
observations.
Introduction
Simulating Arctic halogen chemistry is a persistent problem for global
models because processes appear to differ between the Arctic and middle
latitudes
(Parrella
et al., 2012; Schmidt et al., 2016). Space-based instruments observe large
column densities of reactive bromine across swaths of the Arctic Ocean
during the Arctic spring (Chance,
1998; Richter et al., 1998; Wagner and Platt, 1998). Increased levels of
tropospheric reactive bromine are associated with ozone depletion events
(Barrie
et al., 1988; Foster et al., 2001; Koo et al., 2012; Halfacre et al., 2014)
as well as oxidation of gaseous elemental mercury
(Schroeder
et al., 1998; Nghiem, 2013; Moore et al., 2014). Bromine radicals have been
observed to lead directly to ozone depletion and mercury oxidation
(S. Wang et al., 2019). Deposition of oxidized mercury to the
snowpack can have deleterious effects on the health of Arctic humans and
animals (AMAP, 2011). Arctic reactive bromine
chemistry impacts tropospheric oxidative chemistry but is not typically
accounted for in global models. Model studies have found that reactive
halogen chemistry can explain the oxidation of gaseous elemental mercury
(Holmes et al., 2010) and
reduce radiative forcing from ozone
(Sherwen et al., 2017).
Replicating reactive halogen chemistry in models requires inclusion of
multiphase chemical reactions as well as mechanisms affecting sea salt aerosol particle production and chemical reactions within the snowpack.
These increased levels of tropospheric reactive bromine radicals are a
product of heterogeneous photochemical reactions at the interface between
air and saline surfaces such as surface snowpack and sea salt aerosols
(Saiz-Lopez and von Glasow, 2012; Simpson et al.,
2015). Figure 1 depicts the gas-phase, heterogeneous, and photochemical
reactions thought to control tropospheric bromine, all of which are included
in the model and results presented in this paper. Bromine radicals (Br) are produced by photolysis of molecular bromine (P1) or by self-reaction of
BrO (R6) and react with ozone to form bromine monoxide (BrO) (R2). Under
sunlit conditions, BrO is most often photolyzed back to Br radicals and an
oxygen atom (P2) that then most often reforms ozone, resulting in a null
cycle. Due to this rapid interchange of Br and BrO, these two compounds form
the BrOx family. If processes other than BrO photolysis (P2) convert
BrO back to Br without producing ozone, the imbalance between these other
processes and P2 result in net ozone depletion. For example, ozone is
depleted through R6 or R7 when BrO reacts with another halogen oxide to form
either Br2 or BrCl or through other more extended processes. A reactive halogen-activating cycle occurs when a BrO radical reacts with a
hydroperoxy (HO2) radical in R5 to form gaseous hypobromous acid
(HOBr). Heterogeneous chemistry can occur on a saline surface between HOBr
and particulate bromide (p-Br-) in HR1 forming Br2 or particle
chloride (p-Cl-) in HR6-forming BrCl. For each cycle of reactions P1, R2, R5, and HR1, one hydroperoxy radical is removed from the atmosphere, one
bromine atom is released into the atmosphere, and one ozone molecule is destroyed. This process of activation of particulate and snow bromide to
Br2 by consuming other radicals (e.g., HO2) is known as the “bromine explosion” (Wennberg, 1999). Ground-based instruments
have observed sharp increases in reactive bromine levels over the course of
a single day from below 2 pmol mol-1 up to a maximum of 41 pmol mol-1
(Pöhler et al., 2010). Reactions may
also sequester reactive bromine into more stable bromine reservoir species.
BrO may react with nitrogen dioxide (NO2) in R8 to form bromine nitrate
(BrNO3), which can also undergo hydrolysis on aqueous and ice surfaces
to form HOBr as in HR3.
GEOS-Chem tropospheric bromine reactions. Tropospheric
bromide reservoirs are shown in black boxes, with attached lines indicating reactions. Solid black lines R1–R11 indicate gas-phase chemical reactions,
solid orange lines P1–P8 indicate photolysis reactions, and dashed black
lines HR1–HR8 indicate heterogeneous reactions. All gaseous species may
undergo dry deposition. Additional sources of tropospheric bromine include
the production of particulate bromide by the BLOW mechanisms and the
production of Br2 by the PACK mechanism as well as the degradation of organobromines to form Br (OR1). Table 3 enumerates the specific species involved in each equation and shows the reaction rates for each respective
equation.
A potentially important competitor for recycling of reactive bromine through
HOBr is its reaction with sulfur (IV) species, such as the reaction between
HSO3- and HOBr in HR2
(Chen et al., 2017). To the
extent that this reaction competes with HR1, it can slow the release of
bromide from surfaces and reduce gas-phase reactive bromine (e.g., reduce
BrO). Deposition of the HBr formed from HOBr by HR2 can remove reactive
bromine from the troposphere. In general, the termination of this chemistry
leads to formation of HBr, which undergoes gas-particulate uptake to
particulate bromide (p-Br-).
Ozone deposited on a saline surface can oxidize Br- to form HOBr (similar to p-Br- reactions HR4a and HR4b), which is then converted to
Br2 or another dihalogen (e.g., BrCl). Production of reactive bromine
during ozone deposition does not require light and can occur at night
(Oum et al., 1998; Artiglia et al.,
2017). The production of Br2 is increased at low pH levels
(Halfacre et al., 2019).
We define the inorganic bromine family, Bry, in this paper as the sum of the bromine species: Br, BrO, HOBr, BrNO3, 2×Br2, BrCl,
BrI, and HBr, excluding p-Br-. The release of bromine from sea salt
aerosol particles was found to be the dominant global source of reactive
bromine
(Sander
et al., 2003; Zhu et al., 2019). Sea salt aerosol particles (SSAs) are one of the most abundant aerosol particle types present in the troposphere
(De Leeuw et al., 2011). Due to their
abundance, SSA particles greatly increase the particulate bromide on aerosol
surfaces available for heterogeneous reactive bromine chemistry.
Debromination of acidified aerosol increases reactive bromine by 30 %,
although global models may underestimate Arctic reactive bromine when
considering only open-ocean-sourced SSA (Schmidt et al.,
2016). The initial literature on Arctic reactive bromine chemistry identified aerosol particles as a potential saline surface for reactive bromine
photochemistry (Fan and Jacob, 1992; Vogt et al., 1996), and field studies confirmed that SSA is depleted in bromide
(Ayers et al., 1999;
Hara et al., 2018). If one supposes that SSA can only be produced from the
open-ocean source of SSA, the lack of Arctic Ocean open water during the winter/spring is at odds with observations of high SSA concentrations
observed during the winter months in polar regions
(Wagenbach et al., 1998; Huang et al., 2018). The
formation of SSA from the sublimation of blowing snow particles over the
Arctic Ocean was proposed as an alternate SSA production mechanism
(Yang et al., 2008, 2010,
2019). Recent field studies have confirmed the direct production of SSA from
blowing snow (Frey et al., 2020). A
blowing snow SSA mechanism was implemented in the global chemical model
GEOS-Chem and was able to explain wintertime SSA enhancements over the
Arctic (Huang and Jaeglé,
2017) as well as CALIOP-detected aerosol particle abundance
(Huang et al., 2018) and high levels of Arctic BrO detected by
satellites in spring
(Huang et al., 2020).
Snowpack containing bromide salts was also identified as a source of
reactive bromine (Tang and McConnell, 1996).
Molecular bromine was measured above the snowpack at levels up to 25 pmol mol-1 (Foster et al., 2001). Field experiments demonstrate
that the snowpack emits Br2, Cl2, and BrCl, with emission
affected by ambient ozone levels, the snowpack ratio of bromide to chloride,
and exposure to sunlight (Pratt et
al., 2013; Custard et al., 2017). Box modeling found that the flux of
reactive bromine from the surface of the Arctic Ocean sea ice is a
prerequisite for bromine activation
(Lehrer et al., 2004). Box modeling found
that both HOBr and BrNO3 can be converted to Br2 in the snowpack
(Wang and Pratt, 2017). Detailed one-dimensional models of the snowpack–air interface find that reactive bromine production can occur in the interstitial air between snowpack grains
(Thomas et al., 2011; Toyota
et al., 2014), with ozone depletion events arising from snowpack reactive
bromine production
(Thomas
et al., 2011; Toyota et al., 2014; Cao et al., 2016). However, a detailed
snowpack model coupled to an atmospheric model would be sensitive to
important parameters such as snowpack bromide content and acidity of the
air–ice interface that are highly variable across the Arctic (Toom-Sauntry and
Barrie, 2002; Krnavek et al., 2012). A mechanism to parameterize the release
of molecular bromine from snowpack upon deposition of ozone, HOBr, and
BrNO3 was implemented in the GEM-AQ model and captured many of the
observed features of reactive bromine in the Arctic troposphere
(Toyota et al., 2011). The mechanisms from
Toyota et al. (2011) assume a 100 % yield of molecular bromine on deposition of HOBr or BrNO3 (see Fig. 1 PACK)
and a diurnally varying yield of Br2 on ozone deposition of 7.5 %
during the daytime (solar elevation angle > 5∘) and
0.1 % during the nighttime (solar elevation angle < 5∘)
(see Fig. 1, PACK). In the Toyota et al. (2011) parameterization, the daytime yield of Br2 from ozone was increased to 7.5 % to match
surface ozone depletion observations and is based on the assumption that
photochemical reactions in the snowpack would trigger a bromine explosion
and amplify the net release of Br2
(Toyota et al., 2011). Herrmann et al. (2021) implemented the Toyota et al. (2011)
mechanism in WRF-Chem and found that snowpack Br2 production was capable of replicating ozone depletion events observed in multiple datasets. Marelle et
al. (2021) implemented a surface snowpack mechanism based on Toyota et al. (2011) and a blowing snow SSA mechanism based on Yang et al. (2008) and
Huang and Jaeglé (2017) and found improved prediction of ozone depletion
events, the majority of which were triggered by the snowpack mechanism. The
Toyota et al. (2011) mechanism was also implemented in the EMAC model and
replicated many of the features of reactive bromine events observed by the satellite-based GOME sensor (Falk and
Sinnhuber, 2018).
Field campaigns have directly observed the production of SSA from blowing
snow (Frey et al., 2020) as well as
production of Br2 from the snowpack
(Pratt et al., 2013) in the environment.
This paper uses both production mechanisms for the first time in the global chemical model GEOS-Chem. We devised a set of six model runs to test
each mechanism individually and together as well as one control run using
neither mechanism. We compare BrO simulated in each model run against
extensive ground-based observations of BrO made from February to June 2015.
This set of modeling scenarios allows identification of the effects of each
mechanism on BrO as well as the synergistic effects of both mechanisms
working together.
Data sources and methodsMultiple-axis differential optical absorption spectroscopy (MAX-DOAS) observation platforms
MAX-DOAS remotely measures the vertical profile of BrO (Hönninger
and Platt, 2002; Carlson et al., 2010; Frieß et al., 2011; Peterson
et al., 2015; Simpson et al., 2017). BrO is commonly used as a proxy for
total tropospheric reactive bromine
(Chance,
1998; Richter et al., 1998; Wagner and Platt, 1998; Theys et al., 2011; Choi
et al., 2012). MAX-DOAS instruments were mounted on all of the 15 floating autonomous platforms (O-Buoys) deployed in the Arctic sea ice as a
part of the National Science Foundation-funded Arctic Observing Network
project (Knepp et al., 2010).
Since MAX-DOAS requires sunlight to operate, measurements are not available
in winter. Spring observations on the O-Buoys typically begin in April, when there is enough O-Buoy solar power to defrost the MAX-DOAS view port. Figure 2 shows the O-Buoys active during 2015. O-Buoy 10 was deployed in sea ice
in fall 2013 and measured reactive halogen chemistry in spring 2014 and
2015. Most O-Buoys were destroyed in the summer, crushed between fragments
of melting sea ice. However, O-Buoy 10 survived summer 2014 in an intact ice
floe, survived the winter of 2014–2015, and re-started MAX-DOAS observations
in April 2015. O-Buoys 11 and 12 were deployed in fall 2014 and also
re-started observing BrO in April 2015. Figure 2 shows the GPS-derived
tracks of the O-Buoys for their full deployment and highlights the O-Buoy
locations from April to June 2015 when the BrO observations considered in
this analysis were gathered. A MAX-DOAS instrument of the same design was
deployed at the Barrow Arctic Research Center (BARC) on the coast of the
Arctic Ocean located at 156.6679∘ W, 71.3249∘ N near
Utqiaġvik, AK (Simpson, 2018), also shown in Fig. 2. Unlike the O-Buoy MAX-DOAS systems, which were powered by batteries and
solar panels, the BARC MAX-DOAS was powered by local utilities and was able to defrost its view port to gather BrO observations earlier in the year,
including February and March 2015. The BARC MAX-DOAS data were compared with two O-Buoy-style MAX-DOAS instruments deployed on Icelander platforms
(deployed on top of sea ice instead of within), and measurements from the various MAX-DOAS systems were found to be comparable (Simpson
et al., 2017). The reactive bromine season ends when the BrO slant column
densities fall below the instrument detection limit and do not recover,
which we call the seasonal end date (Burd et al., 2017).
All O-Buoy and BARC (Utqiaġvik) data are available at arcticdata.io
(Simpson et al., 2009; Simpson, 2018). More
information on the time periods of spring BrO observations can be found in
Swanson et al. (2020) and Burd et al. (2017). For comparison to
the MAX-DOAS BrO observations, GEOS-Chem model simulations are sampled along
the GPS-derived paths of O-Buoys 10, 11, and 12 as well as at BARC.
Locations of MAX-DOAS BrO observations used in this work.
Blue lines show the drift tracks of O-Buoys, with green showing the
locations with valid BrO measurements in spring 2015. Location of Barrow
Arctic Research Center (BARC) in Utqiaġvik indicated by green dots. True color MODIS imagery on 1 April 2015 shows typical sea ice coverage (NASA,
2015). The inset map shows the location of the map grid within the Northern Hemisphere.
MAX-DOAS profile retrieval
Vertical profiles of BrO were derived from MAX-DOAS observations by means of
optimal estimation inversion procedures detailed in Peterson et al. (2015)
with settings detailed in Simpson et al. (2017). The HeiPro
optimal estimation algorithm
(Frieß
et al., 2006, 2019) is used to retrieve vertical profiles of BrO between the surface and 4 km from the MAX-DOAS observations. Examination of the
averaging kernels from each MAX-DOAS retrieval finds the retrieved vertical
profile of BrO is best represented by two quantities: the vertical column
density of BrO in the lowest 200 m and the vertical column density of BrO in the lowest 2000 m of the troposphere, referred to in this paper as
BrOLTcol (Peterson et al., 2015). It was
shown in Peterson et al. (2015) that these two
quantities were largely independent of each other, were fairly insensitive
to variations in the assumed prior profile, and represented the
∼ 2–3 degrees of freedom for the signal indicated by the optimal estimation retrieval. An important consideration of this method is that when
the visibility is poor, MAX-DOAS is unable to traverse the lowest 2000 m a.g.l. and BrOLTcol cannot be measured accurately. Therefore, our quality-control algorithm eliminates BrOLTcol observations when the
degrees of freedom for the signal in the lofted (200–2000 m a.g.l.) layer were below 0.5 (Simpson et al., 2017). The average fitting error (1σ
error) of BrOLTcol during spring 2015 was 5.6×1012 molecules cm-2.
SSA production from the open ocean
Sea foam from breaking waves and bursting of bubbles forms aerosol droplets suspended in the marine boundary layer (Lewis and Schwartz,
2004). We calculate emission of sea salt aerosol particles from the open
ocean as a function of wind speed and sea surface temperature (SST) using
the mechanism initially described in Jaeglé et al. (2011) and updated with
decreased emissions over cold (SST < 5 ∘C) ocean waters
(Huang and Jaeglé, 2017).
Two separate SSA tracers are transported: accumulation-mode SSA (rdry= 0.01–0.5 µm) and coarse-mode SSA (rdry=0.5–8 µm). Sea salt bromide is emitted assuming a bromine content of 2.11×10-3 kg Br per kilogram of dry SSA (primarily NaCl) based on the mean ionic composition of seawater (Sander et
al., 2003). Bromide content is tracked separately on accumulation-mode SSA and on coarse-mode SSA. Freshly emitted SSA is alkaline and can be titrated
to a pH of 5 by uptake of acid gases SO2, H2SO4, and
HNO3 (Alexander et al., 2005).
Heterogeneous chemical reactions can convert SSA-transported bromide into
gaseous reactive bromine species in the atmosphere. We run our open-ocean SSA calculations at 0.5∘ latitude × 0.625∘ longitude
spatial resolution using the harmonized emissions component (HEMCO) for the highest possible detail
(Keller
et al., 2014; Lin et al., 2021), including cold water corrections used in Jaeglé et al. (2011). Production of SSA from open oceans followed by
advection can lead to reactive bromine recycling over Arctic Ocean sea ice.
Each of our model runs reads the dataset generated offline by HEMCO rather
than spending computational time replicating open-ocean SSA emissions. We call our control run using only open-ocean SSA emissions BASE.
Blowing snow SSA production
Snow can be lofted from the snowpack into the lowest layers of the
troposphere by high wind speeds, where it can undergo saltation (bouncing
leading to fragmentation) and sublimation to form SSA
(Yang et al., 2008, 2010; Frey et al., 2020). This process is modeled as a
function of humidity, ambient temperature, wind speed, snow particle size
distribution, and the salinity of the blowing snow
(Yang et al., 2008, 2010).
We assume that snowpack exists on all sea ice surfaces during the Arctic
spring after snow accumulation during winter on sea ice of all ages. Three thresholds must be met for SSA production from blowing snow
(Déry and Yau, 1999, 2001).
A temperature threshold restricts SSA production from blowing snow to
temperatures below freezing. The humidity threshold is based on relative
humidity with respect to ice. Sublimation from snow crystals cannot occur if
the air is saturated, and no SSA is produced if RHice is greater than
100 %. The wind speed threshold requires 10 m wind speed to be greater
than a threshold value defined in Eq. (1) for any production of SSA
(Déry and Yau, 1999, 2001).
Ut=6.975+0.0033(Ts+27.27)2
The wind speed threshold (Ut) is dependent on surface temperature
(Ts) in Celsius with a minimum threshold of 6.975 m s-1 at -27.27 ∘C and a maximum threshold at 0 ∘C of 9.429 m s-1. The
10 m wind speed threshold is the most stringent and often controls the
production of SSA from blowing snow.
Production of blowing snow SSA is highly sensitive to surface wind speed. We
use the highest-resolution surface wind speed dataset to ensure the most accurate modeling of SSA and reactive bromine. The MERRA-2 Global Reanalysis
Product has a 0.5∘ latitude × 0.625∘ longitude
resolution which is typically re-gridded to a lower resolution for global
chemical modeling. Previous use of the snowpack blowing snow SSA mechanism
used MERRA-2 data re-gridded to either 2∘× 2.5∘ or
4∘× 5∘ latitude and longitude
(Huang
and Jaeglé, 2017; Huang et al., 2018, 2020). Re-gridding to a coarser spatial resolution may smooth out the highest 10 m wind speeds by averaging
them with lower wind speeds in the grid cell. The Utqiaġvik MERRA-2 10 m
wind speeds at different spatial resolutions are shown in Supplement
Figs. S1, S2, and S3 to illustrate this effect. Average Utqiaġvik 10 m wind speeds for 2015 are 5.3 m s-1 at 2∘× 2.5∘ resolution
and 5.5 m s-1 at 0.5∘× 0.625∘ resolution. The maximum
Utqiaġvik 10 m wind speed at MERRA-2 2×2.5 is 16.3 m s-1, while the
maximum wind speed at MERRA-2 0.5∘× 0.625∘ is 19.3 m s-1.
These extremely high wind speed events are more common at higher spatial
resolution and can contribute an outsized amount of SSA to the marine
boundary layer. Supplement Fig. S4 shows the measured 10 m wind speed at
BARC along with a daily average threshold wind speed (Eq. 1). Spikes in daily averaged wind speed at BARC in April can contribute to SSA formation
and justify the use of high-resolution MERRA-2 wind speed data.
Snow salinity is influenced by snow age and the material underlying the snow
(Krnavek et al., 2012). The median
surface snowpack salinity near Utqiaġvik was measured at 0.67 practical
salinity units (PSUz) for 2–3-week old sea ice, 0.12 PSU for thicker first-year ice, and 0.01 PSU for multi-year ice (MYI) (Krnavek et al., 2012). Snow salinity is also a function of snow depth above sea ice, with blowing surface snow
having much lower salinity than snow at depth that is in contact with the
sea ice (Frey et al., 2020). Domine et
al. (2004) measured
median salinity at 0.1 PSU on snowpack over first-year ice and 0.02 on snowpack over multi-year ice. In this analysis we use a salinity of 0.1 PSU
on first-year sea ice as in Huang et al. (2020). The production of reactive
bromine from sea ice types is entirely dependent on PSU in this parameterization. Previous modeling efforts have used 0.01 PSU for MYI
(Huang et al., 2018) and underestimate BrO production in high
Arctic areas with increased MYI coverage. The bromide content of surface
snow over MYI is enriched by deposition of SSA and trace gases, and MYI
regions may play a role in springtime halogen chemistry
(Peterson et al., 2019). Previous analysis of O-Buoy data found
no statistically significant differences in springtime BrO between regions
of the Arctic (Swanson et al., 2020). We use 0.05 PSU for
snowpack on MYI as in Huang et al. (2020).
Another important parameter for SSA formation is the number of SSA particles
formed from each blowing snowflake. A value of five particles per snowflake was used in Huang and Jaeglé (2017) based on wintertime
observations of supermicron and sub-micron SSA at Barrow. Values of 1 and 20
particles per snowflake have been tested (Yang et al., 2019), but it was unclear which value was more realistic. We use a particle
formation value of five particles per snow grain as in Huang et al. (2020).
Snowpack may be enriched or depleted in bromide compared to seawater, which
is thought to be an effect of atmospheric deposition or release of bromine
from the snowpack (Krnavek et al., 2012). Snowpack enrichment due to atmospheric deposition is less pronounced when
snowpack salinity is high, with snowpack containing 1000 µM Na+
(approximately 0.06 PSU) or more, never exceeding twice the seawater ratio of bromine to chloride (Krnavek et al.,
2012). Domine et al. (2004) found an increased enrichment factor of 5 times seawater in snow with a salinity of 100 µM Cl- (approximately 0.006 PSU). We use a snowpack enrichment factor of bromide
5 times that of seawater as in Huang et al. (2020), where this enrichment best agreed with GOME-2 observations. However, we note that a bromide
enrichment factor of 5 times seawater exceeds an enrichment factor of 2 measured in snowpack with a salinity of 0.1 PSU
(Krnavek et al., 2012).
Our choice of model input settings is similar to Huang et al. (2020), but we will be running the blowing snow SSA mechanism in HEMCO at a 0.5∘
latitude × 0.625∘ longitude spatial resolution. The model run
using the results of our high-resolution blowing snow SSA HEMCO simulation
is called BLOW.
Snowpack emissions of molecular bromine
We base our Br2 emissions scheme on Toyota et al. (2011) and Marelle et al. (2021), which
prescribe a yield of Br2 upon snowpack deposition of ozone, BrNO3, and HOBr. In other modeling studies, this simplified deposition-based
mechanism captured the synoptic-scale behavior of reactive bromine
production across the Arctic
(Toyota
et al., 2011; Falk and Sinnhuber, 2018; Herrmann et al., 2021; Marelle et
al., 2021). These modeling studies used different yields of Br2 upon
deposition over land snowpack, multi-year ice, and first-year ice, restricting the production of molecular bromine from ozone deposition to
first-year ice surfaces. None of these studies were coupled to a snowpack model tracking snow bromide and effectively assume an infinite bromide
reservoir with Br2 production limited only by the deposition flux and
Br2 yield.
Field studies indicate that snowpack over multi-year ice, first-year ice,
and land regions may contribute to reactive bromine chemistry. Krnavek et
al. (2012) found snow bromide content spanning 6 orders of magnitude, with individual samples taken from multi-year ice, first-year ice, and land
regions showing variability of up to 3 orders of magnitude for each region. Analysis of variance in tropospheric BrO from 2011 to 2016 found no statistically significant differences in tropospheric BrO between different
regions of the Arctic (Swanson et al., 2020). Both coastal
snowpack and multi-year ice regions may produce reactive bromine. Molecular
bromine production has been observed from coastal snowpacks on exposure to ozone (Pratt et al., 2013; Custard
et al., 2017). Airborne sampling has observed enhanced BrO up to 200 km
inland (Peterson et al., 2018). Snow above
multi-year sea ice regions is depleted in bromide, indicating that it may
play a role in Arctic bromine chemistry (Peterson et al.,
2019).
Our modeling study tests the hypothesis that all snow has a uniform ability
to produce molecular bromine, effectively assuming an infinite bromide
reservoir with Br2 production limited only by the deposition flux. We
differ from previous model parameterizations in allowing uniform Br2
production upon snowpack deposition of ozone, BrNO3, and HOBr over all sea ice surfaces and selected coastal snowpack regions. We only allow
snowpack Br2 production when the surface temperature is below freezing.
Surface temperatures may rise above freezing and drop back below freezing in
the Arctic spring, which may allow for snowpack Br2 to simulate
late-season bromine production events after snowpack melt such as those
observed in Burd et al. (2017). We expect higher predictions of snowpack
molecular bromine production than recent modeling efforts
(Herrmann et al., 2021;
Marelle et al., 2021) in which ozone deposition over land and multi-year ice
surfaces did not produce molecular bromine.
Snowpack Br2 production over sea ice
We assume a uniform production of Br2 on deposition to snowpack over
oceanic ice, whether the ice is first-year sea ice or multi-year sea ice. We use MERRA-2 fractional ocean ice coverage fields, which introduces some artifacts. MERRA-2 classifies the freshwater Great Lakes as ocean, but sea
ice and snowpack on those frozen lakes are unlikely to have sufficient bromide to support large Br2 fluxes due to their distance from the ocean.
Therefore, we specifically prohibit snowpack Br2 emissions in the Great
Lakes region (between 41 and 49∘ N latitude and between 75 and 93∘ W longitude). This choice is in agreement
with McNamara et al. (2020), who found that road-salt-derived aerosol particles are responsible for 80 %–100 % of atmospheric ClNO2 in
Michigan, with no strong indication of a source of reactive halogens from the nearby Great Lakes.
Snowpack Br2 production over land
We wish to only enable production of Br2 over land if the snowpack is
sufficiently enriched in bromide. Snowpack over land surfaces and glaciers
may be enriched in bromide by oceanic SSA sources
(Jacobi et al., 2012, 2019).
The distance that SSA may be transported inland from the coast is limited by
geographical features such as mountains. Based on direct observations of
reactive bromine chemistry up to 200 km from the Alaskan coastline
(Peterson et al., 2018), we include
unlimited production of Br2 from specific land grid cells within 200 km
of the coast upon deposition of ozone, HOBr, and BrNO3. We only allow
the fraction of each grid cell that is within 200 km of the coastline
(Stumpf, 2021) to produce molecular bromine. We
further restrict snowpack Br2 emissions to locations that are less than
500 m above sea level, because higher-elevation locations are unlikely to be enriched by sea spray. This altitude screen eliminates Br2 emissions
from coastal mountains such as the Alaskan Rockies, the Brooks Range in
Alaska, and the Scandinavian Mountains as well as from the Greenland
Plateau. Halogen chemistry may occur over the Greenland ice sheet
(Stutz et al., 2011), contrary to this screen, but this will have minimal impact on the regions of
interest in this paper.
Our final screen is based on the average snow depth in each land grid cell.
Both modeling studies
(Thomas et al., 2011; Toyota
et al., 2014) and field studies
(Domine
et al., 2004; Pratt et al., 2013; Custard et al., 2017; Frey et al., 2020)
agree that bromine chemistry can occur in the better ventilated and
illuminated top of the snowpack. Regions with less than 10 cm of snowpack
may not have sufficient snow for reactive bromine chemistry, and thus we only produce snowpack Br2 when the average snow depth in a land grid cell is
10 cm or greater. This filter prevents molecular bromine production in the
lower-latitude regions with minimal snow coverage and is necessary because ozone deposition to plants in snow-free grid cells often exceeds the slow
deposition of ozone to snowpack and would not be expected to produce
Br2.
Diurnal yield of Br2 on ozone deposition
We choose two alternate assumptions for the yield of Br2 during the
day. Toyota et al. (2011) initially assumed a
constant yield of Br2 from ozone deposition of 0.1 % based on
laboratory observations of nighttime bromine activation on ozone deposition
(Oum et al., 1998; Wren et al., 2010, 2013) and then adjusted the daytime yield
of Br2 on ozone deposition to 7.5 % to better match surface ozone
mixing ratios measured at coastal stations. This increased daytime yield
value was chosen based on the assumption that photochemistry may trigger an
autocatalytic cycle leading to a 75-fold increase in Br2 yield. The
PHOTOPACK runs use the increased daytime Br2 yield of 7.5 % when the solar elevation angle is 5∘ or greater. Previous implementations
of the snowpack mechanism
(Toyota
et al., 2011; Herrmann et al., 2021; Marelle et al., 2021) predict ozone
deposition velocities over Arctic sea ice on the order of 0.01 cm s-1. Their
findings agree with a modeling sensitivity study finding best agreement with
observations using ozone deposition rates between 0.00 and 0.01 cm s-1 (Helmig et al., 2007). Our model
predicts similar polar open-ocean ozone deposition rates of 0.009 cm s-1 (Pound et al., 2020) but
predicts higher modeled deposition velocity of ozone over Arctic sea ice
between 0.02 and 0.1 cm s-1 based on the month (see Supplement Fig. S5), with higher values influenced by proximity to the coast as observed
along non-Arctic coastlines in Bariteau et al. (2010). Thus, our PHOTOPACK run may
predict much higher Br emissions than previous snowpack predictions despite
the same yield values due to differences in deposition. To match our
magnitude of Br2 production with previous implementations of the
snowpack mechanism
(Toyota
et al., 2011; Herrmann et al., 2021; Marelle et al., 2021), we add two PACK runs with a constant Br2 yield on ozone deposition of 0.1 % based on
yield values in Toyota et al. (2011). Both PACK and PHOTOPACK runs assume
100 % conversion of deposited HOBr and BrNO3 to Br2. Table 1
shows further model run yield details.
Model run settings. Sea salt aerosol particles are produced from blowing snow as detailed in
Sect. 2.5. Daytime is defined as when the solar elevation angle is greater
than 5∘, and nighttime is defined as when the solar elevation angle is less than 5∘.
Model runBlowing snowMillimoles Br yieldedMillimoles Br yieldedSSA producedper mole O3 depositedper mole O3 deposited(daytime)(nighttime)BASEFalse00BLOWTrue00PACKFalse11BLOW+PACKTrue11PHOTOPACKFalse751BLOW+PHOTOPACKTrue751GEOS-Chem chemistry and transport model
The GEOS-Chem global atmospheric chemistry and transport model
(Bey et al., 2001)
simulates emissions, transport, and chemistry of atmospheric trace gases and
aerosols, including halogens. The chemical mechanism in GEOS-Chem 12.9.3
(http://www.geos-chem.org, last access: 29 October 2019,
10.5281/zenodo.3974569, The International GEOS-Chem User Community, 2020) includes
HOx–NOx–VOC-O3–halogen–aerosol tropospheric chemistry (Mao
et al., 2013; Fischer et al., 2014, 2016; Travis et al.,
2016; Wang et al., 2021). The model has been regularly and consistently
updated to reflect the current understanding of heterogeneous and gas-phase halogen chemistry.
Halogens in the troposphere may be sourced from photooxidation of
halocarbons, emissions of iodine from the ocean surface, downward transport
of halogens from the stratosphere, and release of halogens through
heterogeneous chemistry on SSA. Figure 1 shows a simplified version of the
GEOS-Chem reaction scheme focusing on tropospheric bromine reactions and
reservoirs. Heterogeneous reactions for release of reactive bromine from
aerosol surfaces were added to GEOS-Chem
(Parrella et al., 2012)
and have been updated to include multiphase reactions involving cloud
aerosols, cloud droplets, and ice aerosols as well as inter-halogen
reactions between bromine, chlorine, and iodine species (Schmidt
et al., 2016; Sherwen et al., 2016a; X. Wang et al., 2019) and input from the
stratosphere (Eastham et
al., 2014). Recent updates also include reactions between sulfur (IV)
species and HOBr, which lead to a 50 % decrease in Bry due to the
scavenging of HOBr on aerosol surfaces containing sulfur
(Chen et al., 2017). These
HOBr–sulfur(IV) reactions are critical in moderating tropospheric BrO at the mid latitudes (Zhu et al., 2019). In GEOS-Chem 12.9 the halogen
chemical mechanism was modified extensively to include chlorine chemistry as
detailed in X. Wang et al. (2019) with updated halogen–sulfur (IV) rates (Liu et al., 2021), reaction of S(IV) + HOCl, and
improved cloud pH calculation from Shah et al. (2020). For the
simulations here, GEOS-Chem uses the Modern-Era Retrospective Analysis for
Research and Applications, version 2 (MERRA-2) assimilated meteorological
fields (Gelaro et al., 2017)
re-gridded from a native resolution of 0.5∘× 0.625∘ latitude and longitude to 2∘× 2.5∘ using a reduced
vertical grid of 47 layers.
We initialize our model in October 2014 from a full-chemistry benchmark
file, allowing for 6 months of spinup before our period of interest spanning from March to November 2015. We run six different model simulations
with settings detailed in Table 1. The base model (BASE) includes the
halogen sources described above but no Arctic-specific halogen sources. The
BLOW simulation adds SSA production from blowing snow following Huang et al. (2020) but using a more recent version of GEOS-Chem. The PACK simulation
adds snowpack Br2 emissions using a constant yield from O3
deposition. The PHOTOPACK simulation also emits Br2 from snowpack but
increases the Br2 yield from O3 deposition under sunlight. These
blowing snow SSA and snowpack sources are combined in the BLOW+PACK and
BLOW+PHOTOPACK simulations.
Comparing GEOS-Chem results to MAX-DOAS vertical column densities
GEOS-Chem simulates BrO mixing ratios for each of its 47 atmospheric layers.
Reducing the vertical resolution of the more-resolved GEOS-Chem predictions
to be comparable to the coarser MAX-DOAS data is necessary for appropriate
comparison (Rodgers and Connor, 2003). To compare the GEOS-Chem
profiles to these two grid-coarsened quantities, we grid-coarsen the averaging kernels produced by the HeiPro retrieval algorithm using
Supplement Eq. (S1) from Payne et al. (2009) to the partial
column averaging kernels shown in Fig. 3. We use the average of all April averaging kernels that pass our quality criteria (> 0.5 DOFS in the lofted layer), which generally represents non-cloudy conditions. We
calculate modeled BrOLTcol by applying the partial column averaging kernels shown in Fig. 3 to the GEOS-Chem modeled vertical BrO profiles.
Figure 3 shows that the average partial column averaging kernel for the surface layer (0–200 m a.g.l.) has near-unit sensitivity to BrO at the ground, decaying
to about 0.5 at 200 m a.g.l. and then to 0 at about 400 m a.g.l., as desired. The sensitivity of BrOLTcol is near unity from about the surface to 600 m a.g.l. and then slowly decays with 0.5 sensitivity at 2000 m a.g.l. The resulting sensitivity to mid-tropospheric BrO means that free-tropospheric
BrO produced by the GEOS-Chem model contributes to modeled BrOLTcol,
albeit at 50 % or lower sensitivity, even if the GEOS-Chem-predicted
free-tropospheric BrO is above the nominal 2000 m top of the integration
window. The residual sensitivity of the BrOLTcol averaging kernel above
2000 m is caused by the limited ability of ground-based MAX-DOAS to
distinguish the true altitude of BrO at non-tangent geometries (higher
viewing elevation angles) that are required to view BrO at these higher
altitudes. Figure 3 shows that BrO above 4 km makes only a small
contribution to the modeled BrOLTcol, which was not included in BrOLTcol.
Averaging kernels showing the sensitivity of retrieved
BrOLTcol and retrieved BrOsurf
to BrO at a range of altitudes.
Each line represents a row of the averaging kernel matrix. BrOsurf is
the column from the surface to 200 m, and BrOLTcol is the column up to 2000 m.
Although it has been suggested in the literature (von
Clarmann and Glatthor, 2019) that averaged averaging kernels can cause
problems, we do not report data when there are clouds, and thus we only use the more consistent averaging kernels that occur under clear-sky conditions. We use other criteria related to vertical visibility to identify
clear skies. As described in Peterson et al. (2015), the information content (DOFS) in the
lofted layer is nearly linearly related to the aerosol optical depth. We
find that the slant column density of the O2–O2 collisional dimer (a.k.a. O4) observed at a 20∘ elevation angle is correlated with the lofted DOFS (Supplement Fig. S6). From this correlation we find that
observations of clear-sky conditions have a 20∘ elevation angle for O4 dSCD > 1043 molecule2 cm-5, and we use this cut to distinguish clear sky versus clouds. To ensure that GEOS-Chem results are
only compared to the clear-sky observational data, we apply this clear-sky screen to the measured BrOLTcol time series. The use of this screen also assists in minimizing variability in the averaging kernels and thus allows
the April averaged partial column averaging kernels (Fig. 3) to be applied
for clear skies at any time of the year.
Mean snowpack Br2 and p-Br- emissions by month, as
simulated by GEOS-Chem.
The top row shows emissions of Br2 in the PHOTOPACK run, the middle row
shows the emissions of Br2 in the PACK run, and the bottom row shows
emissions of p-Br- from adding the BLOW mechanism.
Examining reactive bromine in the Arctic springSnowpack Br2 emissions
The top two rows of Fig. 4 show PHOTOPACK and PACK average snowpack Br2 emissions for each spring month. The emission of Br2 in
PHOTOPACK increases over the Arctic Ocean in May and June, when the Sun is above the horizon for up to 24 h d-1 and ozone deposition yield is
almost always at the photo-enhanced level of 7.5 %. Notably, Br2
emissions over the Arctic Ocean in the PHOTOPACK and BLOW+PHOTOPACK runs
are highest in June, when the Sun is nearly always 5 ∘ above the horizon and surface temperatures may drop below freezing. The PACK emissions are lower than the PHOTOPACK Br2 emissions by an order of magnitude and show a seasonal cycle with a high BrOLTcol in April and May with a decrease in May and June. While our ozone deposition
velocities (see Supplement Fig. S5) over Arctic sea ice are much higher
than previous estimates of an approximate magnitude of 0.01 cm s-1 (Toyota et
al., 2011), the PHOTOPACK run highlights that a 75-fold increase in daytime
Br2 yield can lead to predictions of increased Br2 production over
the North Pole in June. Monthly satellite observations show that BrO reaches
a minimum over the Arctic Ocean in June (Richter et
al., 1998).
Coastal land regions within 200 km of the coastline have some of the highest
modeled snowpack Br2 emissions (see Fig. 4, rows 1 and 2). Within GEOS-Chem, deposition rates are greatest over land, less rapid over
the ice-covered ocean, and lowest over the open ocean (see Supplement Fig. S5). Lower dry deposition velocities over the ice-covered Arctic Ocean lead to decreased deposition and conversion to Br2. In GEOS-Chem, ozone mixing
ratios and deposition are over 3 orders of magnitude larger than BrNO3 and HOBr mixing ratios and deposition over the Arctic Ocean, and
ozone deposition contributes more than half of total Br2 emitted in the
PACK and BLOW+PACK runs. Our snowpack mechanism assumes that all ozone
deposited to the surface of a grid cell reacts with the snowpack cover, with
those reactions yielding a set percentage of Br2. This assumption is
more appropriate in the barren snow-covered coastal tundra but may be less
accurate in areas where deposition to vegetation dominates. This
nonconservative approach may lead to overestimation of Br2 emissions
from snowy vegetated surfaces. Our screens for snowpack emissions described
in Sect. 1.3.5 tried to minimize these effects but may not work perfectly
due to finite grid cell resolution and other challenges. Increased Br2
emissions observed in Fig. 4 in northern Europe may also be partially
driven by increased local mixing ratios of ozone and NOx over
industrialized regions such as the Kola Peninsula.
Blowing snow aerosol bromide emissions
The bottom row of Fig. 4 shows the total quantity of particulate bromide
released by the blowing snow SSA mechanism in the BLOW runs. Emissions over
the Arctic Ocean decline each month after the March maximum as rising
temperatures increase the wind speed threshold for blowing snow SSA
production. Some icy coastal regions with frequently high wind speeds such
as the Aleutian Islands south of Alaska and the eastern coast of Greenland
continue to emit SSA p-Br- in April, and the extremely high winds in
the Aleutians enable SSA production into May. The location of specific
high-wind storm systems in spring 2015 may be evident in the darker red
spots over the Arctic Ocean, which are particularly noticeable over the
Eurasian and central Arctic in March. These monthly averages are only accurate for the months in spring 2015 and may not be spatially
representative of blowing snow SSA production in other years.
The impact of the blowing snow SSA emissions on measured BrO is minimal on
O-Buoys in the Beaufort Gyre, possibly due to the spatial and seasonal
variations in SSA p-Br- emissions. Figure 4 shows that 2015 SSA
production was highest in March and April in the Eurasian and central sectors of the Arctic, and thus the O-Buoys deployed as shown in Fig. 2 are less exposed to the effects of SSA production than the Arctic as a whole.
Particulate bromide must be activated from SSA by heterogeneous reactions as
in Fig. 1 and Table 3, leading to photochemical cycles that sustain
further activation of bromide from SSA. The dearth of sunlight over the
Arctic Ocean in early March coincides with the greatest SSA p-Br- production and means that the increased February SSA p-Br- emissions may not lead to a direct increase in BrO.
Model root mean square error by run and location. Root mean squared model error (RMSE) shown in BrOLTcol/1012 molecules cm-2 RMSE calculated as the square root of the mean of the squared
errors for all times with valid observed BrOLTcol in spring 2015.
Units in BrOLTcol/1012 molecules cm-2OB10OB11OB12UtqiaġvikBASE9.912.922.913.0BLOW9.712.722.412.5PACK9.910.018.615.2BLOW+PACK10.110.115.717.5PHOTOPACK30.024.826.230.1BLOW+PHOTOPACK30.324.626.331.4
Monthly average BrOLTcol in the observations and model. Monthly averages of BrO at (a) O-Buoy 10, (b) O-Buoy 11, (c) O-Buoy 12, and (d) BARC at Utqiaġvik only using predictions and observations when
dSCDO4>1×1043 molecules2 cm-5. Observations
with average 1σ error shown in red. All units in 1013 molecules cm-2.
Snowpack Br2 emissions have more impact than blowing snow SSA on
monthly BrO abundance
Increased levels of bromine have been historically seen at Utqiaġvik
during February, March, April, and May (Berg et al., 1983). Previous
O-Buoy data analysis noted BrO dropping to zero in June
(Burd et al., 2017). Figure 5 shows monthly averaged
modeled BrOLTcol at Utqiaġvik and on the O-Buoys for each model
configuration. The difference in GEOS-Chem modeled monthly averaged
BrOLTcol for O-Buoys is minimal between the BASE and BLOW runs, the
PHOTOPACK and BLOW+PHOTOPACK runs, and the PACK and BLOW+PACK runs. Both
the BASE and BLOW runs predict near-zero BrOLTcol on all O-Buoys and during most months at Utqiaġvik. The exception to this is the slight increases
in monthly modeled BrOLTcol to 1×1013 molecules cm-2 in March
and April. This BASE increase in BrOLTcol indicates that oceanic SSA
rather than blowing snow SSA can affect modeled BrO at Utqiaġvik due to
its closer proximity to open-ocean regions than the O-Buoys. The increases in BrO from the BASE model due to the addition of BLOW, most evident at
Utqiaġvik in March 2015, are a result of increased particulate bromide
available for activation on aerosol surfaces. The PACK and BLOW+PACK runs
show the highest skill in reproducing observations, falling within the
monthly average of hourly measured BrOLTcol error for 9 of the 13
months plotted in Fig. 5. Both PACK and BLOW+PACK replicate the observed
monthly pattern especially well on O-Buoy 11 and at Utqiaġvik. Both runs
replicate the seasonal pattern of maximum modeled BrOLTcol at
Utqiaġvik in March followed by a decrease to near-zero modeled
BrOLTcol in May despite model overprediction of BrOLTcol in
February and March. The BLOW+PACK monthly BrOLTcol is between
1×1012 and 1×1013 molecules cm-2, higher than PACK monthly BrOLTcol due to the addition of blowing snow SSA.
This increase is most pronounced in February and March at Utqiaġvik, when lower temperatures lead to lower threshold wind speeds and increased SSA
production (see Supplement Fig. S4).
The inclusion of increased daytime yield of snowpack Br2 drives monthly
average BrOLTcol above 3×1013 molecules cm-2 in the PHOTOPACK
and BLOW+PHOTOPACK runs from February until June, far above the peak-observed monthly BrOLTcol of 2×1013 molecules cm-2. The PHOTOPACK and
BLOW+PHOTOPACK runs show a steady decline in BrOLTcol from February to June at Utqiaġvik. Predictions of PHOTOPACK and BLOW+PHOTOPACK
monthly June BrOLTcol above 2×1013 molecules cm-2 on the
O-Buoys is due to increasing photo-assisted local snowpack Br2
emissions over the Arctic Ocean (see Fig. 5). The PHOTOPACK mechanism
predicts monthly average BrOLTcol within observational error only on
O-Buoy 12 in April. Aside from this replication of the sparsely sampled
O-Buoy 12 April BrOLTcol, the PHOTOPACK mechanism overestimates
BrOLTcol.
Table 2 shows the root mean squared error (RMSE) of each model run as
compared to BrOLTcol observations in each different location in spring 2015. The PACK and BLOW+PACK runs have the lowest RMSE on O-Buoys 11 and 12 and among the lowest RMSEs on O-Buoy 10. Utqiaġvik shows the
lowest RMSE of 1.25×1013 molecules cm-2 for the BLOW run, although the PACK run is not too far off at 1.57×1013 molecules cm-2. Despite the
fact that BLOW+PACK has a higher RMSE of 1.75×1013 molecules cm-2 at Utqiaġvik, the BLOW+PACK run performs the best or near the best of all
runs on the O-Buoys and includes both known processes for Arctic reactive
bromine production. The PHOTOPACK and BLOW+PHOTOPACK runs with increased
daytime yield have a consistently high RMSE of 2.46×1013 molecules cm-2 or higher, often double the RMSE of other model runs.
May hourly BrOLTcol time series. Hourly time series of BLOW+PACK, PACK, and BASE BrOLTcol on (a) O-Buoy
10, (b) O-Buoy 11, (c) O-Buoy 12, and (d) BARC at Utqiaġvik in the 2015 Arctic spring. Observations and error bars in red, BASE BrOLTcol in brown, PACK BrOLTcol in purple, and BLOW+PACK BrOLTcol in
orange. All BrOLTcol plotted continuously except for gaps where
dSCDO4>1×1043 molecules2 cm-5.
The BLOW+PACK run best replicates hourly BrO events in mid and late May
The model's hourly predictions of BrOLTcol in May 2015 are shown in
Fig. 6 for the BASE, PACK, and BLOW+PACK runs. Figure 6 screens the modeled BrOLTcol for times when dSCD O4>1×1043 molecules2 cm-5, while Supplement Figs. S7 and S8 make direct
comparisons between observations of BrOLTcol for O-Buoys (S7) and at
Utqiaġvik (S8) throughout all of spring 2015. The O-Buoys show fluctuations in observed BrOLTcol during May and show consistent
increased columns of BrOLTcol from 10 to 20 May. The BASE run never
rises above 1013 molecules cm-2 and underpredicts most May hourly
BrOLTcol, although it predicts monthly BrOLTcol on OB10 within
observational errors in May and June. Both PACK and BLOW+PACK runs show
better skill in replicating BrOLTcol. The addition of the snowpack
mechanism allows us to predict increased BrOLTcol in late May on
O-Buoys 10 and 11. This points to the role of the surface snowpack in late-season events, in agreement with the findings of Burd et al. (2017).
We can identify the role of blowing snow SSA by comparing the PACK and
BLOW+PACK runs. Both the PACK and BLOW+PACK runs underestimate BrOLTcol during the first 10 d of May. BrO predictions show higher variability and peaks starting on 10 May. The blowing snow SSA mechanism
increases BLOW+PACK BrOLTcol on 12 and 13 May. PACK is skilled at
replicating observed O-Buoy 11 BrOLTcol on both days, and both PACK and
BLOW+PACK are within observational BrOLTcol error on 13 May.
A BrO event also occurs on 13 May on O-Buoy 10. While the strength of the
O-Buoy 10 BrO event is overestimated by PACK and BLOW+PACK, the shape of
that event is reproduced in both runs. Observed BrOLTcol decreases
rapidly on all O-Buoys after 14 May, and the model is unable to track this
sharp decrease. Rapid changes in BrOLTcol may be caused by sharp edges
in BrO-enriched air masses such as those seen by Simpson et al. (2017). The GEOS-Chem run at this resolution cannot replicate abrupt changes in BrO, but it does slowly decrease BrOLTcol to reach BrOLTcol to less than 1013 molecules cm-2 on 16 May. The
BLOW+PACK mechanism is skilled in replicating the magnitude and features
of a mid-May BrO event on O-Buoys 10 and 11.
Figure 7 shows all spring 2015 BrOLTcol observations on O-Buoys 10, 11, and 12 and BARC plotted against PACK BrOLTcol and BLOW+PACK
BrOLTcol. The increase in BrOLTcol on adding BLOW leads to fewer
underpredictions of observations (see the bottom right section of Fig. 7b). The Pearson correlation coefficient (r) between PACK BrOLTcol and
observed BrOLTcol is 0.33, improving to 0.39 on addition of BLOW in the
BLOW+PACK run. Other runs show less skill in replicating observations,
with a BASE BrOLTcol Pearson correlation with observations of 0.19 and a BLOW BrOLTcol Pearson correlation with observations of 0.23. We also performed a simple linear regression to determine the relationship between
predictions and observations for each run. The slope of the line of best fit
improves drastically on addition of PACK, changing from 0.06 for BASE and
0.07 for BLOW to 0.33 for PACK and 0.44 for BLOW+PACK. There is a positive
synergistic effect on the slope of the line of best fit when using both BLOW
and PACK in combination rather than individually. The use of both BLOW and
PACK mechanisms corroborates literature findings on the processes
influencing Arctic reactive bromine and increases correlation between
GEOS-Chem predictions and observations.
Hourly modeled BrOLTcol versus BrOLTcol observations.
Two-dimensional histograms showing the density of GEOS-Chem-predicted BrOLTcol versus all observed spring 2015 hourly BrLTcol, with (a) PACK BrOLTcol shown on the left sorted into square bins of 0.2 with a Pearson r correlation with observations of 0.33 and (b) BLOW+PACK BrOLTcol on the bottom sorted into square bins of 0.2 with a Pearson
r correlation with observations to 0.39. All units are in molecules cm-2. 1:1 line drawn in the center in black, with a margin of the average
observational error plotted in dashed black lines around the central 1:1
line.
Arctic spring reactive bromine modeling discussionUse of both mechanisms in conjunction leads to the best prediction of tropospheric BrO results
Initial implementation of this snowpack mechanism in Toyota et al. (2011)
increased the daytime yield of Br2 from ozone deposition to 7.5 % to
improve agreement between observed and modeled surface ozone mixing ratios.
Toyota et al. (2011) also increased the surface resistance of ozone to
104 s m-1, which decreased deposition velocities on Arctic snowpack to
approximately 0.01 cm s-1. Our model using a constant yield of Br2 from
ozone deposition at 0.1 % performs best despite observations that sunlight
has an effect on reactive bromine recycling in the snowpack
(Pratt et al., 2013; Custard et
al., 2017). GEOS-Chem does not explicitly model heterogeneous photochemistry
within the snowpack interstitial space but does include heterogeneous
bromine chemistry on aerosol particle surfaces after the Br2 is emitted
from the snowpack into the lowest model layer. The updates to GEOS-Chem
halogen chemistry
(Schmidt
et al., 2016; Sherwen et al., 2016b; Chen et al., 2017; X. Wang et al., 2019)
should be mechanistically sufficient to model daytime heterogeneous
chemistry of reactive bromine on aerosol surfaces. We note that improvements
to GEOS-Chem have increased the explicit modeling of these photochemical
recycling and amplification processes, possibly reducing the need for
empirical increases to daytime yields.
Our findings differ from recent implementations of the snowpack mechanism in
Herrmann et al. (2021) and Marelle et al. (2021). While all snowpack
mechanisms are based on Toyota et al. (2011),
several large differences in model configuration and mechanism
implementation explain these differences. We allow Br2 production from
ozone deposition over all snow surfaces, leading to much higher Br2
production over MYI and coastal regions. Land snowpack can produce Br2
on exposure to ozone and sunlight
(Pratt et al., 2013; Custard et
al., 2017), and Fig. 4 shows our coastal snowpack producing large quantities of Br2. Tropospheric reactive bromine chemistry has been
observed up to 200 km inland from the coast
(Peterson et al., 2018). Marelle et al. (2021) underestimate BrO in late March and overestimate Utqiaġvik BrO in early April. This seasonal pattern may be due to increased daytime ozone
yield on first-year ice near Utqiaġvik in April. Herrmann et al. (2021) found that HOBr and BrNO3 deposition was more important in driving snowpack Br2 production and that the daytime yield
of 7.5 % Br2 on ozone deposition underpredicted BrO. We find that
ozone contributes slightly more than HOBr and BrNO3 because we allow
for Br2 production on ozone deposition over the multi-year ice and coastal snowpack regions. The temporal coverage of this study spans the entire year,
while Herrmann et al. (2021) only span February, March, and April. Our longer timescale highlights the issue of increased daytime Br2 yield
during May and June (see Fig. 4, PHOTOPACK) with increased emissions over the Arctic Ocean that are not in agreement with satellite observations of
minimal Arctic tropospheric BrO in June (Richter et
al., 1998).
Addition of the PACK mechanism increases surface ozone predictive skill
The Barrow Arctic Research Center (BARC) in Utqiaġvik has the most
comprehensive coverage of surface ozone in spring 2015. A constant yield of 0.1 % Br2 from ozone deposition allows us to approximate the average
vertical extent of ozone depletion events at Utqiaġvik in May 2015. The
increase in Bry in the PACK and BLOW+PACK runs is confined to the
lowest 1000 m of the atmosphere (see Supplement Fig. S9). Ozone
depletions, caused by reactive bromine chemistry, often only occur within
the lowest 1000 m of the troposphere (Bottenheim et
al., 2002; Salawitch et al., 2010). Previous studies have found evidence of
lofted BrO in plumes at altitudes up to 900 m a.g.l.
(Peterson et al., 2017). The
monthly average Utqiaġvik May surface ozone in BLOW and BLOW+PACK is
22 nmol mol-1, matching mean May surface ozone from 1999 to 2008 (Oltmans et al., 2012). The PHOTOPACK runs
generate mean May surface ozone depletion to approximately 5 nmol mol-1, far
below the May mean. The PACK and BLOW+PACK runs duplicate the approximate
vertical extent of elevated bromine levels and the strength of typical May
ozone depletion.
Figure 8 shows hourly ozone predictions alongside BARC ozone observations
(McClure-Begley et al., 2014).
The BASE model fails to replicate variance in ozone measured at BARC in
Utqiaġvik, with a Pearson correlation coefficient to observations of
0.35. Adding PACK improves Pearson correlation to 0.47, within a rounding error of BLOW+PACK Pearson correlation of 0.47. Both PACK and BLOW+PACK
significantly improve model performance in replicating ozone depletions such
as those below 30 nmol mol-1 from 20 to 29 March but fail to track the
subsequent recovery of ozone to background levels on 1 April. Predicted PACK
ozone does not recover to background levels up to a height of roughly 1000 m. A similar pattern where our model replicates low ozone but fails to
predict the recovery of ozone to background levels occurs on 5 and 15 April.
Examination of ozone profiles in GEOS-Chem found that GEOS-Chem
underpredicts tropospheric ozone by 10–20 ppb north of 60∘
latitude (Wang et al., 2021),
which contributes to the low ozone predictions in our runs. Previous
modeling of Utqiaġvik spring 2012 ozone in WRF-Chem found a similar
linear correlation coefficient of 0.5 to BROMEX observations
(Simpson et al., 2017) when using both blowing snow and
snowpack mechanisms (Marelle et
al., 2021). We are biased low compared to observations, with a root mean
square error of 17.0 nmol mol-1 in BLOW+PACK compared to a root mean square
error of 12.9 nmol mol-1 in Marelle et al. (2021). This may be partially due to
limited vertical resolution in GEOS-Chem that may be inadequate for describing shallow surface-based temperature inversions and subsequent recovery. The
high bias in ozone deposition velocity over sea ice surfaces may also
contribute to low ozone mixing ratios near the surface
(Helmig et al., 2007).
Hourly Utqiaġvik ozone time series. Hourly time series of BLOW+PACK, PACK, and BASE ozone at Utqiaġvik in the 2015 Arctic spring. Ozone observations at BARC in black (McClure-Begley et al., 2014),
BASE ozone in brown, PACK ozone in purple, and BLOW+PACK ozone in orange.
Gaps indicate missing observational data.
A similar improvement in ozone predictions on the addition of PACK is seen
on the O-Buoys but is harder to quantify due to observational gaps in ozone data. Supplement Fig. S10 shows hourly ozone predictions graphed over
O-Buoy 11 observations, and Supplement Fig. S11 shows hourly ozone predictions graphed over O-Buoy 12. O-Buoy 10 was not able to gather an
observation of ozone in 2015. The clearest impact of PACK in Figs. S10 and S11 is seen in early April, with observed ozone dropping near 0 nmol mol-1 and PACK and BLOW+PACK runs dropping to 5 nmol mol-1, while the BASE run remains near 20 nmol mol-1. Figure S10 shows that ozone predictions on O-Buoy 10 in May are less accurate, failing to fall below 10 nmol mol-1
ozone, while observations show ozone dropping near the detection limit. The O-Buoys appear to experience more late-season ozone depletion events that
GEOS-Chem fails to replicate, possibly due to warming temperatures
increasing vertical mixing and replenishing ozone near the surface.
Arctic tropospheric reaction rates by model run. Rates for each of the simulated reactions listed in Fig. 1 grouped by
GEOS-Chem run. All units are listed as millions of moles per hour across the
region shown in Supplement Fig. S14. R in Reaction (R3) refers to any
organic molecule. Y in Eq. (1) represents NO, Cl, or H. X in Eq. (HR6a) represents either Br or Cl. PHOTOPACK and BLOW+PHOTOPACK are excluded
as they severely overpredict BrO, as seen in Figs. 4 and 5.
O-Buoys deployed during fall 2015 measured BrO slant column densities
characterized by noise around zero (see Supplement Figs. S12 and S13).
We do not retrieve vertical column density from these fall slant column
densities, because the resulting retrievals would be biased positive due to
an algorithm requirement that only positive BrO column densities are allowed
in the optimal estimation inversion. These differential slant column
densities (dSCDs) can be used qualitatively to determine the presence or
absence of BrO above the detection limit. If the dSCDs display noise
around zero at all viewing angles, the BrO in the troposphere is below the
detection limit of the spectrometer. A pattern of larger BrO dSCDs at
near-horizon viewing elevation angles indicating the presence of
tropospheric BrO above the detection limit is only observed at Utqiaġvik
during Arctic spring (see Supplement Fig. S12). Any BrO present in the Arctic troposphere in September and October falls below detection limits at Utqiaġvik (see Supplement Fig. S12) and on each O-Buoy (see
Supplement Fig. S13). The average Arctic spring 2015 MAX-DOAS BrOLTcol detection limits are 5×1012 molecules cm-2
(Peterson et al., 2015; Simpson et
al., 2017; Swanson et al., 2020). Both BLOW and PACK mechanisms lead to
prediction of increased fall BrO because the weather and sea ice conditions
specified in the emission algorithms occur in fall as well as spring.
Fall GEOS-Chem-predicted BrOLTcol. Hourly time series of BLOW+PACK, PACK, and BASE BrOLTcol on (a) O-Buoy 10, (b) O-Buoy 11, (c) O-Buoy 12 and (d) BARC at Utqiaġvik during September
and October 2015 BASE BrOLTcol in brown, BLOW BrOLTcol in yellow,
PACK BrOLTcol in purple, and BLOW+PACK BrOLTcol in orange. All
BrOLTcol plotted continuously except for gaps where the solar elevation angle was less than 5∘.
Figure 9 shows fall predictions of BrOLTcol filtered for times when
the solar elevation angle was greater than 5∘. BASE and PACK BrOLTcol remain near zero in September but rise above the MAX-DOAS
detection limit of 5×1012 molecules cm-2 BrOLTcol in October.
The addition of the blowing snow SSA mechanism propels BLOW BrOLTcol up
to 6×1013 molecules cm-2 in October. O-Buoys 13 and 14 have the
highest modeled fall BrOLTcol, but even Utqiaġvik has several days of BrOLTcol above 5×1012 molecules cm-2 in late October.
There is no clear evidence of any BrO above MAX-DOAS detection limits at
Utqiaġvik or on any O-Buoy in October, as seen by the dSCDs scattered
around zero in Supplement Figs. S12 and S13.
Both mechanisms assume that snowpack and SSA are just as capable of
recycling reactive bromine as in the springtime. High fall and winter SSA
agrees with observations of peak SSA during polar winter in both Antarctica
(Wagenbach et al., 1998) and the Arctic (Jacobi et al., 2012). The deposition of
Arctic haze (Douglas and Sturm, 2004) and SSA
(Jacobi et al., 2019) increases snowpack salinity
and sulfate content over the course of winter and spring. This seasonal
change in snowpack salinity and acidity may enable reactive bromine
recycling in the Arctic spring, but there may not be sufficient haze and SSA deposition in the fall to decrease snowpack pH and increase snowpack bromide content. Additional observations of fall snowpack over sea ice including ion
content could show different snowpack composition in spring and fall. Thus,
the GEOS-Chem model overestimates fall BrO by assuming the fall snowpack is
equally capable of reactive bromine recycling as the spring snowpack and providing an infinite reservoir of snowpack bromide in all seasons. Most
other modeling exercises have focused on spring with unknown predictions in
fall, possibly indicating problems in the mechanisms or parameterizations being employed, so we suggest that modeling should be done for a full year to
improve the underlying chemistry and physics. We also suggest a fall snow sampling campaign to validate modeled fall BrO.
Conclusions
We add snowpack Br2 production to GEOS-Chem based on multiple field
observations demonstrating molecular bromine production in snowpack
interstitial air. We use a mechanistic parameterization of snowpack Br2
production based on Toyota et al. (2011) in
which Br2 is emitted from all snowpacks of sufficient salinity and depth over land and sea ice upon deposition of the precursor species HOBr,
BrNO3, and ozone. Prior work has also added a blowing snow SSA
production mechanism that increases aerosol particulate bromide and thus
facilitates heterogeneous recycling of reactive bromine on these aerosol
particle surfaces. We update the halogen scheme to GEOS-Chem 12.9.3 and
performed six model simulations including a BASE run with neither blowing
snow SSA nor snowpack emissions, a PACK run assuming a constant yield of Br2 on ozone deposition over all snow surfaces, a PHOTOPACK run
assuming an increased daytime yield of Br2 on ozone deposition (similar to Toyota et al., 2011), a BLOW run using only blowing snow SSA formation, and
two additional runs combining BLOW and each respective PACK mechanism. The increased daytime yield of Br2 in PHOTOPACK leads to overprediction of
BrO in these simulations, but the PACK run (with constant Br2 yield day
and night) matches monthly averaged BrO vertical column densities within
the measurement error for 9 of 13 cases at O-Buoy and Utqiaġvik in springtime months. The PACK and BLOW+PACK runs were successful in replicating observed BrO events on O-Buoys in May. The BLOW mechanism effectively
increases aerosol surface available for turnover of reactive bromine. The
snowpack mechanism has more impact on modeled BrO mixing ratios than the
blowing snow SSA mechanism, but both contribute to tropospheric reactive
bromine. We extend our model run to the full year and find that enhanced
daytime Br2 yield can lead to increased Arctic Ocean Br2
production in the summer. Examining modeled BrO in fall 2015 reveals
predictions of BrO when using these mechanisms that are at odds with observations.
The inclusion of two Arctic reactive bromine production mechanisms based on
literature observations of snowpack Br2 emission and blowing snow SSA
formation improves model skill in replicating Arctic tropospheric BrO in
spring 2015. The snowpack is an important source of reactive bromine, and
SSA particles provide an abundant surface for sustained reactive bromine
recycling in the troposphere. We find that using both snowpack and blowing
snow SSA bromine production mechanisms is necessary for modeling BrO in the
Arctic.
Code availability
Community authored code is available through GitHub at https://github.com/geoschem/geos-chem (last access: 29 October 2019) and 10.5281/zenodo.3974569 (The International GEOS-Chem User Community, 2020). First described in Bey et al. (2001). Documentation and guides may be found at http://wiki.seas.harvard.edu/geos-chem/index.php/Main_Page (last access: 15 January 2022).
Data availability
The Utqiaġvik MAX-DOAS and meteorology data are available at 10.18739/A2R49G90M (Simpson, 2018), and the O-Buoy MAX-DOAS and meteorology data are available at 10.18739/a2wd4w (Simpson et al., 2009). We would like to thank the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Division for the provision of ozone and temperature data near Utqiaġvik, which is available online at 10.7289/V57P8WBF (NOAA ESRL Global Monitoring Division, 2018). We are grateful for the use of the coastline distance dataset from the Pacific Islands Ocean Observing System. We are grateful for the use of imagery from the Land Atmosphere Near Real-Time Capability for EOS (LANCE) system and services from the Global Imagery Browse Services (GIBS), both operated by the NASA/GSFC/Earth Science Data and Information System (ESDIS; https://earthdata.nasa.gov), with funding provided by NASA/HQ.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-14467-2022-supplement.
Author contributions
WFS, WRS, and CH designed the study. WRS collected and curated MAX-DOAS data. KC, LM, JT, LJ, and JH contributed code for reactive
bromine mechanisms. CH, KC, LJ, JH, BA, SZ, QC, XW, and TS contributed model
updates. WFS carried out modeling and analysis. WFS wrote the paper, with
input from all the authors.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the global GEOS-Chem community for their tireless work in improving the model. We recognize the work of Jiayue Huang in
adding the blowing snow SSA mechanism to GEOS-Chem. We owe a debt of gratitude to all members of the Atmospheric Chemistry
and Global Change group at Florida State University for their support in working with GEOS-Chem and Python. We also thank all involved in the O-Buoy project for data collection and analysis.
Financial support
This research has been supported by the National Science Foundation (grant nos. ARC-1602716, AGS-1702266, AGS-2109323, and ARC-1602883) and by the CNRS INSU LEFE-CHAT program (grant no. Brom-Arc) and NASA (grant no. 80NSSC19K1273). This research has received funding from the European Union's Horizon 2020 Research And Innovation Program (grant no. 689443) via project the iCUPE (Integrative and Comprehensive
Understanding on Polar Environments).
Review statement
This paper was edited by Thorsten Bartels-Rausch and reviewed by two anonymous referees.
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