Fluxes of halogenated volatile organic compounds (VOCs) over the Southern
Ocean remain poorly understood, and few atmospheric measurements exist to
constrain modeled emissions of these compounds. We present observations of
CHBr3, CH2Br2, CH3I, CHClBr2, CHBrCl2, and
CH3Br during the O2/N2 Ratio and CO2 Airborne Southern
Ocean (ORCAS) study and the second Atmospheric Tomography mission
(ATom-2) in January and February of 2016 and 2017. Good model–measurement
correlations were obtained between these observations and simulations from
the Community Earth System Model (CESM) atmospheric component with chemistry
(CAM-Chem) for CHBr3, CH2Br2, CH3I, and CHClBr2 but
all showed significant differences in model : measurement ratios. The
model : measurement comparison for CH3Br was satisfactory and for
CHBrCl2 the low levels present precluded us from making a complete
assessment. Thereafter, we demonstrate two novel approaches to estimate
halogenated VOC fluxes; the first approach takes advantage of the robust
relationships that were found between airborne observations of O2 and
CHBr3, CH2Br2, and CHClBr2. We use these linear
regressions with O2 and modeled O2 distributions to infer a
biological flux of halogenated VOCs. The second approach uses the Stochastic
Time-Inverted Lagrangian Transport (STILT) particle dispersion model to
explore the relationships between observed mixing ratios and the product of
the upstream surface influence of sea ice, chl a, absorption due to
detritus, and downward shortwave radiation at the surface, which in turn
relate to various regional hypothesized sources of halogenated VOCs such as
marine phytoplankton, phytoplankton in sea-ice brines, and decomposing
organic matter in surface seawater. These relationships can help evaluate
the likelihood of particular halogenated VOC sources and in the case of
statistically significant correlations, such as was found for CH3I, may
be used to derive an estimated flux field. Our results are consistent with a
biogenic regional source of CHBr3 and both nonbiological and
biological sources of CH3I over these regions.
Introduction
Emissions of halogenated volatile organic compounds (VOCs) influence
regional atmospheric chemistry and global climate. Through the production of
reactive halogen radicals at high latitudes, halogenated VOCs contribute to
tropospheric and stratospheric ozone destruction and alter the sulfur,
mercury, nitrogen oxide, and hydrogen oxide cycles (e.g., Schroeder et al.,
1998; Boucher et al., 2003; Bloss et al., 2005; von Glasow and Crutzen,
2004; Saiz-Lopez et al., 2007; Obrist et al., 2010; Engel et al., 2018).
In the marine boundary layer and lower troposphere, sea salt is the main
source of reactive bromine (Finlayson-Pitts, 2003; Simpson et al., 2015). Yet
halogenated VOCs may also be a more important source of inorganic bromine to
the whole atmosphere than previously thought according to a recent study,
which indicates that sea salt is scarce and insufficient to control the
bromine budget in the middle and upper troposphere (Murphy et al., 2019).
Phytoplankton and macroalgae in the ocean are the main sources to the
atmosphere of several very short-lived bromocarbons, including bromoform
(CHBr3), dibromomethane (CH2Br2), dibromochloromethane
(CHClBr2), and bromodichloromethane (CHBrCl2) (Moore et al., 1996;
Carpenter et al., 2003; Butler et al., 2007; Raimund et al., 2011). Other
halogenated VOCs, such as methyl iodide (CH3I) and methyl bromide
(CH3Br), have many natural sources, such as coastal macroalgae,
phytoplankton, temperate forest soil and litter, and biomass burning (e.g.,
Bell et al., 2002; Sive et al., 2007; Colomb et al., 2008; Drewer et al.,
2008). CH3I is also formed through nonbiological reactions in surface
seawater, and CH3Br is emitted as a result of quarantine and
pre-shipment activities, which are not regulated by the Montreal Protocol
(e.g., Moore and Zafiriou, 1994; Engel et al., 2018). Over the Southern
Ocean specifically, hypothesized sources of halogenated VOCs include
coastal macroalgae, phytoplankton, sea-ice algae, and photochemical or dust-stimulated nonbiological production at the sea surface (e.g., Abrahamsson
et al., 2018; Manley and Dastoor, 1998; Moore and Zafiriou, 1994; Moore et al.,
1996; Richter and Wallace, 2004; Williams et al., 2007; Tokarczyk and Moore,
1994; Sturges et al., 1992).
We largely owe our current understanding of marine halogenated VOC emissions
over the Southern Ocean to ship-based field campaigns and laboratory process
studies (e.g., Abrahamsson et al., 2004a, b; Atkinson et al., 2012; Carpenter
et al., 2007; Moore et al., 1996; Chuck, 2005; Butler et al., 2007;
Raimund et al., 2011; Hughes et al., 2009, 2013; Mattsson et al., 2013). These studies have reported surface water and sea-ice
halogenated VOC supersaturation and corresponding elevated levels of
halogenated VOCs in the marine boundary layer (MBL) in summer and have
identified numerous biological and nonbiological ocean sources for these
compounds. Mattsson et al. (2013) noted that the ocean also acts as a sink
for halogenated VOCs when undersaturated surface waters equilibrate with
air masses transported from halogenated VOC source regions. The spatially
heterogeneous ocean sources of CHBr3 and CH2Br2 at high
latitudes in the Southern Hemisphere are often underestimated in global
atmospheric models (Hossaini et al., 2013; Ordoñez et al., 2012; Ziska
et al., 2013). Ship-based and Lagrangian float observations provide
invaluable information on the sources and temporal variability of compounds
in the surface ocean. These methods offer the advantage of simultaneous
measurements of both air and seawater to evaluate the gases' saturation
state in the surface ocean and calculate fluxes. Yet ship-based measurements
onboard these slow-moving platforms also have drawbacks: they under-sample
the spatial variability of halogenated VOCs (e.g., Butler et al., 2007) and
require assumptions about gas exchange rates to estimate fluxes.
Disentangling the roles of the atmospheric transport and spatial variability of
emissions in halogenated VOC distributions requires large-scale atmospheric
observations. At low latitudes, large-scale convection at the intertropical
convergence zone carries bromocarbons and other halogenated VOCs into the
free troposphere and lower stratosphere (e.g., Liang et al., 2014; Navarro
et al., 2015). Polar regions are characterized by stable boundary layers in
summer. Wind shear, frontal systems, and internal gravity waves create
turbulence and control vertical mixing within and across a stable polar
boundary layer (e.g., Anderson et al., 2008), and small, convective plumes
may form over the marginal sea-ice zone, related to sea-ice leads as well as
winds from ice-covered to open-ocean waters (e.g., Schnell et al., 1989). As
a result of limited vertical transport in these regions, however, air–sea
fluxes lead to strong vertical gradients. Zonal transport from lower
latitudes has a large impact on the vertical gradients of trace gas mixing
ratios over polar regions (Salawitch et al., 2010). Given their extended
photochemical lifetimes at high latitudes (see Sect. 2.3 for a brief
discussion), many halogenated VOC distributions are particularly sensitive
to zonal transport at altitude.
Aircraft observations can rapidly map basin-wide vertical distributions,
support quantitative flux estimates, and provide spatial constraints to
atmospheric models (e.g., Xiang et al., 2013; Stephens et al., 2018; Wofsy, 2011). Few airborne observations of halogenated VOCs exist at high
latitudes in the Southern Hemisphere. Two earlier aircraft campaigns that
measured summertime halogenated VOCs in this region are the first
Aerosol Characterization Experiment (ACE-1; Bates, 1999) and the
first High-performance Instrumented Airborne Platform for Environmental
Research (HIAPER) Pole-to-Pole Observations (HIPPO; Wofsy, 2011) campaign.
For these two aircraft campaigns, whole-air samples were collected onboard
the NSF/NCAR C-130 and the NSF/NCAR Gulfstream V (GV) during latitudinal
transects over the Pacific Ocean as far south as 60 and
67∘ S, respectively. However, the ACE-1 and HIPPO campaigns
obtained relatively few whole-air samples in this region, with ≤100
samples poleward of 60∘ S combined (e.g., Blake et al., 1999;
Hossaini et al., 2013). ACE-1 measurements of CH3I in the MBL indicate
a strong ocean source between 40 and 50∘ S in austral
summer, with mixing ratios above 1.2 pmol below ∼1 km (Blake
et al., 1999).
Halogenated VOC emissions are frequently incorporated into Earth system
models, using either climatologies or parameterizations based on satellite
observations of chlorophyll and geographical region, and evaluated using
mixing ratio comparisons with airborne observations. In Sect. 3.1 and 3.2,
we report new airborne observations of CHBr3, CH2Br2,
CH3I, CHClBr2, CHBrCl2, and CH3Br from high latitudes in
the Southern Hemisphere, where data are scarce, and large-scale regional
mixing ratio comparisons for halogenated VOCs with the Community Earth
System Model (CESM) atmospheric component with chemistry (CAM-Chem). In
Sect. 3.4, we present two novel approaches to estimate regional fluxes of
halogenated VOCs for comparison with global climate model
parameterizations or climatologies. One approach uses correlations of
halogenated VOCs with oxygen (O2) of marine origin, as measured
by deviations in the ratio of O2 to nitrogen (N2) (δ(O2/N2); see Sects. 2.1.2 and 3.1.2). We exploit robust ratios of
halogenated VOCs to oxygen (O2) determined from linear regressions
(i.e., the enrichment ratio), and the ocean flux of O2 from CESM's ocean
component, to estimate the marine biogenic flux of several halogenated VOCs.
The second approach relies on observed halogenated VOC mixing ratios, the
Stochastic Time-Inverted Lagrangian Transport (STILT) particle dispersion
model, and geophysical datasets (see Sects. 2.3 and 3.3). We assess
contributions from previously hypothesized regional sources for the Southern
Ocean and estimate halogenated VOC fluxes based on regressions between
upstream influences, observed mixing ratios, and distributions of remotely
sensed data.
MethodsMeasurements
Atmospheric measurements for this study were collected at high latitudes in
the Southern Hemisphere as part of the O2/N2 Ratio and CO2
Airborne Southern Ocean (ORCAS) study (Stephens et al., 2018) and the
second NASA Atmospheric Tomography Mission (ATom-2) near Punta Arenas,
Chile (Fig. 1). The ORCAS field campaign took place from 15 January to 29 February 2016 onboard the NSF/NCAR GV. On 10 and 13 February 2017 the sixth and seventh
ATom-2 research flights passed over the eastern Pacific sector poleward of
60∘ S (defined here as Region 1) and over the Patagonian Shelf
between 40 and 55∘ S and between 70 and
50∘ W (defined here as Region 2), respectively. The two regions
for this study are defined based loosely on dynamic biogeochemical provinces
identified using bathymetry, algal biomass, sea surface temperature, and
salinity (Reygondeau et al., 2013).
Overview map of ORCAS and ATom-2 flight tracks in the study regions: (1) high latitudes in the Southern Hemisphere poleward of 60∘ S and (2) the Patagonian Shelf. The ORCAS and ATom-2 aircraft flights and dips below 200 m that took place within these regions are also
shown.
Both projects featured en route vertical profiling from near the ocean
surface (∼150 m) to the upper troposphere, with 74 ORCAS and
seven ATom-2 (during the sixth and seventh flights) low-altitude level legs
in the MBL. These campaigns shared a number of instruments, including the
NCAR Trace Gas Organic Analyzer (TOGA), the NCAR Atmospheric Oxygen (AO2)
instrument, and a Picarro cavity ring-down spectrometer operated by NOAA,
discussed below. More information about individual instruments may be found
in Stephens et al. (2018) and at
https://www.eol.ucar.edu/field_projects/orcas (last access: 12 January 2019) and
https://espo.nasa.gov/atom/content/ATom (last access: 25 October 2019).
Halogenated VOCs
During ORCAS and ATom-2 TOGA provided mixing ratios of over 60 organic
compounds, including halogenated VOCs. The instrument, described in Apel et
al. (2015), continuously collects and analyzes samples for CHBr3,
CH2Br2, CHClBr2, CHBrCl2, and CH3I among other
compounds, with a 35 s sampling period and repeats the cycle every
2 min using online fast gas chromatography and mass spectrometry. This
study also leverages measurements of CH3Br from whole-air samples from
the U. Miami/NCAR Advanced Whole Air Sampler (AWAS; Schauffler et al.,
1999) onboard the GV during the ORCAS campaign and the UC Irvine Whole Air
Sampler (WAS; Blake et al., 2001) onboard the DC-8 during the ATom-2
campaign. Halogenated VOCs reported here have an overall ±15 %
accuracy and ±3 % relative precision, with detection limits of 0.03 ppt for CH3I, 0.2 ppt for CHBr3, 0.03 ppt for CH2Br2,
0.03 ppt for CHClBr2, 0.05 ppt for CHBrCl2, and 0.2 ppt for
CH3Br – 0.2 ppt. In addition, comparisons between onboard collected
whole-air samples and in-flight TOGA measurements, when sharing over half of
their sampling period with TOGA measurements, showed good correlations for
CHBr3, CH2Br2, CH3I, and CHClBr2, although there
were some calibration differences (Figs. S1 and S2 in the Supplement). In addition to the
comparison between colocated atmospheric measurements, we also conducted a
lab intercomparison following the campaign between NOAA's programmable
flask package (PFP) and TOGA (Table S1; see the Supplement for details).
δ(O2/N2) and CO2
The AO2 instrument measures variations in atmospheric O2, which are
reported as relative deviations in the oxygen-to-nitrogen ratio (δ(O2/N2)), following a dilution correction for CO2 (Keeling
et al., 1998; Stephens et al., 2018). The instrument's precision is ±2 per meg unit (one in 1 million relative) for a 5 s measurement
(Stephens et al., 2003).
Anthropogenic, biogenic, and oceanic processes introduce O2
perturbations that are superimposed on the background concentrations of
O2 in air (XO2; in dry air 0.2095). Air–sea O2 fluxes are
driven by both the biological production and consumption of O2 and by
the heating and cooling of surface waters. O2 is consumed when fossil fuels
are burned and produced and consumed during terrestrial photosynthesis and
respiration. Seasonal changes in the ocean heat content lead to small
changes in atmospheric N2. As others have done, we isolated the air–sea
O2 signal by subtracting model estimates of the terrestrial O2,
fossil fuel O2, and air–sea N2 flux influences from the δ(O2/N2) measurements (Eq. 1; Keeling et al., 1998; Garcia
and Keeling, 2001; Stephens et al., 2018). The difference of the δ(O2/N2) measurement and these modeled components is multiplied by
XO2 to convert to parts per million equivalent as needed (ppm eq; Keeling et al.,
1998; Eq. 1).
O2-ppm-equiv=[δ(O2/N2)-δ(O2/N2)land-δ(O2/N2)fossilfuel-δ(O2/N2)N2]×XO2
We obtained the modeled δ(O2/N2) signal terrestrial
influences from the land model component of the CESM, the fossil fuel
combustion influences from the Carbon Dioxide Information Analysis Center
(CDIAC; Boden et al., 2017), and the air–sea N2 influences from the
oceanic component of CESM. These fluxes were all advected through the
specified dynamics version of CESM's atmosphere component, as described
below in Sect. 2.2 and in Stephens et al. (2018).
CO2 measurements were provided by NOAA's Picarro G2401-m cavity ring-down spectrometer modified to have a ∼1.2 s measurement
interval and a lower cell pressure of 80 Torr, which enabled the instrument
to function at the full range of GV altitudes (Karion et al., 2013). Dry-air mole fractions were calculated using empirical
corrections to account for dilution and pressure-broadening effects as
determined in the laboratory before and after the campaign deployments, and
in-flight calibrations were used to determine an offset correction for each
flight. Corrected CO2 data have a total average uncertainty of 0.07 ppm
(Karion et al., 2013). To merge them with the TOGA
data, these faster O2 and CO2 measurements were arithmetically
averaged over TOGA's 35 s sampling periods (Stephens, 2017, and
https://espo.nasa.gov/atom/content/ATom, last access: 20 December 2018).
CAM-Chem model configuration
The CESM version 1 atmospheric component with chemistry (CAM-Chem) is a
global three-dimensional chemistry climate model that extends from the
Earth's surface to the stratopause. CAM-Chem version 1.2 includes all the
physical parameterizations of Neale et al. (2013) and a finite-volume
dynamical core (Lin, 2003) for tracer advection. The model has a horizontal
resolution of 0.9∘ latitude × 1.25∘ longitude,
with 56 vertical hybrid levels and a time step of 30 min. Meteorology is
specified using the NASA Global Modeling and Assimilation Office (GMAO)
Goddard Earth Observing System Model version 5 (GEOS-5; Rienecker et al.,
2008) (GEOS-5), following the specified dynamic procedure described by
Lamarque et al. (2012). Winds, temperatures, surface pressure, surface
stress, and latent and sensible heat fluxes are nudged using a 5 h
relaxation timescale to GEOS-5 1∘× 1∘
meteorology. The sea surface temperature boundary condition is derived from
the Merged Hadley–NOAA Optimal Interpolation Sea Surface Temperature and
Sea-Ice Concentration product (Hurrell et al., 2008). The model uses
chemistry described by Tilmes et al. (2016), biomass burning and biogenic
emissions from the Fire INventory of NCAR (FINN; Wiedinmyer et al., 2011)
and MEGAN (Model of Emissions of Gases and Aerosols from Nature) 2.1
products (Guenther et al., 2012), and additional tropospheric halogen
chemistry described in Fernandez et al. (2014) and Saiz-Lopez et al. (2014).
These include ocean emissions of CHBr3, CH2Br2, CHBr2Cl,
and CHBrCl2, with parameterized emissions based on chlorophyll a (chl a) concentrations and scaled by a factor of 2.5 over coastal regions, as
opposed to open-ocean regions (Ordóñez et al., 2012). The model used an
existing CH3I flux climatology (Bell et al., 2002), and CH3Br was
constrained to a surface lower boundary condition, also described by
Ordóñez et al. (2012). This version of the model was run for the period
of the ORCAS field campaign (January and February 2016), following a
24-month spin-up. To facilitate comparisons to ORCAS observations, output
included vertical profiles of modeled constituents from the two nearest
latitude and two nearest longitude model grid points (four profiles in
total) to the airborne observations at every 30 min model time step.
Following the run, simulated constituent distributions were linearly
interpolated to the altitude, latitude and longitude along the flight track,
yielding colocated modeled constituents and airborne observations. This
version of the model has not yet been run for the ATom-2 period.
STILT model configuration
The Stochastic Time-Inverted Lagrangian Transport (STILT; Lin, 2003)
particle dispersion model uses a receptor-oriented framework to infer
surface sources or sinks of trace gases from atmospheric observations
collected downstream, thus simulating the upstream influences that are
ultimately measured at the receptor site. The model tracks ensembles of
particle trajectories backward in time and the resulting distributions of
these particles can be used to define surface influence maps for each
observation. STILT was run using 0.5∘ Global Data
Assimilation System (GDAS) reanalysis winds to investigate the transport
history of air sampled along the flight track (Stephens et al., 2018). For
each TOGA observation, an ensemble of 4096 particles was released from the
sampling location and followed over a backwards simulation period of 7 d. Particles in the lower half of the simulated MBL are assigned a
surface influence value, which quantitatively links observed mixing ratios
to surface sources (Lin, 2003). The average surface influence of all
4096 particles per sampling location yields an hourly and spatially gridded
surface influence value (ppt m2 s pmol-1) at a spatial resolution
of 0.25∘× 0.25∘ for each sample point.
Uncertainty in the surface influence value is strongly influenced by the
accuracy of the underlying meteorological transport, as discussed in Xiang
et al. (2013). We evaluated the GDAS reanalysis winds by comparing model
winds interpolated in space and averaged between corresponding time points
and pressure levels to match aircraft observations. By evaluating observed
winds compared with modeled winds along the flight tracks we can estimate
uncertainty in the surface influence values. We consider the
observation–model differences in both wind speed and direction to
approximate errors in surface influence strength and location. For wind
speed, a small bias may be present; we find a median difference
between observations and reanalysis of 0.68 m s-1, a 5 % relative bias. The
1σ of the wind speed difference is 2.3 m s-1, corresponding to a 19 %
1σ uncertainty in wind speed. In its simplest approximation, the
surface influence strength error is perfectly correlated with the wind speed
error, and thus we take 19 % as an approximation of the surface influence
strength uncertainty. The uncertainty in surface influence location depends
on the error in the wind direction. We find a 1σ error of 14∘ in
wind speed, which corresponds to a possible error of 260 km d-1.
Finally, we note that photochemical loss during transport is not accounted
for in this analysis. Low OH mixing ratios, cold temperatures, and lower
photolysis rates due to angled sunlight at high latitudes lead to longer
than average halogenated VOC lifetimes. For instance, assuming an average
diurnal OH concentration of 0.03 ppt and average photochemical loss
according to the Tropospheric Ultraviolet and Visible (TUV) radiation model
and the Mainz Spectral data site
(http://satellite.mpic.de/spectral_atlas, last access: 9 January 2019) for 29 January under
clear-sky conditions at 60∘ S, CHBr3 has a lifetime of 30 d, CH2Br2 has a lifetime of 270 d, CH3I has a lifetime
of 7 d, and CHClBr2 has a lifetime of 63 d. As such, the
photochemical lifetimes of these gases are greater than or equal to the time
of our back-trajectory analysis. Moreover, OH concentrations in this region
have large uncertainties, the inclusion of which would lead to more, not
less, uncertainty in surface-influence-based regression coefficients and
estimated fluxes (see Sects. 2.3 and 3.3 for details).
STILT surface influence functions
For this study, we used STILT surface influence distributions with remotely
sensed ocean surface and reanalysis data (i.e., surface source fields) in
linear and multi-linear regressions to generate empirical STILT influence
functions. Surface influence functions can help explain observed mixing
ratios of CHBr3, CH2Br2, CH3Br, and CH3I, evaluate
the likelihood of particular halogenated VOC sources, and in the case of
statistically significant correlations may be used to derive an estimated
flux field (see Sect. 3.3 and 3.4.2 for details).
We tested whether observed mixing ratios (Z) could be explained by a linear
relationship in which the predictor variable is a surface influence
function equal to the product of the surface influence (H) and a potential
geophysical surface source field(s), such as chl a, as well as an intercept (b), a slope (a), and error term ξ (Eq. 2; Fig. S5). This
relationship can be generalized as a multiple linear regression with
multiple surface influence functions (Hs1, Hs2…) and
slope coefficients (a1, a2; Eq. 3) when multiple sources
contribute to observed halogenated VOC mixing ratios. The multiple linear
regression may also include an interaction term (Hs2) between predictor
variables (e.g., Hs1 and Hs2) with a slope coefficient (a3) to
improve the fit. Statistical correlations between mixing ratios and surface
influence functions may be used to support or reject hypothesized sources. A
flux (µmol m-2 s-1) may then be estimated for each grid cell
based on the product of the slopes (a1, a2…) and the
potential source fields (s1, s2…). Grid cell fluxes
are averaged over a geographical region to yield the average regional flux.
We used the standard deviation of the regression coefficients and the
relative uncertainty in the surface source, added in quadrature, to estimate
the uncertainty in the flux (see Sect. 3.4.2 for fractional uncertainties).
We note that the uncertainty in STILT transport (see Sect. 2.3 for details)
is inherently reflected in the relative uncertainty of the regression
coefficients (a1, a2…).
2Z=aHs+b+ξ3Z=a1Hs1+a2Hs2+(a3Hs1Hs2)…+b+ξ
Meridional–altitudinal cross sections of mixing ratios of (a)CH3I, (b)CHBr3, (c)CH2Br2, (d)CHClBr2, and (e)CHBrCl2 from the TOGA and mixing ratios of (f)CH3Br from AWAS and WAS in 2016 and 2017, respectively, during the ORCAS and ATom-2 campaigns
over the Southern Ocean in austral summer. Note the different color bar scales. Gray points
denote measurements below the detection limit of each species (CH3I – 0.03 ppt, CHBr3 – 0.2 ppt, CH2Br2 – 0.03 ppt, CHClBr2 – 0.03 ppt, CHBrCl2 – 0.05 ppt, CH3Br – 0.2 ppt).
Surface source fields
Geophysical surface source fields of remotely sensed and reanalysis data
included a combination of sea-ice concentration, chl a, absorption due to
ocean detrital material, and downward shortwave radiation at the ocean
surface.
We used daily sea-ice concentration data (https://nsidc.org/data/nsidc-0081, last access: 7 January 2019)
at a 25 km × 25 km spatial resolution between 39.23– 90∘ S and 180∘ W–180∘ E from
the NASA National Snow and Ice Data Center Distributed Active Archive Center
(NSIDC; Maslanik, 1999). These data report the fraction of sea-ice
cover, land-ice cover, and open water. Unfortunately, these data do not
provide any information on sea-ice thickness or the presence of brine
channels or melt ponds, which may modulate emissions from sea-ice-covered
regions. Sea-ice concentration data were calculated using measurements of
near-real-time passive microwave brightness temperature from the Special
Sensor Microwave Image/Sounder (SSMIS) on the Defense Meteorological
Satellite Program (DMSP) satellites. NSIDC sea-ice concentration data were
arithmetically averaged to yield a 0.25∘× 0.25∘ binned sea-ice fraction for use with gridded surface
influences.
Due to persistent cloud cover over the Southern Ocean, which often precludes
the retrieval of remotely sensed ocean color data, we used 8 d mean
composite Aqua MODIS L3 distributions of chl a from the Ocean Color Index
(OCI) algorithm and absorption due to gelbstoff and detrital material at 443 nm from the Generalized Inherent Optical Properties (GIOP) model (NASA
Goddard Space Flight Center, 2014). Absorption due to gelbstoff and detrital
material at 443 nm is used as a proxy for colored dissolved organic matter
(CDOM; https://oceancolor.gsfc.nasa.gov/atbd/giop/, last access: 3 October 2018). CDOM is
hypothesized to be an important source of carbon for the photochemical
production of CH3I (Moore and Zafiriou, 1994). The GIOP model also publishes
an uncertainty in the absorption due to gelbstoff and detrital material at
443 nm. Raw 4 km × 4 km data were geometrically averaged, based on lognormal
probability density functions, to a spatial resolution of
0.25∘× 0.25∘ for use with gridded surface
influences. We used the ratio of the 0.25∘× 0.25∘ gridded uncertainty in the detrital material
absorption to the absorption as the relative uncertainty for flux
calculations (see Sect. 3.4.2).
The National Center for Environmental Prediction (NCEP) provides Final
Global Data Assimilation System (GDAS/FNL) global data of downward shortwave
radiation at the surface at 0.25∘ and 6 h resolution (NCEP, 2015).
We chose downward shortwave radiation for use with gridded surface
influences because the photo-production of CH3I has been observed at
all visible wavelengths (Moore and Zafiriou, 1994). These reanalysis data are
available at a higher temporal resolution and better spatial coverage than
satellite retrievals of photosynthetically active radiation (PAR) or
temperature.
Results and discussionObserved halogenated VOC patterns and relationships
Zonal cross sections of halogenated VOC data collected on ORCAS and ATom-2
illustrate unprecedented spatial sampling across our study area between the
surface and 12 km (Fig. 2). Above average mixing ratios of CH3I,
CHBr3, and CHClBr2 typically remain confined to the lower
∼ 2–4 km of the atmosphere (Fig. 2a, b, d). These compounds
have lifetimes of approximately 2 months or less. Conversely, weak sources
and longer lifetimes (≥3 months) may have contributed to similar
concentrations of CH2Br2 and CHBrCl2 throughout the
troposphere and above average mixing ratios as high as 8 km (Fig. 2c, e).
Unfortunately, the availability of data above the detection limit and
absence of BL enhancements for CHBrCl2 preclude the identification of
strong regional sources at this time. Meridional distributions also indicate
lower-latitude sources of CH3I and CH3Br (<50∘ S), potentially resulting from terrestrial and anthropogenic contributions,
and higher-latitude sources (>60∘ S) of CHBr3,
CH2Br2, and CHClBr2 (Fig. 2a–d, f).
Observed halogenated VOC interrelationships
Across our study area in both 2016 and 2017, we found that CHBr3 and
CH2Br2 exhibit a consistent enhancement ratio with each other in
the bottom 2 km of the atmosphere both in Region 1 and Region 2, which
suggests that these bromocarbon fluxes are closely coupled. Previous studies
have documented colocated source regions of CHBr3 and CH2Br2
in the Southern Ocean (e.g., Hughes et al., 2009;
Nightingale et al., 1995; Laturnus 1996), and laboratory studies
have demonstrated that phytoplankton and their associated bacteria cultures,
including a cold-water diatom isolated from coastal waters along the
Antarctic Peninsula and common to the Southern Ocean, produce both
CHBr3 and CH2Br2 (Hughes et al., 2013; Tokarczyk and Moore
1994; Sturges et al., 1992). The nonlinearity observed in ratios of these
two gases at low CHBr3 may reflect the different rates of their
production or loss in seawater or, possibly, the influence of air masses
from distant, more productive low-latitude source regions. Several studies
have documented the bacterially mediated loss of CH2Br2, but not
CHBr3, and report distinct ratios of CH2Br2 to CHBr3 in
seawater during the growth and senescent phases of a phytoplankton bloom
(e.g., Carpenter et al., 2009; Hughes et al., 2013). Although this analysis
is restricted to the bottom 2 km of the atmosphere, zonal transport of air
masses with lower ratios of CH2Br2 to CHBr3, as have
been observed in the MBL over productive, low-latitude regions, may also
have influenced our observations (Yokouchi et al., 2005). Mixing ratios of
CHBr3 and CHClBr2 were also correlated (Fig. 3d) in Region 2, and
a similar, weaker relationship was observed in Region 1 (Fig. 3b).
CHClBr2 is a less well-studied compound than CH2Br2. Yet
these consistent relationships suggest that CHBr3 and CHClBr2 may
either share some of the same sources or have sources that covary.
Mixing ratios of CHBr3 vs. CH2Br2 and CHClBr2 across the ORCAS and ATom-2
campaigns in Region 1 (a, b) and in Region 2 (c, d), respectively. Type II major axis regression
models (bivariate least squares regressions) are based on ORCAS data below 2 km and illustrate
regional enhancement ratios. Error bars represent the uncertainty in halogenated VOC
measurements.
Mixing ratios of CHBr3, CH2Br2, and CH3I vs. O2
on ORCAS and ATom-2 in Region 1 poleward of 60∘ S (a–d) and Region 2 over the Patagonian Shelf (e–h). Slopes ± standard
errors from type II major axis regression model (bivariate least squares regression) fits of
ORCAS data for regressions with r2>0.2 (fits were calculated on variables scaled to their full
range). The slopes reported in the figure are converted to picomole-to-mole ratios prior to estimating
biogenic halogenated VOC fluxes based on modeled CESM O2
fluxes. Data from above 7 km
were excluded due to the influence of air masses transported from further north.
Observed halogenated VOC relationships to δ(O2/N2)
and CO2
We sought to test if the biologically mediated production of bromocarbons
and oxygen results in similar atmospheric distributions. Conversely, we
expected halogenated VOC atmospheric distributions and CO2
distributions to anticorrelate because CO2 fixation in surface waters
is proportional to the production of oxygen.
For these comparisons, both O2 and CO2 mixing ratios from the
upper troposphere (5–7 km) were subtracted from the data to detrend for
seasonal and interannual variability (Figs. 4; S3). To isolate the
contribution of ocean O2 fluxes, the ORCAS δ(O2/N2)
values reported here represent the Δδ(O2/N2) to
observed values between 5 and 7 km adjusted for CESM O2 land and fossil
fuel contributions and the influence of air–sea N2 fluxes. In Fig. 4
we present type II major axis regression fits to data (fits were calculated
using data scaled to their full range) between the ocean surface and the
lowest 7 km for bromocarbons with photochemical lifetimes of ≥1 month
and from the lowest 2 km for CH3I with a photochemical lifetime of
∼1 week. We used a type II major axis regression model to
balance the influences of uncorrelated processes and measurement uncertainty
in halogenated VOCs (on the y axis) and uncorrelated processes and
measurement uncertainty in O2 and CO2 (on the x axis) on the
regression slope (Ayers, 2001; Glover et al., 2011). As noted by
previous studies, simple least squares linear regressions fail to account
for uncertainties in predictor variables (e.g., Cantrell, 2008).
CAM-Chem1.2 model–aircraft measurement comparison during the ORCAS campaign
between 1 and 12 km in Region 1 high latitudes in the Southern Hemisphere poleward of 60∘ S. All
regressions are type II major axis regression models with bivariate least squares regressions (slopes
are shown when r2≥0.2). The bold black line in each vertical profile represents the binned
(mean) mixing ratio of halogenated VOC measurements at that altitude. The binned mean includes measurements below the detection limit (DL), which for this calculation are assigned a
value equal to the DL multiplied by the percentage of data below detection. Modeled values include locations where observations were below the DL.
The robust correlations of CHBr3 and CH2Br2 with δ(O2/N2), in both 2016 and 2017 and in Region 1 and Region 2,
provide support for a regional biogenic source of these two halogenated
VOCs (Fig. 4a, b, e, and f). The air–sea exchange of O2 during
summer in the Southern Ocean is driven by net community production (the
excess of photosynthesis over respiration) in the surface mixed layer,
surface warming, and to a lesser extent ocean advection and mixing (e.g.,
Stephens et al., 1998; Tortell and Long 2009; Tortell et al., 2014). Note
that we adjust for influences on the δ(O2/N2) from thermal
N2 fluxes (see Eq. 1, Sect. 2.1.2 for details). Biological O2
supersaturation in the surface mixed layer develops quickly in the first
few days of a phytoplankton bloom and diminishes as community
respiration increases and air–sea gas exchange equilibrates the surface
layer with the atmosphere on a timescale of ∼1 week.
CHBr3 and CH2Br2 are emitted from phytoplankton during the
exponential growth phase (Hughes et al., 2013), which often coincides with
high net community production and the accumulation of O2 in surface
waters. The bulk air–sea equilibration time for an excess of CHBr3 and
other halogenated VOCs is less than 2 weeks, although the photochemical
loss of halogenated VOCs will alter their ratio over time (see the Supplement
for details on calculations of bulk sea–air equilibration times).
Our observations suggest a biological source for CHBr3 and
CH2Br2 in both Region 1 and Region 2 (Fig. 4). Interestingly, the
slope of the regression between CHBr3 and O2 appears distinct in
Region 1 and Region 2, but for CH2Br2 it is the same. Molar
enrichment ratios are 0.20±0.01 and 0.07±0.004 pmol : mol
for CHBr3 and CH2Br2 to O2 in Region 1, and they are 0.32±0.02 and 0.07±0.004 pmol : mol in Region 2. We observe a
weaker relationship between CH3I and CHClBr2 and O2 in Region 1 (Fig. 4c, d), consistent with the existence of other nonbiological
sources of CH3I in this region. Figure 4g and h illustrate a strong
relationship between CH3I and O2, as well as CHClBr2 and
O2, in Region 2, however, which implies that the dominant sources of
CH3I and CHClBr2 emissions over the Patagonian Shelf are
biological. The corresponding molar enrichment ratios of CH3I to
O2 and CHClBr2 to O2 in Region 2 are 0.38±0.03 pmol : mol and 0.19±0.04 pmol : mol, respectively.
In contrast to O2, air–sea fluxes of CO2 over the Southern Ocean
during summer reflect the balance of opposing thermal and biological drivers
(e.g., Stephens et al., 1998, 2018). Ocean buffering chemistry results in
CO2 equilibration across the air–sea interface on a timescale of
several months. ORCAS observations showed a depletion of CO2 in the
MBL, indicating that uptake driven by net photosynthesis dominated over
thermally driven outgassing during the few months preceding the campaign
(Stephens et al., 2018). CHBr3 and CH2Br2 in the lowest 7 km
were negatively correlated with CO2 in both years in Region 1 and
Region 2 (Fig. S3a, b, e, f). Interestingly, CH3I was not correlated
with CO2 in Region 1, likely due to the long air–sea equilibration
timescale of CO2 compared with a 9 d air–sea equilibration time and a
∼7 d photochemical lifetime for CH3I. For longer-lived
species, correlations for halogenated VOCs to CO2 have similar
r2 values as those for halogenated VOCs to δ(O2/N2),
but model and climatological estimates of Southern Ocean CO2 fluxes are
much less certain than for O2 (Anav et al., 2015; Nevison et al.,
2016). As a result, we use modeled O2 fluxes as the basis for our
halogenated VOC flux estimates (see Sect. 3.4.1 for details).
Model–observation comparisons
The ORCAS dataset provides an exceptional opportunity to evaluate the
CAM-Chem halogenated VOC emission scheme (Ordóñez et al., 2012) at high
latitudes in the Southern Hemisphere. We compared modeled halogenated VOC
constituents to corresponding observations along the ORCAS flight track
(Figs. 5; 6). In these figures, we used type II major axis regression
models to balance the measurement uncertainty (on the y axis) and the
inherent, yet difficult to quantify representativeness and errors in a
global atmospheric chemistry model (on the x axis). We note that this
comparison may favor constituents with longer photochemical lifetimes when
transport and mixing dominate over source heterogeneity.
CAM-Chem 1.2 model–aircraft measurement (TOGA and AWAS) comparison during the ORCAS campaign between 1 and 12 km in Region 2 for the Patagonian Shelf. All regressions are type
II major axis regression models with bivariate least squares regressions (slopes are shown when the r2≥0.2). The bold black line in each vertical profile represents the binned (mean) mixing ratio of halogenated VOC measurements at that altitude. Again, the binned mean includes measurements
below the detection limit (DL), which for this calculation are assigned a value equal to the DL
multiplied by the percentage of data below detection. Modeled values include locations where observations were below the DL.
In Region 1 and Region 2, both the model and observations indicate that
elevated mixing ratios of CH3I remain confined to the MBL (Figs. 5a and
6a), presumably due to its relatively short photochemical lifetime.
Modeled and observed CH3I are poorly correlated in Region 1 (r2=0.20; Fig. 5b) and better correlated in Region 2 (r2=0.70; Fig. 6b). In both regions, the model most likely underpredicts CH3I in the
upper troposphere and lower stratosphere (UTLS), likely stemming from the
poleward transport of lower-latitude air masses, for which CAM-Chem also
exhibits a negative bias. Mixing ratio comparisons with CAM-Chem over the
tropics (see Fig. 10 in Ordóñez et al., 2012) depict similar or larger
discrepancies and have been attributed to stronger than anticipated
convective cells in the tropics. We found strong correlations and agreement
to within a factor of ∼2 between modeled and observed
CHBr3 and CH2Br2 (Figs. 5c–f and 6c–f). Relatively long
lifetimes (≥1 month) in Region 1 likely enable the vertical and zonal
transport of CHBr3 and CH2Br2 to the middle and upper
troposphere (Fig. 5c and e). The model was biased low with respect to
measurements of CH3Br by ∼25 % in Region 1 and Region 2 (Figs. 5g–h and 6g–h), potentially as a result of an incorrect surface
lower boundary condition. The model underpredicted the mean vertical
gradient in CHClBr2, although it did a reasonable job of representing
the mean vertical gradient in CHBrCl2 in both Region 1 and Region 2.
In both cases, however, the model failed to capture the spatial variability
in both CHClBr2 and CHBrCl2 observations (Figs. 5i–l and
6i–l). Region 2 contains stronger sources of halogenated VOCs than Region 1,
which has been documented in numerous ship-based campaigns and archived in
the Halocarbons in the Ocean and Atmosphere database (HalOcAt; https://halocat.geomar.de/, last access: 8 January 2019). Region 2 also has much higher chl a (Fig. S4),
supporting biogenic sources for these gases.
Linear type II regressions between influence functions convolved with sea-ice
distributions, which exclude land ice, and mixing ratios for CHBr3, CH2Br2, and CH3I in Region 1 poleward of 60∘ S. Surface influence (ppt m2 s pmol-1) in each grid cell was multiplied by fractional sea-ice concentration surface field, which is unitless, yielding sea-ice surface influence function units (ppt m2 s pmol-1), as shown on the x axis. Linear regression lines are not shown, as p≥0.001.
Linear type II regressions between influence functions of 8 d composites of chl a and mixing ratios of halogenated VOCs (a–d) poleward of 60∘ S (Region 1). Surface influence
(ppt m2 s pmol-1) in each grid cell was multiplied by the chl a (µg m-3) surface field, resulting in
surface influence function units (µg ppt s pmol-1 m-1) shown on the x axis. Linear regression
lines are shown where when p<0.001.
Observed CH3I plotted against the surface influence functions of downward shortwave
radiation (a) and absorption due to detritus (b). Predicted mixing ratios of CH3I based on a
multiple linear regressions (MLRs) using these two predictors in Region 1 are shown in (c) according to Eq. (3). Surface influence (ppt m2 s pmol-1) in each grid cell was multiplied by
the surface source field, such as shortwave radiation at the surface (W m-2; ppt W s pmol-1), and the surface ocean's detrital absorption (m-1; ppt m s pmol-1),
shown on the x axes.
Relationships between STILT surface influence functions and observations
We used the STILT model to explore the relationships between observed mixing
ratios and the upstream surface influence functions (Eqs. 2–3) of sea
ice, chl a, absorption due to detritus, and downward shortwave radiation at
the surface, which relate to various regional hypothesized sources of
halogenated VOCs such as marine phytoplankton, phytoplankton in sea-ice
brines, and decomposing organic matter in surface seawater (e.g., Moore and
Zafiriou 1994; Moore et al., 1996; Tokarczyk and Moore, 1994; Sturges et al.,
1992).
We found no positive relationships between upstream sea-ice influence and
any measured halogenated VOC in Region 1 (Fig. 7). We interpret this result to
mean that increased summertime sea ice acts either to reduce the production
of halogenated VOCs by blocking sunlight or as a physical barrier to oceanic
emissions of halogenated VOCs from under-ice algae. Both of these mechanisms
are also consistent with a link between enhanced CHBr3 and
CH2Br2 emissions due to sea-ice retreat and surface sea-ice meltwater (Carpenter et al., 2007).
Resulting mean January–February 2016 O2-based (parameterized) CHBr3, CH2Br2, and
CHClBr2 fluxes (pmol m-2 s-1) in Region 1 (a–c) poleward of 60∘ S and Region 2 (d–f) over the
Patagonian Shelf. CESM modeled O2
fluxes are scaled by the slope between the oceanic
contribution to δ(O2/N2) and CHBr3, CH2Br2, and CHClBr2 reported in Fig. 4. Note that these
fluxes represent mean estimated biogenic fluxes in January–February 2016 (see Sect. 3.4.1 for details).
In other studies, it has also been proposed that sea ice could be an
important source for CHBr3 and other halogenated VOCs, since high
mixing ratios of CHBr3 have been observed at the sea-ice and ice–snow
interface in the austral winter (Abrahamsson et al., 2018) and in under-ice
algae in the austral spring (Sturges et al., 1992). At present, CAM-Chem
v1.2 with very short-lived halogen chemistry does not include a regional
flux of halogenated VOCs over sea-ice-covered waters in summer, and our
results do not indicate a need to include one. Our data, which were
collected in January and February, however, cannot assess the importance of
sea ice as a source of halogenated VOCs in other seasons, such as winter or
spring (Abrahamsson et al., 2018; Sturges et al., 1992). More field
campaigns are needed to further study the seasonality and regional strength
of sea-ice-related halogenated VOC emissions.
We observed a statistically significant positive correlation between the
surface influence function of 8 d satellite composites of chl a concentration,
which is widely used as a proxy for near-surface phytoplankton biomass, and
mixing ratios of CHBr3 and CH2Br2 in Region 1 (Fig. 8a, b).
This corroborates previous findings from shipborne field campaigns
and laboratory studies that have suggested a biogenic source for these two
bromocarbons (e.g., Moore et al., 1996; Hughes et al., 2013) and further
substantiates the current CAM-Chem parameterization of regional bromocarbon
emissions using satellite retrievals of chl a in polar regions. CH3Br
mixing ratios were not significantly correlated with chl a surface influence
functions (Fig. 8c). Although potentially suggesting that marine
phytoplankton and microalgae were not a strong regional source of CH3Br
during ORCAS, it is also possible that the relatively long lifetime of
CH3Br precludes a definitive analysis of its origin based on chl a using
7 d back trajectories. Neither CHClBr2 nor CHBrCl2 was
significantly correlated with chl a composite surface influence functions
(data not shown); however, more observations of these short-lived species in
the remote MBL are needed to substantiate this result.
Similar to Lai et al. (2011), we observed a significant correlation between
mixing ratios of CH3I and total weekly upstream influence functions of
8 d chl a composites (Fig. 8d). Weaker correlations were observed with
upstream influence functions on timescales shorter than 7 d. We found
that CH3I, particularly in Region 1, was better explained by a
multi-linear regression with two predictors: (1) the influence function of
downward shortwave radiation at the surface (Fig. 9a) and (2) the absorption
of light due to detrital material (Fig. 9b), yielding improved agreement
between predicted and observed CH3I (Fig. 9c). Several previous studies
have correlated mixing ratios of CH3I with satellite retrievals of PAR
and surface ocean temperature, revealing a link to solar radiation (e.g.,
Happell et al., 1996; Yokouchi et al., 2001).
Although certain species of phytoplankton are capable of producing CH3I
(e.g., Manley and de la Cuesta, 1997; Hughes et al., 2011), several studies
also indicate a nonbiological source for CH3I in the surface ocean.
This nonbiological source, though not fully understood, requires light, a
humic-like substance at the surface ocean supplying a carbon source and
methyl group, and reactive iodine (Moore and Zarifou, 1994; Richter and
Wallace, 2004). Thus far, two chemical mechanisms have been proposed for the
nonbiological production of methyl iodide; the first is a radical recombination
of a methyl group and iodine involving UV photolysis (e.g., Moore and Zarifou,
1994), and the second is a substitution reaction involving the reduction of an
oxidant, such as iron III (e.g., Williams et al., 2007).
Flux estimationO2-based emission estimates
We present a novel approach that facilitates a basin-wide halogenated VOC
flux estimate using the robust relationship between airborne observations of
O2 and halogenated VOCs combined with modeled O2 fluxes. Unlike
the existing CAM-Chem halogenated VOC biogenic flux parameterization, this
method does not rely on weekly retrievals of chl a at high latitudes, which
are often patchy. In addition, our study indicates that CHBr3,
CH2Br2, and CHClBr2 and CH3I are better correlated with
marine-derived O2 than the upstream influence of chl a.
For CHBr3, CH2Br2, and CHClBr2 we construct ocean
emission inventories for January and February using a scaled version of
gridded modeled air–sea O2 fluxes and the slopes (i.e., molar ratios) of
linear correlations between δ(O2/N2) and halogenated VOC
mixing ratios (Fig. 10). O2 fluxes were obtained from simulations using
a configuration of CESM nudged to reanalysis temperatures and
winds as described in Stephens et al. (2018). An earlier free-running
version of CESM was one of the best evaluated for reproducing the seasonal
cycle of δ(O2/N2) over the Southern Ocean (Nevinson et
al., 2015, 2016). To date, the north–south gradient in atmospheric O2
has not been well reproduced by any models (Resplandy et al., 2016).
Vertical gradients in O2 on ORCAS indicate that CESM overestimated
gradients by 47 % on average; accordingly, O2 fluxes were adjusted
downward by 47 % to better match the observations. This is obviously a
very simple adjustment to the modeled fluxes, and the actual air–sea O2
flux biases in CESM likely have a great deal of spatial and temporal
heterogeneity. We calculated an uncertainty for the CESM flux using a
second independent estimate of O2 fluxes based on dissolved O2
measurements in surface seawater. The Garcia and Keeling (2001) climatology
has much smoother temporal and spatial patterns than CESM flux estimates but
also results in overestimated atmospheric O2 spatial gradients. We
calculate the relative uncertainty in O2 flux as the ratio of the mean
absolute difference between gridded Garcia and Keeling values (2001; also
adjusted down by 51 % everywhere to better match ORCAS observations) to
the CESM flux estimates in Regions 1 and 2 (adjusted down by 47 %
everywhere). These disagreements were 7.3 % and 3.4 % for Regions 1
and 2, respectively. Based on the ratios of halogenated VOC to O2
mixing ratios in bivariate least squares regressions and these adjusted
O2 fluxes, we estimate mean emissions of CHBr3 and
CH2Br2 in Region 1 and Region 2. Relative uncertainty in the
slopes (i.e., the standard deviation of the slopes) from these regressions
and the mean relative uncertainties in regional O2 fluxes were added in
quadrature to yield uncertainties in calculated halogenated VOC emission
rates.
Figure 10 shows the mean emissions for January and February of CHBr3,
CH2Br2, and CHClBr2 in Region 1 and Region 2. Mean regional
emissions of CHBr3, CH2Br2, and CHClBr2 are 91±8, 31±17, and 11±4 pmol m-2 h-1 in Region 1 and
329±23, 69±5, and 24±5 pmol m-2 h-1 in
Region 2 (Table 1). The mean flux of CH3I in Region 2 is 392±32
(Table 1). Table 1 also lists the mean January and February CAM-Chem emissions from
Region 1 and Region 2, as well as emissions from several other observational
and modeling Antarctic polar studies. Our estimates fall within the range of
these other studies, which span every month of the year and whose estimated
fluxes range from negative (i.e., from the atmosphere into the ocean) to 3500 pmol m-2 h-1CHBr3 in a coastal bay during its peak in
primary production. CAM-Chem emissions for all species are significantly
lower than our observationally derived values in Region 1, with the
exception of CH3I. Conversely, CAM-Chem emissions are significantly
higher than our estimated emissions in Region 2, with the exception of
CHClBr2 in Region 1, which remains underpredicted by the model (Table 1). We note that in Region 2, CAM-Chem fluxes of CHBr3 and
CH2Br2, although still significantly different, are more similar
to our estimated fluxes.
Mean ± uncertainty (see Sect. 3.4.1 and 3.4.2 for details)
of halogenated VOC emission estimates (pmol m-2 h-1) in Region 1 and
Region 2 calculated in this study (with the method indicated below each value),
from CAM-Chem (Ordóñez et al., 2012), and from several other modeling and
ship-based observational studies.
Region and monthsCHBr3CH2Br2CH3ICHClBr2ReferenceRegion 1 (JF)91±831±1835±2911±4This study<60∘ SO2 Regr.O2 Regr.MLRO2 Regr.Region 2 (JF)329±2369±5392±3225±5This study>55∘ S and <40∘ SO2 Regr.O2 Regr.O2 Regr.O2 Regr.Region 1 (JF)101.91200.38CAM-ChemRegion 2 (JF)360448008.7CAM-ChemSouthern Ocean(≥50∘ S), (DJ)200200200–Ziska et al. (2013) (model)Marguerite Bay (DJF)3500875––Hughes et al. (2009) (obs)70–72∘ S Antarctica1300–––Carpenter et al. (2007) (obs)Southern Ocean(≥50∘ S) (Feb–Apr)225312708–Butler et al. (2007) (obs)40–52∘ S S. Atlantic (Sep–Feb)-1670–250–Chuck (2005)Southern Ocean(≥50∘ S), (DJ)-330–––Mattson et al. (2013) (model)STILT-based emission estimates
Similar to our O2-based emission estimates, we used the relationship
between surface influence functions and CH3I mixing ratios (Fig. 9) to
predict a flux field in Region 1 (Fig. 11). We used a multiple linear
regression (±1 standard deviations; Eq. 2), wherein Hs1 and
Hs2 are the downward shortwave radiation and detrital absorption
surface influence functions, respectively, with an intercept b=0.19±0.01, influence coefficients a1=3.7×10-5±1.3×10-5 and
a2=3.5±0.74, and an interaction term with the coefficient
a3=-5.2×10-4±1.5×10-4 (Fig. 9c). These regression coefficients
and interaction term were used to estimate an average nonbiological flux of
CH3I (Fig. 11; Table 1). This method could be used in place of the
current Bell et al. (2002) climatology to update near weekly
(∼8 d) emissions of CH3I in future versions of
CAM-Chem. Our estimated mean CH3I flux in Region 1 (35±29 pmol m-2 h-1) is significantly lower than the current CAM-Chem
estimated emissions (Table 1). As noted in Sect. 3.2, our observations of
CH3I are also much lower than the modeled mixing ratios. As discussed
above, the strong correlations between CH3I and O2 in Region 2
also suggest a dominant biological source for this compound in this region.
As a result, we have not used this relationship to parameterize a flux for
CH3I in Region 2 (see Sect. 3.1.2 and 3.4.1 for details). We note that
although it would be possible to provide STILT-based emission estimates for
other halogenated VOCs (e.g., CHBr3 and CH2Br2), the
correlations of these compounds were less strong with surface influence
functions than those with O2/N2.
Mean estimated CH3I fluxes for January–February. The multi-linear regression in Fig. 9
between CH3I mixing ratios and geophysical influence functions related to shortwave radiation
and detrital material at the sea surface was used to derive a mean flux field in January–February 2016 for Region 1.
Conclusions
Our work combined TOGA and AWAS halogenated VOC airborne observations from
the ORCAS and ATom-2 campaigns, with coincident measurements of O2 and
CO2, geophysical datasets, and numerical models, including the global
atmospheric chemistry model CAM-Chem and the Lagrangian transport model
STILT. We evaluated model predictions, calculated molar enrichment ratios,
inferred regional sources, and provided novel means of parameterizing ocean
fluxes. We found that the Southern Ocean MBL is enriched in halogenated
VOCs but that these MBL enhancements are less pronounced at higher
latitudes, i.e., poleward of 60∘ S (Region 1), than over the
productive Patagonian Shelf (Region 2). Overall, our results indicated that
the Southern Ocean is a moderate regional source of CHBr3,
CH2Br2, and CH3I and a weak source of CHClBr2 and
CHBrCl2 in January and February. Good model–measurement correlations
were obtained between our observations and simulations from the Community
Earth System Model (CESM) atmospheric component with chemistry (CAM-Chem)
for CHBr3, CH2Br2, CH3I, and CHClBr2 but all showed
significant differences in model : measurement ratios. The model : measurement
comparison for CH3Br was satisfactory and for CHBrCl2 the low
levels present precluded us from making a complete assessment.
CHBr3 and CH2Br2 exhibited strong and robust correlations
with each other and with O2 and weaker but statistically significant
correlations with the influence of chl a, which is a proxy for phytoplankton
biomass. CHClBr2 and CHBr3 were well correlated with one another,
particularly in Region 2. Together, these correlations suggested a
biological source for these gases over the Southern Ocean. We found that
CH3I mixing ratios in Region 1 were best correlated with a
nonbiological surface influence function, although biogenic CH3I
emissions appear important in Region 2.
Our flux estimates based on the relationship of halogenated VOC mixing
ratios to O2 and remotely sensed parameters (for CH3I) were
compared with those derived from global models and ship-based studies (Table 1). Our emission estimates of CHBr3, CH2Br2, and CHClBr2
are significantly higher than CAM-Chem's globally prescribed emissions in
Region 1, where halogenated VOC mixing ratios are underpredicted (Table 1;
Fig. 5). Similarly, our estimate of CHClBr2 emissions is also
significantly higher than CAM-Chem's in Region 2, where CHClBr2 mixing
ratios remained underpredicted. Yet, to the best of our knowledge,
CAM-Chem's global parameterization of halogenated VOC fluxes has not been
compared with data at high latitudes. Indeed, our emission estimates of
CHBr3, CH2Br2, and CH3I fall within a range of CAM-Chem's
estimates (on the low end) and most prior estimates based on either other
models or localized studies using seawater-side measurements from the
Antarctic polar region in summer (on the high end). In the case of
CH3I, our estimated emissions suggest that the prescribed emissions in
CAM-Chem may be too high in Region 1 and Region 2. Our parameterizations of
the CH3I flux could be used to explore interannual variability in
emissions, which is not captured by the Bell et al. (2002) CH3I
climatology currently employed in CAM-Chem.
To extend these relationships to year-round and global parameterizations for
use in global climate models, they must be studied using airborne
observations in other seasons and regions. These approaches may help
parameterize emissions of new species that can be correlated with surface
influence functions or the biological production of oxygen, or they may improve
existing emissions where persistent biases exist. Finally, future airborne
observations of halogenated VOCs have the potential to further improve our
understanding of air–sea flux rates and their drivers for these chemically
and climatically important gases over the Southern Ocean.
Data availability
Data used in this publication are publicly available at 10.5065/D6639N5B (Apel, 2017)
and 10.3334/ORNLDAAC/1581 (Wofsy et al., 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-14071-2019-supplement.
Author contributions
EA is responsible for the bulk of the conceptualization, formal analysis,
writing, review, and editing with contributions from all authors. BBS and
ECA were instrumental in the investigation and supervision related to this
paper. RSH contributed to the conceptualization, as well as the
investigation and halogenated VOC data curation for this project. BBS, EJM,
and RFK were responsible for the curation of δ(O2/N2)
data and contributed to formal analysis involving these data. MSHM and EAK were responsible for STILT data curation and formal analysis, as well as
the conceptualization and formal analysis of SITLT-based geostatistical
influence functions; flux estimates were also informed by these two. DK,
ST, JFL, and ASL were responsible for constructing CAM halogenated
VOC emissions and conducting CAM runs. MCL was responsible for CESM
simulations yielding O2 fluxes and comparing this product alongside the
Garcia and Keeling O2 climatology in CAM. KMC and CM were responsible
for the data curation of CO2 observations. AJH contributed to the
investigation for halogenated VOC data.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the ORCAS and ATom-2 science teams as well as the NCAR
Research Aviation Facility and NASA DC-8 pilots, technicians, and mechanics
for their support during the field campaigns. In addition, we appreciate the
NCAR EOL staff, who facilitated computing and data archival. In
particular, we thank Tim Newberger for his help in supporting the NOAA
Picarro CO2 observations and Andrew Watt for his help in supporting the
AO2 O2 observations. This work was made possible by grants from NSF
Polar Programs (1501993, 1501997, 1501292, 1502301, 1543457), NSF
Atmospheric Chemistry grants 1535364, 1623745, and 1623748, and NASA funding
of the EVS2 Atmospheric Tomography (ATom) project, as well as the support of
the NCAR Advanced Study Program (ASP) Postdoctoral Fellowship Program and
computing support from Yellowstone, provided by NCAR's Computational and
Information Systems Laboratory. The National Center for Atmospheric Research
is sponsored by the National Science Foundation.
Financial support
This research has been supported by the NSF (grant nos. 1501993, 1501997, 1501292, 1502301, and 1543457) and NSF
Atmospheric Chemistry (grant nos. 1535364, 1623745, and 1623748).
Review statement
This paper was edited by Andreas Engel and reviewed by three anonymous referees.
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