Introduction
The oceans emit large amounts of halogen- (Penkett et al., 1985; Quack and
Wallace, 2003) and sulfur-containing substances (Bates et al., 1992; Watts,
2000) that influence atmospheric chemistry. Organic bromine and iodine in
the atmosphere is largely supplied by oceanic emissions of very short-lived
substances (VSLS) such as dibromomethane (CH2Br2), bromoform
(CHBr3) and methyliodide (CH3I) (Lovelock and Maggs, 1973;
Hossaini et al., 2013). Also, a large fraction of the atmospheric sulfur
loading is due to oceanic emissions of OCS, CS2, H2S and dimethyl
sulfide (DMS; CH3SCH3), the latter being the major compound
transporting sulfur from the ocean to the atmosphere (Watts, 2000; Sheng et
al., 2015). Thus, we focus on DMS in this study.
Assessing marine emissions of VSLS is crucial, as they significantly
influence Earth's atmosphere in both the troposphere and the
stratosphere. In the troposphere, bromine-containing VSLS such as CHBr3
and CH2Br2 contribute to ozone destruction and alter the oxidative
capacity (von Glasow et al., 2004; Salawitch, 2006). Oceanic CH3I is
the main organic iodine compound in the atmosphere (Lovelock and Maggs,
1973) and impacts tropospheric oxidative capacity and ozone destruction
(Chameides and Davis, 1980; Saiz-Lopez et al., 2012). Iodine oxides, which
can be product gases of CH3I are likely to contribute to nucleation and
growth of secondary marine aerosol production (O'Dowd and De Leeuw, 2007).
DMS emitted to the troposphere is a precursor of secondary organic aerosol
and potentially cloud condensation nuclei and thus influences the radiative
budget (Charlson et al., 1987). Halogenated VSLS also enhance stratospheric
ozone depletion (Sinnhuber and Meul, 2015) and thus contribute to the
ozone-driven radiative forcing of climate (Hossaini et al., 2015). Despite
the short lifetime of CH3I (4–7 days) compared to the bromocarbons
(6–120 days), there is potential for a small fraction of marine-produced
CH3I to be transported to the stratosphere where it also contributes to
ozone depletion (Tegtmeier et al., 2013; Solomon et al., 1994). DMS has a
shorter lifetime of 11 min–46 h (Barnes et al., 2006; Osthoff et al.,
2009) compared to CH3I. Despite the short lifetime, there is potential
even for the very short-lived DMS to be transported to the tropical
tropopause layer (TTL) in convective hot spot regions (Marandino et al.,
2013a, b).
The impact of marine VSLS emissions on atmospheric chemistry has been
studied in chemistry–climate and transport models (e.g. Salawitch et al.,
2005; Kerkweg et al., 2006b; Sinnhuber et al., 2009; Liang et al., 2010;
Ordóñez et al., 2012). Therein, marine emissions of the VSLS have
mainly been based on prescribed boundary layer mixing ratios (Aschmann et
al., 2009) or emission scenarios (Warwick et al., 2006; Liang et al., 2010;
Ordóñez et al., 2012; Hossaini et al., 2013). However, prescribing
emissions in atmospheric models lacks the impact of the atmospheric boundary
layer mixing ratio on the concentration gradient. This concentration
gradient at the interface between ocean and atmosphere directly influences
the emissions, as it determines the direction and magnitude of the flux. The
lack of potential feedbacks can result in a modelled atmospheric surface
concentration inconsistent with the oceanic surface concentration.
Here, we evaluate a conceptually different way of considering marine
emissions in chemical climate models that is based on a consistent
concentration gradient between ocean and atmosphere. In contrast to previous
approaches of either specifying atmospheric surface mixing ratios or
specifying sea-to-air fluxes, water concentrations are prescribed and
emissions are calculated online. Thus, the concentration gradient at the
interface and the emissions are consistent with the atmospheric boundary
layer and the ocean surface, and the emissions can respond to the actual
state of the atmosphere. The approach is applied to established concentration
climatologies of short-lived halocarbons (CH2Br2, CHBr3,
CH3I) and sulfur compounds (DMS) that share common characteristics such
as supersaturation in the surface ocean and marine production. For the
halocarbons, this set-up is applied for the first time and uses surface ocean
concentration climatologies derived from observations by Ziska et al. (2013).
Oceanic DMS emissions have been evaluated in coupled ocean–atmosphere models
(Kloster et al., 2006; Cameron-Smith et al., 2011) or modelled online during
a test for the implementation of different submodels (Kerkweg et al., 2006b).
In our study, the focus lies on how to consider oceanic emissions in a
stand-alone atmospheric model, and uses the most updated DMS concentrations
available (Lana et al., 2011). Additionally, we compare the output of the two
methods with observations from aircraft and ship campaigns.
Prescribing water concentrations and calculating emissions online enables
convenient testing of different air–sea gas exchange parameterizations.
Air–sea gas exchange is calculated as the product of the concentration
gradient between air and water at the surface and the transfer velocity. The
latter needs to be parameterized, and many different parameterizations have
been published (see e.g. Wanninkhof et al., 2009 for a summary). Most
parameterizations relate the transfer velocity to wind speed (e.g. Liss and
Merlivat, 1986; Wanninkhof and McGillis, 1999; Nightingale et al., 2000; Ho
et al., 2006), but others take the effect of bubble-mediated transfer (Asher
and Wanninkhof, 1998) or enhancement by rain (Ho et al., 1997, 2004) into
account. Testing a variety of different parameterizations on prescribed water
concentrations to calculate atmospheric abundances provides information on
the uncertainties of global emission estimates.
The experimental set-up consists of two steps. First, we prescribed surface
water concentrations in the chemistry climate model EMAC (ECHAM5/MESSy
Atmospheric Chemistry) (Jöckel et al., 2006, 2010)
and air–sea exchange of VSLS was then calculated online by the submodel
AIRSEA (Pozzer et al., 2006). The model results are then evaluated and
compared to a simulation where the difference results from prescribed VSLS
emissions (PE). To compare the simulation set-up with prescribed emissions
to the set-up with prescribed water concentrations, we used the same
concentration climatologies that were used to create the emission
climatologies. In our study, these concentration and corresponding emission
climatologies were published by Ziska et al. (2013) for the halocarbons and
Lana et al. (2011) for DMS. The modelled atmospheric mixing ratios of the
gases are compared to measurements from time series of ground-based
stations, ship and aircraft campaigns in order to identify whether the
online calculation is simulating the atmospheric mixing ratios more
accurately. In a second step, we use the coupled module to test the
sensitivity of the global emissions to eight different, frequently used or
recently published, transfer velocity parameterizations.
Model set-up and data description
The atmosphere-chemistry model EMAC
The EMAC model is a global atmospheric
chemistry climate model described in Jöckel et al. (2006, 2010). ECHAM5/MESSy includes submodels describing
processes of the troposphere and middle atmosphere as well as interaction
with land and human influences. Air–sea gas exchange is calculated in EMAC
with the submodule AIRSEA, as described by Pozzer et al. (2006).
The numerical simulations were nudged towards the European Centre for
Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (Dee et al.,
2011) every 6 h (temperature, divergence, vorticity, surface pressure).
The resolution of the EMAC atmosphere was ∼ 2.8∘ × 2.8∘ (T42) and 39 vertical hybrid pressure levels up to 0.01 hPa
(L39). The effect of resolution on the results tested with a finer
resolution (T106) was only minor (see Table S2 in the Supplement). The
atmospheric model as well as the submodel AIRSEA uses a time step of 600 s.
The convective transport follows the scheme of Tiedtke (1989) and the tracer
advection is described in Lin and Rood (1996). An overview of these nudged
simulation set-ups can be found in Sect. 2.3.
The simulations include the four very short-lived species, CH2Br2,
CHBr3, CH3I and DMS, and simplified atmospheric loss reactions for
them. The loss reactions include
oxidation with OH, O(1D), Cl and photolysis for CHBr3 and CH2Br2 following the reactions rates by Sander et
al. (2011);
oxidation with OH, Cl and photolysis for CH3I (Sander et al.,
2011);
and oxidation with OH and O(3P) for DMS (Sander et al., 2011).
EMAC uses monthly mean concentrations of OH, developed and evaluated for the
TransCom-CH4 model intercomparison project, and discussed in detail by Patra
et al. (2014). Monthly mean photolysis rates for VSLS were calculated by the
TOMCAT CTM (chemical transport model) which has been used extensively to
examine the tropospheric chemistry of VSLS (e.g. Hossaini et al., 2013).
These fields were provided at a horizontal resolution of
2.8∘ × 2.8∘ (longitude × latitude) and on
60 vertical levels (surface to ∼ 60 km). TOMCAT calculates photolysis
rates online using the code of Hough (1988) which considers both direct and
scattered radiation. Within TOMCAT, this scheme is supplied with surface
albedo, monthly mean climatological cloud fields and ozone and temperature
profiles. The photolysis rates have recently been used and evaluated as part
of the ongoing TransCom-VSLS model intercomparison project
(http://www.transcom-vsls.com).
The simulated atmospheric lifetimes in our set-up generally agree well with
published estimates for these gases, indicating that the basic assumption of
the simplified chemistry applied here is valid. The local mean tropical
(20∘ N–20∘ S) lifetime of CH2Br2 in the
troposphere in our model study is 143 days and thus lies below 167 days,
which was found in Hossaini et al. (2010). The mean tropospheric tropical
lifetime of CHBr3 is 20 days in our study, which is consistent within
10 % with a recent reevaluation of CHBr3 lifetime by Papanastasiou et
al. (2014), together with a recent reevaluation of the reaction of OH with
CHBr3 by Orkin et al. (2013). The local lifetime of CH3I in our
study is 3 days, which is in accordance with the study of Tegtmeier et al. (2013).
The tropical lifetime of DMS in our study ranges between less than 1 day and up to
3 days and is thus within but at the higher end of the range
of 11 min–46 h (Barnes et al., 2006; Osthoff et al., 2009).
Parameterizations of air-sea gas exchange
In this study, the AIRSEA submodel (Pozzer et al., 2006) and its approach for
air–sea gas exchange was adopted, using the two-layer model (Liss and
Slater, 1974). Marine emissions (F) of gases are calculated as the product of
the concentration gradient between air and water concentration of the gas
(Δc) and the transfer velocity (k; Eq. 1), which needs to be
parameterized.
F=k⋅Δc=k⋅(cw-H⋅cair)
with cw being the water concentration, H the Henry constant
(dimensionless, water over air) and cair the concentration of the gas in
air which was taken from the modelled atmosphere in the respective time
step. Henry constants and their temperature dependencies are taken from
Moore et al. (1995) for the halocarbons and De Bruyn et al. (1995) for DMS.
The transfer velocity k comprises air- (kair) and water-side (kw)
transfer velocities (Eq. 2) in all parameterizations with the Henry constant
(H), air temperature (Tair) and the ideal gas constant (R):
k=1kw+R⋅H⋅Tairkair-1.
The water-side transfer velocity kw is often parameterized in
relation to wind speed with linear (e.g. Liss and Merlivat, 1986), quadratic
(e.g. Ho et al., 2006) or cubic (e.g. Wanninkhof and McGillis, 1999)
dependencies. Differences between these parameterizations arise from
different techniques to determine kw. The kw
parameterizations tested in our study result from tracer release experiments
in wind tanks (Liss and Merlivat, 1986), from deliberate tracer techniques in
the open ocean (Nightingale et al., 2001; Ho et al., 2006) or from direct
flux measurements using eddy covariance (Wanninkhof et al., 1999; Marandino
et al., 2009; Bell et al., 2013). Additional drivers of gas exchange, e.g.
bubble-mediated transfer (e.g. Asher and Wanninkhof, 1998) and enhancement in
the presence of rain (e.g. Ho et al., 2004) are discussed. Bubble-mediated
transfer has been suggested to be influential for gases with low solubilities
since they more quickly escape from the liquid phase into the bubbles. Asher
and Wanninkhof (1998) reanalysed data from a dual tracer experiment and found
a better fit when bubble-mediated gas transfer was considered in the flux
calculations. Bubbles are more easily transported to the surface and released
to the atmosphere, thereby adding to the total flux. Rain is believed to
increase the flux under calm wind conditions due to an alteration of the sea
surface, which was tested in a dual tracer experiment in the laboratory (Ho
et al., 2004). Other factors are known to influence air–sea gas exchange
such as the presence of surfactants, but parameterizations including that
effect are only marginally explored (Tsai and Liu, 2003) and require global
distributions of surfactants that are currently not available. First steps of
including surfactants in global models are currently discussed (Elliott et
al., 2014; Burrows et al., 2014).
Schematic overview of the set-up of prescribed emissions (PE, left
panel) and online-calculated fluxes based on prescribed water concentrations
(PWC, right panel) implemented in EMAC. Climatologies of fixed water and
atmospheric concentrations in Ziska et al. (2013; Z13) and Lana et al. (2011; L11) were used to compute a global emission estimate, and the
resulting interannual mean emission climatology is prescribed in EMAC using
the submodule OFFLEM (PE, left panel). Calculating emissions online based on
prescribed concentration (Z13, L11) considers the current state of the
atmosphere during the calculation of emissions in the submodule AIRSEA (PWC,
right panel).
For sparingly soluble gases, kw dominates the transfer velocity, and
kair is often neglected as a simplification. For more soluble gases,
McGillis et al. (2000) found that considering kair alters the flux to
the atmosphere significantly when low temperatures or moderate wind speeds
prevail. The parameterizations of kair according to Kerkweg et al. (2006a, Eqs. 3 and 4 therein) assumes a dependency on the friction velocity
and surface wind speed, which is considered in the AIRSEA submodel.
The transfer velocity needs to be adapted to each gas by scaling it with the
dimensionless Schmidt number in water for kw and the Schmidt
number in air for kair divided by the Schmidt number that the
specific parameterization was normalized to, which is in most cases either
600 or 660. The Schmidt number is the ratio of the diffusion coefficient of
the compound to the kinematic viscosity of the surrounding medium. Following
the approach of the AIRSEA submodel, the Schmidt number in water is estimated
by scaling the CO2 Schmidt number in water (Wannikof, 1992; Wilke and
Chang, 1955), while the Schmidt number in air is calculated from air
viscosity and diffusivity of the gas in air (Lymann et al., 1990).
Experimental set-up
Prescribed concentrations and prescribed emissions
The experimental set-up consists of two steps. First, we compare emissions
and atmospheric mixing ratios from prescribed water concentrations (PWC)
with those derived from prescribed emissions (PE) (Fig. 1). For the PWC and
PE set-up, two different submodels are used to calculate the emissions in
EMAC: in the PE approach, emission climatologies are prescribed offline
using the submodel OFFLEM (Kerkweg et al., 2006b). For the PWC set-up,
emissions (or depositions) are calculated online using the submodel AIRSEA
(Pozzer et al., 2006). Details of the simulation set-ups for simulations
1 (PWC) and 2 (PE) can be found in Table 1. Both simulations cover a period
of 24 years (1990–2013) to average out interannual variabilities in
emissions and to ensure that the model output can be subsampled specifically
at the times of atmospheric observations specified in Sect. 2.4.
Set-up of model simulations evaluated in this study.
PWC: prescribed water concentration, PE: prescribed emissions,
AIRSEA: submodel for online calculation of emissions, OFFLEM: submodel for
prescribing emissions.
Abbreviation
kw-
Emission calculation,
Rain
White cap
Period
parameterization
submodule
effect
coverage effect
1
PWC
Nightingale et al. (2000)
PWC, AIRSEA
No
No
1990–2013
2
PE
Prescribed emissions, no online calculation, kw in original publications N00
PE, OFFLEM
No
No
1990–2013
3
LM86
Liss and Merlivat (1986)
PWC, AIRSEA
No
No
2010–2011
4
W99
Wanninkhof et al. (1999)
PWC, AIRSEA
No
No
2010–2011
5
N00
Nightingale et al. (2000)
PWC, AIRSEA
No
No
2010–2011
6
H06
Ho et al. (2006)
PWC, AIRSEA
No
No
2010–2011
7
H06r
Ho et al. (2006)
PWC, AIRSEA
Yes
No
2010–2011
8
A98
Asher and Wanninkhof (1998)
PWC, AIRSEA
No
Yes
2010–2011
9
B13m
Bell et al. (2013) modified, only DMS
PWC, AIRSEA
No
No
2004–2013
10
M09
Marandino et al. (2009)
PWC, AIRSEA
No
No
2004–2013
In simulation 1, we prescribe water concentration climatologies for the
halocarbons from Ziska et al. (2013, Z13) and for DMS from Lana et al. (2011,
L11). The assumption of constant water concentrations despite loss by
emissions is justified by the relatively small emissions compared to the
absolute amount of gas in the oceanic mixed layer and the fast production of
the compounds in water (e.g. Hopkins and Archer, 2014; Hepach et al., 2015).
The modelled emissions from the PWC set-up are compared to the original Z13
and L11 emission climatologies. In the same manner, resulting atmospheric
mixing ratios in the PWC simulation are compared to atmospheric
concentrations from the PE set-up with prescribed emissions from Z13 and L11.
The emission climatology from Z13 is based on constant water and atmospheric
concentrations extrapolated from ∼ 5000 measurements, using 6-hourly
ERA-Interim wind fields and the Nightingale et al. (2000) parameterization
for water-side transfer velocity. The L11 concentration climatology is based
on ∼ 40 000 measurements and surface wind data for the emission
climatologies from the NCEP/NCAR reanalysis project with a water-side
transfer velocity parameterized according to Nightingale et al. (2000, N00)
and an air-side transfer velocity according to Kondo (1975). The
climatologies, prescribing emissions and concentrations of the gases of
interest (CH2Br2, CHBr3, CH3I and DMS) were regridded to
the T42 grid of EMAC with ncregrid (Jöckel, 2006), which is in all four
cases coarser than the original grid described in Z13 and L11
(1∘ × 1∘ in both). It has to be noted that this
leads to a smoothing of small, local hotspots, but we assume this to be
negligible since we compare emissions on a global scale.
Besides the concentrations taken from the climatologies Z13/L11, the air–sea
calculation requires information on sea surface temperature, salinity and
wind. The mean sea surface temperature in the model for simulation 1
(1990–2013) was 15.95 ∘C, 15.82 ∘C in Z13 and
16.22 ∘C in L11. The mean wind speed in the EMAC simulations (PWC,
PE) was 7.51 m s-1, which is slightly larger than the wind speed used
to calculate the emission climatologies in Z13 (EMAC is 4.7 % larger) and
L11 (EMAC is 2.7 % larger). Sea surface salinity is prescribed with a
constant value of 0.4 mol L-1 in our model simulations as opposed to
spatially varying salinity in Z13 and L11. A 2-year simulation comparing the
effects of a constant salinity versus the Z13 climatology revealed a low
effect on global emissions (< 3 %), which is in accordance with findings
of Ziska et al. (2013). Compared to the calculation of the Schmidt number in
the publications by Z13 and L11, the submodel AIRSEA uses a different
empirical, temperature-dependent equation to calculate the Schmidt number. In
AIRSEA, the Schmidt number of CO2 at the respective temperature is
calculated and then adapted with the molar volume to the Schmidt number of
the gas of interest (Wilke and Chang, 1955; Hayduk and Laudie, 1974). In Z13,
the Schmidt number is calculated by averaging the diffusion coefficient
according to Hayduk and Laudie (1974) and Wilke and Chang (1955) and then
dividing by the dynamic viscosity of seawater at varying temperatures and a
constant salinity of 35. In L11, the Schmidt number is calculated according
to Saltzman et al. (1993). The resulting differences are negligible at sea
surface temperatures higher than 10 ∘C and grow largest at
0 ∘C, where they are still less than 15 %. Since the Schmidt
number is then normalized to the Schmidt number of CO2, the resulting
difference becomes small and does not lead to significant differences in the
global emission estimates of all four compounds. Differences in other
influential input parameters for emission calculation between our PWC set-up
and Z13 and L11 are thus small, ensuring that differences in emissions
between PWC and Z13 and L11 can be attributed to the consideration of the
actual state of the atmosphere in the PWC set-up.
Transfer velocity parameterizations
In the second part of the study, we test the sensitivity of the global
emissions towards eight different transfer velocity parameterizations. These
tests cover a 2-year time span (2010–2011) with 1 year (2009) as
spin-up. The simulations 3–6 (Table 1) test the impact of different
water-side transfer velocity parameterizations related to wind speed. The
parameterizations tested in this study are illustrated in Fig. 2. With
increasing wind speed, the differences between the transfer velocity
parameterizations grow larger; hence, testing these parameterizations yields
a range of global emission estimates that reflects this uncertainty.
Parameterizations and the general description of air–sea gas exchange
calculation are described in Sect. 2.2.
Parameterizations for water-side transfer velocity of air–sea gas
exchange kw for a Schmidt number of 660 that are tested in this study:
the linear parameterization LM96 (Liss and Merlivat, 1986), the cubic
parameterization W99 (Wanninkhof and McGillis, 1999), the quadratic
parameterization N00 (Nightingale et al., 2000) and H06 (Ho et al., 2006),
the parameterization modified according to Bell et al. (2013, B13m) with a
levelling off at wind speeds higher than 11 m s-1, and the linear
parameterization M09 (Marandino et al., 2009).
Locations of atmospheric data for comparison with model output
used in this study. Panel (a) shows locations of atmospheric measurements from
23 aircraft campaigns considered for comparison with halocarbon simulations.
Panel (b) shows location of measurements in the atmospheric boundary layer
from ships (PHASE-1, Knorr-06, Knorr-07, M98) and from aircraft campaign
(HIPPO 1–5) measurements considered for comparison with DMS simulations.
Table 1 provides an overview of all performed simulations. Simulation 3 uses
the 3-step linear parameterization of Liss and Merlivat (1986, LM86),
simulation 4 the cubic relationship by Wanninkhof and McGillis (1999, W99),
simulation 5 the quadratic parameterization by Nightingale et al. (2000,
N00), and simulation 6 the quadratic transfer velocity parameterization by
Ho et al. (2006, H06). The effect of rain (simulation 7 in Table 1) was
tested adding the Ho et al. (1997) rain effect parameterization to the H06
transfer velocity parameterization (see Pozzer et al., 2006, Eqs. 10 and 11).
White cap coverage according to Asher and Wanninkhof (1998, A98) considers
bubble-mediated gas exchange and is used in simulation 8. The different
parameterizations (LM86, W99, N00, H06) were available from the AIRSEA
version of Pozzer et al. (2006). The N00 parameterization was normalized to
the Schmidt number of 600 as in the original publication by Nightingale et
al. (2000), while 660 was used in Z13.
Two additional simulations including only DMS were performed to test the
effect of two recently published parameterizations of kw. These two
parameterizations have been derived from in situ DMS eddy covariance
measurements and deviate from previously published parameterizations. Bell
et al. (2003) observed that the transfer velocity does not increase at wind
speeds higher than 11 m s-1. Marandino et al. (2009) found a linear
dependency between wind speed and the transfer velocity kw for DMS. Both
simulations cover the period of 2004–2013, since observations from this
period were available for comparison. These two parameterizations for
kw were added to the submodule code of AIRSEA (for equations see Table 4). The modification of the code included a parameterization based on
results of the study from Bell et al. (2013, B13m) with a conservative
approach, in which the N00 parameterization was used at wind speeds below 11 m s-1 and kept constant at higher wind speeds to account for the
missing increase of kw with increasing wind speed. Finally, the
parameterization by Marandino et al. (2009, M09) was used in simulation 10
for the same period as B13m. Both newly implemented parameterizations are
part of the most recent release MESSy 2.52.
Observational data
Simulated atmospheric mixing ratios of the trace gases from PWC and PE are
compared to observations from ship campaigns, aircraft campaigns and ground-based time series stations.
A total of 23 aircraft campaigns providing halocarbon data are considered in
order to create annual zonal mean climatologies of these trace gases. The
combined data set ranges from 90∘ N to 75∘ S,
transecting from the surface to the upper troposphere/lower stratosphere
over land and ocean from 1992 to 2012 (see Table S1 for details on the
aircraft campaigns). Many of the more recent data sets are inter-calibrated
(see e.g. Brinckmann et al., 2012; Hall et al., 2014; Sala et al., 2014;
Wisher et al., 2014). The latitudinal and longitudinal distributions and
names of the aircraft campaigns are illustrated in Fig. 3a. The measurements
were averaged in zonal 10∘ wide latitude bins with a vertical
extent ranging from 10 to 50 hPa (10 hPa in boundary layer and TTL regions).
Most of the measurements are located around 30∘ N of latitude with
more than 150 points per bin. The tropical region (20∘ N–20∘ S) has an average of 50 points per bin. Figure S1 in the
Supplement illustrates the numbers of the measurements per bin.
For the comparison of measured and modelled data, the EMAC output of
simulations 1 and 2 is first sampled at the same location as the aircraft
measurements (longitude, latitude, altitude and time) by linear
interpolation. Then, the same process of averaging per bin as for the
measurements is applied to the model output.
Metadata of the ground-based time series stations of halocarbons
(NOAA) considered in this study. For DMS, the data from time series of Cape
Grim and Amsterdam Island were considered.
Nr.
Abbr.
Station name
Latitude
Longitude
Elevation (m)
Period
1
ALT
Alert, Canada
82.45∘ N
62.51∘ W
210
1992–2011
2
AMS
Amsterdam Island
37.80∘ S
77.54∘ E
55
1990–1999
3
SUM
Summit, Greenland
72.58∘ N
38.48∘ W
3209
2004–2011
4
BRW
Barrow, Alaska
71.32∘ N
156.61∘ W
27
1993–2011
5
MHD
Mace Head, Ireland
53.33∘ N
9.90∘ W
42
1998–2011
6
LEF
Park Falls, Wisconsin
45.95∘ N
90.27∘ W
868
1996–2011
7
THD
Trinidad Head, California
41.05∘ N
124.151∘ W
120
2002–2011
8
NWR
Niwot Ridge Forest, Colorado
40.03∘ N
105.55∘ W
3475
1993–2011
9
KUM
Cape Kumukahi, Hawaii
19.5∘ N
154.8∘ E
39
1995–2011
10
MLO
Mauna Loa, Hawaii
19.53∘ N
155.58∘ W
3433
1993–2011
11
CGO
Cape Grim, Tasmania
40.68∘ S
144.69∘ E
164
1993–2011
12
PSA
Palmer Station, Antarctica
64.92∘ S
64.00∘ W
15
1997–2011
13
SPO
South Pole
90.00∘ S
59.00∘ E
2837
1993–2011
Nine coastal ground stations from NOAA/ESRL, where halocarbons have been
measured by the NOAA global flask sampling network starting from 1990–2004
were chosen for comparison due to their location close to the coast (Table 2).
These data are currently available at the HalOcAt (Halocarbons in the Ocean and Atmosphere) database
(https://halocat.geomar.de/). Two time series stations situated distant to
the coast (Park Falls, Wisconsin, Niwot Ridge Forest, Colorado, both USA)
were chosen to assess to contribution of marine halocarbon emissions to the
atmospheric mixing ratio over land. Monthly means of the time series were
compared to monthly means of simulations 1 and 2 for the PWC and PE set-up.
DMS was directly compared to measurements from ship campaigns in the marine
boundary layer, because only few data from ground-based time series stations
is available. The campaigns chosen were PHASE-I (2004, Marandino et al.,
2007), two campaigns on RV Knorr (Marandino et al., 2007, 2008), and
M98 on RV Meteor (2009, A. C. Zavarsky, personal communication,
2014) to ensure a broad spatial coverage (Fig. 3b). Additionally, DMS data
from two time series stations – Cape Grim, Australia, 1990–1993 (Ayers et
al., 1995) and Amsterdam Island in the Indian Ocean, 1990–1999 (Sciare et
al., 2000) – were used for comparison (Table 2). Upper air atmospheric
concentrations of DMS were compared to aircraft measurements from the HIAPER
Pole-to-Pole observation (HIPPO) campaigns 1–5 (Wofsy et al., 2012), again
subsampling the model output for time and location of the observations.
Results and discussion
Global emissions based on prescribed concentrations
Integrated global fluxes from this study (PWC: prescribed water
concentrations, N00: kw parameterization of Nightingale et al., 2000)
compared to previously published emission estimates. Note that Ziska et al. (2013) is a bottom-up approach and the water concentrations were used in the
online flux calculations for the halocarbons; Lana et al. (2011) DMS water
concentrations were used for DMS online calculations. Ordóñez et al. (2012),
Liang et al. (2010), and Warwick et al. (2006) are top-down approaches, Bell et
al. (2002) is an oceanic mixed-layer bottom-up model approach for CH3I.
CH2Br2 (Gg yr-1)
CHBr3 (Gg yr-1)
CH3I (Gg yr-1)
DMS (Tg yr-1)
This study
Ziska et al.
Ordóñez et al.
Liang et al.
Warwick et al.
This study
Ziska et al.
Ordóñez et al.
Liang et al.
Warwick et al.
This study
Ziska et al.
Bell et al.
This study
Lana et al.
(PWC, N00)
(2013)
(2012)
(2010)
(2006)
(PWC, N00)
(2013)
(2012)
(2010)
(2006)
(PWC, N00)
(2013)
(2002)
(PWC, N00)
(2011)
90–50∘ N
1.3
-4.0
1.6
1.3
0.3
26.7
44.8
13.3
9.4
0.9
13.4
20.3
14.0
2.1
2.3
50–20∘ N
12.5
16.5
15.3
14.9
10.5
49.0
33.9
123.2
108.1
27.9
36.8
40.5
89.9
7.2
8.5
20∘ N–20∘ S
32.2
38.4
41.1
34.3
84.5
108.5
94.1
286.9
249.0
517.4
63.3
59.3
91.2
18.0
21.1
20–50∘ S
7.8
19.3
7.7
9.7
16.5
41.4
42.0
98.0
70.5
43.8
80.7
67.7
82.4
13.9
16.5
50–90∘ S
9.1
17.2
0.9
1.6
0.9
12.8
0.1
7.0
11.6
2.4
15.5
17.0
14.9
4.2
6.0
Total
63.0
87.4
66.6
61.8
112.7
238.4
214.9
528.4
448.6
592.4
209.7
204.8
291.7
45.5
54.4
The long-term mean of global emissions (1990–2013, simulation 1 in Table 1)
based on PWC is different from the offline calculated emission climatologies
for all four gases. The magnitude of this difference varies between the
gases +11 % (CHBr3) to -28 % (CH2Br2) (Table 3). The global
spatial pattern of the PWC emissions is similar to the spatial patterns in
Z13 and L11 (Figs. 4, 5). Although global emissions for CH2Br2
were reduced in the PWC set-up compared to the Z13 scenario, they still lie
in the range of previously published estimates (61.8–112.7 Tg yr-1;
Table 3). The global PWC emissions for CHBr3 are 11 % higher than
from Z13, but still 47–60 % lower than top-down approaches by Warwick et
al. (2006), Liang et al. (2010) and Ordóñez et al. (2012). The PWC
CHBr3 emissions lie at the lower end of emission scenarios, closest to
Z13. The same holds for CH3I, where emissions are 2 % higher compared
to Z13 but still 18 % lower than the published estimate from Bell et al. (2002). Emission estimates in PWC are closest to Z13 and thus at the lower
end of the range of published global emission estimates. DMS emissions in
PWC compared to L11 were 17 % lower (Table 3).
Emissions from PWC (N00
parameterization for kw) for the trace gases dibromomethane
(CH2Br2, panel a), bromoform (CHBr3, panel b), methyliodide
(CH3I, panel c) and dimethyl sulfide (DMS, panel d), their annual mean of the
period 1990–2013 (simulation 1, Table 1).
The main differences between PE and PWC result from considering the actual
state of the atmosphere when calculating emissions from PWC, since the
atmospheric mixing ratio of the gas has a direct feedback on its emissions
through the concentration gradient (Eq. 1). Higher atmospheric
concentrations lead to lower marine emissions (or can even lead to
deposition) and vice versa. In the PWC set-up where the actual concentration
gradient between the ocean surface concentration and the model's atmospheric
mixing ratio is considered, the emissions thus respond consistently to this
feedback. The most obvious example for that is the global emission of DMS.
In L11, an atmospheric concentration of 0 ppt is assumed justified by the
high supersaturation in the water and the short lifetime of DMS. In the PWC
approach in our study, the atmospheric mixing ratio is always higher than 0 ppt,
on average 133 (±125) ppt, and this is likely the main reason
for the resulting 17 % reduction in the modelled flux vs. L11 (Fig. 5).
Considering the actual state of the atmosphere leads to altered
concentration gradients and thus emissions for any gas in the PWC set-up,
but the impact on global emissions depends on the specific characteristics
and global distribution of the gas in the surface ocean. For example, the
impact of the PWC approach on global emissions for CH2Br2 (28 %
difference between PWC and Z13) is larger than that for CH3I (2 %
difference) (Table 3). This difference can be explained by the saturation of
the two gases: CH3I is mainly oversaturated in the surface ocean with a
mean saturation ratio (actual concentration divided by equilibrium
concentration) of 18.2 in Z13. CH2Br2 with a mean saturation ratio
of 2 is concentrated closer to equilibrium. The distance from equilibrium is
thus larger for CH3I than for CH2Br2. Changes in atmospheric
mixing ratio therefore affect the concentration gradient for
CH2Br2 more than for CH3I. For CHBr3 with a similar
global ocean surface saturation ratio as CH2Br2, a drastic change
in emissions between PWC and Z13 can be seen in the Southern Hemisphere
(50–90∘ S; Table 3), where the emissions increase 2
orders of magnitude in the PWC compared to Z13. The Z13 emission climatology
displays a latitudinal band of elevated atmospheric mixing ratios around 60∘ S, which result in this region being a sink for atmospheric
CHBr3. In our PWC set-up, atmospheric mixing ratios in this region are
not as elevated and hence PWC leads to larger emissions. In general, gases
that are concentrated close to equilibrium in the surface ocean respond more
strongly to changes in atmospheric concentrations and thus to the PWC set-up
than more supersaturated gases.
Comparing integrated regional fluxes, the halocarbons display the largest
differences in the polar regions (Table 3). Besides dynamic atmospheric
concentrations that may alter emissions in the PE set-up, two other reasons
for differences in this specific set-up apply for the halocarbons. First, no
sea ice is considered in Z13 whereas EMAC uses prescribed sea ice in our PWC
set-up. L11 considers sea-ice. When sea ice is present in the model
EMAC/AIRSEA, the flux is reduced by the fraction of surface that is covered
by it. This may lead to the lower flux estimations in our PWC set-up and may
partly explain e.g. the reduced emissions in the Arctic for CHBr3.
Furthermore, our PWC approach takes into account air-side transfer velocity
(Eq. 2) instead of only the water-side transfer velocity as Z13, which can
control the flux of more soluble gases at low temperatures and thus decrease
emissions (McGillis et al., 2000). At high latitudes (60–90∘ N and
S), where low temperatures and high winds prevail, the transfer velocity can
be reduced by up to 68 % (CH2Br2), 32 % (CHBr3) and 61 % (CH3I)
using kair in the PWC set-up. L11 takes the kair
and sea ice into account, so this difference does not apply.
Differences (PWC–PE) in emissions between PWC (simulation 1, Table 1,
2010–2011) and PE (simulation 2, Table 1, 2010–2011). Red indicates a larger
flux in the PWC set-up, blue a larger one in the PE set-up.
Atmospheric mixing ratios based on PWC and PE
The atmospheric mixing ratios in EMAC sustained by emissions either from PWC
or PE are compared to available atmospheric observations from aircraft
campaigns (halocarbons, DMS), ground-based time series stations from
NOAA/ESRL (halocarbons) and ship campaigns (DMS). The model output of
simulations 1 and 2 (Tables 1, 4) was subsampled at the times and locations of
the observations. A scatterplot for direct comparison between model output
and observations is provided in the Supplement in Fig. S2.
Atmospheric mixing ratios (in ppt) of the trace gases dibromomethane
(CH2Br2, upper row), bromoform (CHBr3, middle row), and
methyliodide (CH3I, lower row) derived from measurements (see Fig. 3
for locations of aircraft campaigns) and EMAC runs with prescribed water
concentrations and prescribed emissions. Model output was subsampled at
locations and times of observations and binned for direct comparison.
The largest difference between PWC and PE in the atmospheric mixing ratio is
again found for CH2Br2 in the Southern Hemisphere (Fig. 6), where
the PWC set-up yields lower emissions and therefore also lower atmospheric
mixing ratios. For CH2Br2, atmospheric mixing ratios globally
decrease on average by 28 % compared to the PE set-up, which is the same
percentage as the reduction in the global emissions. Concentrations derived
from these reduced fluxes generally agree better with the measurements, even
though Arctic emissions still seem to be underestimated in the model
compared to the observations. A possible explanation for this
underestimation could be emissions of VSLS from sea ice that are not
considered in the model, as e.g. Karlsson et al. (2013) observed elevated
CH2Br2 in brines on top of sea ice. Mixing ratios of CHBr3
are similar in the PWC and PE set-ups (difference globally only 1.2 %),
but both do not show the same pattern as the measurements: for both set-ups,
atmospheric mixing ratios are underestimated in the Southern Hemisphere up
to the northern tropics (Fig. 6). The same is evident for CH3I, where
PWC and PE also vary only slightly, while both set-ups underestimate
atmospheric CH3I concentration in the tropics. Since atmospheric
concentrations were derived from emissions based on the Z13/L11 water
concentration climatology in the PWC set-up, negative discrepancies to
atmospheric observations indicate regions where the concentration
climatologies lack hotspots and can thus identify missing oceanic source
regions. For all three halocarbons, the concentration climatologies seem to
represent water concentrations that are too low in the Northern Hemisphere
and the tropics to explain the observed atmospheric mixing ratios. It has to
be noted that coastal areas are large source regions of halocarbon emissions
with global contributions of up to 70 % (Ziska et al., 2013), which might
be underrepresented in our modelled approach and thus might at least partly
explain these missing sources.
Mean seasonal variation of CH2Br2 mixing ratios (in ppt)
using model output based on PE (in red) and PWC (in blue), subsampled at the location of the NOAA
ground-based time series stations. Black dots indicate the long-term monthly
means of the time series at the specific locations (± standard
deviation of the monthly means), vertical lines indicate the corresponding
standard deviations. Monthly time series of at least 7 years were averaged,
the exact periods are listed in Table 2.
Modelled concentrations matched observations from NOAA/ESRL ground stations
in most of the cases better in the PWC set-up compared to PE. The agreement
between simulation and measurements increases with the atmospheric lifetime
of the gases: modelled mixing ratios for CH2Br2, with the longest
lifetime of the tested gases, reflect the observed seasonality at all 12
stations well (Fig. 7). The modelled seasonality of the atmospheric mixing
ratios is similar in both the PWC and PE set-ups, indicating that the main
fluctuations at these locations comes from seasonality in atmospheric
transport and chemistry rather than from seasonality in emissions, since
emissions are constant in PE. For all stations except for Mace Head, PWC
yields atmospheric mixing ratios closer to the measurements for
CH2Br2, reducing overestimations of modelled atmospheric mixing
ratios compared to measurements of up to 75 % as e.g. at the South Pole.
Discrepancies between observations and model simulations are larger in most
of the ground-based stations for CHBr3 (lifetime ∼ 20 days in our simulation) than for CH2Br2, and again PWC
yields as good or more accurate mixing ratios than PE compared to the
measurements (Fig. 8). However, the observed seasonality is not well
reflected in either the PWC or the PE set-up. This mismatch indicates that a
further seasonality in the sources is required, which can e.g. be accounted
for by introducing a seasonality in the water concentrations prescribed.
This finding is opposite to findings from Liang et al. (2010), who concluded
that atmospheric CHBr3 mixing ratios are mainly driven by transport and
atmospheric chemistry. Furthermore, the good agreement between model and
observations at continental sites away from the coast (Park Falls,
Wisconsin, USA; Niwot Ridge Forest, Colorado, USA) for CH2Br2 and
CHBr3 indicates that the ocean is the dominant source of these
compounds also over land. CH3I, the gas with the shortest lifetime in
the range of a few days, shows the largest discrepancies between modelled
mixing ratios and observations (Fig. 9). The PWC set-up yields mixing ratios
in the range of the observations for only two stations (Alert, Canada, and
Barrow, Alaska, USA) and, in most of the stations, the seasonality was not
well reflected in the model runs. CH3I seasonality in water
concentrations has previously been observed (Shi et al., 2014), indicating
that seasonally resolved water concentrations are needed to reproduce
atmospheric concentrations of the shortest-lived compounds in a more
accurate way. Oceanic emissions in PE and PWC were too large to explain
atmospheric mixing ratios at stations in high latitudes (Summit, Mace Head,
Cape Grim, Palmer Station, South Pole), but too low to explain atmospheric
mixing ratios in lower latitudes (Park Falls, Trinidad Head, Niwot Ridge,
Cape Kumukahi, Mauna Loa), which agrees with findings from aircraft
campaigns (Fig. 6).
Same as Fig. 7, for CHBr3.
Integrated global emissions during 2010–2011 for sensitivity tests
using different parameterizations for the transfer velocity kw
(simulations 3–6, same as in Table 1) and the effects of rain (simulation 7),
bubble-mediated transfer parameterized using white cap coverage (simulation
8) and parameterizations recently suggested for DMS (simulations 9 and 10).
Equations for the parameterizations using wind speed u are given for the
Schmidt number (subscript after k) as in the original publications listed.
u: wind speed at 10 m a.s.l. in metres per second. k is given in centimetres per hour.
No.
Parameterization
CH2Br2
CHBr3
CH3I
DMS
Gg yr-1
Gg yr-1
Gg yr-1
Tg yr-1
3
Liss and Merlivat (1986)
for u≤3.6, k660=0.17u for 3.6 < u < 13, k660=2.85u-9.65 for u≥13, k660=5.9u
53.74
189.10
151.88
33.38
4
Wanninkhof et al. (1999)
k660=0.0283u3
58.38
211.17
223.52
45.22
5
Nightingale et al. (2000)
k600=0.22u2+0.333u
63.04
238.46
209.73
45.49
6
Ho et al. (2006)
k660=0.266u2
62.71
236.10
213.47
45.91
7
Ho et al. (2006) + rain
–
65.08
249.66
225.67
48.70
8
White cap coverage
–
62.76
238.51
197.44
42.53
9
Bell et al. (2013), modified
for u≤11, k600=0.22u2+0.333u for u > 11, k600=30.283
–
–
–
40.63
10
Marandino et al. (2009)
k720=1.92u-1.0*
–
–
–
42.45
Mean (simulations 3–6)
59.47
218.71
199.65
42.5
* Units converted, in original publication: k720=0.46u-0.24 (m day-1).
Error metrics for the comparison of model output from PWC
(simulation 1) and PE (simulation 2) for all of the compounds including all
aircraft campaigns and ship observations, illustrated in Fig. S2 in the Supplement.
Determination of error metrics according to Yu et al. (2006).
CH2Br2 PE
CH2Br2 PWC
CHBr3 PE
CHBr3 PWC
CH3I PE
CH3I PWC
DMS PE
DMS PWC
Mean bias (ppt)
0.24
-0.036
-0.23
-0.24
-0.14
-0.14
86.21
42.12
Mean absolute gross error (ppt)
0.30
0.15
0.31
0.31
0.15
0.16
102.9
67.39
RMSE (ppt)
0.381
0.21
0.53
0.53
0.26
0.26
236.2
135.8
Fractional bias (ppt)
0.26
0.0001
-0.23
-0.20
-0.89
-0.96
0.23
0.10
Fractional absolute error (ppt)
0.31
0.20
0.56
0.56
1.13
1.19
1.23
1.18
Normalized mean bias factor (–)
0.27
-0.04
-0.49
-0.53
-1.71
-1.96
1.31
0.64
Four ship campaigns were chosen for comparison of DMS, since only few long-term
measurements of atmospheric mixing ratios of DMS are available. Simulation with both the PWC (N00) and the PE approach overestimate DMS
mixing ratios in the marine boundary layer from ship campaigns (see positive
mean bias in Table 5). However, the PWC reduces discrepancies within both
ship and aircraft campaigns by a factor of 2 (Table 5), as the mixing ratio
is overestimated by a factor of 0.61 in PWC as opposed to 1.31 in PE. The
observed seasonality of DMS mixing ratios at Amsterdam Island is well
reflected in the simulations except for the summer months, where PWC and PE
overestimate the monthly mean by a factor of up to 4.6 (PWC) and 6.7 (PE)
(Fig. 10). At Amsterdam Island, the simulated annual mean atmospheric mixing
ratio of 180.7 ppt in the PWC set-up agrees very well with the observed annual
mean of 181.2 ppt, whereas the simulated annual mean in the PE set-up is
268.5 ppt. At Cape Grim, the results of the two set-ups do not differ that
much, and both simulations underestimate the mixing ratios measured during
austral summer.
Same as Fig. 7, for CH3I.
Same as Fig. 7, for DMS.
An overall comparison of the agreement of both set-ups with observations is
summarized in a Taylor diagram (Fig. 11). This diagram is a statistical
summary that shows how well two patterns match each other with regard to
their correlation, variance and root-mean-square difference (Taylor, 2001).
The closer a point of a specific set-up is located to the reference point of
observations (here 1.0 on x axis), the more the simulation resembles the
observed measurements. PWC simulations increased the agreement with
observations for CH2Br2, especially the correlation (0.4 in PE to
0.6 in PWC), and for DMS (0.53 in PE to 0.65 in PWC), but only very slightly
for CHBr3 and CH3I. Centred statistics for all compounds can be
found in Table 5, with the equations used to compute the statistics according
to Yu et al. (2006) listed in the Supplement.
Taylor diagram of PE (triangles) compared to
PWC (circles) runs using the same
parameterization for kw (N00) for comparison. The Taylor diagram relates
model simulations to observations according to their root-mean-square error
(given as the distance to the reference point, x axis 1.0), correlation and
standard deviation. Simulations located closest to the reference point agree
best with observations.
Comparison of different transfer velocity (kw)
parameterizations
A large uncertainty of global emission estimates is related to different
parameterizations of the transfer velocity in Eq. (1). Calculating emissions
online enables a simple way of testing different transfer velocity
parameterizations, which was realized here with eight 2-year simulations
described in Table 1 (simulations 3–10).
The largest sensitivity for the emissions of all gases is introduced by
different parameterizations of the water-side transfer velocity kw
tested in simulations 3–6 (Table 4). The 4 parameterizations that were tested
(simulation 3–6, Table 1) comprised linear (LM86, simulation 3), cubic (W99,
simulation 4) and quadratic (N00, simulation 5, H06, simulation 6) relations
to wind speed. The resulting global emission estimates in these
parameterizations range 53.7–65.1 Gg yr-1 for
CH2Br2, 189.0–249.7 Gg yr-1 for CHBr3, 151.9–225.7 Gg yr-1 for CH3I and 33.4–48.7 Tg yr-1 for DMS (Table 4).
As expected, the linear kw parameterization (LM86) yields the lowest
global emission estimates, since it produces the lowest kw values (Fig. 2).
The N00 parameterization produces the highest global fluxes for CHBr3
and CH2Br2 but not for DMS and CH3I, where the highest
fluxes were obtained by H06 (DMS) and W99 (CH3I) (Table 4). The fact
that different parameterizations lead to higher global estimates for
different gases is explained by the varying spatial distribution of
concentration hot spots and regional variations of wind.
The kw parameterization in simulation 7 increases the flux under calm
conditions due to precipitation. This increase ranged from 4 %
(CH2Br2) to 6 % (DMS) (Table 4) when compared to the reference
flux using H06 alone (simulation 6, Table 4). Additional flux due to
precipitation is inversely correlated to the Schmidt number, so that under
identical conditions, increasing flux would be added in the order
CHBr3 > CH2Br2 > DMS > CH3I. The global flux estimations compared to the reference run do not
increase in this order (Table 4), rather DMS >
CHBr3∼ CH3I > CH2Br2. This
non-uniform response among the gases is explained by the globally and
regionally varying distance from equilibrium for the four gases, which
together with regional precipitation patterns leads to variations in the
emissions increased by rain. The parameterization based on white-cap
coverage (A98) also has small but ambivalent effects on the global flux for
the different compounds (simulation 8, Table 4). Compared to the mean of all
nonlinear parameterizations for each gas, global emissions were higher when
the white cap coverage parameterization was used for CHBr3 (4 %) and
CH2Br2 (2 %) but lower for CH3I (-8 %) and DMS
(-6 %) (Table 4).
The parameterizations tested only for DMS are both derived from eddy
covariance measurements at sea. Both parameterizations changed the global
emissions by -4.4 % (B13m) and -1.2 % (M09) compared to the average
flux of simulations 3–6 (Table 4). Although the modelled atmospheric mixing
ratios at the time and location of observations is for both of the
parameterizations higher than the observations, discrepancies between
simulated and observed mixing ratios were reduced compared to the N00
parameterization by factors of 1.4 (B13m) and 1.2 (M09).
Summary and conclusions
Two different ways of considering marine emissions of trace gases in global
atmospheric chemistry models are discussed here for the halocarbons
CH2Br2, CHBr3, CH3I and the sulfur-containing compound
DMS. In contrast to prescribing emissions (PE) from oceanic and atmospheric
concentration climatologies in the model, prescribing water concentrations
(PWC) with an online calculation of emissions results in a consistent
concentration gradient between ocean and atmosphere. The approach of
modelling emissions online was successfully applied for the very short-lived
halocarbons for the first time. The approach is based on the submodel AIRSEA
coupled to EMAC by Pozzer et al. (2006). The method has a number of
conceptual and practical advantages, as in this framework the modelled flux
can respond in a consistent way to changes in sea surface temperature,
surface wind speed, possible sea ice cover and marine atmospheric mixing
ratios in the model.
Global emission estimates of the four gases differ between +11 %
(CHBr3) and -28 % (CH2Br2) between PWC and PE when the
transfer velocity kw is parameterized according to Nightingale et
al. (2000) in both set-ups. Prescribing water concentrations instead of
emissions has the strongest effect for gases close to equilibrium in the
surface ocean such as CH2Br2 (28 % reduced emissions in PWC
compared to PE), as its emissions are most sensitive to atmospheric
concentrations. In contrast, only a 2 % difference is found for the highly
supersaturated gas CH3I. Considering PWC reduces the global emissions of
DMS by 17 %, a comparison to observations revealed that PWC compared to PE
reproduces observations slightly (CHBr3, CH3I) or much
(CH2Br2, DMS) better for measurements made at ground-based time
series stations, aircraft campaigns and ship cruises. Even though it is clear
that more data for all compounds are needed globally, the PWC set-up can be
used to identify oceanic regions where more measurements will be needed to
improve the global emission estimate. For example, there are clear
discrepancies in the Northern Hemisphere for CHBr3 and the tropics for
CH3I.
Global emission estimates display a large sensitivity towards the
parameterization of the transfer velocity kw, with relative differences
between 15.6 % (CH2Br2) and 35.9 % (CH3I) compared to
the mean global emissions of the four tested simulations including kw
parameterizations according to Liss and Merlivat (1986, LM86), Wanninkhof
and McGillis (1999, W99), Nightingale et al. (2000, N00) and Ho et al. (2006, H06). Sensitivity towards rain or bubble-mediated transfer was
generally low (< 10 % change in global emission estimate). Two
parameterization-adapting results that have recently been suggested for DMS
(Marandino et al., 2009, M09; Bell et al., 2013, B13m) produced both a lower
global emission estimate, which at the same time reduced discrepancies
between simulated and observed atmospheric mixing ratios and yielded
simulated atmospheric mixing ratios closer to observations than simulated
mixing ratios with the N00 parameterization.
In summary, prescribing water concentrations instead of prescribing
emissions in global atmospheric chemistry models leads to a consistent
concentration gradient between ocean and atmosphere and enables convenient
testing of different air–sea gas exchange parameterizations. Based on the
results of our comparison between the PE and PWC, prescribing concentrations
leads to more consistent emissions and mainly more accurate reproduction of
observations of atmospheric mixing ratios of the VSLS described here.