Introduction
Bromoform (CHBr3) is one of the most abundant bromine-containing volatile halocarbons and is a considerable source of
reactive bromine species in the atmosphere
e.g.. Due
to its lifetime of approximately 3–4 weeks
, bromoform alters the bromine
budget in both the troposphere and the stratosphere and can lead to
ozone depletion with potential impacts on the radiation budget of the
atmosphere .
Troposphere–stratosphere transport of short-lived volatile compounds
(including bromoform) highly depends on the location of the emissions
; thus the
spatio-temporal quantification of emissions is essential for assessing
its impact on atmospheric chemistry and climate. However, bromoform
emissions are so far poorly constrained and represent a significant
uncertainty in global atmospheric chemistry models
. Bromoform has both natural and
anthropogenic sources. Anthropogenic sources e.g. desalination
or disinfection of water; e.g. are
thought to contribute relatively little to the global emissions
. Natural bromoform synthesis in the open ocean is mainly
related to phytoplankton .
However, it is unclear whether bromoform is formed extra-
or intracellularly. In any case, the enzyme bromoperoxidase drives the process
in which bromide is oxidized in the presence of H2O2 followed
by a halogenation of organic compounds (haloform reaction). There
are indications of intracellular production; for example, some laboratory studies show
that bromoform is released during phytoplankton growth (e.g. by diatoms;
). In contrast, there is also evidence
that bromoform is extracellularly produced, as the components that are necessary
for bromoform production (dissolved organic compounds and the enzyme
bromoperoxidase) may escape via cell lysis or exudation of phytoplankton
.
Enhanced bromoform production during stress, as shown for macroalgae
(e.g. ), has not been demonstrated for
phytoplankton. However, the amount of bromoform produced can be related to
different phytoplankton species. Differences between
typical open ocean microalgae, i.e. the coccolithophores
(Emiliana and Calcidiscus) and diatoms
(Chaetoceros), are rather small (within a factor of 2)
. These different phytoplankton groups
show different global distribution patterns ().
In addition, open ocean bromoform may partly originate from coastal
sources via lateral transport
. In fact, the coastal
sources can be much stronger than the open ocean source
.
In these regions production occurs predominantly by macroalgae
. All
these aspects are important to understand current open ocean
concentrations and emissions, and to potentially project its future
development under a changing climate. Here, we address the question
of the impact of phytoplankton and lateral transport from the coast on
open ocean bromoform concentrations. For this purpose we implement
a refined version of the bromoform module of
into a marine biogeochemistry model (the Hamburg
Ocean Carbon Cycle model HAMOCC: ) which is coupled
to a global ocean general circulation model (the Max Planck Institute ocean model,
MPIOM: ). In a suite of present-day equilibrium
simulations we investigate the contribution of bulk phytoplankton,
diatom and non-diatom phytoplankton to bromoform production. We assess
the relevance of CHBr3 advection from the coast and
characterize emissions to the atmosphere based on simulated oceanic
concentrations and observed atmospheric concentrations.
Material and methods
Bromoform module
We use the bromoform cycling module as presented in
. They used the module within the
one-dimensional water column model GOTM (General Ocean Turbulence Model;
) together with a simple
nutrients–phytoplankton–zooplankton–detritus (NPZD)-type
ecosystem model to represent
conditions during Meteor cruise M55 in the Cabo Verde
region. Here, we use the module within the three-dimensional ocean
general circulation model MPIOM
that includes the biogeochemistry model HAMOCC .
Only mean features of the
bromoform module and modifications to the earlier parameterization are presented; details on the original parameterizations can be found in
.
Bromoform B (in mmolm-3) in the model is updated at
every time step following production, decay, advection, diffusion, and
gas exchange with the atmosphere. The only bromoform production
process considered in the current study is CHBr3 production
during phytoplankton growth. We do not consider bromoform synthesis
linked to phytoplankton sinks, i.e. the extracellular production of
bromoform. implemented this process and did
not find differences in CHBr3 concentrations, because
phytoplankton sinks are closely co-located with its sources. This is
also the case in HAMOCC; thus large-scale features will be the same,
despite moderate differences in timing of maximum bromoform
production. As the biogeochemistry model does not resolve plankton
functional groups, we can not directly calculate species- (or
group-) specific bromoform production. However, the contribution of
diatoms can be assessed indirectly from the availability of silicate,
as done previously for fractionating carbon export production and for
parameterizing dimethylsulfide production
. It is assumed that diatoms
grow faster than other phytoplankton groups; thus, whenever silicate is
available, diatoms are dominant, whereas residual plankton groups
dominate under silicate-limiting conditions. The bromoform production
ratio β is derived from the bulk bromoform production ratio
β0:
β=β0⋅fac1⋅Si(OH)4KphySi(OH)4+Si(OH)4+fac2⋅KphySi(OH)4KphySi(OH)4+Si(OH)4,
where KphySi(OH)4 denotes the half-saturation constant
for silicate (Si(OH)4) uptake. We test different factors fac1 and
fac2 for the relative contribution of diatoms and non-diatom
phytoplankton (see Sect. ).
Bromoform degradation processes considered in the model are
photolysis, halide substitution, hydrolysis, and bacterial degradation
during nitrification. We omit degradation during remineralization of
detritus in this study, because showed that it
leads to unrealistic accumulation of bromoform in the deep ocean. An
increase in the degradation rate did not solve this issue but instead led to
too low subsurface maxima. With regard to degradation by ammonium
oxidizing bacteria, we introduce one modification. As it was shown for
freshwater nitrifiers that these bacteria can oxidize volatile
halogenated organic compounds (including CHBr3; see
e.g. ) during oxidation of
ammonium hydroxylamine, it seems reasonable to exclude this process
for low-oxygen conditions. Therefore, a threshold of [O2]>50 µmolm-3 for the occurrence of this process is
implemented.
represented halide substitution and hydrolysis
as one first-order decay process with a half-life of
4.37 years. As both processes are strongly temperature-dependent and follow different kinetics, hydrolysis and halogen
substitution are implemented in the current study as separate
sinks. The former is implemented as a first-order decay process with
a temperature-dependent decay rate lhyd:
S=lhyd(T)⋅B.
reviewed hydrolysis rates of organic halogens
and suggested the following temperature dependence of the basic
hydrolysis rate kB in molmin-1:
kB=A1⋅exp-EART,
with A1=1.23×1017 molmin-1,
EA=107 300 Jmol-1,
R=8.314 JK-1mol-1, and T the seawater
temperature in K. The hydrolysis rate then follows from
lhyd=kB⋅[OH-],
where [OH-] is calculated from the dissociation product of
water and the hydrogen ion concentration, which are part of the
carbonate chemistry formulation in HAMOCC which uses the
formulation of.
Also, halide substitution is implemented as a first-order degradation
process with a temperature-dependent rate constant,
(lsubst)
S=lsubst(T)⋅B.
The rate is chosen to vary exponentially and represents a half-life of
τ1/2=5 years at 25 ∘C and τ1/2=74 years at
2 ∘C .
lsubst=lrefexpA2⋅1Tref-1T,
with lref=7.33×10-10 s-1 at
Tref=298 K, A2=12 507.13 K, and T the
seawater temperature in K.
In CHBr3 gas exchange with the
atmosphere was calculated from the two-film model assuming it is
controlled by the water side:
Fair–sea=kw⋅B-caH.
Hence, the flux was calculated from atmospheric concentrations ca,
solubility Henry's law constant H;, bulk
surface water concentrations (B), and a transfer velocity (kw).
We modify the description of the transfer velocity given by
to resolve the temperature dependence of the Schmidt number ScCHBr3 :
kw=(0.222u2+0.33u)⋅660ScCHBr3,ScCHBr3=4662.8-319.45T+9.9012T2-0.1159T3.
In the equations, u denotes wind speed (ms-1) and T temperature (K).
Observations
CHBr3 observations are taken from the supporting information of
, who extrapolated cruise data submitted to the
HalOcAt database (https://halocat.geomar.de) into a global gridded
field of bromoform concentrations and calculated emissions.
provide gridded data derived from a robust fit (RF) method
and ordinary least-squares (OLS) regression. The largest difference in these
methods is in the treatment of outliers. Gridded atmospheric mixing ratios are used as boundary
conditions for the model after conservative spatial interpolation onto the
model grid. We use the data derived from
the RF method that is less sensitive to outliers.
For the model evaluation we use the individual ship cruise data to avoid the influence of
patterns arising from extrapolation of the sparse data matrix. We compare
observed data from a particular month to modelled monthly means.
The exact origin of the individual data can be identified from the
supporting information in .
The observation-based net primary productivity (NPP) that we use in the model
evaluation was downloaded from
http://www.science.oregonstate.edu/ocean.productivity/index.php
(accessed in June 2014). NPP is calculated from NASA's SeaWIFS (Sea-viewing
Wide Field-of-view Sensor) level 3 data (PAR and Chl a) and NOAA's AVHRR
(Advanced Very High Resolution Radiometer) sea surface temperature for
1997–2009 using the Vertically Generalized Productivity Model (VGPM;
).
Model setup
Seven model experiments are set up to assess different aspects of
bromoform cycling (Table ). Of these, four experiments are designed to
study bromoform synthesis by phytoplankton. All of these experiments
use the climatological atmospheric concentrations of
as upper boundary conditions and resolve
all other bromoform cycling processes as described above. The
reference experiment Ref uses the constant bulk bromoform
production ratio derived from a laboratory study with diatoms
. For usage in HAMOCC, this ratio is
converted from nitrogen to phosphorus units using a constant Redfield
ratio (N : P =16:1). Following we
derive a factor of about 2 in the mean bromoform production ratio
between the groups of the open ocean microalgae, i.e. the
coccolithophores (Emiliana and Calcidiscus) and the
diatom (Chaetoceros). As almost nothing is known about other
phytoplankton groups (e.g. little on Baltic cyanobacteria and
nothing on flagellates), we test both a lower
and a higher production ratio by diatoms and residual (non-diatom)
phytoplankton. In the experiment Dia, the production ratio by
diatoms is modified to be half that of residual plankton,
i.e. fac1=0.5 and fac2=1. In the experiment
NDia, the opposite is implemented, i.e. production by
non-diatoms is reduced: fac1=1 and
fac2=0.5. Additionally, an experiment (Half) is
conducted in which the constant bulk production ratio is only half of
that in Ref in order to separate the impact of a pure reduction of
the global rate from fractionation among phytoplankton groups.
Model experiments. All experiments consider the degradation processes described in Sect. .
CHBr3production
Boundary conditions
Diatoms
Non-diatoms
Atmospheric CHBr3 (ca)
Prescribed coastal CHBr3
fac1
fac2
climatological
monthly
<200 m 80 pmolL-1
Ref
1.0
1.0
+
-
-
Dia
0.5
1.0
+
-
-
NDia
1.0
0.5
+
-
-
Half
0.5
0.5
+
-
-
Coast
0.0
0.0
+
-
+
Equi
0.0
0.0
+
-
-
Seas-at
1.0
1.0
-
+
-
To test the hypothesis that open ocean bromoform is produced at the
coast and advected to the open ocean, we conduct two joint
experiments. In both experiments we eliminate the production of
bromoform by phytoplankton, while we use the same atmospheric boundary
conditions as in Ref. In the first one (Coast) we
prescribe a bromoform concentration of 80 pmolL-1 in
waters shallower than 200 m. We choose this artificial
approach as it is impractical to resolve coastal sources
(i.e. macroalgae with tide-dependent bromoform release, release from
benthic algae and seagrass) explicitly in a global model with
approximately 1.5∘ horizontal resolution (curvilinear grid).
As a result of the constant atmospheric boundary conditions, a flux
from air to sea takes place because no bromoform is produced
offshore. To quantify this bromoform source to the ocean, we perform
a second experiment without prescribed bromoform on the shelf,
Equi (which stands for equilibrium with the atmosphere). The
comparison of Coast and Equi allows us to assess the
relevance of lateral transport of bromoform from the coast to the open
ocean.
An additional experiment is performed to address the effect of
variability in the atmospheric concentrations. The lifetime of
bromoform in air leads to a distinct seasonal cycle in atmospheric
mixing ratios
e.g.. The
additional experiment, Seas-at, differs from Ref
only in the atmospheric boundary conditions for bromoform
gas exchange.
In Seas-at, atmospheric boundary conditions follow
a seasonal cycle. We derive that seasonal cycle from the surface ocean
concentrations calculated in experiment Ref, because the extrapolated fields of
do not resolve temporal variability.
In particular, the ratio between seawater concentration
monthly means and their annual mean is used to construct the monthly
means of atmospheric concentrations from the climatological mean. We
thereby assume for simplicity that dynamics of atmospheric bromoform
are controlled by oceanic concentrations. This is possible when the
ocean is oversaturated with CHBr3 and CHBr3 is not
accumulating in air.
For all simulations the model restarts from a 1000-year spin-up
under pre-industrial conditions (CO2=278 ppm) followed by
a 200-year spin-up under present-day conditions
(CO2=353 ppm). Following these spin-ups the model
experiments are run into steady state (starting from a constant
CHBr3 concentration of 0.01 pmolL-1). The model results
are analysed for the last year of the simulation, when surface waters down
to 500 m are in quasi-steady state. All experiment use NCEP 6-hourly
forcing interpolated to the model time step of 72 min
and monthly mean model output is analysed. In the following, whenever we use
the term “model”, we are referring to MPIOM-HAMOCC.
Results and discussion
Simulated open ocean sources and sinks of bromoform
The aim of the study is to investigate the impact of planktonic production
and lateral transport on bromoform surface concentration patterns and on
sea–air gas exchange. The spatial distribution of bromoform in
seawater reflects the balance between sources (production and uptake from the
atmosphere) and losses via outgassing and degradation. In the experiments
that include planktonic production of bromoform (Ref, Half,
Dia, NDia, Seas-at), large-scale structures of
surface concentrations are controlled by this process: maxima occur in
biologically productive regions (e.g. in upwelling regions) and minima are
located in the oligotrophic subtropical gyres in the Pacific and Atlantic
Ocean (Figs. a, d and a, d). A reduction of the bulk
production ratio of bromoform relative to primary production (Half)
leads to a reduction of bromoform concentrations almost everywhere, apart
from regions with uptake of bromoform from the atmosphere (e.g. the Southern
Ocean and the northern extratropics in the local winter seasons,
Fig. c and f). Lower marine CHBr3 production leads here to
lower surface ocean concentrations during the phytoplankton bloom and thus
a larger difference between seawater and atmospheric concentrations and hence
enhanced uptake from the atmosphere, which in turn leads to higher surface
seawater concentrations after the phytoplankton bloom.
Similarly, differences
between Ref and Seas-at are highest where the seasonal cycle
of CHBr3 surface concentrations is pronounced, particularly in the extratropics, where
the variability of bromoform production is strong (Figs. 1a, b, d, e; e.g. around
50∘ N). This is because the seasonal cycle of the
atmospheric forcing field in Seas-at is derived from the sea surface
concentrations. Atmospheric concentrations higher than the
climatological mean lead to a reduction of the
flux and subsequently higher seawater concentrations. A comparison of the
experiments Ref and Equi, in which the only bromoform
source is uptake from the atmosphere, reveals that uptake is particularly
relevant in higher latitudes (Fig. S1 in the Supplement), where it can deliver
more than 70 % of the bromoform in surface seawater (not shown). In the
polar regions bromoform production in the model is very low, as primary
production is limited by light availability even during summer because of
the sea ice. However, particularly in this specific region,
uncertainties are large and bromoform cycling is not well captured in the model.
First, this is because of the importance of
uptake from the atmosphere. Our stand-alone ocean model is forced by
extrapolated atmospheric bromoform concentrations from
, where data from CHBr3 measurements are
sparse. Second, our model does not consider a potentially important source
process: production within sea ice and subsequent discharge into seawater
during melting or by diffusion through brine channels
.
Mean surface bromoform concentrations (pmolL-1) in experiment Ref in
boreal winter (a) and boreal summer (d) and the percentage difference
(e.g. 100⋅Seas-at-RefRef) of Seas-at
(b, e) and Half (c, f) in the same season.
Mean surface bromoform concentrations (pmolL-1) in
experiment Half in
boreal winter (a) and boreal summer (d) and the percentage
difference
(e.g. 100⋅Dia-HalfHalf) of
Dia (b, e) and NDia (c, f) in the same season.
Mesh patterns show regions where the fraction of
diatoms (b, e) or non-diatoms (c, f) in bulk phytoplankton
dominates (i.e. fraction >0.5) (inclined mesh for
diatoms, straight mesh for non-diatoms).
As mentioned above, simulated bromoform distribution patterns mainly
follow the patterns of simulated primary productivity. In the experiments
Dia and NDia, a relative reduction of the bromoform
production ratio for diatom (Dia) or residual phytoplankton
(NDia) dominance by a factor of 2 is implemented. As
a consequence of silicate availability the model predicts largest
diatom abundances in the northern and southern extratropics
(Fig. b and e) and non-diatom phytoplankton in the lower
latitudes (Fig. c and f). This distribution of diatoms is in
line with the one predicted by models with explicit implementation of
functional phytoplankton groups e.g. or
diagnosed from satellite retrievals
. As a direct consequence of the model
configuration,
the bromoform production in both experiments is lower than in the experiment
Ref, and bromoform concentrations are consequently
lower. Compared to the uniform reduction of the bulk production rate
in Half, concentrations are of course higher in regions in
which the phytoplankton group with the unchanged (i.e. not reduced)
production ratio dominates (Fig. ). Similar to
experiment Half, the reduction of the production rate in Dia
leads to a reduction of the global bromoform inventory (Table ),
as diatoms dominate in productive regions.
When focusing on certain regions, though, differences
in the two approaches become apparent, e.g. in lower latitudes where
non-diatom species dominate and the bromoform production (and
concentration) is hence higher in Dia than in Half.
Simulated global annual bromoform production and loss (GmolCHBr3yr-1), inventory (GmolCHBr3), and residence time
(days); the first number refers to gas exchange and
the second number to degradation
Process
Ref
Seas-at
Half
Dia
NDia
Uptake
0.018
0.016
0.024
0.022
0.019
Outgassing
0.3142
0.311
0.149
0.22
0.24
Planktonic source
0.37
0.37
0.18
0.26
0.29
Degradation
0.069
0.066
0.057
0.063
0.063
Inventory
0.215
0.205
0.1822
0.1966
0.200
Residence times τa (days)
205
197
322
253
241
(τgasxb, τdegrc ) (days)
(249, 1141)
(239, 1124)
(445, 1167)
(326, 1144)
(304, 1161)
a:
τ=11τdegr+1τgasx;
b:
τgasx=inventoryoutgassing;
c: τdegr=inventorydegradation
Primary production depends on light, temperature, and nutrient
availability. Therefore in some ocean regions, such as the
oligotrophic subtropical gyres, where surface nutrient concentrations
are very low, production maxima are located in intermediate waters (at
approximately 50–80 m). In most open ocean regions, though,
bromoform production maxima in the model are located within the upper
mixed layer (Fig. ). This suggests that
seasonal mixing with deeper ocean layers, i.e. the dynamics of the
mixed layer depth, play only a minor role in shaping the temporal
evolution of bromoform surface concentrations. However, in regions
with subsurface production maxima in summer, like the subtropical North
Atlantic, the deepening of the mixed layer in winter mixes bromoform
upwards and leads to surface maxima that do not correspond to surface
production maxima as also described in.
Location of bromoform production maxima in Ref: within or below mixed
layer for boreal winter (a) and boreal summer (b). The mixed-layer depth is defined as the
depth where a density (σT) difference of 0.125 relative to the surface value
occurs.
The experiments Coast and Equi are designed to study
lateral transport of bromoform from the coast to the open ocean within its
lifetime. The mean global CHBr3 residence time in steady state in
the experiments with planktonic production is approximately 200 days
(Table ). The global residence time does not reflect the local lifetime of bromoform at certain water depths or locations. At the global scale
the residence time is dominated by gas exchange
(τ≈250 days, Table ); the
residence time with regard to degradation is much longer (τ≈1100 days, Table ). Thus, the lifetime within the mixed layer is
much shorter than in the deeper ocean. However, it could be still long enough
to allow for considerable transport of bromoform from the coast to the open
ocean. In the experiment Coast, bromoform is reset to a concentration
of 80 pmolL-1 in waters shallower than 200 m at each
model time step. The comparison between Coast and Equi
allows us to assess the relevance of lateral transport, as Equi
accounts for the contribution of uptake from air in pristine open ocean
waters. As expected, lateral transport from shelf regions is
particularly relevant in the Arctic surface ocean (Figs. c, f and ),
because the Arctic Sea is semi-enclosed by land and outgassing
is low at cold temperatures. In contrast, the surface concentrations in the open
Pacific Ocean are least influenced by coastal bromoform. Our chosen value of
80 pmolL-1 is an arbitrary value; however, it is more than 15
times higher compared to the mean open ocean concentration and therefore high
enough to roughly represent the gradient between open ocean coastal regions.
In comparison to Ref, most open ocean regions in the Atlantic and
Pacific show lower surface concentrations in Coast (not shown).
However, even in deep open ocean waters (water depths > 1500 m),
surface bromoform concentrations reach 10–30 % of the coastal value
in 10 % of the model grid cells (Figs. and c, f).
Thus, in the North Atlantic,
downstream of high coastal production a considerable fraction of open ocean
surface concentrations may be attributed to lateral transport of bromoform.
Mean surface bromoform concentrations (pmolL-1) in
experiment Equi in
boreal winter (a) and boreal summer (d) and the percentage
difference
(e.g. 100⋅Coast-EquiEqui) of
experiment Coast (b, e) and 100⋅Coast-Equi80pmolL-1 (c, f) in the same
season.
Histograms of 100⋅Coast-Equi80pmolL-1
surface concentrations [%] for different local water depths in the Atlantic Ocean (a), Arctic
Ocean (b), Pacific and Indian Ocean (c), and Southern Ocean (d).
Evaluation of simulated surface concentrations
The evaluation of simulated surface concentrations is clustered
regionally; that is, the Atlantic, the Pacific, the Southern Ocean, and
the Arctic Ocean are discussed separately.
Atlantic Ocean
Simulated surface concentrations in the Atlantic show a distinct spatial and
temporal pattern. The temporal coverage of bromoform observations does not
allow for an extensive evaluation of the temporal evolution of bromoform
concentrations. The spatial coverage, however, is high enough to compare
spatial, in particular latitudinal, features of the CHBr3
distribution. Data from three cruises allow us to evaluate the latitudinal gradient in the Atlantic: Polarstern cruise Blast 2
, Polarstern cruise ANT X/1
, and Polarstern cruise ANT XVII/1
. Blast 2 (Fig. S6) and ANT X/1 (Fig. S14) cross the Atlantic from the northeast (off the European continent and North Africa) to South America in boreal autumn (October, November). The cruise ANT XVII/1
leads along the African coast from the subtropical North to the South Atlantic in August (Fig. S24). Roughly, both simulated
and observed concentrations in autumn show high bromoform levels in the
extratropics (3–10 pmolL-1), a decrease towards the subtropics
(approximately 1 pmolL-1), and a peak at the Equator
(approximately 2 pmolL-1, Figs. S14 and S24). In comparison to
the Blast 2 cruise , the
general distribution pattern is well represented in all experiments with an open
ocean bromoform source, but the model overestimates the peak at the Equator
(factor of 1.35–1.8 between model and observation), concentrations at
the secondary peak at 10–20∘ N (factor of 1.95–2.6 between model
and observation), and concentrations close to the Patagonian Shelf (factor of
2–3; Fig. S6). There are observations of bromoform at the Equator in the
same season that show higher bromoform levels (8–14 pmolL-1;
). Both maxima (at the Equator and in the
subtropics) are caused by spatial primary production maxima triggered by
nitrate availability. Our parameterization of CHBr3 production
strongly relies on the quality of simulated spatio-temporal distribution of
primary production. Primary production is not a primary target parameter of
HAMOCC, which is designed to capture global features of the carbon cycle; for example, it is configured to reproduce realistic organic carbon export rates. We
simulate a global net primary productivity (NPP) of
59.3 GtCyr-1, which is in the range of published estimates
(e.g. 52 GtCyr-1 – ; NPP = 51±10 GtCyr-1 – ). Observations of primary production are not
available from the ship cruises when CHBr3 was measured. Therefore we
compare the simulated primary production to a NPP product derived from
satellite-based ocean colour data (details in Sect. ) to evaluate
bromoform production in more detail. Indeed, the simulated primary production
exceeds the observed NPP in locations of equatorial upwelling in boreal
summer (Fig. S3f). However, direct comparison along the ship track of Blast 2
indicates that this overestimation is just slightly higher than the observed
maximum (650 vs. 590 mgCm-2d-1, Fig. S7). The secondary
maximum close to 15∘ N is within the range of observed primary
production. This indicates either that conditions during the cruise are not
captured in this satellite-based estimate or that the implementation of the
production process as a linear function of plankton growth does not fully
capture characteristics of bromoform production. Furthermore, our model
experiments are designed to reflect present-day conditions in the open ocean
rather than to represent historic conditions. In the experiments with
a reduced bromoform production rate (Half, Dia,
NDia), the simulated bromoform concentrations
(2–2.7 pmolL-1 vs. 3.6 pmolL-1 in Ref) in
the northern subtropical Atlantic are slightly closer to observations, which
are around 1.0 pmolL-1. This is also true when looking into the
broader latitudinal bands (Fig. S5); in all bands of
50∘ S–20∘ N and 40–60∘ N, bromoform seems to be
better represented with a reduced production rate. The comparison between
other individual ship cruises, e.g. MSM 18/3 (Fig. S12) and DRIVE
(Fig. S10), shows that this method (reduction of the production
ratio) does not improve uniformly the model results. Ideally, simulated primary
productivity, production rate, and even species composition need to reflect the
conditions during the cruise to obtain the best possible representation of
bromoform distribution patterns.
Pacific
To evaluate bromoform in the Pacific we closer look at data from four cruises
in the eastern Pacific (Blast 1, Gas Ex 98, Phase 1-04, RB-99-06:
Figs. S26–S33; ) and one cruise in the western Pacific
TransBrom: Figs. S34–35;; please that note overlaps exist. Simulated
concentrations in the northwestern Pacific in autumn represent observations
during RB-99-06 very well (between 2 and 10 pmolL-1), apart from
underestimations close to the coast (Fig. S32). In spring–summer, concentrations in
the model along the same track (Gas Ex 98) are 3 times
higher than observations at some locations (Fig. S28), likely because primary production is
overestimated by the model (Fig. S29). Similar mismatches of simulated and
observed concentrations due to too high primary production in the model also show
up in the equatorial Pacific when comparing to Phase 1-04 (Figs. S30,
S31) in spring/early summer. In the eastern tropical Pacific, overestimations
of the primary production can be linked to a commonly known weakness of most
biogeochemistry models, the so-called “nutrient trapping” in the equatorial
Pacific details in, where too high nutrient
concentrations at the surface lead to too high primary and export production.
For both the northern and the equatorial eastern Pacific, bromoform
concentrations in autumn and winter match observations well (Blast 1,
Fig. S26; RB-99-06, Fig. S32). As the CHBr3 concentrations are overestimated in spring
during highest bromoform production, the underestimation
in later months indicates that the too strong source is compensated for by
a strong sink, e.g. strong outgassing. In the western Pacific only data from
the TransBrom cruise are available to compare simulated and observed
concentrations. Simulated bromoform concentrations are almost identical in
all experiments and closely match observations in the open ocean. Close to
the Indonesian Shelf, simulated concentrations are underestimated compared to
observations, probably because of macroalgae or other coastal sources, which
are not implemented in the model.
Southern Ocean and Arctic
The comparison of HAMOCC-simulated primary production to the that
derived by the VGPM model shows that NPP is overestimated in austral
summer (Fig. S3) along several ship tracks (ADOX, Figs. S44–45; CLIVAR01, Figs. S40–41; SWEDARP, Figs. S36–37). The representation of primary production by satellite-based
estimates (including VGPM) is poor in the Southern Ocean
. However, the overestimation of NPP
could also indicate shortcomings of the biogeochemistry model,
e.g. that iron limitation is not strong enough, as iron is the
limiting nutrient for phytoplankton growth in this region or that there is too
strong mixing in the physical model. For the Southern Ocean it is
difficult to directly conclude from deviations between simulated and
observed NPP about the quality of simulated
bromoform. For example, overestimations in NPP do not always go in line with
an overestimation of bromoform concentrations (e.g. S44 for
ADOX). Apparently other parameters such as mixing have a strong impact
on concentration patterns, too. This can be also seen for SWEDARP
(Fig. S36; ), where bromoform concentrations do
not follow the pattern of primary productivity or chlorophyll in both
model results and observations. However, there are also examples of
a good model representation of observed bromoform concentrations and
primary production, i.e. for BLAST3 (February–April, Figs. S38–39) and CLIVAR01
(October–November, Figs. S40–41; 140–250∘ E).
As noted in Sect. , production of bromoform within sea
ice by ice algae is not represented in the model. Therefore, open
ocean CHBr3 concentrations downstream of melting sea ice and
close to sea ice are likely to be underestimated. Furthermore, the
contribution of uptake from the atmosphere to bromoform sources is
large in polar regions (e.g. around the Antarctic Peninsula) and
atmospheric boundary conditions rely on extrapolation of very sparse
data. Therefore, we can not expect to simulate seawater bromoform
concentrations in polar regions correctly. For this reason, the
evaluation of Southern Ocean bromoform concentrations is only of
preliminary nature and the detailed evaluation of bromoform
concentrations in the Arctic is omitted. However, for completeness
a figure showing bromoform concentrations in the Arctic compared to
observations from a ship cruise in June 2002 can be found in the Supplement
(Figs. S52 and S53).
Summary
Overall the model is capable of representing large-scale features of
observed bromoform concentrations, considering that no tuning of the
model is performed. Discrepancies mostly arise from regionally weak
representation of primary production or insufficient representation of
environmental conditions during the ship cruises in this
non-historical present-day model configuration. Note that we compare
monthly mean model output to observations along ship tracks, which
usually lasted a couple of weeks. We refrain from analysing temporally
higher resolved model output, because the atmospheric bromoform
concentrations used in the gas exchange do not resolve such high
temporal variability. Differences among the model experiments are
often smaller than differences between model results and
observations. The best match with observations is achieved when either
reducing the bulk bromoform production rate or considering different
production rates for different phytoplankton groups (Fig. , S4–5). A reduced
diatom bromoform production ratio slightly improves the representation of the bromoform
concentrations in the Southern Hemisphere, while the concentrations in the
Northern Hemisphere are better depicted for a reduced non-diatom bromoform
production ratio.
Box-and-whisker plot of simulated and observed surface ocean bromoform concentrations
(pmolL-1). Box widths are determined by the 25th and 75th percentiles of data
within each 10∘ latitude box, outliers (grey) are located outside 1.5 times the
differences of the percentiles, and the middle line of each box shows the median. Simulated
concentrations are averaged over one grid cell around the location of observations. Different colours
denote different experiments (Ref: blue; Seas-at: red; Half: green;
Dia: pale purple; NDia: pale red); observations are shown in black.
Gas exchange with the atmosphere
Simulated bromoform emissions follow a pronounced seasonal cycle, dictated by
seawater concentrations and meteorological conditions. High emissions (>1200 pmolm-2h-1) occur in regions of high bromoform
production, i.e. in boreal winter (DJF) in the Southern Ocean (Fig. a) and in boreal summer (JJA) in the North Pacific and the Atlantic Ocean (Fig. e). In contrast, in the oligotrophic subtropical gyres, bromoform emissions are low but still positive, i.e. into air (<50 pmolm-2h-1). Tropical upwelling regions always show
high emissions, as bromoform production is high all year. In the Southern
Ocean and the northern North Atlantic, emissions in local winter seasons, as
well as Arctic emissions, are characterized by net uptake from the atmosphere. In the
latter two regions this feature also persists in the annual mean. In the
Southern Ocean, high emissions in summer compensate for the uptake in winter,
and over the year the ocean is a net source to the atmosphere. Also, at the
global scale, the open ocean is a bromoform source to the atmosphere, and
delivers approximately 0.9 GmolBryr-1 (Table ).
For the North Atlantic and the Arctic Ocean,
the experiment Coast suggests that coastal sources
could enhance oceanic concentrations and counteract the undersaturation of the ocean.
Furthermore, in the Arctic and Southern Ocean,
bromoform production in sea ice could have a similar effect
with an increased sea–air flux, a feature that is
also not resolved in the model. Both mechanisms are currently
not included but would lead to higher simulated global bromoform emissions. In addition,
the seasonal reversal of gas exchange is also strongly influenced by the
atmospheric boundary conditions. Thus, it is important to
choose these carefully for simulating realistic bromoform
emissions with a stand-alone ocean model.
Generally, simulated emissions are higher in the extratropics of the Southern Hemisphere than those of the Northern Hemisphere (Fig. ). Note that we choose a different unit here
than in the residual discussion of gas exchange to ease the comparison with
the recent evaluation of CHBr3 emission inventories by
, who showed a similar figure. Zonal maxima are
higher than 0.8×10-13 kgm-2s-1 in the southern
extratropics compared to 0.4×10-13 kgm-2s-1 in the
tropics (not shown). This pattern is different from the distribution often used in atmospheric chemistry modelling that shows largest emissions from the tropical oceans
. Lowest
emissions are simulated in experiments Half and Dia due to
the lower bromoform production (Table ). In these experiments the
relative contribution of the Northern and Southern Hemisphere to total
emissions is similar. Compared to Half, emissions in the lower
latitudes in Dia are higher because of low diatom presence.
Mean bromoform sea–air flux (pmolm-2h-1) in
experiment Ref in
boreal winter (a) and boreal summer (e) and the percentage
difference
(e.g. 100⋅Seas-at-RefRef) of
Seas-at
(b, f), Half (c, g), and Dia (d, h) in the same
season.
Zonal median of bromoform sea–air flux (kgm-2s-1); mean of JJA (blue),
DJF (black), MAM (green), and SON (orange); and annual mean (dashed grey). Results are from
Ref (a), Seas-at (b), and Half (c), and
Dia (d).
Previously reported and simulated global annual bromoform net emissions
(GmolBryr-1) from the ocean.
Source type
Lit. value
Reference
Open ocean
10.01 (3–22)
10.26
4.75–7.06
Global ocean
10.0
Open ocean
1.9
10.3
Tropics
4.35
Global ocean
5.31
Open ocean
3.19
6.33
Global ocean
2.49
(OLS)
Global ocean
1.5
(RF)
Global ocean
3.5
Open ocean
0.9
This study: Ref, Seas-at (net flux)
Previous estimates of global annual marine bromoform emissions range from 1.5
to 10.3 GmolBryr-1 (Table ), considering either both coastal and
open ocean regions or treating them individually. Most of these global
estimates except are derived from indirect
methods. This means that either bromoform measured in the marine boundary
layer during ship cruises is used to calculate local fluxes which are
extrapolated to the global scale
or that emissions needed as
boundary conditions in atmospheric modelling studies are constrained to lead
to least deviation of simulated air concentrations from observations
e.g..
In the latter so-called “top-down” approach,
the estimates are all based on the same concept; the
global ocean is split into latitudinal bands for which different emissions
are applied. However, the number and extent of these zones,
the treatment of the tropics and coastal regions, and the temporal resolution
considered differ among the different studies. As mentioned above, performed
a detailed evaluation of global bromoform emission inventories for
atmospheric modelling. They include three top-down inventories , as well as the bottom-up (based on
observations
in air and water) inventory by based on
the OLS method. The only inventory used in the study of Hossaini et al. (2013) that considers temporal variability
in the emissions is the one by Ordóñez et al. (2012). They
indirectly resolve seasonally varying bromoform fluxes within
the tropics (±20∘) because
they relate air–sea fluxes to satellite-based chlorophyll
concentrations
which are in turn temporally variable. are,
however, able to reproduce most of the seasonality of bromoform atmospheric mixing ratios
even with the temporally invariant emissions. They argue that this is presumably because the seasonality
is driven by photolytic degradation in air. The seasonality in our simulated
emissions sometimes encompasses a variation of more than a factor of 2, in
particular in the productive extratropical regions. The impact of
this seasonality on the evolution of atmospheric mixing ratios needs to be
tested in dynamic ocean–atmosphere coupling.
In , good agreement between observed and simulated
atmospheric mixing ratios, in particular within the tropics, could be achieved
when using the emission inventory by , which was
the lowest of the previous estimates (Table ). Therefore, we will focus on the comparison of our results with those of that inventory. Our open ocean emissions are even lower than this
observation-based estimate by . Our approaches
differ in the oceanic concentrations that drive the saturation anomaly. Our
simulated ocean concentrations represent the observations used in the
extrapolation by well, with a tendency towards
overestimating seawater concentrations (Sect. ). Global fluxes
are lower in our approach for several reasons. First, our model
considers the seasonality of oceanic concentrations in contrast to . Simulated concentrations
match observations well; however, they are often lower in the winter season,
for which observations are rare. Furthermore, we do not include (and do not
intend to represent) coastal emissions, which are generally higher than open
ocean emissions. Another reason why our global emissions are lower than the
ones in is that their high emissions often occur
in locations where no data exist as a result from the extrapolation
method used, e.g. in the northern North Atlantic and in the subtropical eastern South
Pacific. Our emissions indicate uptake from the atmosphere in the northern North
Atlantic and the Gulf of
Alaska and the Bering Sea in boreal winter (DJF) but fluxes into air in all
other seasons, which leads to lower overall emissions. The only ship cruise
in the subtropical eastern South Pacific is Blast 1, which does not show high
concentrations close to 30∘ S. Another region where our simulated
fluxes deviate from the previous estimates is the subtropics. As primary
production is low in subtropical gyres due to the nutrient limitation of
plankton growth, CHBr3 emissions are also low, approximately one-third of the emissions by (compare
Fig. with Fig. 3 in ). However, modelled concentrations match observations well, i.e. in
the subtropical Atlantic
(e.g. compared to data from the Blast 2 cruise
Fig. S6;, or data from the M60 cruise
(Fig. S18;)). Thus, as gas exchange is the primary sink
of ocean bromoform in this region, we have confidence in
the simulated emissions.