Introduction
Nitrous oxide (N2O) is a gaseous compound responsible for two key
feedback mechanisms within the Earth's climate. First, it acts as a
long-lived and powerful greenhouse gas (Prather et al., 2012)
ranking third in anthropogenic radiative forcing after carbon
dioxide (CO2) and methane (CH4) (Myhre et al., 2013). Secondly,
the ozone (O3) layer depletion in the future might be driven mostly by
N2O after the drastic reductions in CFCs emissions start to show their
effect on stratospheric chlorine levels (Ravishankara et al., 2009). The
atmospheric concentration of N2O is determined by the natural balance
between sources from land and ocean and the destruction of N2O in the
atmosphere largely by photolysis (Crutzen, 1970; Johnston, 1971). The
natural sources from land and ocean amount to ∼ 6.6 and 3.8 TgN yr-1,
respectively (Ciais et al., 2013). Anthropogenic activities
currently add an additional 6.7 TgN yr-1 to the atmosphere, which has
caused atmospheric N2O to increase by 18 % since preindustrial times
(Ciais et al., 2013), reaching 325 ppb in the year 2012 (NOAA ESRL Global
Monitoring Division, Boulder, Colorado, USA, http://esrl.noaa.gov/gmd/).
Using a compilation of 60 000 surface ocean observations of the partial
pressure of N2O (pN2O), Nevison et al. (2004) computed a global
ocean source of 4 TgN yr-1, with a large range of uncertainty from 1.2
to 6.8 TgN yr-1. Model-derived estimates also differ widely, i.e.,
between 1.7 and 8 TgN yr-1 (Nevison et al., 2003; Suntharalingam et
al., 2000). These large uncertainties are a consequence of too few
observations and of poorly known N2O formation mechanisms, reflecting a
general lack of understanding of key elements of the oceanic nitrogen cycle
(Gruber and Galloway, 2008; Zehr and Ward, 2002), and of N2O in
particular (e.g., Zamora et al., 2012; Bange et al., 2009; Freing et al.,
2012). A limited number of interior ocean N2O
observations were made available only recently (Bange et al., 2009), but
they contain large temporal and spatial gaps. Information on the rates of
many important processes remains insufficient, particularly in natural
settings. There are only a few studies from a limited number of specific
regions such as the Arabian Sea, central and North Pacific, Black Sea, the
Bedford Basin and the Scheldt estuary, which can be used to derive and test
model parameterizations (Mantoura et al., 1993; Bange et al., 2000; Elkins
et al., 1978; Farias et al., 2007; Frame and Casciotti, 2010; Westley et
al., 2006; Yoshida et al., 1989; Punshon and Moore, 2004; de Wilde and de
Bie, 2000).
N2O is formed in the ocean interior through two major pathways and
consumed only in oxygen minimum zones (OMZs) through denitrification (Zamora et
al., 2012). The first production pathway is associated with nitrification
(conversion of ammonia, NH4+, into nitrate, NO3-), and
occurs when dissolved O2 concentrations are above 20 µmol L-1.
We subsequently refer to this pathway as the high-O2 pathway.
The second production pathway is associated with a series of processes when
O2 concentrations fall below ∼ 5 µmol L-1 and
involves a combination of nitrification and denitrification (hereinafter
referred to as low-O2 pathway) (Cohen and Gordon, 1978; Goreau et al.,
1980; Elkins et al., 1978). As nitrification is one of the processes
involved in the aerobic remineralization of organic matter, it occurs nearly
everywhere in the global ocean with a global rate at least one order of
magnitude larger than the global rate of water column denitrification
(Gruber, 2008). One of the main reasons is that denitrification in the water column is
limited to the OMZs, which occupy only a few percent of the total ocean
volume (Bianchi et al., 2012). This is also the only place in the water
column where N2O is being consumed.
The two production pathways have very different N2O yields, i.e.,
fractions of nitrogen-bearing products that are transformed to N2O. For
the high-O2 pathway, the yield is typically rather low, i.e., only
about one in several hundred molecules of ammonium escapes as N2O (Cohen
and Gordon, 1979). In contrast, in the low-O2 pathway, and particularly
during denitrification, this fraction may go up to as high as 1 : 1, i.e.,
that all nitrate is turned into N2O (Tiedje, 1988). The relative
contribution of the two pathways to global N2O production is not well
established. Sarmiento and Gruber (2006) suggested that the two may be of
equal importance, but more recent estimates suggest that the high-O2
production pathway dominates global oceanic N2O production (Freing et
al., 2012).
Two strategies have been pursued in the development of parameterizations for
N2O production in global biogeochemical models. The first approach
builds on the importance of the nitrification pathway and its close
association with the aerobic remineralization of organic matter. As a result
the production of N2O and the consumption of O2 are closely tied
to each other, leading to a strong correlation between the concentration of
N2O and the apparent oxygen utilization (AOU). This has led to the
development of two sets of parameterizations, one based on concentrations,
i.e., directly as a function of AOU (Butler et al., 1989), and the other
based on the rate of oxygen utilization, i.e., OUR (Freing et al., 2009).
Additional variables have been introduced to allow for differences in the
yield, i.e., the ratio of N2O produced over oxygen consumed, such as
temperature (Butler et al., 1989) or depth (Freing et al., 2009). In the
second approach, the formation of N2O is modeled more mechanistically
and tied to both nitrification and denitrification by an O2-dependent
yield (Suntharalingam and Sarmiento, 2000; Nevison et al., 2003; Jin and
Gruber, 2003). Since most models do not include nitrification explicitly,
the formation rate is actually coupled directly to the remineralization of
organic matter. Regardless of the employed strategy, all parameterizations
depend to first order on the amount of organic matter that is being
remineralized in the ocean interior, which is governed by the export of
organic carbon to depth. The dependence of N2O production on oxygen
levels and on other parameters such as temperature plays a secondary role. This has important implications not only for the modeling of the
present-day distribution of N2O in the ocean but also for the
sensitivity of marine N2O to future climate change.
Over this century, climate change will perturb marine N2O formation in
multiple ways. Changes in productivity will drive changes in the export of
organic matter to the ocean interior (Steinacher et al., 2010; Bopp et al.,
2013) and hence affect the level of marine nitrification. Ocean warming
might change the rate of N2O production during nitrification (Freing et
al., 2012). Changes in carbonate chemistry (Bindoff et al., 2007) might
cause changes in the C : N ratio of the exported organic matter (Riebesell et
al., 2007), altering not only the rates of nitrification but also the ocean
interior oxygen levels (Gehlen et al., 2011). Finally, the expected general
loss of oxygen (Keeling et al., 2010; Cocco et al., 2013; Bopp et al., 2013)
could substantially affect N2O production via both nitrifier
denitrification and classic denitrification.
Ocean biogeochemical models used for IPCC's Fourth Assessment Report
estimated a decrease between 2 and 13 % in primary production (PP)
under the business-as-usual high-CO2 concentration scenario A2
(Steinacher et al., 2010). A more recent multi-model analysis based on the
models used in IPCC's Fifth Assessment Report also suggests a large
reduction of PP down to 18 % by 2100 for the RCP8.5 scenario (Bopp et al.,
2013). In these simulations, the export of organic matter is projected to
decrease between 6 and 18 % in 2100 (Bopp et al., 2013), with a
spatially distinct pattern: in general, productivity and export are
projected to decrease at mid- to low latitudes in all basins, while
productivity and export are projected to increase in the high latitudes and
in the South Pacific subtropical gyre (Bopp et al., 2013). A wider spectrum
of responses was reported regarding changes in the ocean oxygen content.
While all models simulate decreased oxygen concentrations in
response to anthropogenic climate change (by about 2 to 4 % in 2100), and
particularly in the mid-latitude thermocline regions, no agreement exists
with regard to the hypoxic regions, i.e., those having oxygen levels below
60 and 5 µmol L-1 (Cocco et al., 2013; Bopp et al., 2013). Some models
project these regions to expand, while others project a contraction. Even
more divergence in the results exists for the suboxic regions, i.e., those
having O2 concentrations below 5 µmol L-1 (Keeling et al.,
2010; Deutsch et al., 2011; Cocco et al., 2013; Bopp et al., 2013), although
the trend for most models is pointing towards an expansion. At the same
time, practically none of the models is able to correctly simulate the
current distribution of oxygen in the OMZ (Bopp et al., 2013). In summary,
while it is clear that major changes in ocean biogeochemistry are looming
ahead (Gruber, 2011), with substantial impacts on the production and
emission of N2O, our ability to project these changes with confidence
is limited.
In this study, we explore the implications of these future changes in ocean
physics and biogeochemistry on the marine N2O cycle, and make
projections of the oceanic N2O emissions from year 2005 to 2100 under
the high-CO2 concentration scenario RCP8.5. We analyze how changes in
biogeochemical and physical processes such as net primary production (NPP),
export production and vertical stratification in this century translate into
changes in oceanic N2O emissions to the atmosphere. To this end, we use
the NEMO-PISCES ocean biogeochemical model, which we have augmented with two
different N2O parameterizations, permitting us to evaluate changes in
the marine N2O cycle at the process level, especially with regard to
production pathways in high- and low-oxygen regimes. We demonstrate that
while future changes in the marine N2O cycle will be substantial, the
net emissions of N2O appear to change relatively little – i.e., they are
projected to decrease by about 10 % in 2100.
Methodology
NEMO-PISCES model
Future projections of the changes in the oceanic N2O cycle were
performed using the PISCES ocean biogeochemical model (Aumont and Bopp,
2006) in offline mode with physical forcings derived from the IPSL-CM5A-LR
coupled model (Dufresne et al., 2013). The horizontal resolution of NEMO
ocean general circulation model is 2∘ × 2∘ cos
∅
(∅ being the latitude) with enhanced latitudinal resolution at the
Equator of 0.5∘. PISCES is a biogeochemical model with five
nutrients (NO3, NH4, PO4, Si and Fe), two phytoplankton
groups (diatoms and nanophytoplankton), two zooplankton groups (micro- and
mesozooplankton) and two non-living compartments (particulate and dissolved
organic matter). Phytoplankton growth is limited by nutrient availability
and light. Constant Redfield C : N : P ratios of 122 : 16 : 1 are assumed (Takahashi
et al., 1985), while all other ratios, i.e., those associated with
chlorophyll, iron and silicon (Chl : C, Fe : C and Si : C), vary dynamically.
N2O parameterizations in PISCES
We implemented two different parameterizations of N2O production in
NEMO-PISCES. The first one, adapted from Butler et al. (1989), follows the
oxygen consumption approach, with a temperature-dependent modification of
the N2O yield (P.TEMP). The second one is based on Jin and Gruber (2003)
(P.OMZ), following the more mechanistic approach, i.e., it considers
the different processes occurring at differing oxygen concentrations in a
more explicit manner.
The P.TEMP parameterization assumes that the N2O production is tied to
nitrification only. This is
implemented in the model by tying the N2O formation in a linear manner
to O2 consumption. A small temperature dependence is added to the yield
to reflect the potential impact of temperature on metabolic rates. The
production term of N2O, i.e., JP.TEMP(N2O), is then mathematically
formulated as
JP.TEMP(N2O)=(γ+θT)J(O2)consumption,
where γ is a background yield (0.53 × 10-4 mol N2O
(mol O2)-1 consumed), θ is the temperature dependency of γ
(4.6 × 10-6 mol N2O (mol O2)-1 K-1), T is temperature (K)
and J(O2)consumption is the sum of all biological O2 consumption
terms within the model. The same ratio between constants γ and
θ is used in the model as in the original formulation from Butler et al. (1989). Although this parameterization is very simple, a recent analysis
of N2O observations supports such an essentially constant yield, even
in the OMZ of the eastern tropical Pacific (Zamora et al., 2012).
The P.OMZ parameterization, formulated after Jin and Gruber (2003), assumes
that the overall yield consists of a constant background yield and an oxygen-dependent yield. The former is presumed to represent the N2O production
by nitrification, while the latter is presumed to reflect the enhanced
production of N2O at low oxygen concentrations, in part driven by
denitrification, but possibly including nitrification as well. This
parameterization includes the consumption of N2O in suboxic conditions.
This gives
JP.OMZ(N2O)=(α+βf(O2))J(O2)consumption-kN2O,
where α is, as in Eq. (1), a background yield (0.9 × 10-4 mol N2O (mol O2)-1 consumed); β is a yield parameter that
scales the oxygen-dependent function (6.2 × 10-4);
f(O2) is a unitless oxygen-dependent step-like modulating function, as
suggested by laboratory experiments (Goreau et al., 1980) (Fig. S1,
Supplement); and k is the first-order rate constant of
N2O consumption close to anoxia (zero otherwise). For k, we have adopted
a value of 0.138 yr-1 following Bianchi et al. (2012) while we set the
consumption regime for O2 concentrations below 5 µmol L-1.
The constant α is on the same order of magnitude as the one proposed
by Jin and Gruber (2003), while β is two orders of magnitude smaller. The use
of the original value would result in a significant increase in N2O
production associated with OMZs and, hence, in a departure from the
assumption of dominant nitrification.
The P.OMZ parameterization allows us to independently quantify the
N2O formation pathways associated with nitrification and those
associated with low oxygen concentrations (nitrification/denitrification)
and their evolution in time over the next century. Specifically, we consider
the source term αJ(O2)consumption as that associated with
the nitrification pathway, while we associated the source term βf(O2) J(O2)consumption
with the low-oxygen processes (Fig. S2, Supplement).
N2O production is inhibited by light in the model, and therefore
N2O production in P.TEMP and P.OMZ parameterizations only occurs below
a fixed depth of 100 m.
N2O sea-to-air flux (in mgN m-2 yr-1) from
(a) P.TEMP parameterization averaged for the 1985 to 2005 time period
in the historical simulation, (b) P.OMZ parameterization over the
same time period, (c) data product of Nevison et al. (2004) and
(d) latitudinal N2O sea-to-air flux
(in TgN degree-1 yr-1) from Nevison et al. (2004) (black).
P.TEMP: blue; P.OMZ: red.
We employ a standard bulk approach for simulating the loss of N2O to
the atmosphere via gas exchange. We use the formulation of Wanninkhof et al. (1992) for estimating the gas transfer velocity, adjusting the Schmidt
number for N2O and using the solubility constants of N2O given by
Weiss and Price (1980). We assume a constant atmospheric N2O
concentration of 284 ppb in all simulations to explore future changes
inherent to ocean processes without feedbacks due to changes in the
atmosphere.
Experimental design
NEMO-PISCES was first spun up over 3000 years using constant
preindustrial dynamical forcings fields from IPSL-CM5A-LR (Dufresne et al.,
2013) without activating the N2O parameterizations. This spin-up phase
was followed by a 150-year-long simulation, forced by the same dynamical
fields now with N2O production and N2O sea-to-air flux embedded.
The N2O concentration at all grid points was prescribed initially as 20 nmol L-1, which is consistent with the MEMENTO database average value
of 18 nmol L-1 below 1500 m (Bange et al., 2009). During the 150-year
spin-up, we diagnosed the total N2O production and N2O sea-to-air
flux and adjusted the α, β, γ and θ parameters in
order to achieve a total N2O sea-to-air flux in the two
parameterizations at equilibrium close to 3.85 TgN yr-1 (Ciais et
al., 2013). In addition, the relative contribution of the high-O2
pathway in the P.OMZ parameterization was set to 75 % of the total
N2O production based on Suntharalingam et al. (2000), where a
sensitivity model analysis on the relative contribution of high- and
low-O2 production pathways showed that a higher contribution of
nitrification (75 %) than denitrification (25 %) achieved the best model
performance compared to the data product from Nevison et al. (1995). P.TEMP
can be considered as 100 % nitrification, testing in this way the
hypothesis of nitrification as the dominant pathway of N2O production
on a global scale. Nitrification could contribute with up to 93 % of the
total production based on estimations considering N2O production along
with water mass transport (Freing et al., 2012).
Projections in NEMO-PISCES of historical (from 1851 to 2005) and future (from
2005 to 2100) simulated periods were done using dynamical forcing fields from
IPSL-CM5A-LR. These dynamical forcings were applied in an offline mode – i.e.,
monthly means of temperature, velocity, wind speed or radiative flux were
used to force NEMO-PISCES. Future simulations used the business-as-usual
high-CO2 concentration scenario (RCP8.5) until year 2100. Century-scale model
drifts for all the biogeochemical variables presented, including N2O
sea-to-air flux, production and inventory, were removed using an additional
control simulation with IPSL-CM5A-LR preindustrial dynamical forcing fields
from year 1851 to 2100. Despite the fact that primary production and the
export of organic matter to depth were stable in the control simulation, the
air–sea N2O emissions drifted (an increase of 5 to 12 % in 200 years
depending on the parameterization) due to the short spin-up phase (150 years)
and the choice of the initial conditions for N2O concentrations.
Present-day oceanic N2O
Contemporary N2O fluxes
The model simulated air–sea N2O emissions show large spatial contrasts,
with flux densities varying by one order of magnitude, but with relatively
small differences between the two parameterizations (Fig. 1a and b). This
is largely caused by our assumption that the dominant contribution (75 %)
to the total N2O production in the P.OMZ parameterization is the
nitrification pathway, which is then not so different from the P.TEMP
parameterization, where it is 100 %. As a result, the major part of
N2O is produced in the subsurface via nitrification, contributing
directly to imprint changes into the sea-to-air N2O flux without
significant meridional transport (Suntharalingam and Sarmiento, 2000).
Elevated N2O emission regions (> 50 mgN m-2 yr-1)
are found in the equatorial and eastern tropical Pacific, in
the northern Indian ocean, in the northwestern Pacific, in the North
Atlantic and in the Agulhas Current. In contrast, low fluxes (< 10 mgN m-2 yr-1) are simulated in the Southern Ocean, Atlantic and
Pacific subtropical gyres, and southern Indian Ocean. The large-scale
distribution of N2O fluxes is coherent with Nevison et al. (2004) (Fig. 1c).
This comes as a natural consequence of the relatively high contribution
of nitrification, and hence hotspots of N2O emissions are associated
with regions where higher export of organic matter occurs in the model.
There are, however, several discrepancies between the model and the data
product. At high latitudes, the high N2O emissions observed in the
North Pacific are not well represented in our model, with a significant
shift towards the western part of the Pacific Basin, similar to other
modeling studies (e.g., Goldstein et al., 2003; Jin and Gruber, 2003). The
OMZ in the North Pacific, located at approximately 600 m deep, is
underestimated in the model due to the deficient representation of the
meridional overturning circulation (MOC) in the North Pacific in global
ocean biogeochemical models, which in turn might suppress areas of low
oxygenation and therefore one potential N2O source. Discrepancies between
model and observations also occur in the Southern Ocean, a region whose role
in global N2O fluxes remains debated due to the lack of observations
and the occurrence of potential artifacts due to interpolation techniques
reflected in data products such as that from Nevison et al. (1995) (e.g.,
Suntharalingam and Sarmiento, 2000; Nevison et al, 2003). The model also
overestimates N2O emissions in the North Atlantic. The emphasis put on
the nitrification pathway suggests that hotspots of carbon export are at the
origin of elevated concentrations of N2O in the subsurface. N2O is
quickly outgassed to the atmosphere, leading to such areas of high N2O
emissions in the model.
Model–data discrepancies can be seen as a function of latitude in Figure 1d.
The modeled N2O flux maxima peak at around 40∘ S, i.e.,
around 10∘ north to that estimated by Nevison et al. (2004),
although Southern Ocean data must be interpreted with caution. In the
Northern Hemisphere the stripe in the North Pacific in not captured by the
model, splitting the flux from the 45∘ N band into two peaks at
38 and 55∘ N.
Contemporary N2O concentrations and the relationship to
O2
The model results at present day were evaluated against the MEMENTO database
(Bange et al., 2009), which contains about 25 000 measurements of colocated
N2O and dissolved O2 concentrations. Table 1 summarizes the
standard deviation and correlation coefficients for P.TEMP and P.OMZ compared
to MEMENTO. The standard deviation of the model output is very similar to
MEMENTO, i.e., around 16 nmol L-1 of N2O. However, the
correlation coefficients between the sampled data points from MEMENTO and
P.TEMP/P.OMZ are 0.49 and 0.42, respectively. Largest discrepancies are found
mostly in the deep ocean and in the OMZs.
Standard deviation and correlation coefficients between P.TEMP and
P.OMZ parameterizations with respect to MEMENTO database observations (Bange
et al., 2009).
P.TEMP
P.OMZ
OBS
Standard deviation (in nmol N2O L-1)
12
18
16
Correlation coefficient with obs.
0.49
0.42
–
Figure 2 compares the global average vertical profile of the observed
N2O against the results from the two parameterizations. The in situ
observations show three characteristic layers: the upper 100 m layer with low
(∼ 10 nmol L-1) N2O concentration due to gas
exchange keeping N2O close to its saturation concentration, the
mesopelagic layer, between 100 and 1500 m, where N2O is enriched via
nitrification and denitrification in the OMZs, and the deep ocean beyond
1500 m, with a relatively constant concentration of 18 nmol L-1 on
average. Both parameterizations underestimate the N2O concentration in
the upper 100 m, where most of the N2O is potentially outgassed to
the atmosphere. In the second layer, P.OMZ shows a fairly good agreement
with the observations in the 500 to 900 m band, whereas P.TEMP is too low by
∼ 10 nmol L-1. Below 1500 m, both parameterizations
simulate too high N2O compared to the observations. This may be caused
by the lack or underestimation of a sink process in the deep ocean, or by
the too high concentrations used to initialize the model, which persist due
to the rather short spin-up time of only 150 years.
The analysis of the model simulated N2O concentrations as a function of
model simulated O2 shows the differences between the two
parameterizations more clearly (Fig. 3a and b). Such a plot allows us to
assess the model performance with regard to N2O (Jin and Gruber, 2003),
without being subject to the strong potential biases introduced by the
model's deficiencies in simulating the distribution of O2. This is
particularly critical in the OMZs, where all models exhibit strong biases
(Cocco et al., 2013; Bopp et al., 2013). P.TEMP (Fig. 3a)
slightly overestimates N2O for dissolved O2 concentrations above
100 µmol L-1, and does not fully reproduce either the high
N2O values in the OMZs or the N2O depletion when O2 is almost
completely consumed. P.OMZ (Fig. 3b) overestimates the N2O
concentration over the whole range of O2, with particularly high values
of N2O above 100 nmol L-1 due to the exponential function used
in the OMZs. There, the observations suggest concentrations below 80 nmol L-1
for the same low O2 values, consistent with the linear trend
observed for higher O2, which seems to govern over most of the O2
spectrum, as suggested by Zamora et al. (2012). The discrepancy at low
O2 concentration may also stem from our choice of a too low N2O
consumption rate under essentially anoxic conditions. Finally, it should be
considered that most of the MEMENTO data points are from OMZs and therefore
N2O measurements could be biased towards higher values than the actual
open-ocean average, where our model performs better.
Global average depth profile of N2O concentration (in
nmol L-1) from the MEMENTO database (dots) (Bange et al., 2009). P.TEMP: blue; P.OMZ: red. Model parameterizations are averaged over the 1985 to
2005 time period from the historical simulation.
Caveats in estimating N2O using ocean biogeochemical models
The state variables upon which representation of N2O in models rely,
i.e., oxygen and export of carbon, are compared to the CMIP5 model ensemble
to put our analysis into the context of the current state-of-the-art model
capabilities. We focus here our analysis on suboxic waters (O2 < 5 µmol L-1) and export production. Whereas CMIP5
models tend to have large volumes of O2 concentrations in the suboxic
regime, it is not the case for our NEMO-PISCES simulation, which clearly
underestimates the volume of low-oxygen waters as compared to the oxygen-corrected World Ocean Atlas 2005 (WOA2005*) (Bianchi et al., 2012). The fact
that NEMO-PISCES forced by IPSL-CM5A-LR is highly oxygenated is confirmed by
Fig. 8, where the histogram of the full O2 spectrum of WOA2005* and
NEMO-PISCES is shown. The O2 distribution in the model shows a
deficient representation of the OMZs, with higher concentrations than those
from observations. The rest of the O2 spectrum is well represented in
our model.
The O2 distribution in the model (Fig. 9) shows a deficient
representation of the OMZs, with higher concentrations than those from
observations in WOA2005* and the other CMIP5 models. NEMO-PISCES is
therefore biased towards the high O2 production pathway of N2O due
to the modeled O2 fields.
When turning to the export of organic matter, NEMO-PISCES is close to the
CMIP5 average value of 6.9 PgC yr-1. The overall distribution of export
is also very similar to the CMIP5 model mean, and both show smaller values
than those from the data-based estimate of 9.84 PgC yr-1 from Dunne et
al. (2007) (Fig. 9).
Distribution of O2 concentration in the NEMO-PISCES 1985 to 2005
averaged time period (black) compared to the oxygen-corrected World Ocean
Atlas (red) from Bianchi et al. (2012). Interval widths are O2
concentrations at steps of 5 µmol L-1.
Averaged O2 concentration between 200 and 600 m depth
(in µmol L-1) (left) and export of carbon (in
mmolC m-2 d-1) (right) in (a) WOA2005* and Dunne et
al. (2007), (b) CMIP5 model mean historical simulations over the
1985–2005 time period and (c) NEMO-PISCES for the present
1985–2005 time period.
The uncertainties derived from present and future model projections can be
estimated using the spread in the CMIP5 model projection of export of
organic matter and assuming a linear response between nitrification (or
export) and N2O production in the subsurface, which is assumed to be
quickly outgassed to the atmosphere. In NEMO-PISCES, a decrease of 13 % in
export leads to a maximum decrease in N2O emissions of 12 % in the
P.OMZ scenario. Based on results by Bopp et al. (2013), changes in export of
carbon span -7 to -18 % in the CMIP5 model ensemble at the end of the
21st century and for RCP8.5. The spread would propagate to a similar range
in projected N2O emissions across the CMIP5 model ensemble. When these values are applied to present N2O emissions of 3.6 TgN yr-1,
uncertainties are then bracketed between -0.25 and -0.65 TgN yr-1.
Regarding the low-O2 pathway, a similar approach is not that straightforward. Zamora et al. (2012) found that a linear relationship between AOU
and N2O production might occur even at the OMZ of the eastern tropical Pacific. Zamora
et al. (2012) acknowledged the fact that the MEMENTO database includes N2O
advected from other regions and that mixing could play a relevant role,
smoothing the fit between N2O and AOU from exponential to linear.
However, Zamora et al. (2012) quoting Frame and Casciotti (2010), suggested
that regions where an exponential relationship in N2O production is
present might be rare and that other non-exponential N2O production
processes might occur. Therefore the plot they presented could describe
the actual linear relationship between N2O production and oxygen
consumption. Based on this hypothesis, we could refer again to the linear
relationship suggested in the high-O2 and export scenario. However, in
this case the CMIP5 model projections of changes in the hypoxic and suboxic
volumes differ substantially. Most models project an expansion of the OMZs
in the +2 to +16 % range in the suboxic volume (O2 < 5 µmol L-1). There are, however, models that project a slight
reduction of 2 %. Spatial variability in projections add to the spread
between CMIP5 models. These discrepancies suggest that uncertainties from
this spread must be interpreted with caution when estimating potential
future N2O emissions.
The use of O2 consumption as a proxy for the actual N2O production
therefore plays a pivotal role in the uncertainties in N2O model
estimations. Future model development should aim at the implementation of
mechanistic parameterizations of N2O production based on nitrification
and denitrification rates. Further, in order to determine accurate O2
boundaries for both N2O production and N2O consumption at the core
of OMZs, additional measurements and microbial experiments are needed. The
contribution of the high-O2 pathway that was considered in this model
analysis might be a conservative estimate. Freing et al. (2012) suggested
that the high-O2 pathway could be responsible of 93 % of the total
N2O production. Assuming that changes in the N2O flux are mostly
driven by N2O production via nitrification, it would suggest a larger
reduction in the marine N2O emissions in the future. However, the
mismatch between NEMO-PISCES and the Nevison et al. (2004) spatial
distribution of N2O emissions in the western part of the basins
suggests that changes in the future might not be as big as those projected
in the model in such regions. Changes would be then distributed more
homogeneously.
The model assumption neglecting N2O production in the upper 100 m avoids
one important source of uncertainty in estimating global oceanic N2O
fluxes. In the case of nitrification occurring in the euphotic layer, our results
would be facing a significant uncertainty of at least ±25 % in
N2O emissions according to Zamora and Oschlies (2014) analysis using
the UVic Earth system climate model. Finally, Zamora et al. (2012) observed
a higher than expected N2O consumption at the core of the OMZ in the
eastern tropical Pacific, occurring at an upper threshold of 10 µmol L-1.
The contribution of OMZs to total N2O production remains an
open question. N2O formation associated with OMZs might be
counterbalanced by its own local consumption, leading to the attenuation of
the only increasing source of N2O attributable to the projected future
expansion of OMZs (Steinacher et al., 2010; Bopp et al., 2013).
The combined effect of climate change and ocean acidification has not been
analyzed in this study. N2O production processes might be altered by
the response of nitrification to increasing levels of seawater pCO2
(Huesemann et al., 2002; Beman et al., 2011). Beman et al. (2011) reported a
reduction in nitrification in response to decreasing pH. This result
suggests that N2O production might decrease beyond what we have
estimated only due to climate change. Conversely, negative changes in the
ballast effect could potentially reinforce nitrification at shallow depth in
response to less efficient particulate organic carbon export to depth and shallow remineralization
(Gehlen et al., 2011). Regarding N2O formation via denitrification,
changes in seawater pH as a consequence of higher levels of CO2 might
not be substantial enough to change the N2O production efficiency,
assuming a similar response of marine denitrifiers as reported for
denitrifying bacteria in terrestrial systems (Liu et al., 2010).
Finally, the C : N ratio in export production (Riebesell et al., 2007) might
increase in response to ocean acidification, potentially leading to a
greater expansion of OMZs than simulated here (Oschlies et al., 2008;
Tagliabue et al., 2011), and therefore to enhanced N2O production
associated with the low-O2 pathway.
Changes in atmospheric nitrogen deposition have not been considered in this
study. It has been suggested that, due to anthropogenic activities, the
additional amount of reactive nitrogen in the ocean could fuel primary
productivity and N2O production. Estimates are, however, low, around
3–4 % of the total oceanic emissions (Suntharalingam et al., 2012).
Longer simulation periods could reveal additional effects on N2O
transport beyond changes in upwelling or meridional transport of N2O in
the subsurface (Suntharalingam and Sarmiento, 2000) that have been observed
in this transient simulation. Long-term responses might include eventual
ventilation of the N2O reservoir in the Southern Ocean, highlighting
the role of upwelling regions as an important source of N2O when longer
time periods are considered in model projections. Additional studies using
other ocean biogeochemical models might also yield alternative values using
the same parameterizations. N2O production is particularly sensitive to
the distribution and magnitude of export of organic matter and O2
fields defined in models.
Conclusions
Our simulations suggest that anthropogenic climate change could lead to a
global decrease in oceanic N2O emissions during the 21st century.
This maximum projected decrease of 12 % in marine N2O emissions for
the business-as-usual high-CO2 emissions scenario would compensate for
the estimated increase in N2O fluxes from the terrestrial biosphere in
response to anthropogenic climate change (Stocker et al., 2013), so that the
climate–N2O feedback may be more or less neutral over the coming
decades.
The main mechanisms contributing to the reduction of marine N2O
emissions are a decrease in N2O production in highly oxygenated waters as
well as an increase in ocean vertical stratification that acts to decrease
the transport of N2O from the subsurface to the surface ocean. Despite
the decrease in both N2O production and N2O emissions, simulations
suggest that the global marine N2O inventory may increase from 2005 to
2100. This increase is explained by the reduced transport of N2O from
the production zones to the air–sea interface.
Differences between the two parameterizations used here are more related to
biogeochemistry rather than changes in ocean circulation. Despite sharing
the high-O2 N2O production pathway, leading to a decrease in
N2O emissions in both cases, the role of warming in P.TEMP or higher
N2O yields at low O2 concentrations in P.OMZ translate into
notable differences in the evolution of the two production pathways.
However, the dominant effect of changes in stratification in both
parameterizations ultimately drives the homogeneous response of the
parameterizations considered in model projections in the next century.
The N2O production pathways demand, however, a better understanding in
order to enable an improved representation of processes in models. The efficiencies of the production processes in response to
higher temperatures or increased seawater pCO2 are required. Other effects such as changes in the O2 boundaries at which
nitrification and denitrification occur must be also taken into account. In
the absence of process-based parameterizations, N2O production
parameterizations will still rely on export of organic carbon and oxygen
levels. Both need to be improved in global biogeochemical models.
The same combination of mechanisms (i.e., change in export production and
ocean stratification) have been identified as drivers of changes in oceanic
N2O emissions during the Younger Dryas by Goldstein et al. (2003). The
N2O flux decreased, while the N2O reservoir was fueled by longer
residence times of N2O caused by increased stratification. Other
studies point towards changes in the N2O production at the OMZs as the
main reason for variations in N2O observed in the past (Suthhof et al.,
2001). Whether these mechanisms are plausible drivers of changes beyond year
2100 remains an open question that needs to be addressed with longer
simulations.