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GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 22, GB3023, doi:10.1029/2007GB003095, 2008
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Net biological oxygen production in the ocean: Remote in situ
measurements of O2 and N2 in surface waters
Steven Emerson,1 Charles Stump,1 and David Nicholson1
Received 27 August 2007; revised 1 May 2008; accepted 9 May 2008; published 23 August 2008.
[1] We describe a method for determining net annual biological oxygen production in the
euphotic zone of the ocean using remote in situ measurements of oxygen and nitrogen
gas. Temperature, salinity, oxygen, and total dissolved gas pressure were measured
every 2 hours at 10-m depth on a mooring at the Hawaii Ocean time series during the year
2005. Since dissolved N2 is effectively inert to biological processes it can be used as a
tracer for the physical mechanisms affecting the O2 concentration in an upper ocean
model of gas concentrations. We determine a net biological oxygen production in the
surface mixed layer of 4.8 ± 2.7 mol m2 yr1. The most important term in the mixedlayer mass balance other than biological oxygen production is the flux of oxygen across
the air–water interface to the atmosphere. Simultaneous glider surveys of the O2 field
measured in a companion paper (Nicholson et al., 2008) yield net biological oxygen
production below the mixed layer of 0.9 ± 0.1 mol O2 m2 yr1. The upper-ocean mass
balance also includes a potential contribution from diapycnal mixing of O2 into the
pycnocline of 0–0.8 mol O2 m2 yr1. Assuming that the net biological oxygen
production over a period of a year or longer is stoichiometrically related to net biological
carbon production and export via DO2/DC = 1.45, the biological carbon flux from the
euphotic zone at HOT is 4.1 ± 1.9 mol C m2 yr1 in 2005, with roughly 80% of the
carbon production originating in the mixed layer. Annual estimates of this flux (the
ocean’s ‘‘biological carbon pump’’) have been determined experimentally in only a few
locations of the ocean because of the labor and expense involved in repeated ship board
measurements. With this new in situ method, it may now be possible to better quantify
the global distribution of the net annual biological carbon export, a prominent mechanism
of carbon cycle feedback in response to climate change, both in the past and future.
Citation: Emerson, S., C. Stump, and D. Nicholson (2008), Net biological oxygen production in the ocean: Remote in situ
measurements of O2 and N2 in surface waters, Global Biogeochem. Cycles, 22, GB3023, doi:10.1029/2007GB003095.
1. Introduction
[2] Net biological production and export of carbon from
the euphotic zone of the ocean is sometimes referred to as
the biological pump because it is the biological process that
removes carbon from the surface ocean and atmosphere to
the deeper ocean [Volk and Hoffert, 1985]. Availability of
nutrients, light and the ecological regimen control the
efficiency of the biological pump, and ocean models indicate that changes in any of these will affect the pCO2 of the
atmosphere [e.g., Najjar et al., 2007]. How the biological
pump changes in response to climate change is important
for evaluating both the reasons for past pCO2 changes in the
atmosphere and to predict feedbacks to anthropogenic pCO2
increases.
[3] Determining the annually averaged, biologically induced flux of organic matter from the upper ocean is
1
School of Oceanography, University of Washington, Seattle,
Washington, USA.
Copyright 2008 by the American Geophysical Union.
0886-6236/08/2007GB003095$12.00
complicated because it requires measurements over at least
a whole year. The most direct approach using shallow
sediment traps is not very effective because most practitioners believe they underdetermine the particle flux [e.g.,
Buesseler et al., 2007], and they do not capture the
dissolved organic carbon (DOC) component, which has
been estimated to be 20% of the total organic carbon
production in the euphotic zone [Hansell and Carlson,
1998]. To date, annual estimates of the biological pump
from direct measurements of oxygen, carbon isotopes and
thorium 234 mass balances are available only from locations that are visited by ships year-round at long-term, time
series sites of the ocean: Hawaii Ocean Time series (HOT),
Bermuda Atlantic Time Series (BATS), and Ocean Station
Papa (Stn P). Other less complete but annual estimates are
based on repeated measurements in the Eastern Equatorial
Pacific during the Joint Global Ocean Flux Study (JGOFS)
and by annual estimates of the draw down of nitrate in the
Subarctic North Pacific Ocean. Net annual carbon fluxes
determined by these methods are summarized in Table 1.
[4] Attempts to determine the global biological carbon
export using satellite color images [Falkowski et al., 1998;
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Table 1. Comparison of Annual Biological Carbon Export Out of the Euphotic Zone or Mixed Layer Determined at Five Different
Locations by a Variety of Measurements (Column 2) With Estimates Based on Inverse Model Calculations (Column 3)a
Annual Biological Carbon Export, mol C m2 yr1
Location
Measured
Inverse GCM
W. Subarctic Pacific
E. Subarctic Pacific (Stn. P)
Subtropical Pacific (HOT)
E. equation Pacific
Subtropical Atl. (BATS)
4.5b
2.4d, 2.2b
2.7e, 1.4e, 4.3e, 2.4f, 2.7g, 2.3h
4.4i
4.0j, 2.3k
4.0c
4.0c
1.0c
3.0c
2.5l
a
Means are presented without error estimates which are less than ±50%.
NO3 draw down [Wong et al., 2002], Satellite chl and T [Goés et al., 2004].
c
Schlitzer [2004].
d
O2 mass balance [Emerson et al., 1995].
e
O2 mass balance [Emerson et al., 1997; Hamme and Emerson, 2006; this work, respectively].
f
Thorium-234 deficiency [Benitez-Nelson et al., 2001].
g
Carbon isotope mass balance [Quay and Stutsman, 2003].
h
Carbon isotope mass balance [Keeling et al., 2004].
i
Carbon isotope mass balance [Zhang and Quay, 1997].
j
O2 mass balance [Spitzer and Jenkins, 1989; Jenkins and Doney, 2003].
k
Carbon isotope mass balance [Gruber et al., 1998].
l
Schlitzer [2000].
b
Laws et al., 2000] and General Ocean Circulation Models
(GCMs [e.g., Schlitzer, 2000; Najjar et al., 2007]) all arrive
at global values of 10– 15 petagrams C yr1. However, the
ocean color and GCM estimates are more variable geographically than are the measurements made so far. A comparison
of the measured and inverse model-derived values is presented in Table 1. Two examples of the discrepancy are in the
North Pacific Ocean (Table 1): (1) both satellite color and
model-derived predictions suggest export fluxes in the
Eastern Subarctic Pacific that are at least 4 times those in
the Eastern Subtropical Pacific (4 and 1 mol C m2 yr1,
respectively), whereas measurements at HOT and Stn P
indicate values that are about the same (2 –3 mol C m2
yr1) and (2) inverse model and satellite predictions suggest
roughly equal carbon export between the Eastern and Western Subarctic Pacific Ocean (4.0 mol C m2 yr1) whereas
estimates from observed nitrate draw down indicate carbon
export in the west is about twice that in the east (4.5 and
2.3 mol C m2 yr1).
[5] Reasons for the discrepancies may be due to known
deficiencies in the ocean color and model estimates.
Satellites determine ocean color to only one optical depth
(10 –30 m), the relationships between chlorophyll and productivity vary in different ocean regions [Campbell et al.,
2002], and the connection to carbon export is still uncertain
[Laws et al., 2000]. Perhaps the most serious problem with
prediction of carbon export in large-scale ocean models is
poor resolution of the temporal variability and that they do
not have sufficient mechanisms for transporting nutrients
from the top of the thermocline to the euphotic zone in the
nutrient-starved subtropical ocean [McGillicuddy et al.,
2003; Oschelies, 2001]. Part of the discrepancy between
models and measurements may also result from the fact that
the ocean is so undersampled that annual estimates from the
few time series locations presently available are not representative of regional values. The only way to know whether
generalizations from time series measurements or from the
models are more nearly correct is to measure the net
biological production in additional areas of the ocean. To
do this one must be able to determine the flux remotely using
autonomous instrumentation because of the logistical complexity and expense involved with ship-based, time series
programs.
[6] Here we describe a method developed to determine net
biological oxygen production using measurements of T, S,
O2 and total gas pressure on moorings. The method is tested
at (HOT) using data obtained in 2005. In situ O2 and N2 data
are interpreted in terms of the net biological oxygen production, which over the annual cycle is assumed to be
stoichiometrically related to net biological carbon production via the oxygen to carbon ratio for phytoplankton
growing on nitrate and oxic carbon respiration (DO2/DC =
1.45 [Laws, 1999; Anderson, 1995; Hedges et al., 2002]).
We demonstrate that it is now possible to expand our
estimates of the annual biological carbon pump to as many
regions of the ocean as there are surface moorings. The first
section of the paper describes the in situ data. We then
calculate the net biological oxygen production from an upper
ocean model and elaborate on the errors associated with this
method.
2. Analytical Methods
[7] The method used here to determine the net biological
carbon export from the euphotic zone of the ocean is
based on measuring the oxygen budget in the surface
ocean using a time series of measurements of oxygen
and other inert gases [e.g., Spitzer and Jenkins, 1989;
Emerson et al., 1997; Hamme and Emerson, 2006, Stanley
et al., 2006; see Figure 1]. In this method the physical and
biological processes affecting the concentration and saturation state of oxygen in the upper ocean are separated using
data from inert gases. Our in situ approach uses the same
principles described in previous papers to determine the
oxygen mass balance, applied to values of oxygen and
nitrogen gas concentration determined using in situ sensors
at a single depth in the euphotic zone. The advantages are
that both O2 and N2 concentrations can be determined nearly
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Figure 1. Schematic representation of the oxygen mass balance of the upper ocean and its relationship
to density surfaces (dashed lines) and transport of metabolites in the gaseous O2, dissolved (CH2O as
DOM), and particulate (CH2O as POM) forms. Double- and single-shafted, vertical arrows at the air –
water interface indicate diffusive and bubble-induced gas exchange, respectively. Two-way and single
arrows in the water indicate eddy and advective transport. Wavy vertical arrows illustrate particle settling.
continuously and remotely. The disadvantages are that, at
least in the present configuration, the concentrations are
from a single depth rather than a profile of the upper ocean,
and nitrogen gas is not the ideal tracer for oxygen changes
due to physical processes because of their different solubilities [see Benson, 1964; Spitzer and Jenkins, 1989; Hamme
and Emerson, 2006].
[8] The MOSEAN surface mooring (Dr. Tommy Dickey,
PI, www.opl.ucsb.edu/mosean.html) was in place (22°750N,
158°W) near the Hawaii time series station ALOHA
(22°450N, 158°W) from August 2004 to the summer of
2007. We provided instruments to determine temperature,
salinity, and the concentrations of oxygen and nitrogen
gases at 10 meters on the mooring from the beginning of
the study till the end of 2005 on three separate deployments
(MOSEAN 1, 2 and 3). Temperature and salinity were
measured using a Seabird Electronics Seacat 16+ CTD.
Oxygen was determined with an SBE-43 Clark-type oxygen
sensor and total gas pressure in the water was determined
using a Gas Tension Device (GTD [McNeil et al., 1995]),
both of which were logged in the Seacat. With an accurate
estimate of atmospheric pressure, PA, the pressure of
dissolved gases in the ocean mixed layer, PwGTD and the
oxygen concentration, [O2], it is possible to calculate the
pressure of nitrogen gas in the surface ocean [Emerson et
al., 2002]:
W
A
sat
sat
pW
N2 ¼ PGTD P pH2 O * ðXAr þ XCO2 Þ pH2 O ½O2 =aO2
ð1Þ
where pHsat2O is the pressure of water vapor at saturation, XAr
and XCO2 are the mole fractions of argon and CO2 in the
atmosphere and aO2 is the Henry’s law solubility constant
for oxygen in seawater with units of mol kg1 atm1.
Emerson et al. [2002] argue that the largest error in the
accuracy of this method of determining the nitrogen gas
pressure is the error in the oxygen determination by the
SBE-43 which is standardized by Winkler titration at
roughly monthly intervals.
3. Results
[9] Total gas pressure in the water at 10 meters and
atmospheric pressure from National Data Buoy Center
(NDBC) buoy 51001 in the vicinity (23°24.040N, 162°15.590W)
are presented in Figure 2. Both of these measurements are
made with Paroscientific sensors which are reported to have
accuracies of ±0.01% (www.paroscientific.com). In practice, we have observed that three separate Paroscientific
pressure sensors at the same height on the fantail of a ship at
sea read pressures to within 0.4 mbar (0.04% of one
atmosphere). Heterogeneity due to winds in this non-ideal
setting for intercalibration may play a role in causing some
of these differences. Atmospheric pressures exhibit shortterm (2 week) variability of ±5 mbar with greater excursions in winter due to storms. The GTD data are more
regular because of the damping effect of air– water gas
transfer with pressures that are close to atmospheric values
in winter (November– March) but increase to values that
exceed atmospheric pressures by up to 10 mbar in summer.
The main processes that alter the gas pressure in the water
from that in the atmosphere are the physical effects of
temperature change, injection of air into the water via air
bubbles that are mixed below the water surface and the
biological production or consumption of oxygen. (Nitrogen
fixation rates are too small to create measurable N2 pressure
changes in the surface ocean [Emerson et al., 2002]). In the
winter the effects of cooling of the water and bubble
processes tend to offset each other. Cooling increases the
gas solubility which decreases the partial pressure in equi-
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Figure 2. Pressure of the atmosphere from NDBC buoy 51001 at 23°24.040N, 162°15.590W (shaded
diamonds) and pressure of the dissolved gases at 10 m on the MOSEAN mooring at the Hawaii Ocean
Time series (HOT) from GTD measurements (solid diamonds).
librium with the gas concentration in the water in the mixed
layer. If the pressure in the water becomes lower than that in
the atmosphere there is a gas flux into the ocean while the
system tries to reestablish air – water gas pressure equilibrium. At the same time, bubbles entrained by the breaking
waves during winter storms tend to increase the gas pressure
as does biological oxygen production. The offsetting influences cause the atmospheric and ocean pressures to be
similar in 2005 at HOT. The observed increase in gas
pressure in the water during summer is partly a temperature
effect caused by the decrease in solubility with temperature
and the accompanying increase in gas partial pressure, and
partly due to biological O2 production. Bubble processes
are, if anything, less effective in summer because storms
and high winds are less frequent.
[10] The effects of oxygen and nitrogen changes on the
GTD pressure are separated by measuring the oxygen
concentration using a dissolved oxygen sensor. Oxygen
concentrations determined by the SBE-43 sensor were
calibrated in the ocean by Winkler oxygen titrations on
samples taken when the moorings were deployed and
recovered and by using roughly monthly measurements of
oxygen by the time series scientists at Stn ALOHA. The
Winkler measurements and the magnitude of the correction
made to the SBE-43 sensor based on the calibrations are
presented in Table 2. We observed a regular 0.5% difference between the HOT data and our own and offset their
data to agree with our values. Overtime, the sensor drifts to
lower values due mainly to biofouling in the productive
surface waters. Corrections (less than 3% over a month
except between days 238 and 316) were made to the data
between calibrations by assuming a linear decrease in
sensitivity of the sensor with time.
[11] Nitrogen gas concentrations in the mixed layer were
calculated from the oxygen concentrations, pressure measurements and equation (1). The degree of saturation, D, in
percent (DC = (([C] [Csat]) / [Csat]) 100) for N2 and O2
were determined using the solubilities presented in the
works of Garcia and Gordon [1992] and Hamme and
Emerson [2004]. The results are presented for the calendar
year of 2005 in Figure 3 where symbols indicate independent oxygen and nitrogen measurements on water samples
taken near the moorings. The oxygen sensors are calibrated
by the Winkler oxygen measurements and thus the two
agree perfectly. Nitrogen data, on the other hand, represent
independent measurements using mass spectrometry (see
Table 2. Oxygen and Nitrogen Measurements at the Location of the MOSEAN Mooring and the Hawaii Ocean Time Series (HOT)a
Julian Date 2005
T, °C
Salinity
O2, mmol kg1
DO2, %
Corr, %
N2, mmol kg1
DN2, %
47.8
142.7
150.5
167.0
198.7
227.7
253.0
282.6
316.0
347.0
23.546
25.387
25.601
25.069
26.041
26.406
26.687
26.230
25.252
24.945
35.091
34.867
34.845
35.321
34.996
35.061
35.099
35.228
35.066
35.161
212.6
210.7
212.3
210.4
208.0
207.1
206.2
205.0
212.3
208.3
0.4
2.5
3.6
2.5
2.2
2.4
2.8
1.4
3.1
0.8
2.1
6.5
395.6
0.4
390.7
1.2
388.3
0.3
a
1.0
1.4
0.3
1.0
2.2
5.2
3.0
The first three dates in column 1 are in the first deployment (MOSEAN 2) and the rest in MOSEAN 3. DO2 and DN2 are supersaturation values, and
‘‘Corr’’ is the correction applied on the date indicated to bring the SBE-43 oxygen sensor into agreement with the Winkler titration oxygen measurements.
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EMERSON ET AL.: IN SITU O2 AND N2 MEASUREMENTS
Figure 3. Mean daily oxygen (light line) and nitrogen
(dark line) supersaturation (in percent) at 10 m on the
MOSEAN mooring at HOT. Dark symbols are oxygen
concentrations determined by individual Winkler O2 titrations and are used to calibrate the oxygen sensor. Open
symbols are nitrogen gas measurements from samples taken
during deployment and recovery of the mooring and are
independent of the in situ measurements.
the section 4.3 of the Discussion for more about the data
comparisons).
[12] The in situ data at 10 meters on the MOSEAN mooring
for the calendar year 2005 (Figure 3) indicate slight nitrogen
undersaturation in the winter (October – March) and supersaturation in the summer, following what one might predict
for temperature changes altered by an unknown supersaturation effect of bubbles. Oxygen supersaturations are nearly
the same as those of nitrogen in January and February and
two other short periods but greater during the rest of the
year. The difference in supersaturation between oxygen and
nitrogen, (DO2 DN2), which is a measure of O2 change
due to biological processes, is clearly episodic rather than
continuous, indicating the nature of biological oxygen production at this location (see later). Measurements from two
previous deployments at 50 meters at this location in 1997
and 1998 (Figure 4, reproduced from Emerson et al. [2002])
also reveal the pulsed nature of the (DO2 DN2) difference.
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budget is sufficient for these gases; however, there has been
little success in constraining the rate of diapycnal exchange
between the mixed layer and waters below using either heat
or gas mass balance [Hamme and Emerson, 2006].
[14] We begin the discussion of our results with a model
which includes entrainment but assumes the vertical (diapycnal) exchange of gases between the mixed layer and
below is negligible. The result is then compared with
estimates of the biological oxygen production below the
mixed layer determined by glider surveys described in a
separate paper [Nicholson et al., 2008] and the exchange
across the top of the pycnocline (110 m) calculated
assuming eddy diffusion transport and a range of eddy
diffusion coefficients. In this way we evaluate the net
biological oxygen production in the entire euphotic zone
and the potential underestimate that would be made from
using the mixed-layer-determined value only.
[15] The model for gases in the upper ocean mixed
layer states that the change in concentration of gas C,
[C] (mol m3), as a function of time, t (d), in the ocean
surface mixed layer of depth h (m) is due to gas exchange at
the air water interface, GEAW, injection of the gas by
bubbles caused by breaking waves that are mixed below the
surface, B, entrainment of water with different gas concentration during mixed layer deepening, E, and the net
community biological production by photosynthesis and
respiration, J (units of mol m2 d1).
h dC
¼ GEAW þ B þ E þ J
dt
ð2Þ
[16] By measuring oxygen and an inert gas simultaneously
it is possible to isolate the physical processes causing O2
change in the upper ocean. It has long been realized that the
noble gas argon is ideal for this purpose because it has a
similar solubility to oxygen [Benson, 1964] and the O2 –Ar
pair have been used over the years [e.g., Craig and Hayward,
1987; Spitzer and Jenkins, 1989; Emerson et al., 1995;
Kaiser et al., 2005; Hamme and Emerson, 2006; Reuer
4. Discussion
4.1. Mixed-Layer Model of Processes Controlling
Gas Concentration
[13] Processes that affect dissolved gas concentrations in
the mixed layer of the surface ocean are (1) diffusive
exchange with the atmosphere, (2) wave-induced subduction of air bubbles below the air– water interface where they
dissolve, (3) entrainment of water from below during mixed
layer deepening, (4) exchange or transport of water between
the local mixed layer and the surrounding waters, and
(5) biological processes. Horizontal advection and diffusion
fluxes of the major atmospheric gases, N2, O2 and Ar are
small compared to other terms in the upper ocean gas mass
balance because of weak horizontal gradients resulting from
relatively rapid exchange with the atmosphere [Emerson et
al., 1995]. A one dimensional model of the upper ocean gas
Figure 4. The same figure as in Figure 3 except for data
from the HALE ALOHA mooring at HOT in 1997 and
1998. Diamonds indicate nitrogen saturation values determined independently by mass spectrometry [from Emerson
et al., 2002].
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EMERSON ET AL.: IN SITU O2 AND N2 MEASUREMENTS
et al., 2007; Juranek, 2007; Stanley, 2007]. Our goal is to
demonstrate the utility of simultaneous measurements of
oxygen and nitrogen gases to separate the physical and
biological processes causing oxygen change because it is
presently possible to measure these two in situ and remotely,
which is not yet the case for argon.
[17] Gas solubility (a) and its temperature dependence are
important terms in the mechanisms that control gas exchange by air – sea diffusive transfer and bubble processes.
The values for oxygen are about twice those for nitrogen
gas (aO2/aN2 = 2.0, (daO2/dT) / (daN2/dt) = 2.3 between
20 –30°C, at S = 35). When seawater warms gases become
less soluble, and when it cools more soluble. If the temperature dependence of the solubility for O2 and N2 were the
same, the difference in their saturation values ((DO2 DN2),
Figure 3) would not change with temperature. Because the
solubility change with respect to temperature for oxygen is
about twice that for nitrogen a positive difference of (DO2
DN2) increases during warming and decreases during
cooling. Fortunately the influence of solubility temperature
dependence on supersaturation change can be determined
exactly as long as the temperature history is available.
[18] Using nitrogen gas supersaturation to separate out the
influence of bubble process on the degree of oxygen
supersaturation is not as easily remedied. Most models
simplify the mechanism of gas exchange by bubbles into
two categories dependent on their size [e.g., see Fuchs et
al., 1987]. Small bubbles that totally collapse and inject
their atmospheric content into the surface water change the
gas concentration depending on the gas solubility. Because
O2 solubility is almost exactly twice that of nitrogen the
effect of these bubbles is twice as strong for N2 as it is for O2.
Thus a positive supersaturation difference, (DO2 DN2),
becomes smaller because of this bubble processes. Larger
bubbles that do not collapse exchange gases by diffusion
across the bubble interface. The gas concentrations change
according to the solubility and diffusion coefficients of the
gases in a similar way to the mechanism that controls
exchange across the air– water interface (see later). If the
bubble effect were totally by this process the difference in
supersaturation of oxygen and nitrogen (DO2 DN2)
caused by this mechanism would be far smaller than that
by total collapse of bubbles even if the supersaturation due to
bubbles were large (see Schudlich and Emerson [1996] for
an explanation of the expected ratios for different bubble
processes). Thus it is important to be able to determine the
relative importance of the two mechanisms. Gas exchange
studies using a suite of inert gases at the Hawaii and
Bermuda time series sites [Hamme and Emerson, 2006;
Stanley, 2007] indicate that these two mechanisms are of
similar importance with total collapse being responsible for
more than half of bubble processes. Since we are dealing
with only one inert gas, N2, we have to guess about the
relative importance of these mechanisms, and that introduces
an element of uncertainty in calculation of the effect bubble
fluxes on O2 concentration.
[19] Each of the terms in equation (2) except the biological one, J, can be written in terms of a model of the process
involved. The diffusive exchange of gases across the air –
water interface (GE, positive into the ocean) is assumed to
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follow laboratory-determined parameterizations in which
the flux is proportional to a mass transfer coefficient that
is specific to gas C, GC (m d1), and the difference between
the gas concentration measured in the mixed layer, [C], and
the value in saturation equilibrium with the atmosphere,
[Csat], at the measured temperature, salinity and pressure,
GEAW ¼ GC ð½C bCsat cÞ
ð3Þ
The saturation concentration represents the concentration at
the air–water interface which is cooler than the bulk mixed
layer because heat loss from the ocean to the atmosphere in this
location creates a ‘‘cool skin’’. This temperature difference has
been calculated to be 0.1°C at HOT, with little seasonal
variation [Emerson et al., 1995] so mixed layer temperatures
were lowered by this amount to determine [Csat].
[20] The mass transfer coefficient can be normalized to a
single fluid, gas, temperature and salinity via the Schmidt
number, SC, which is the ratio of the kinematic viscosity of
the fluid, n, and the molecular diffusion coefficient, DC
of the gas, SC = u/DC [see Emerson and Hedges, 2008,
chapter 10]. Laboratory experiments in which the exchange
rates of different gases were measured simultaneously in
water indicate that the process of gas exchange is proportional to the square root of the molecular diffusion coefficient of the individual gases (i.e., n = 0.5 [Ledwell, 1984;
Jahne et al., 1984]). Thus
GC ¼ Sc0:5
C G*
ð4Þ
where G* is the gas exchange mass transfer coefficient
normalized to a single fluid, temperature, salinity and gas.
[21] Gas exchange rates in the ocean have been determined by a variety of different gas tracers at different
conditions [e.g., Wanninkhof, 1992; Nightingale et al.,
2000]. These tracer experiments have been normalized to
a single Schmidt number and compiled as a function of the
observed wind speed at ten meters above the air– water
interface, U10. (We normalize to SC = 600, which is nearly
the same as the Schmidt number for CO2 at 20°C and zero
salinity, following Nightingale et al. [2000]. Other compilations sometimes use SC = 660, which is closer to the value
for CO2 at 20°C and salinity = 35.) Now the relationship
between the exchange mass transfer coefficient for gas C at
the ambient conditions of temperature and salinity is:
GC ¼
ScC 0:5
G*
600
ð5Þ
[22] The bubble term in equation (2) parameterizes the two
types of bubble mechanisms discussed earlier. The collapse
of small bubbles when they are injected below the air– water
interface is designated by the empirical exchange coefficient,
Vinj, times the mole fraction of the gas in the atmosphere, XC.
The second mechanism depicts the transport resulting from
larger bubbles that exchange gases across the bubble– water
interface before they pop back out to the atmosphere. In the
simplest case this mechanism is designated by a separate
empirical coefficient, Vex, combined with the molecular
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diffusion coefficient, D, and gas solubility, a, in the same
proportionality as in the air– water interface exchange [see
Hamme and Emerson, 2006]. Thus
BC ¼ Vinj þ Vex D0:5 a XC
ð6Þ
We define the ratio of these mechanisms as an independent
dimensionless parameter, b
b¼
Vinj 0:5
D aO
Vex O2 2
ð7Þ
and determine the sensitivity of the oxygen mass balance to
a range of b values.
[23] The entrainment flux, E, in equation (2) is equal to
the change in depth of the mixed layer, h, with time
multiplied by the difference in concentration between the
water below the mixed layer that is entrained, [CT], and the
value in the mixed layer, [C].
E¼
dh T
½C ½C
dt
ð8Þ
Because entrainment occurs only when the mixed layer
deepens this value is zero for dh/dt values less than or equal
to zero. The terms on the right side of equation (8) cannot be
evaluated from the data described here so we use fourdimensional data from a glider survey of T, S and O2 around
the MOSEAN mooring that took place in 2005 [Nicholson
et al., 2008]. The glider survey did not measure nitrogen
gas, but many previous determinations of N2 over this depth
interval [Hamme and Emerson, 2006] indicate that the
saturation state does not change significantly with depth so
the gradient is close to that predicted by the depth
dependence of Nsat
2 .
[24] We can now combine equations (2) – (8) into two
separate equations for [N2] and [O2] in the ocean mixed layer
with the minimum number of unknowns: G600, Vinj, b, and J:
0:5
h d½N2 SN2
½N2 ½Nsat
¼ G600
2 þ Vinj XN2
660
dt
!
1 DN2 0:5 aN2
dh T
½N2 ½N2 1þ
þ
b DO2
dt
aO2
ð9Þ
0:5
h d½O2 SO2
1
¼ G600
½O2 ½Osat
þ
V
X
1
þ
inj O2
2
dt
b
660
dh T
þ
ð10Þ
½O2 ½O2 þ J
dt
These equations are solved simultaneously using wind
speed from NDBC mooring 51001 and the relationship
G600 = 0.22 U210 + 0.33 U10, from Nightingale et al. [2000]
and corroborated by Sweeney et al. [2007], where G600 is in
units of cm hr1 and U10 in m s1. Wind speed was
measured at a mooring height of about three meters and
extrapolated to 10 meters using the log-layer wind speed
relationship, resulting in values at 10 meters that are 10%
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higher than the measurements. The wind speed – G600
relationship from Nightingale et al. [2000] derives from
purposeful tracer release experiments and wind speeds that
are averages over periods of several days to about one week.
Since the G600 versus U10 relationship is nonlinear, there is a
bias in calculation of G600 from averaged wind speeds
[Wanninkhof, 1992]. We tested this empirically using the
weighted wind speed correction described by Reuer et al.
[2007] over a period of 20 days prior to the time of sampling
and found that the averaging increased the mass transfer
coefficient by only 5%. This is much less of an effect than
described by Reuer et al. [2007] because HOT is in the
region of the Trade Winds and has a mean year-round wind
speed of about 7 m s1. Variability is greater in the winter
because of storms, but the period of these events is less than
a week. The calculation was made using the 20 day weighted
average for the mass transfer coefficient.
[25] Molecular diffusion coefficients are from Wise and
Houghton [1966] and kinematic viscosities of water from
Pilson [1998]. Changes in the mixed layer depth and the
entrainment of oxygen into the mixed layer were estimated
from T, S, and O2 data determined in a nearly yearlong
glider survey of the area surrounding the mooring in 2005.
The mixed-layer was defined as the depth where the density
of the water became 0.15 sq units greater than the mean
value between 5 and 15 meters. With a nearly continuous
measure of the oxygen field and the mixed layer depth it is
possible to accurately evaluate the exchange of oxygen from
below to within the mixed layer as it deepens. These data
are presented in Nicholson et al. [2008].
4.2. Calculation of the Net Biological Oxygen
Production
[26] The data in Figure 3 indicate nitrogen gas supersaturation of 0.5–1.0% in summer and near zero in the winter
accompanied by relatively large differences between the oxygen and nitrogen supersaturation (DO2 DN2). The processes
of temperature change and bubbles conspire to keep the
nitrogen gas concentration near zero in the winter. A preliminary estimate of the role of these two processes can be assessed
by solving the nitrogen gas equation (equation (9)) for Vinj
assuming b = 1 and then calculating the oxygen concentration
expected from equation (10) considering only gas exchange
and bubble processes. The concentration of oxygen on day,
i + 1, can be evaluated from that at time, i, from:
dt
SO2 ;i 0:5
G600;i
660
h i
!
1
þ
V
X
1
þ
½O2 i ½Osat
inj;i O2
2 i
b
½O2 iþ1 ¼ ½O2 i þ
ð11Þ
The results of stepping through this calculation for 2005
(Figure 5) indicate large differences between the ‘‘biologically inert’’ oxygen supersaturation and the measured values
that follow the same pattern as the (DO2 DN2) difference
in Figure 3. Most of this difference is due to biological
oxygen production.
[27] The solution to equations (9) and (10) for the net
biological oxygen production presented as the cumulative
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EMERSON ET AL.: IN SITU O2 AND N2 MEASUREMENTS
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Figure 5. Oxygen supersaturation at 10 m at HOT during 2005. The dark line is the oxygen data in
Figure 3. The gray line is the supersaturation determined from equation (11). Vertical shaded regions are
four periods of low sea surface height as indicated by the altimeter data [see Nicholson et al., 2008].
oxygen flux during the year 2005 (Figure 6) illustrates the
importance of the different terms controlling the oxygen
concentration in the mixed layer. The sign of the terms in
this figure are derived from equation (2) rewritten to solve
for the net biological oxygen production, J. The calculated
annual biological oxygen production at HOT for the year
2005 using the MOSEAN data is 4.8 mol O2 m2 yr1, and
the most important term used to calculate J in the mass
balance is oxygen exchange to the atmosphere.
[28] The time rate of change term is very noisy (not
shown) because small daily fluctuations in the measured
oxygen concentration cause large fluxes, but they are both
positive and negative and nearly cancel in the cumulative
plot of Figure 6. The cumulative entrainment flux to the
mixed layer is a relatively small term in the mass balance
and becomes most important after day 240 (September –
November) because this is the period of mixed layer
deepening. The oxygen concentration is greater below the
mixed layer than within it in summer and autumn because
of biological production that cannot escape to the atmosphere and because oxygen decreases in the mixed layer
through the summer in response to the decrease in saturation
due to warming and subsequent gas exchange. When the
mixed layer deepens some of the oxygen produced by
biological processes below the mixed layer is entrained.
This flux is negative in Figure 6 because the net biological
production, J, calculated here is oxygen production in the
mixed layer only.
[29] The bubble flux in the cumulative mass balance
(Figure 6) is nearly zero and plays very little role in the
oxygen mass balance for the year 2005 at HOT. The reason
the bubble flux is so small in 2005 is that nitrogen is not
very supersaturated during this period (Figure 3). This is not
always case as bubble fluxes determined for previous years
Figure 6. The cumulative biological oxygen production calculated from equations (9) and (10) and the
data presented in Figure 3. Different lines are the individual components of the oxygen mass indicated in
equation (2): J = d[O2]/dt GEAW B E, wherein J is the biological oxygen production, d[O2]/dt is
the time rate of change, GE is the air– water interface gas exchange flux, B represents bubble fluxes, and
the entrainment flux is E.
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the previous periods by about 1%. Comparison of wind speed
and surface ocean temperature and salinity during the periods
when there are mooring data (1997, 1998, and 2005; data not
presented) indicate little discernable difference in wind
speed; however, the temperature in surface waters is about
0.5 C warmer in the first 4 months of the year in 2005 than the
other 2 years. The difference between 1997 and 2005 is the
presence of a large mesoscale eddy in the springtime during
the earlier period that brought colder more nitrogen rich
subsurface water to the surface [see Letelier et al., 2000]. This
is a common, but intermittent occurrence at HOT and we
suspect it is the most important reason for the observed
interannual variability in nitrogen supersaturation.
Figure 7. The same as in Figure 6 except using data from
1997 (Figure 4). Entrainment E is assumed to be 0, and the
mixed-layer depth h is idealized from climatological data at
HOT. See the caption to Figure 6 for details.
have often been much more important relative to the gas
exchange flux (Emerson et al., 1995; Hamme and Emerson,
2006; and the mooring results in 1997 and 1998, see later).
Because the nitrogen supersaturations are not large in
comparison the oxygen supersaturation in 2005 at HOT
the ratio of the bubble mechanisms, b, has little influence on
the calculated net biological oxygen production. Varying b
from 0.1 to 10 causes a change in J of only 5% .
[30] An assessment of the interannual variability of the
net biological oxygen production at HOT attained by the
(DO2 DN2) method is estimated by applying the same
model to earlier data from the HALE mooring deployments
in 1997 and 1998. We cannot determine a net annual
oxygen production from these data because the sensors
were at 50 meters during this experiment and thus were
out of the mixed layer during the period of strongest
stratification in summer. We are also lacking simultaneous
glider deployments at that time so we cannot include
changes in the mixed layer depth and a calculation of
entrainment. The importance of the main terms in equation
(3) for the HALE data (Figures 7 and 8) indicates that the
effect of bubbles during these years was much greater than
during the MOSEAN deployment. Consequently, the error
in the final result caused by uncertainty in b is greater.
Assuming a range in b from 0.1 to 10 results in an
uncertainty in the net biological oxygen production of
±10% for 1997 and ±25% for 1998. Neither of the two
HALE experiments lasted the entire year; however, the
mean daily net biological O2 production rates over the
period of the data in years 1997, 1998 and 2005 are
0.013, 0.006 and 0.013 moles O2 m2 d1, respectively
indicating that net biological oxygen production is significantly different from year to year at this location.
[31] In an attempt to identify the reason for the variable
importance of the bubble flux in the surface waters at HOT,
we collected the mean annual DN2 values for the four
different years when it has been determined (Table 3). Both
GTD and mass spectrometer values for 2005 are lower than
4.3. An Assessment of Errors in This Determination
of the Net Biological Oxygen Production
[32] An evaluation of the errors of our determination of
the net biological oxygen production are presented here in
three forms. First, we assess the errors in the mixed layer
mass balance by compounding our best estimates of the
uncertainties of the main data inputs to equations (9) and
(10) using a Monte Carlo approach. We then evaluate how
much this determination underestimates the biological O2
production in the euphotic zone by comparing the mixedlayer result with biological production estimates for below
the euphotic zone derived from glider surveys in 2005
[Nicholson et al., 2008], and estimates of the diapycnal
flux of O2 to the top of the pycnocline at about 110 meters.
[33] The Monte Carlo error estimate is made by varying
the parameters that are most important to the calculation
(the gas exchange mass transfer coefficient, the concentration of O2 and the total gas pressure) through their range of
uncertainty. It is assumed that our assessment of the
magnitude of the uncertainties represents one standard
deviation and is normally distributed about the mean. Input
values are chosen randomly from weighted distributions
about the mean. Solutions to equations (9) and (10) were
determined 1000 times from different random selections and
Figure 8. The same as Figure 6 except using data from
1998 (Figure 4). Entrainment E is assumed to be 0, and the
mixed-layer depth h is idealized from climatological data at
HOT. See the caption to Figure 6 for details.
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Table 3. Mean Annual Observed Nitrogen Supersaturation, DN2,
in the Mixed Layer of the Ocean at HOT Determined by Both
Mass Spectrometer and GTD – O2 Measurementsa
DN2, %
Year-Reference
Mass Spec
1990 [Emerson et al., 1995]
1997/1998 [Emerson et al., 2002]
2000/2001 [Hamme and Emerson, 2006]
2005-This study
1.5 ± 0.8 (9)
1.4 ± 0.8 (7)
1.0 ± 0.8 (11)
0.4 ± 0.8 (3)
GTD – O2
1.0 ± 0.4
0.1 ± 0.4
a
The value in parentheses is the number of measurements.
the resulting net biological oxygen production values, while
not a perfect Gaussian distribution, were assigned a mean
and standard deviation about the mean.
[34] Assumed errors in the MOSEAN data and results of
the Monte Carlo calculation are presented in Table 4. We set
the random error in the value of the gas exchange, mass
transfer coefficient determined from the wind speed to be
±30% because this is the value suggested for the experimental error during the tracer release experiments [Nightingale
et al., 2000]. Values calculated from the four most quoted
wind speed relationships vary by about 30% at 7 m s1,
the mean wind speed at HOT. (Liss and Merlivat [1986]
exchange rates are 30% lower than those from Wanninkhof
[1992], and determinations by Nightingale et al. [2000] and
Sweeney et al. [2007] fall between these.) Thus the uncertainty in the method used to create the regressions is greater
than the difference of the regressions at 7 m s1, and we use
the larger as our error estimate.
[35] The assumed uncertainty in the measured oxygen
concentration is ±0.5%. We believe that the accuracy based
on different standards and reproducibility of duplicate measurements can be ±0.2% [Emerson et al., 1998]. However,
since we have corrected the values of the Winkler titrations
from HOT by 0.5% to agree with our measurements, we
adopt this difference as an upper-limit estimate of the
accuracy uncertainty.
[36] The assumed uncertainty of the nitrogen measurement due to the GTD determination is ±0.2%. The accuracy
of the GTD measurements should be much better than this
(see Methods); however, the nitrogen concentration is
determined from both GTD and oxygen measurements
and thus the O2 and N2 errors are not totally independent.
An overestimate (underestimate) of oxygen pressure of
0.5% would result in an underestimate (overestimate) of
N2 of 0.1% assuming no error in the total gas pressure
measurement. The interdependence of the O2 and N2
determinations is included in the Monte Carlo estimates of
both O2 and N2 errors. We suspect that the uncertainty in the
total gas pressure measurement is small enough that the error
in the N2 concentration by GTD – O2 is primarily due to the
inaccuracies in the oxygen determination. Until publication
of this paper, however, we have no independent laboratory
comparisons of N2 concentrations determined by GTD – O2
and by mass spectrometry. We thus adopt a conservative
estimate of the error of the N2 concentration of ±0.2%. (The
few field comparisons of N2 gas concentrations between
mass spectrometer determinations and GTD – O2 values
(Table 3) reveal a difference of 0.4 ± 0.8% with the mass
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spectrometer values being higher. The nitrogen values
determined in this comparison were calculated from the
mass spectrometer 28/32 mass ratio in combination with
the Winkler O2 determinations [see Emerson et al., 1998];
thus these mass spectrometer N2 data are also limited by the
accuracy of the oxygen concentration. We presently determine nitrogen concentrations by isotope dilution mass spectrometry, and future comparisons with GTD – O2 data will
result in entirely independent estimates of the nitrogen gas
concentration.)
[37] The error in the net biological oxygen production due
to each of the error inputs individually and their combination (Table 4) indicates that the most critical uncertainty is
in the oxygen concentration measurement. The Monte Carlo
combination of the errors suggests the net biological oxygen
production can presently be determined to ±54% by this
method.
[38] The mixed layer biological O2 production of 4.8 ±
2.7 mol O2 m2 yr1 underestimates the total biological
production in the euphotic zone because it does not consider
the biological production below the mixed layer or the flux
of oxygen to the pycnocline. The first of these values was
determined at this location during 2005 by Nicholson et al.
[2008] using glider surveys of the T, S and O2 field in the
upper 1000 meters around HOT. Using a mass balance
similar to that described here they determine a biological O2
production of 0.9 ± 0.1 for this depth interval. The reader is
referred to this paper for a description of the data and
calculation. Adding this value to the mixed-layer result
yields a biological oxygen production in the euphotic zone
is 5.7 ± 2.7 moles O2 m2 yr1, without considering export
of oxygen to the pycnocline.
[39] The gradient of O2 across the top of the pycnocline
indicates a flux of oxygen to the region below 110 m
where there is a net consumption of oxygen. The diapycnal
flux of O2 to the pycnocline is supported by biological
oxygen production in the region from 110 m to the mixed
layer. If we assume oxygen is transported to the pycnocline
by eddy diffusion, and use a maximum proposed diffusion
coefficient of 1.0 cm2 s2 (see estimates for this value by
Hamme and Emerson [2006], Keeling et al. [2004], and
Quay and Stutsman [2003]) along with gradients of oxygen
across the 110 m depth horizon we calculate a flux of
0.8 mol m2 yr1, which is close to the maximum estimates
by Nicholson et al. [2008] for 2005 and the value deterTable 4. Error Estimates Determined From a Monte Carlo
Calculation of Net Biological Oxygen Production From the
MOSEAN Mooring Data During the Year 2005a
Error Source
Gas exchange rate (G600)
O2 concentration [O2]
N2 concentration [N2]
G600, [O2], [N2] together
Error of the
Input, %
±30
±0.5
±0.2
Biological O2 Production,
mol m2 yr1
4.8
4.8
4.8
4.8
±
±
±
±
1.3
2.5
0.5
2.7
a
The error sources are listed in column 1, and the assumed magnitude of
error presented as the percent standard deviation about the mean is in
column 2. The values following the ± sign in the last column represent the
standard deviation of the results of 1000 runs of the calculation described
in the text.
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mined by Hamme and Emerson [2006] for the year 2001.
We include this value into the net production of the euphotic
zone by assuming a mean and standard deviation of 0.4 ±
0.4 mol m2 yr1. Thus the Biological oxygen production
at HOT for 2005 is 6.1 ± 3.1 mol O2 m2 yr1. Almost
eighty percent of this production takes place in the mixed
layer with the rest produced between the mixed layer and
the pycnocline. The maximum estimate of the flux to the
pycnocline is only 10– 15% of the total biological oxygen
production.
5. Conclusions
[40] Net biological oxygen production determined here of
6.1 ± 3.1 mol O2 m2 yr1 corresponds to a net community
carbon production rate of 4.2 ± 1.9 mol C m2 yr1 assuming
a stoichiometric relationship between oxygen production and
carbon fixation of DO2/DC = 1.45. We assume that the net
community production is equivalent to the annual transport
of carbon from the euphotic zone at steady state. This value is
on the high end of the six annual carbon and oxygen balance
estimates at this location (Table 1) which have a mean and
standard deviation of the means of 2.6 ± 0.9 mol C m2 yr1.
It is becoming evident that there is a measurable interannual
variability in the net carbon production at HOT by perhaps a
factor of two, but there is not currently enough data to
compare changes with other observations at this time series
site.
[41] Nicholson et al. [2008] demonstrated correlations
among sea surface height from satellite altimeter data, the
depth of isotherms and the oxygen concentration on isotherms below the mixed layer at HOT during 2005. They
concluded that shoaling of isotherms associated with
Rossby Waves in this location elevated relatively nutrientrich isopycnals into the euphotic zone, which was responsible for enhancing biological productivity in the depth
region below the mixed layer. The four periods associated
with shallow isothermal surfaces during 2005 are indicated
by shading in Figure 4. While the time period in this figure
is too short to be conclusive, it is not obvious that isopycnal
shoaling events coinside in any way with times of enhanced
(DO2 DN2) in the mixed layer. This leads us to conclude
that the elevation of isopycnals by Rossby waves, which
plays a strong role in deep euphotic zone productivity,
probably is not controlling the bulk of the net biological
oxygen and organic carbon production in the mixed layer. It
may be that biological fluxes in the upper regions of the
euphotic zone in this region are more sensitive to larger
eddies that visit the area intermittently [Letelier et al.,
2000].
[42] There are two issues that should be addressed in
order to efficiently apply the in situ (DO2 DN2) difference to determining annual net biological carbon production
to other ocean regions. The first is analytical and involves
the weakest aspect of the in situ method, which is drift of the
oxygen sensors because of biofouling if they remain in the
euphotic zone for longer than a few months. We believe that
the error of the mass balance method can be substantially
improved by decreasing the problem of biofouling and
implementing a method of in situ O2 calibration in which
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dissolved pO2 data are compared with atmospheric values,
as done for in situ pCO 2 measurements. Until these
improvements are made it will be possible to use the method
described here only at locations where manual oxygen
measurements can be made intermittently.
[43] The second issue is more conceptual and has to do
with the relationships among net biological production of
O2 and C and the uptake of nutrients. One of the conundrums of the oxygen mass balance method is the basic
mechanism causing O2 supersaturation in the surface ocean
[Jenkins and Doney, 2003]. In a one-dimensional, steady
state, Redfield ocean where productivity is limited by
nutrient fluxes from the top of the thermocline and carbon
export is dominated by rapid particulate carbon export, one
would not expect there to be any oxygen excess in the
euphotic zone caused by net biological organic matter
production unless the nutrients being transported to the
euphotic zone are preformed (originated from water that
had a measurable nutrient concentration when it left the
ocean surface). This is because, in this simple one-dimensional model, vertical transport brings to the ocean surface
nonpreformed nutrients and apparent oxygen utilization
(AOU = [O2] [Osat
2 ]) in the proportion required to return
the oxygen concentration exactly back to saturation by net
community organic matter production. There is no oxygen
supersaturation formed so long as the particulate carbon
production and export is much more rapid than gas exchange. There is little time for the oxygen deficit brought to
the surface to be replenished by gas transfer, which requires
several weeks in an ocean with a 40 meter mixed layer,
before net biological O2 production occurs. Based on
measurements of inorganic nutrients and particulate matter,
it appears that these stipulations are generally followed, at
least in the subtropical oceans, where dissolved nitrate and
particulate carbon concentrations in surface waters are
uniformly low (less than a few mmol kg1 [Buesseler et
al., 2007]).
[44] We believe part of the reason for observing biologically produced supersaturation in the surface layers of the
ocean, and a serious problem with the above simple
conceptual model, has to do with the production of dissolved organic carbon (DOC) during photosynthesis. The
semilabile concentration of DOC in the upper ocean is about
30 mmol kg1 and these concentrations change on seasonal
timescales in the temperate ocean regions [Hansell and
Carlson, 1998]. On the shallowest layers below the compensation depth in the subtropics (the depth below the ocean
surface where net biological community production and
respiration are equal) the DDOC/DAOU ratio is at least
0.5 [Abell et al., 2000; Doval and Hansell, 2000] indicting
that downward transport of DOC accounts for more than
half of the organic carbon export to the shallow subeuphotic
zone depths at these locations. If the residence time of
oxygen produced by the formation of DOC in the mixed
layer is of the same magnitude as that for gas exchange and
transport to the thermocline then there will be an appreciable biological oxygen supersaturation.
[45] Modeling studies indicate a surface-ocean DOC
residence time of about six months with respect to production is required to reproduce the horizontal DOC distribu-
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EMERSON ET AL.: IN SITU O2 AND N2 MEASUREMENTS
tion in ocean global circulation models [Najjar et al., 2007;
Yamanaka and Tajika, 1997]. If we assume half of the net
annual biological production determined in this study
results in DOC production and a 30 mmol m3 semilabile
DOC reservoir in a 50 meter deep mixed layer, then the
residence time for DOC with respect to biological production at HOT is 9 months (t J = [DOC] h/JDOC = (30 103 mol m3 50 m) / (4.8 mol m2 yr1 0.5) = 0.6 yr
7 mo.)
[46] We have demonstrated in this research that the
biological oxygen supersaturation in the mixed layer is
easily measurable in situ and the flux to the atmosphere is
of greater importance to the mass balance than either the O2
production below the mixed layer or the flux to the
pycnocline in the subtropical Pacific Ocean. How this flux
is related to both the particulate and dissolved organic
carbon export is not yet totally clear theoretically and since
C:N and N:P ratios in dissolved organic matter (DOM) are
as high as 15 and 40 in the subtropics [Abell et al., 2000]
rather than Redfield values of 7 and 16, the relationship to
nutrient fluxes is also still vague.
[47] These issues indicate we still have a way to go to
satisfactorily describe the relationship between oxygen
supersaturation and processes of net carbon export and
new production. Estimates of the net biological production
in other locations of the ocean along with seasonal distributions of DOM will help to sort this out using upper-ocean
models of their production and respiration.
[48] Acknowledgments. We would like to express our appreciation to
the Hawaii Ocean Time series (HOT) personnel headed by Matt Church and
Dave Karl and to the scientists involved in the MOSEAN mooring program
headed by Tommy Dickey for their enthusiastic support of our contribution
to their large test bed programs. Critical reviews by two anonymous
reviewers and Paul Quay greatly improved the manuscript. This research
was financially supported by the grants that made these infrastructure
programs possible and our own NSF grant OCE-0628663.
References
Abell, J. S., Emerson, and P. Renaud (2000), Distributions of TOP, TON
and TOC in the North Pacific subtropical gyre: Implications for nutrient
supply in the surface ocean an remineralization in the upper thermocline,
J. Mar. Res., 58, 203 – 222.
Anderson, L. A. (1995), On the hydrogen and oxygen content of marine
phytoplankton, Deep-Sea Res. I, 42, 1675 – 1680.
Benitez-Nelson, C., K. O. Buesseler, D. M. Karl, and J. Andrews (2001), A
time-series study of particulate matter export in the North Pacific Subtropical Gyre based on 234Th:238U disequilibrium, Deep-Sea Res. I, 48,
2595 – 2611.
Benson, B. (1964), Some thoughts on gases dissolved in the ocean, paper
presented at Symposium on Marine Geochemistry, Univ. of R. I., Kingston.
Buesseler, K. O., et al. (2007), An assessment of the use of sediment traps
for estimating upper ocean particle fluxes, J. Mar. Res., 65(3), 345 – 416.
Campbell, J., et al. (2002), Comparison of algorithms for estimating ocean
primary production from surface chlorophyll, temperature, and irradiance,
Global Biogeochem. Cycles, 16(3), 1035, doi:10.1029/2001GB001444.
Craig, H., and T. Hayward (1987), Oxygen supersaturation in the ocean—
biological versus physical contributions, Science, 235, 199 – 202.
Doval, M. D., and D. A. Hansell (2000), Organic carbon and apparent
oxygen utilization in the western South Pacific and the central Indian
Oceans, Mar. Chem., 68, 249 – 264.
Emerson, S., and J. I. Hedges (2008), Chemical Oceanography and the
Carbon Cycle, Cambridge Univ. Press, Cambridge, U.K.
Emerson, S., P. D. Quay, D. M. Karl, and M. Landry (1997), Experimental
determination of the organic carbon flux from open-ocean surface waters,
Nature, 389, 951 – 954.
Emerson, S., P. D. Quay, C. Stump, D. Wilbur, and R. Schudlich (1995),
Chemical tracers of productivity and respiration in the subtropical Pacific
Ocean, J. Geophys. Res., 100(C8), 15,873 – 15,887.
GB3023
Emerson, S., C. Stump, B. Johnson, and D. M. Karl (2002), In situ determination of oxygen and nitrogen dynamics in the upper ocean, Deep-Sea
Res. I, 49, 941 – 952.
Emerson, S., D. Wilber, C. Stump, and P. D. Quay (1998), Accurate measurements of O2, N2, and Ar gases in water and the solubility of N2, Mar.
Chem., 64, 337 – 347.
Falkowski, P. G., R. T. Barber, and V. Smetacek (1998), Biogeochemical
controls and feedbacks on ocean primary production, Science, 281, 200 –
206.
Fuchs, G., W. Roether, and P. Schlosser (1987), Excess 3He in the ocean
surface layer, J. Geophys. Res., 92(C6), 6559 – 6568.
Garcia, H. E., and L. I. Gordon (1992), Oxygen solubility in seawater:
Better fitting equations, Limnol. Oceanogr., 37, 1307 – 1312.
Goés, J. I., H. R. Gomes, A. Limsakul, and T. Saino (2004), The influence
of large-scale environmental changes on carbon export in the North
Pacific Ocean using satellite and shipboard data, Deep-Sea Res., 51,
247 – 279.
Gruber, N., C. D. Keeling, and T. F. Stocker (1998), Carbon-13 constraints
on the seasonal inorganic carbon budget at the BATS site in the northwestern Sargasso Sea, Deep-Sea Res. I, 45, 673 – 717.
Hamme, R. C., and S. Emerson (2006), Constraining bubble dynamics and
mixing with dissolved gases: Implications for productivity measurements
by oxygen mass balance, J. Mar. Res., 64, 73 – 95.
Hamme, R., and S. Emerson (2004), The solubility of neon, nitrogen and
argon in distilled water and seawater, Deep Sea Res. I, 51, 1517 – 1528.
Hansell, D. A., and C. A. Carlson (1998), Net community production of
dissolved organic carbon, Global Biogeochem. Cycles, 12(3), 443 – 453.
Hedges, J. I., J. A. Baldock, Y. Gelinas, C. Lee, M. L. Peterson, and S. G.
Wakeham (2002), The biochemical and elemental compositions of marine plankton: A NMR perspective, Mar. Chem., 78, 47 – 63.
Jahne, B., W. Huber, A. Dutzi, T. Wais, and J. Imberger (1984), Wind/
wave tunnel experiment on the Schmidt number and wave field dependence of air/water gas exchange, in: Gas Transfer at Water Surfaces, edited by W. Brutsart and G. H. Jirka, pp. 303 – 309, D. Reidel
Pub. Co., Dordrecht.
Jenkins, W. J., and S. C. Doney (2003), The subtropical nutrient spiral,
Global Biogeochem. Cycles, 17(4), 1110, doi:10.1029/2003GB002085.
Juranek, L. (2007), Assessment of Pacific Ocean organic matter production
and export using measurements of dissolved oxygen isotopes and oxygen/argon gas ratios, Ph. D. thesis, University of Washington, Seattle,
WA.
Kaiser, J., M. K. Reuer, B. B. Barnett, and M. L. Bender (2005), Marine
productivity estimates from continuous O2/Ar ratio measurements by
membrane inlet mass spectrometry, Geophys. Res. Lett., 32, L19605,
doi:10.1029/2005GL023459.
Keeling, C. D., H. Brix, and N. Gruber (2004), Seasonal and long-term
dynamics of the upper ocean carbon cycle at Station ALOHA near Hawaii, Global Biogeochem. Cycles, 18, GB4006, doi:10.1029/
2004GB002227.
Laws, E. A. (1999), Photosynthetic quotients, new production and net
community production in the open ocean, Deep-Sea Res., 38, 143 – 167.
Laws, E. A., P. G. Falkowski, W. O. Smith Jr., H. Ducklow, and J. J.
McCarthy (2000), Temperature effects on export production in the open
ocean, Global Biogeochem. Cycles, 14(4), 1231 – 1246.
Ledwell, J. R. (1984), The variation of gas transfer coefficient with molecular diffusivity, in Gas Transfer at Water Surfaces, edited by W. Brutsart
and G. H. Jirka, pp. 293 – 302, D. Reidel., Dordrecht, 639.
Letelier, R. M., et al. (2000), Role of late winter mesoscale events in the
biogeochemical variability of the upper water column of the North Pacific
Subtropical Gyre, J. Geophys. Res., 105(C12), 28,723 – 28,739.
Liss, P. S., and L. Merlivat (1986), Air-sea gas exchange rates: Introduction
and synthesis, in The Role of Air-Sea Gas Exchange in Geochemical
Cycling, edited by P. Buat-Menard, pp. 113 – 127.
McGillicuddy, D. J., L. A. Anderson, S. C. Doney, and M. E. Maltrud
(2003), Eddy – driven sources and sinks of nutrients in the upper ocean:
Results from a 0.1° resolution model of the North Atlantic, Global Biogeochem. Cycles, 17(2), 1035, doi:10.1029/2002GB001987.
McNeil, C. L., B. D. Johnson, and D. M. Farmer (1995), In situ measurements of dissolved nitrogen and oxygen in the ocean, Deep-Sea Res. I,
42, 819 – 826.
Najjar, R. G., et al. (2007), Impact of circulation on export production,
dissolved organic matter and dissolved oxygen in the ocean: Results from
OCMIP-2, Global Biogeochem. Cycles, 21, GB3007, doi:10.1029/
2006GB002857.
Nicholson, D., S. Emerson, and C. Eriksen (2008), In situ determination
of T, S, and O2 in the Subtropical North Pacific using glider surveys,
Limnol. Oceanogr., in press.
12 of 13
GB3023
EMERSON ET AL.: IN SITU O2 AND N2 MEASUREMENTS
Nightingale, P. D., G. Malin, C. S. Law, A. J. Watson, P. S. Liss, M. I.
Liddicoat, J. Boutin, and R. C. Upstill-Goddard (2000), In situ evaluation
of air-sea gas exchange parameterizations using novel conservative and
volatile tracers, Global Biogeochem. Cycles, 14(1), 373 – 387.
Oschelies, A. (2001), Model-derived estimates of new production: New
results point towards lower values, Deep-Sea Res. II, 48, 2173 – 2197.
Pilson, M. E. Q. (1998), An Introduction to the Chemistry of the Sea,
Prentice Hall, Upper Saddle River, N. J., 431 pp.
Quay, P., and J. Stutsman (2003), Surface layer carbon budget for the
subtropical N. Pacific: 13C constraints at station ALOHA, Deep-Sea
Res. I, 50, 1045 – 1061.
Reuer, M. K., B. A. Barnett, M. L. Bender, P. G. Falkowski, and M. B.
Hendricks (2007), New estimates of southern ocean biological production
rates from O2/Ar ratios and triple isotope composition of O2, Deep Sea
Res., Part I, 54, 951 – 974.
Schlitzer, R. (2000), Applying the adjoint method for global biogeochemical modeling, in Inverse Methods in Global Biogeochemical
Cycles, Geophys. Monogr. Ser., vol., 114, edited by P. Kasibhatla et
al., pp. 107 – 124, AGU Washington, D. C.
Schlitzer, R. (2004), Export production in the Equatorial and North Pacific
derived from dissolved oxygen, nutrient and carbon data, J. Oceanogr.,
60, 53 – 62.
Schudlich, R., and S. Emerson (1996), Gas supersaturation in the surface
ocean: The roles of heat flux, gas exchange and bubbles, Deep-Sea Res.
II, 43, 569 – 589.
Spitzer, W. S., and W. J. Jenkins (1989), Rates of vertical mixing, gas
exchange and new production: Estimates from seasonal gas cycles in
the upper ocean near Bermuda, J. Mar. Res., 47, 169 – 196.
Stanley, R. (2007), A determination of air-sea gas exchange and upper
ocean biological production from five noble gases and tritiugenic helium-3, Ph.D. thesis, Massachusetts Institute of Technology and Woods
Hole Oceanographic Institution, Cambridge, MA.
GB3023
Stanley, R. H., W. J. Jenkins, and S. C. Doney (2006), Quantifying seasonal
air-sea gas exchange processes using noble gas time-series: A design
experiment, J. Mar. Res., 64(2), 267 – 295.
Sweeney, X., C. E. Gloor, A. R. Jacobson, R. M. Key, G. McKinley, J. L.
Sarmiento, and R. Wanninkof (2007), Constraining global air-sea exchange for CO2 with recent bomb 14C measurements, Global Biogeochem. Cycles, 21, GB2015, doi:10.1029/2006GB002784.
Volk, T., and M. I. Hoffert (1985), Ocean carbon pumps: Analysis of
relative strengths and efficiencies in ocean-driven atmospheric CO2
changes, in The Carbon Cycle and Atmospheric CO2: Natural Variations
Archean to Present, edited by E. T. Sundquist and W. S. Broecker,
pp. 99 – 110, AGU, Washington, D. C.
Wanninkhof, R. (1992), Relationship between wind speed and gas exchange over the ocean, J. Geophys. Res., 97(C5), 7373 – 7382.
Wise, D. L., and H. G. Houghton (1966), The diffusion coefficients of ten
slightly soluble gases in water at 10 – 60 C, Chem. Eng. Sci., 21, 999 –
1010.
Wong, C. S., N. A. D. Waser, Y. Nojiri, F. A. Whitney, J. S. Page, and J. Zeng
(2002), Seasonal cycles of nutrients and dissolved inorganic carbon at
high and mid latitudes in the North Pacific Ocean during the Skaugran
cruises: Determination of new production and nutrient uptake ratios,
Deep-Sea Res. II, 49, 5317 – 5338.
Yamanaka, Y., and E. Tajika (1997), Role of dissolved organic matter in the
marine biogeochemical cycle: Studies using an ocean biogeochemical
general circulation model, Global Biogeochem. Cycles, 11(4), 599 – 612.
Zhang, J., and P. D. Quay (1997), The total organic carbon export rate based
on 13C and 12C of DIC budgets in the equatorial Pacific region, Deep-Sea
Res. II, 44, 2163 – 2190.
S. Emerson, D. Nicholson, and C. Stump, School of Oceanography,
University of Washington, Box 355351, Seattle, WA 98195, USA.
([email protected])
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