Introduction
Oceanic uptake of anthropogenic carbon dioxide (CO2) moderates the
rise of atmospheric CO2 concentrations and leads to changes in the
ocean carbon cycle. CO2 uptake acidifies the oceans, potentially
altering ecosystems and leading to adverse effects for the societies and
industries that depend on them. Assessing the vulnerability of these
resources to long-term change in ocean acidification requires a detailed
understanding of the carbonate system's natural variability that any
anthropogenic trend may add to or alter. Coastal ecosystems vary on shorter
temporal and spatial scales than the shelf or open ocean so predicting how
they may change under future climate scenarios is more difficult and
requires an understanding of the system's high-frequency components.
The Bay of Fundy (Fig. , inset) is an approximately
200 km long, 50 km wide, 75 m deep bay that extends
northeastward into Canada from the Gulf of Maine in the northwest Atlantic
Ocean. The regional circulation flows southward along the Scotian Shelf as
the Nova Scotia Current, follows the coastline around southern Nova Scotia to
enter the southern side of the Bay of Fundy, and exits the bay along the
northern side to join the Eastern Maine Coastal Current in the Gulf of Maine
. The
mean circulation and water properties of the Gulf of Maine and the Nova
Scotia Current are well described (e.g., ; ; ). Within the bay the mean flow recirculates cyclonically around
the outer Bay of Fundy . The geometry of the basin makes it
resonant with the M2 tidal frequency and generates the highest tidal range in
the world, over 16 m at the head of the bay . Turbulence from the fast tidal flows keeps much of the basin
well mixed, while the deeper regions of the outer bay develop seasonal
stratification. The largest freshwater source to the bay is the St. John
River, on the northern coast, so owing to river plume dynamics and the
general circulation of the region, this river water primarily propagates
along the northern coast, into the Gulf of Maine.
Map of Grand Passage, which cuts through a peninsula in southwest
Nova Scotia. Instrument location indicated by the pink dot. Flood and ebb
tide flow directions shown in blue arrows. Inset: map of the region with the
red box indicating the field site at the mouth of the Bay of Fundy.
Seasonal and inter-annual variability in air–sea CO2 flux, along with
the key carbon cycling processes, have been documented both upstream from the
Bay of Fundy on the Scotian Shelf e.g., and downstream in the Gulf of Maine . Within the bay, salt marshes have been shown to be weak
emitters of CO2 to the atmosphere , but these
results might not be representative of fully submerged regions. Basin-wide
estimates of CO2 fluxes have come as part of larger-scale coastal
studies where the Bay of Fundy is included as part of the Gulf of Maine
region. Using historical pCO2 data and satellite algorithms,
identified the Gulf of Maine as a weak source of
CO2 to the atmosphere. In contrast, results from a coupled
biogeochemical-circulation model indicate the Gulf of Maine to be a
relatively strong sink of CO2 compared to surrounding areas
.
The coastal carbon budget and thus the rate at which alkalinity will be able
to buffer future ocean acidification depend on the exchange processes between
coastal and open oceans. In shallow marshy estuaries, tidal budgets have been
estimated for oxygen from dissolved oxygen measurements and for
carbon from a linear model based on pH and oxidation-reduction potential
. However, to the best of our knowledge, the tidal transport
of carbon in this macrotidal system, or far from large freshwater or nutrient
sources, has never been addressed.
To investigate this issue, a year-long, high-frequency time series of
pCO2, temperature, salinity, and currents was measured via a
cabled-to-shore platform in Grand Passage, a tidal channel at the mouth of
the Bay of Fundy, Nova Scotia (Fig. ). This location is ideal
for tracing the main input of water into the Bay from the Scotian Shelf. We
quantify carbonate system variability on hourly to seasonal timescales,
unravel the interaction between the daily and tidal cycles, determine the
phase relationship between tidal currents and carbonate system variables, and
estimate lateral transports by tidal pumping, which moves alkalinity and
dissolved inorganic carbon (DIC) out of the bay, opposite to the mean flow in
the region.
Measurements and data processing
Grand Passage is a narrow channel (1 km wide, 4 km long) separating Brier
Island from Long Island at the end of the Digby Neck peninsula, which juts
out into the mouth of the Bay of Fundy from the southwest corner of Nova
Scotia (Fig. , inset). The tidal range is 5 to 6 m and
peak tidal velocities range from 2 to 3 ms-1.
Time series measurements
A year-long record of high-frequency measurements of pCO2
(Fig. a), temperature (Fig. b),
and pressure, 4 months of salinity (Fig. c), and
1 month of velocity data were collected via a cabled-to-shore observatory
on a bottom frame that was deployed in approximately 10.5 m mean
water depth (location indicated on Fig. ).
(a–c) Measured variables and (d, e) time series
generated from the carbonate system equilibrium solution for data shown
in (a–c). (a) pCO2 in Grand Passage
(blue) and NOAA's weekly atmospheric zonal average for 44–45∘ N
(yellow). (b) Temperature. (c) Salinity (right y axis) and
alkalinity (left y axis) from periods with salinity data (blue) and
generated from a tidal prediction when measurements were not available owing
to instrument failure (orange). (d) DIC. (e) pH.
(f) Alkalinity vs. salinity from bottle samples at the Grand Passage
field site in 2016 and 2017 (purple) as well as from the Scotian Shelf via
the Atlantic Zone Monitoring Program (AZMP) in 2013 (blue) and 2014 (orange) and
the Gulf of Maine in 2015 (yellow). Linear regression (black) used to
generate alkalinity shown in (c).
The primary instrument for this experiment was a CONTROS HydroC CO2
sensor, which uses non-dispersive infrared spectrometry (NDIR) to measure gas
concentrations that have equilibrated across a hydrophobic membrane. The
HydroC was calibrated by the manufacturer before and after deployment and has
a resolution of <1 µatm, an accuracy of ±1 %, and a
response time of 65 s at 15 ∘C and 70 s at 5 ∘C.
All field measurements fell within the calibrated measurement range of
200–1000 µatm. The HydroC was mounted 1 m above the sea
floor and cabled to shore for continuous power and data transfer. It recorded
pCO2 every 1 s from March 2015 to April 2016. The
instrument was zeroed every 64 min until 16 June 2015 and every 735 min
for the remainder of the experiment. During zeroing the gas stream is
isolated from the membrane and CO2 is removed. Zero-channel values
indicate no sensor drift over the deployment period. Following zeroing,
partial pressure re-equilibrated over roughly 1 h and data from these
periods were omitted from analyses.
A CTD (conductivity, temperature, and depth instrument) was
collocated with the HydroC and recorded salinity, temperature, and pressure
data every 30 s from March through July 2015. The CTD was recovered
and redeployed to record data every 60 s until April 2016. The
conductivity sensor biofouled soon after redeployment so only temperature and
pressure are available for the second half of the experiment.
Salinity measurements were despiked and a linear drift of 0.41 over the first
deployment period was removed. The drift was determined by matching the final
measured tidal cycle to the initial tidal cycle values from the redeployment
of the CTD on the following day (prefouling). Salinity was estimated for the
remainder of the year using tidal harmonic analysis of
the available 4 months of data. Salinity variation at frequencies lower or
higher than the tidal harmonics is absent from the latter part of the data
set, but does not change the final results of this work because the direct
effect of salinity and alkalinity (Sect. ) variation is
subtracted from the DICex variable (Sect. ) used
for quantitative analyses.
An RDI Workhorse 600 kHz ADCP (Acoustic Doppler Current Profiler) was deployed nearby, in approximately
27 m water depth, for part of the experimental period. The
upward-looking ADCP recorded velocity in 0.5 m bins at 2 Hz
from 8 to 26 April 2015.
All time series variables were low-pass filtered with a cutoff period of
5 min and then subsampled at a 5 min interval.
Bottle samples
Bottle samples were collected from the ferry wharf on the west side of Grand
Passage over 2 days in February 2016. On 16 February 2016, 12 bottles were
filled hourly from 07:30 to 18:30 local time, using a Niskin
bottle cast off the wharf, from
0.5 to 1 m below the surface. On 17 February 2016, 22 samples were collected
half-hourly from 07:00 to 18:00. The Niskin bottle was used for seven samples, until it broke. Later samples
were collected from the adjacent shore, approximately 5 m from shore in
1.5 m water depth, from 0.5 m below the surface. On 18 March 2017,
15 bottle samples were collected between 09:00 and 13:00 ADT (UTC-3) via
Niskin casts off the Nova Endeavor,
alternating between the center, east, and west sides of Grand Passage across
a transect adjacent to the cabled instruments.
Bottle samples for total alkalinity analysis were poisoned with a
supersaturated mercuric chloride solution to halt biological activity and
stored for later analysis. Total alkalinity was analyzed by potentiometric
titration on a Versatile Instrument for the Determination of Titration
Alkalinity (VINDTA 3C). Analytical methods were based on ,
including the use of certified reference materials for regular instrument
calibration.
Other data sources
Hourly wind speeds and daily precipitation for the entire experiment period
were obtained from the Environment and Climate Change Canada (ECCC) weather
station on Brier Island, on the western side of Grand Passage
(http://climate.weather.gc.ca/climate_data/hourly_data_e.html?StationID=10859,
last access: 14 January 2019).
Atmospheric CO2 concentrations were obtained from the NOAA Greenhouse
Gas Marine Boundary Layer Reference data product . At
the time of data processing, ∼ weekly (1/48 of a year) values were
available through 2015 for both zonal average values for 44–45∘ N
and global average values. Monthly global average values were additionally available
through September 2016
(ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_gl.txt, last
access: 14 January 2019) so estimates for
44–45∘ N were constructed for January–April 2016 by adding the
difference between the zonal and global values for 2013–2015, averaged by
calendar week, to the global values for 2016.
Alkalinity and salinity data from the Gulf of Maine shown in
Fig. f and used in Eq. () were obtained
from the NOAA Ocean Acidification Program .
Alkalinity and salinity data from the Scotian Shelf shown in
Fig. f and used in Eq. () were collected at
all stations as part of the Atlantic Zone Monitoring Program (AZMP)
in Fall 2013 and Spring 2014. Samples were collected and
processed following the methods described in for
the 2007 AZMP data set.
Estimating alkalinity from salinity
A linear relationship between salinity, S, and alkalinity, TA,
(Fig. f) was calculated with data from the bottle
samples collected at the field site in February 2016 (n=34) and
March 2017 (n=15), from the AZMP Fall 2013 (n=357) and Spring 2014
(n=467) cruises, and from the Gulf of Maine in 2015 (n=286).
TA=(45.75±0.39)S+(709.72±12.76)
The Grand Passage data cover a small (<1ΔPSU) salinity range so a
regression from only the Grand Passage field site was not robust. The Grand
Passage salinity–alkalinity relationship was not a priori expected to be identical to the data collected
10 to 200 km offshore in the Scotian Shelf and Gulf of Maine regions, but
Fig. f shows there is no significant change in the
water mass end members between those regions, which are up- and downstream of
Grand Passage in the regional circulation pattern. A time series of
alkalinity (Fig. c) is calculated from salinity using
Eq. ().
Calculating DIC
DIC concentration and pH (Fig. d, e) for the carbonate
system at equilibrium were calculated from measurements of
pCO2, salinity, alkalinity, and temperature, with constants
following and . We used
the MATLAB code from for the CO2SYS implementation from of
the equations for carbonate equilibria.
Results and discussion
Seasonal evolution of carbonate system variables
Grand Passage connects two adjacent embayments, St. Mary's Bay and the Bay of
Fundy, and because the water properties in these embayments can differ, the
strong semi-diurnal (M2) tide causes tidal period variability in the
carbonate system. pCO2 varies on annual, daily, and tidal
timescales, and the magnitude of the daily and tidal signals also changes with
the season. The seasonal evolution of pCO2
(Fig. a) is dominated by the effect of temperature
(Fig. b), rising in summer and declining in winter,
while biological processes only modulate this cycle. pCO2
ranges from a minimum of 307 µatm in spring 2015 up to a maximum
of 557 µatm in early fall, with daily and M2 period variation of
32 and 10 µatm, respectively. In November, the daily range drops
to 11 µatm and pCO2 decreases throughout the
winter. Final measured values in March 2016 were 30 µatm higher
than the those measured the previous spring, owing to the difference in water
temperature.
Temperature (Fig. b) has a strong seasonal cycle,
increasing from 2 ∘C in April 2015 up to 14 ∘C in
September. Temperature decreases from October 2015 through March 2016, when
it reaches an annual minimum of 3.5 ∘C, 1.5∘ higher than the
previous springtime minimum. The ranges of daily and M2 tidal variation are
0.35 and 0.7 ∘C, respectively, in the summer and 0.1 and
0.15 ∘C during fall through spring.
The salinity (Fig. c, right axis) average for our
March–July period of data availability was 31.9 with a 0.18 tidal variation
and no daily signal. Salinity was not correlated with local wind or
precipitation. Alkalinity mean and tidal variation were 2177 and
8.24 µmolkg-1, respectively, based on the linear
relationship between salinity and alkalinity measured with bottle samples
(Fig. f and Sect. ).
The in situ pCO2 value is important because it determines
the air–sea flux of CO2, but is not an ideal variable to assess
biogeochemical carbonate dynamics because of its dependence on temperature
and alkalinity, which obfuscate the biological processes. DIC
(Fig. d) does not have a strong temperature dependence
so it better depicts the biological DIC variation. DIC declines steeply
during the spring bloom and then increases though the winter. During the
spring bloom and throughout the summer there is a large daily range in DIC.
In October, daily variability shrinks, and DIC increases steadily throughout
the winter. pH varies as an approximate inverse of pCO2. pH
has an average value of 8.01 (=-log10〈[H+]〉) and
daily and M2 variation are equivalent to changes in pH of 8 to 8.03 and 8 to
8.01, respectively.
Unraveling daily and tidal cycles of biogeochemically driven changes in DIC
In order to isolate the biogeochemically mediated changes in DIC from the
variability due to purely physical processes, we define the variable “excess
DIC” as the DIC remaining after the concentration expected from the physical
mixing of water masses of different salinities and alkalinities has been
subtracted. The DIC dependence on salinity and alkalinity,
DICmix, is estimated numerically with CO2SYS using fixed values
of pCO2 and temperature. DICmix includes both
a salinity-dependent component and a background constant that depends on
pCO2 and temperature. The mean pCO2,
446 µatm, and temperature, 8 ∘C, for the deployment
period are chosen for the calculation. DICex is calculated by
subtracting DICmix from the full (observed) DIC,
DICobs (Fig. ).
DICex=DICobs-DICmixwhereDICmix=DIC(pCO2=446,T=8,S,TA)
DIC vs. salinity for measured (blue) and tidal reconstruction
(orange) carbonate system solutions. DIC at fixed pCO2
(446 µatm) and temperature (8 ∘C) (black) is subtracted
from observed DIC to create DICex.
Only the time variation of the resulting DICex is meaningful,
not the absolute value. This time variation is not sensitive to the choice of
fixed pCO2 and temperature from within the ranges typical of
this field site. The relationship between pCO2 and DIC that
is captured in DICex is unchanged over the small natural range
of alkalinity in this region, so DICex is not sensitive to the
choice of a measured, tidal harmonic, or even constant salinity estimate.
DICex is presumed to be predominantly biogeochemically driven,
but also includes any changes in DIC due to air–sea exchange, which we could
not calculate on daily timescales, but are shown to be small on weekly timescales in Sect. .
DICex (Fig. a) decreases rapidly April
and May due to the spring bloom and more gradually over the summer, as
explained by . Following a decline in October
(yearday 270–290) suggestive of a
fall bloom, DICex increases steadily through fall and winter.
The daily cycle of DICex is evident throughout the year when
highlighted by the use of color to indicate local time of day in
Fig. a. Daily maxima occur in early morning
(∼ 06:00, pink dots) following nighttime respiration and minima in late
afternoon (∼ 17:00, green dots) following the peak hours of sunlight
and photosynthesis. This cycle is shifted several hours earlier than the one
reported on the Scotian Shelf by . The daily range of
DICex is approximately 3 times as large in summer as in winter,
consistent with higher summer sunlight supporting higher phytoplankton
growth.
(a) DICex for a calendar year with local time
of day indicated by color. The time variation of the resulting
DICex is meaningful, while the total value depends on the
specific choice of fixed pCO2 used for the computation.
(b) DICex (color) for each hour of the day over a
calendar year.
The solar and tidal cycles combine to create a fortnightly cycle (visible in
Fig. a, but more clear in
Fig. b). This beating pattern in the time series is
due to the difference between the 12.42 h M2 tidal and 24 h
diel periods. Mathematically, this effect is identical to a spring–neap tidal variation, but here the
daily cycle is due to solar insolation, rather than the solar gravitational
force. The diagonal banding pattern in Fig. b shows
the M2 tide progressing 50 min later each day. The daily morning peak and
afternoon low appear as broad horizontal stripes and are most visible for
yeardays 100–275. A similar daily pattern has been observed on the Scotian
Shelf , but no tidal signal was detected there. The pulsing of
the strength of the morning high and afternoon low is due to the coincidence
of an M2 maximum or minimum with the time of the daily
maximum or minimum.
The overlapping daily and tidal cycles apparent in
Fig. can be separated and quantified by spectral
analysis. Power spectral densities of DICex,
PDICex(f) for frequency f, were calculated by
Welch's method on detrended time series for each month with eight
approximately week-long Hamming windowed segments with 50 % overlap. The
position of peaks in spectra of month-long DICex time series
(Fig. a) identify the frequencies with the greatest
variability, and the area under each peak equals the contribution of that
frequency to the total signal variance. These February and August examples
show a large daily peak and a slightly smaller M2 tidal peak for both months,
and they show that DICex is more variable in August than February at
all frequencies. The third and fourth peaks visible in
Fig. a are harmonics of the 24 h and M2 frequencies and
do not substantially contribute to the total signal variance. The variance of
DICex at the 24 h and M2 frequencies are calculated from the
area under the spectra using a five-point peak width,
σDICex2=∑PDICex(f)Δf. The total DICex
variance is 10 times higher in August than in February but in both seasons
the daily and M2 frequencies represent the majority of the variability:
56 % (56 %) and 19 % (7 %), respectively, of the total
DICex signal variance in August (February).
(a) Spectra of DICex in February (blue) and
August (orange). (b) DICex range over daily (black,
solid) and M2 (red, dashed) cycles for each month of the year. Amplitude
determined from area beneath peaks in spectra, such as examples shown
in (a). The 95 % confidence intervals are shown in lighter colors.
The strengths of M2 and daily cycles in DICex evolve with the
season (Fig. b). The range of DICex
variation at a particular frequency is defined as 2A for amplitude A,
i.e., the “peak-to-trough” difference of a sine wave. The rms
value of a sinusoid equals A/2, so the range of the daily or tidal DIC cycle equals 2×2×σDICex. Months from April
through September have a similar size daily signal near
15 µmolkg-1, with lower values throughout the winter. The
tidal variation is always smaller than the daily variation but is also
generally larger in summer and smaller in winter. However, unlike the daily
signal, June and July have smaller tidal variation than the shoulder months
of April, May, August, and September. Changes in the strength of the tidal
signal reflect changes in spatial gradients of DICex,
presumably owing to season fluctuations in the spatial variation of
biological activity.
Tidal phasing
The relationships between the tidal flow and DICex,
temperature, salinity and alkalinity, and
Hex+ are depicted in Fig. for April 2015, when
velocity measurements were available. This tidal-phase information
complements the daily cycle of DICex emphasized in
Fig. a.
The relationships between the tidal flow and
(a) salinity and alkalinity, (b) temperature,
(c) DICex, and (d) Hex+ are
depicted for April 2015, when velocity measurements were recorded. The phase
of the tide is indicated by ADCP water depth on the y axis and
depth-averaged along-channel velocity on the x axis (positive in flood
direction, i.e., northward, into the Bay of Fundy), with mean values
indicated by black lines. Phase progression is counterclockwise, shown by
arrows in (a). Scalar concentration is indicated by color; for
example, more yellow data points visible on the left side of
(a) indicate higher salinity during ebb tide, when water is leaving
the Bay of Fundy. The variation in position of the tidal ellipse between each
pass of the tidal cycle reflects the changes in tidal range and maximum speed
associated with the spring–neap cycle. For all variables shown by colors,
18 h high-pass-filtered values plus mean are plotted to visually highlight
the M2 variability by eliminating the daily signal; the filtering is not used
for data analyses. Hex+ is computed using the same method as
DICex, for H+=10-pH.
Salinity and corresponding alkalinity (Fig. a) are lowest
during late flood and highest between maximum ebb and early flood (see ebb and flood directions on
Fig. ). Salinity values are lower during flood compared to those
on ebb, which indicates that the water from St. Mary's Bay/Scotian Shelf that enters
the Bay of Fundy through Grand Passage each flood tide is fresher than what
exits during ebb. This tidal asymmetry in salinity holds for all months of
the year.
Temperature (Fig. b) is lowest in late flood and peaks a
short time later during early ebb, and overall the water is colder during
flood than during ebb. Unlike the salinity asymmetry, the temperature
asymmetry changes sign with the seasons. The shallower St. Mary's Bay is more
sensitive to surface heat fluxes than the deeper Bay of Fundy, so it is
warmer in spring and summer and colder in fall and winter. As a result, the
oscillating tides move heat into the Bay of Fundy half of the year and out
half of the year.
DICex (Fig. c) peaks at low slack and the
lowest values occur during late flood and early ebb. Hex+
(Fig. d) also peaks at low slack and has the lowest values
during early ebb. This pattern suggests lower net community production (NCP) in the
bay than on the shelf, likely a result of a stronger spring bloom on the
shelf than in the bay, which is advected by the mean currents around southern
Nova Scotia. Smaller-scale spatial variation in nutrient or light
availability owing to different water depths or mixing rates could also
contribute to different growth rates on the two sides of Grand Passage.
Lateral transport by tidal pumping
Transport of carbon through Grand Passage is driven both by net volume
transport and by tidal pumping – oscillatory tides moving water
masses with different properties back and forth on each tidal cycle. Water
volume flux per meter of channel width, q (m2s-1) =u‾h, for water depth h (m) and depth-averaged velocity
u‾ (ms-1) can be decomposed into a time-mean
(〈〉) and fluctuating (′) part, q=〈q〉+q′.
By definition, there is no net water volume transport by the time-varying
volume flux used to calculate tidal pumping. The fluctuations in volume
transport (Fig. a) that drive tidal pumping vary in magnitude
over the spring–neap cycle, but return to zero each tidal cycle.
Cumulative along-channel transport since the start of the deployment
period by tidal pumping of (a) water volume,
(b) alkalinity, and (c) DICex for March–July
2015 and (d) DICex for the full year; orange color
indicates period not shown in (a)–(c). Salt or alkalinity
fluxes are not computed for the full year because fluxes from the constructed
tidal salinities will only reflect the mean trend already seen in
Fig. b and not contain changes in phase or magnitude that
might occur in the fall or winter.
Any correlation between the tidal water volume flux, q′, and fluctuations
in conserved carbonate system concentration variables, S′=S-〈S〉 (gm-3 or molm-3), leads to a scalar flux
by tidal pumping, QpumpS=〈q′S′〉
(molm-1s-1 or gm-1s-1), when averaged over
timescales longer than a tidal cycle. Salinity is nearly vertically uniform
at this field site , and we assume DIC is also vertically
uniform because the vertical mixing timescale is much shorter than the
timescale of gas exchange owing to the high turbulence in the Bay of Fundy
(Appendix ), i.e., ∫0hS(z)u(z)dz=Su‾. Fluctuations of the conserved variable that are not
correlated with fluctuations in water volume flux, such as the daily or
seasonal cycles shown in Fig. a, are in S′ but do
not contribute to QpumpS because they are not correlated with
q′.
Cumulative along-channel transports, M (m3, mol, or
gC), of water volume, alkalinity, and DICex
(Fig. ) are calculated by multiplying q′, q′TA′,
and q′DICex′ by the width (w=800 m) of
Grand Passage and integrating in time, t. We assume spatial uniformity of
water properties across the section.
MS(t)=∫0tq′S′wdt
The fluctuating water volume flux, q′, used to compute these transports is
a tidal harmonic solution based on a fit to the month of ADCP data from
April 2015. The harmonic fit represents 93 % of the observed variability
(i.e., R=0.97) in water volume flux. By contrast, the tidal harmonic fit
to the 4 months of salinity measurements (Sect. )
is not well correlated with the observations (R=0.35), owing to the
non-sinusoidal shape of the salinity signal as well as longer period
variability. Due to this poor fit, alkalinity fluxes are not computed for the
period without direct salinity measurements. DICex is not
affected by the salinity, as described in Sect. , so
DICex transport is calculated for the full year.
For the first half of 2015, salt and alkalinity (Fig. b) are
pumped southward, out of the Bay of Fundy by the tides. DICex
(Fig. c) also has net negative (southward) transport over
this period, but has shorter periods of near-zero or positive transport in
March, mid-April, and early June.
Tidal pumping for DICex is also calculated for the second half
of the deployment year (Fig. d) and continues the negative
trend until January 2016, when the flux becomes slightly positive for the
last 2 months of the measurement period. Salinity from a seasonal
climatology e.g suggests that the sign
of the lateral salinity gradient is the same all year, indicating that the
sign of the alkalinity flux will stay the same throughout the year.
Notably, these transports move alkalinity and DICex in the
opposite direction of the mean flow of the region, which follows the coast
clockwise around southwestern Nova Scotia, moving northward into the Bay of
Fundy near Grand Passage e.g.,. Within Grand Passage,
salinity and alkalinity transport by tidal pumping is roughly 20 % of that by
the mean volume flux through the channel (Appendix ).
The exact fraction may vary outside the channel, but the important point is
that tidal pumping likely plays a first-order role in carbon transport
budgets anywhere with large tides and spatial gradients in the biogeochemical
water properties, including the entire width of the mouth of the Bay of
Fundy.
While the mean volume transport through Grand Passage cannot be extrapolated
to the full width of the Bay of Fundy, the tidal pumping term could plausibly
apply over the eastern side of the mouth, where salinity gradients are
positive into the bay. If the transport by tidal pumping is applied out to
just 10 km from shore (∼15 % of the width of the mouth of the
bay), which is a ∼50 m deep region, the DICex
transported out of the Bay of Fundy through lateral advection by tidal
pumping would be 5×108 kg C in a year. This value is
3 times what is estimated to leave the Bay of Fundy by outgassing to the
atmosphere if the Grand Passage air–sea fluxes (Sect. )
applied over the whole bay.
Air–sea CO2 flux
High-frequency variability in DICex is assumed to be driven by
biological and biogeochemical processes, but air–sea flux plays a significant
role on long timescales. We assess the importance of air–sea flux to the
carbon budget by calculating weekly and annual fluxes, as well as the equivalent
changes in DIC. Oceanic pCO2 was lower than atmospheric
pCO2 (Fig. a) for the first
2 months of observations, April and May, and then rose and remained higher than
atmospheric pCO2 for the following 10 months, June through
March, giving an annual net negative CO2 flux (i.e., outgassing) at
this site.
Atmospheric and oceanic CO2 concentrations
(Fig. a) and wind speed (Sect. )
are used to calculate the flux of CO2 between the atmosphere and
ocean at the field site. Atmospheric and oceanic pCO2 data
were interpolated onto the hourly wind time base. Air–sea flux, F
(molm-2s-1), is calculated following Eq. (3),
here using the convention that F is positive for a flux of gas from the
atmosphere into the ocean.
F=-kK0(pCO2ocean-pCO2air),
where K0 is the solubility of CO2 (molm-3Pa-1)
and k (ms-1) is gas transfer velocity. k (cmh-1)
can be represented well for wind speeds, U (ms-1), below
15 ms-1 at Schmidt number Sc=660 with the empirical
formula Eq. 37
k660=0.24〈U102〉.
U10 is the 10 m wind speed measured at the ECCC station on Brier
Island. U10 was also computed as the difference between the air and water
speeds (up to ±2 ms-1), which increased the air–sea flux
estimates by approximately 5 %. Air–sea flux is initially computed on an
hourly timescale to fully capture the quadratic wind-speed dependence, but
only weekly (or longer) averages of F are robust owing to the weekly
timescale of the available atmospheric pCO2 data.
The average air–sea CO2 flux at this site is -4.6×10-8 molm-2s-1, which is similar in magnitude to
previous Gulf of Maine flux estimates, but with outgassing most of the year
rather than the more even split between positive and negative fluxes reported
at a deeper site or for regional averages
. If this gas exchange was spatially uniform
over the Bay of Fundy (approx. 104 km2), this air–sea flux would
release 1.75×108 kg carbon (=1.45×1010 mol) into the atmosphere per year.
The annual local flux is equivalent to a -47 µmolkg-1
change in DICex (Eq. ) when applied to
30 m water depth in Grand Passage over the year-long deployment.
ΔDICair–sea=Fair–seahΔt
The weekly averaged flux is typically between 0 and -1×10-7 molm-2s-1, which is equivalent to up to
-2 µmolkg-1 change in DICex over a week. The
maximum weekly value occurred in late September 2015 and was -2×10-7 molm-2s-1, yielding a
-4 µmolkg-1 change in DICex in 1 week.
Consideration of the local DIC budget
Observed DICex variation is due to the difference between local
(water column and sediment) net community production, local air–sea
flux, and advective changes due to spatial gradients of biological production
or air–sea flux (Appendix ). Daily variation in
DICex can reasonably be attributed to the local time rate of
change, and the tidal variation is likely advective. Longer cycles of
variation could have both a local seasonal cycle and advective contributions
from seasonal variation occurring weeks or months upstream.
We did not directly measure spatial gradients of DICex, but can
infer that the local along-channel gradient is positive (increasing
northwards) from the observation that the highest and lowest values tend to
occur near low and high slack water, respectively (Fig. ). A
positive along-channel DICex gradient carried by a northward
regional circulation decreases local DICex.
DICex returns near to its initial value over the full annual
cycle (Fig. a) so the annual mean ∂DICex/∂t is zero. The outgassing air–sea flux
and along-channel advection both decrease DICex, so to close
the DIC budget (Eq. ) the biologically driven change in DIC
must be positive (NCP<0) on average over the year, even though it is negative in
the spring.