Introduction
It has become increasingly apparent that upwelling systems, including the
California Current System (CCS), are particularly vulnerable to anthropogenic
ocean acidification due to their unique physical and chemical traits (Feely
et al., 2008, 2010; Gruber et al., 2012; Hauri et al., 2013a, b). Upwelled
waters have been isolated from the atmosphere and are naturally elevated in
CO2 from remineralization of organic matter; depending on the age of the
upwelled water mass it may also contain anthropogenic carbon (Harris et al.,
2013; Sabine et al., 2002). Recent observations estimate the saturation
horizon with respect to aragonite (depth at which ΩAr= 1)
along the CCS has shoaled by approximately 50 m since preindustrial times,
and undersaturated waters (ΩAr<1) have been observed at the
surface near the California–Oregon border during a strong upwelling event
(Alin et al., 2012; Feely et al., 2008; Harris et al., 2013). Furthermore,
the rate of acidification (i.e., ΔpH yr-1) is expected to be
significantly higher along upwelling margins than observed in the surface
open ocean (Bates et al., 2014; Gruber et al., 2012; Hauri et al., 2013b;
Leinweber and Gruber, 2013; Rykaczewski and Dunne, 2010) due to the reduced
buffering capacity of seawater at higher levels of CO2 (Frankignoulle,
1994). This effect has caused parts of the CCS to venture beyond the envelope
(defined as mean ±1 SD) from modeled preindustrial ΩAr
conditions (Hauri et al., 2013b). This is a concern because organisms may
need to survive outside of the environmental conditions to which they are
acclimatized, and the evolutionary potential for key ecological species to
adapt to such rapid and unprecedented changes is poorly understood. For
example, a significant decrease in calcareous benthic organisms was observed
along a natural pH gradient near a cold volcanic CO2 vent (Hall-Spencer
et al., 2008; Kroeker et al., 2011). However, some calcareous organisms such
as limpets seemed to have adapted to higher CO2 levels compared to
corals and mussels in the same system (Rodolfo-Metalpa et al., 2011).
Furthermore for upwelling margins, CO2 covaries with other environmental
stressors such as temperature and O2 (Reum et al., 2015), making
predictions more difficult due to potential nonlinear synergistic effects
(Frieder et al., 2014).
A critical component in making accurate impact assessments of ocean
acidification is the development of robust, ecosystem-specific projections
of future CO2 conditions
(Andersson
et al., 2013; Cai et al., 2011; Feely et al., 2009, 2010; McNeil and Matear,
2008; Sunda and Cai, 2012). Development of surface, open-ocean acidification
projections has been relatively straightforward, as they rely on
well-defined chemical principles of CO2 equilibrium at the air–sea
interface (Byrne
et al., 2010; Lauvset and Gruber, 2014). Models become more complicated when
attempting to resolve biological and physical processes that contribute
significantly to the natural variability in the system. For example,
biologically mediated “enhanced acidification” was identified in the
northern Gulf of Mexico, causing significantly faster rates of acidification
than the open ocean (Cai et al., 2011). On many tropical
coral reefs, seasonal patterns in CO2 are minimal, whereas the dominant
frequency of variability occurs on diel and tidal frequencies
(Hofmann et al., 2011). On upwelling margins,
both biological and physical processes contribute to the observed natural
variability in carbonate conditions (Fassbender et
al., 2011).
One approach to develop region-specific ocean acidification projections is to
apply an eddy-resolving regional ocean model system (ROMS) coupled with a
biogeochemical component, as has been developed for the CCS (Gruber et al.,
2011, 2012; Hauri et al., 2013b). Such models have highlighted the importance
of capturing physical and biological processes in highly dynamic upwelling
systems. The model simulations show complex spatiotemporal variability (Hauri
et al., 2013b), and predict that the frequency of “corrosive” upwelling
events will intensify in both magnitude and duration by the year 2050 (Hauri
et al., 2013a). However, the eddy-resolving ROMS project pCO2 and
saturation state conditions on a 5 km grid, whereas many marine animals
experience the environment on the scale of centimeters to meters. In
addition, regional models can largely resolve event-scale (weeks) and
seasonal features, but cannot capture fluctuations on diel to tidal timescales, which can be the dominant frequency of variability in many near-shore
environments (Duarte et al., 2013; Frieder et al., 2012; Hofmann et al.,
2011). Due to this discrepancy in both space and time, numerical models tend
to underestimate or entirely miss the high-frequency variability that exists
for the microclimate of organisms.
To transition from region-specific to habitat-specific ocean acidification
projections, high-temporal-resolution data from autonomous chemical sensors
deployed across many habitat types can be used to directly quantify the full
range of present-day carbonate conditions
(Harris
et al., 2013; Hofmann et al., 2011, 2014; Martz et al., 2014; Sutton et al.,
2014). The CCS supports many ecosystems that are of great ecological and
economic value. In particular, there is great habitat and species diversity
near-shore on the shelf, which includes a large number of commercially
important invertebrates and fishes. Habitats in the region include bays and
estuaries, rocky and sandy intertidal, eelgrass beds and kelp forests,
sub-tidal reefs, canyons, and extensive sandy sea floor. There are many
endangered and harvested benthic organisms that inhabit only one or a subset
of these habitats.
Sensor data provide key observations for the mechanistic understanding of the
controls on environmental conditions, and are particularly needed for coastal
marine environments where complex physical and biological processes influence
the observed variability. For example, sensor data from a near-shore kelp
forest in the southern CCS revealed that local biological feedbacks and
episodic upwelling events were the dominant drivers of CO2 variability,
with pCO2 fluctuating by 600 µatm at 17 m water depth
(Frieder et al., 2012). This scale of variability associated with near-shore
environments is not captured by regional model simulations, but is most
relevant for organisms living inside the kelp forest.
Here, we present an anthropogenic ocean acidification model to project
CO2 chemistry into the future by combining autonomous chemical sensor
data, regional empirical relationships for the CO2 system (Alin et al.,
2012), hydrographic data, and the atmospheric CO2 record (Keeling et
al., 2005). This model was applied to four habitats ranging from the surface
to 100 m water depth and all within 5 km of each other in the Southern
California Bight (SCB). Each site showed distinct CO2 variability
signatures and acidification trajectories, highlighting the importance of
interpreting ocean acidification projections in the context of present and
future habitat-specific CO2 signatures. Implications for future ocean
acidification research are discussed.
Methods
Study sites
Moored autonomous sensor packages SeapHOx or SeaFET (Bresnahan et al., 2014) were deployed at four depths (4, 17, 30, and 88 m) within several distinct habitats on the San Diego continental shelf for 1 year starting June 2012 (Fig. 1). All
sensors were deployed near the seafloor; the three shallowest sensors were
deployed within 3 m of the bottom, and the deepest sensor was moored 12 m
above the seafloor (Table 1).
Summary of sensor deployments
Habitat type
Deployment site
DDa
BDb
Latitude
Longitude
Daysc
Surf zone
Scripps pier
4
6
32.87∘ N
117.26∘ W
122
Kelp forest
La Jolla kelp forest
17
20
32.81∘ N
117.29∘ W
128
Canyon edged
La Jolla canyon
30
30
32.86∘ N
117.27∘ W
302
Shelf break
Del Mar buoy
88
100
32.94∘ N
117.32∘ W
335
a Sensor deployment depth in meters. b
Bottom depth in meters. c Total deployment days between June 2012
and June 2013. d A linear drift correction for salinity was
applied for two of the four deployments.
Map of study region. Hydrographic stations (black dots) and sensor
deployment sites (black squares) are shown. Initials are as follows: CE, canyon
edge; SB, shelf break; SZ, surf zone; and KF, kelp forest.
A SeaFET was deployed at the Ellen Browning Scripps Pier 2 m above the
benthos as a part of the Scripps Ocean Acidification Real-time Monitoring
Program. The sensor was located approximately 400 m from the shore in the
surf zone. Weekly discrete samples for total alkalinity (TA) and dissolved
inorganic carbon (DIC) were taken alongside the sensors for calibration and
quality control following standard protocols (Dickson et al., 2007). The
sensor was serviced every 1–2 months to remove biofouling organisms.
The La Jolla kelp forest is part of the South La Jolla State Marine Reserve
and is characterized by a dense population of Macrocystis pyrifera.
The chemical variability in this ecosystem is strongly influenced by regional
physical processes (e.g., upwelling and stratification) and local biological
feedbacks (e.g., production and respiration). A SeapHOx was deployed at 17 m
in the southern portion of the kelp forest, 3 m above the bottom. The reader
is referred to Frieder et al. (2012) for further details on site and
deployment description.
The La Jolla canyon is a submarine canyon plunging from approximately 20 m
to a depth of 1000 m within several kilometers from shore. A SeapHOx was deployed
over a sandy bottom at the southern canyon edge at 30 m depth within the
Matlahuayl state marine reserve; the O2 sensor malfunctioned and thus is
not included. The water in the La Jolla canyon is characterized by higher
salinity and lower temperature, O2, and pH (data not shown). Tidal
energy in submarine canyons is significantly amplified (Swart et al., 2011),
bringing deep water from the canyon to the canyon edge. Therefore, physical
forcings are the dominant drivers for chemical variability at this site
(Navarro et al., 2013).
The Del Mar buoy was first deployed in 2006 at 100 m off of Del Mar in
northern San Diego at the shelf break, and has provided continuous time
series data (e.g., temperature, salinity, oxygen, and current) at discrete
depths (Frieder et al., 2012; Send and Nam, 2012). A SeaFET sensor was
deployed on the mooring at 88 m in 2011, and has provided a near-continuous
time series of pH since. Colocated sensors include temperature, salinity
(SBE 37), and dissolved oxygen (O2; Aanderaa optode). Water at this
depth is isolated from the atmosphere and below the euphotic zone, and thus
influenced primarily by upwelling and tidal dynamics.
Cruise data
Hydrographic data were collected aboard R/V Melville during the
student-led San Diego Coastal Expedition cruises in June/July and
December 2012 (Fig. 1). The SCB is characterized by relatively weak (compared
to the northern CCS) but nearly year-round upwelling. However, there is a
clear seasonal cycle based on climatological data, where upwelling
intensifies generally between April and August, with the maximum occurring in
May (Bograd et al., 2009). The cruises therefore corresponded with upwelling
(June/July) and non-upwelling (December) seasons. Water samples were
collected at stations ranging from > 100 km from shore at 1200 m water
depth, to within 5 km from shore at 30 m depth. Discrete samples were
analyzed for O2, pH, and DIC; duplicate samples were collected during
every cast.
Discrete samples for O2 were collected and analyzed by titration using a
custom-built system (Martz et al., 2012). The titrant was standardized prior
to and after each cruise using KIO3 standard solutions prepared in house
(Fisher, lot 105 595); no detectable drift was observed for either cruise.
Precision was ±0.6 µmol kg-1 (duplicate n= 62,
1 SD), and the accuracy was estimated to be ±0.5 % because KIO3
standards were not recrystallized (Emerson, 1999).
Samples for DIC and pH were collected in 150 or 250 mL Pyrex serum
bottles (13 mm neck) following standard procedures (Dickson et al., 2007).
However, rather than leaving headspace, the bottle was filled completely, and
a gray butyl stopper was inserted to prevent gas exchange; samples were
analyzed within 4 h of collection.
DIC samples were analyzed using a custom-built system based on an infrared
analyzer (LI-COR 7000) similar to systems built by others (Friederich et al.,
2002; O'Sullivan and Millero, 1998). The DIC measurements were calibrated
using certified reference materials provided by the Dickson Lab at SIO by
applying a gain correction (slope) and assuming an offset of zero
(intercept). The reference materials were stored in CO2-impermeable bags
(3 L Scholle DuraShield®) and were measured
frequently throughout the cruise. The stability of the reference material in
the bag was verified by daily measurements of a new bottle; no drift was
observed. Precision and accuracy of the DIC measurements were
±2.5 µmol kg-1 (duplicate n= 67, 1 SD).
Samples for pH were analyzed spectrophotometrically at 20 ∘C
(Clayton and Byrne, 1993) using an automated system (Carter et al., 2013).
The pH is reported on the total hydrogen ion concentration scale. The
indicator dye (m-cresol purple, ACROS lot A0264321) was used as received from
the manufacturer without further purification. An offset was applied based on
measurements in certified Tris buffer provided by the Dickson Lab. The
precision and accuracy of the measurements were estimated to be ±0.0015
(duplicate n= 86, 1 SD) and ±0.02 (Liu et al., 2011),
respectively. TA and pCO2 were calculated using CO2SYS (van Heuven et
al., 2011), with pH and DIC as inputs and carbonic acid dissociation
constants from Mehrbach et al. (1973) refit by Lueker et al. (2000).
Sensor data
The SeapHOx and SeaFET sensor packages utilize a modified Honeywell Durafet
III pH combination electrode for high-frequency pH measurements (Martz et
al., 2010). These sensor packages have been successfully deployed in
ecosystems worldwide (Frieder et al., 2012; Hofmann et al., 2011; Kroeker et
al., 2011; Martz et al., 2014; Price et al., 2012), and have been shown to
have excellent stability in seawater for months to years (Bresnahan et al.,
2014). The SeapHOx is an integrated sensor package that also consists of an
Aanderaa 3835 oxygen optode and a Sea-Bird SBE 37 conductivity–temperature
sensor all plumbed into a pumped flow stream; the SeaFET measures pH using a
passively flushed cell. Sampling frequencies were 1 h-1 or greater at
all depths.
All pH measurements were calibrated based on discrete TA and DIC samples
taken alongside the sensor, at minimum at the beginning and end of each
sensor deployment (n> 4 for every site), as recommended by the best
practices (Bresnahan et al., 2014). The resolution of the pH measurements is
better than 0.0005 pH, stability is estimated to be better than 0.005, and
accuracy is estimated to be ±0.015. Sensors were removed periodically for
maintenance, but all were deployed for > 50 days during both the upwelling
and relaxation season.
At the surf zone (surface waters), a constant TA value of
2240 µmol kg-1 was assumed, since discrete TA samples showed
low variability (2240 ± 7 (1 SD) µmol kg-1, n= 57). For the three subsurface sensors, TA was estimated
(TAest) using a regional empirical relationship developed for the
CCS, with temperature and salinity as inputs (Alin et al., 2012); an offset
of +8 µmol kg-1 was applied to TAest based on
comparisons to discrete samples collected (root mean squared error
(RMSE) = 6 µmol kg-1, n= 25). This offset was a
persistent feature over multiple years (2010–2012), thus most likely
reflecting a regional surface TA influence that is not incorporated in the
empirical relationship developed for the whole CCS. DIC, pCO2, and
ΩAr were calculated using CO2SYS (van Heuven et al., 2011)
with pH sensor data and TAest as inputs. Uncertainty for the
calculated DIC, pCO2, and ΩAr is pH-dependent but is on
average estimated to be ±13 µmol kg-1,
±25 µatm, and ±0.04, respectively. The daily range of sensor
data was calculated by first high-pass-filtering the data with a 36 h
window and then taking the difference between the daily maximum and minimum.
The mean daily range was then calculated by averaging the resultant time
series.
Modeling future carbonate chemistry
Approach
The carbonate conditions were modeled by decreasing or increasing DIC while
using TA conditions from 2012. Modeled projections were made for
preindustrial times and for year t, where t ranges between 2012 and 2100. The
model presented here is based on the ΔCt∗
approach (Gruber et al., 1996), but instead of using tracers to estimate the
age of the water mass (e.g., CFCs), we used the atmospheric CO2 record
as a quasi-age tracer. The age of the water mass ranged between 0 and 50
years in this study region. Although this approach must be used with caution,
we demonstrate that our estimates are in good agreement with previously
published anthropogenic carbon inventory estimates using age tracer
measurements in this region (Feely et al., 2008; Sabine et al., 2002). In
this model, it was assumed that ocean acidification is due to anthropogenic
CO2 invasion through the air–sea interface alone. We also assumed that
both the path of a particular water mass between the subduction and upwelling
site and the rate of remineralization processes remain unchanged. Sensitivity
to these assumptions is explored in the Discussion.
The DIC of the modeled year t (DICt) is calculated by
DICt=DIC2012+ΔDICanth,
where DIC2012 is the DIC observed in 2012, and ΔDICanth is the additional anthropogenic CO2 that the
water mass would have absorbed since 2012. Different formulations for ΔDICanth were used for surface waters (i.e., above the seasonal
mixed layer depth, defined here as σθ≤ 25.2 kg m-3) and subsurface waters (σθ > 25.2 kg m-3), and are outlined below.
For surface waters, ΔDICanth was calculated as the
difference in surface DIC between year t and 2012. Surface DIC was
calculated by assuming atmospheric equilibrium with
TA = 2240 µmol kg-1 (based on water samples from the
Scripps pier) and using pCO2,atm projection under the 2013 IPCC
RCP6.0 scenario (Hijioka et al., 2008). Although large deviations from
equilibrium conditions are often observed in the coastal ocean due to
upwelling and biological production (Hales et al., 2005), the mean
pCO2 calculated from sensor data at the surf zone was 394 ± 43 (1 SD) µatm (Table 2), suggesting that the surface water at the
study site was near atmospheric equilibrium.
Mean ±SD of modeled carbonate parameters at in situ conditions
for preindustrial, 2012, 2060, and 2100 using the RCP6.0 projection at each
habitat.
Year
pCO2
ΩAr
pH
(µatm)
Surf zone
Preind.
267 ± 26
3.09 ± 0.21
8.19 ± 0.034
(4 m)
2012
394 ± 43
2.38 ± 0.25
8.05 ± 0.038
2060
473 ± 56
2.09 ± 0.19
7.98 ± 0.041
2100
619 ± 80
1.71 ± 0.18
7.88 ± 0.045
Kelp forest
Preind.
365 ± 74
2.28 ± 0.42
8.08 ± 0.078
(17 m)
2012
516 ± 108
1.77 ± 0.36
7.95 ± 0.083
2060
683 ± 156
1.43 ± 0.33
7.84 ± 0.094
2100
937 ± 231
1.11 ± 0.29
7.72 ± 0.105
Canyon edge
Preind.
365 ± 68
2.29 ± 0.37
8.08 ± 0.068
(30 m)
2012
529 ± 105
1.75 ± 0.31
7.94 ± 0.075
2060
702 ± 155
1.40 ± 0.29
7.83 ± 0.085
2100
964 ± 231
1.09 ± 0.25
7.70 ± 0.095
Shelf break
Preind.
637 ± 132
1.38 ± 0.27
7.86 ± 0.083
(88 m)
2012
878 ± 149
1.05 ± 0.18
7.73 ± 0.070
2060
1195 ± 200
0.80 ± 0.15
7.61 ± 0.070
2100
1639 ± 246
0.60 ± 0.10
7.47 ± 0.065
For subsurface waters, ΔDICanth was quantified as the
increase in DIC due to anthropogenic CO2 when the water parcel was last
in contact with the atmosphere. The mass balance of DIC for subsurface waters
is
DIC=DIC∘+ΔDICbio,
where DIC∘ is the preformed DIC, and ΔDICbio is the
DIC added by remineralization processes in the ocean interior. DIC∘
can be expressed as the sum of DIC if it were in equilibrium with the
atmosphere (DICeq) and the degree of air–sea disequilibrium due
to slow gas exchange kinetics and biological processes (ΔDICdiseq):
DIC∘=DICeq+ΔDICdiseq.
Since anthropogenic CO2 only enters the ocean at the surface, the
increase in DICeq represents the anthropogenic ocean
acidification signal, ΔDICanth, assuming ΔDICdiseq is invariant with time. However, in order to use this
approach, the age of the water parcel must first be quantified, as this
determines the pCO2,atm with which it was last in contact.
The age of the water parcel was established by combining Eqs. (2) and (3):
DICeq2012-age=DIC-ΔDICbio-ΔDICdiseq,
where the superscript denotes the year at which the water parcel was last at
the surface (i.e., equal to 2012 minus age of the water mass). The age of the
water mass was calculated by comparing the atmospheric CO2 record to the
pCO2,atm that is necessary to generate
DICeq2012-age. ΔDICdiseq
was estimated from published values in the region (Sect. 2.4.3). Using this
information, we calculated ΔDICanth by
ΔDICanth=DICeqt-age-DICeq2012-age,
where the superscripts denote the year at which the water parcel was last at
the surface, and age is the age of the water parcel. For example, if
age = 30 yr and t= 2050, then CO2 projections for the year
2020 would be used to calculate DICeqt-age;
DICeq2012-age was calculated from Eq. (4).
The ΔDICanth for subsurface waters was modeled for each
projection year as a linear function of σθ, and the surface
and subsurface ΔDICanth were connected assuming a
two-end-member linear mixing between σθ 25.2 and 25.5 kg m-3
to prevent step changes (Fig. 2).
ΔDICanth as a function of σθfor
certain modeled years (indicated above line) using the IPCC RCP6.0
projection. The three regimes used in this model – surface, mixing, and
subsurface – are labeled.
Calculation of ΔDICbio
ΔDICbio was quantified following formulations in Sabine et
al. (2002):
ΔDICbio=rC:O(AOU)-0.5TAobs-TA∘+rN:O(AOU),
where AOU (apparent oxygen utilization) = (O2,sat - O2,obs), TA∘ is
the preformed alkalinity, and the r's are the elemental remineralization
ratios (Anderson and Sarmiento, 1994). The oxygen saturation concentration
(O2,sat) was calculated using the equations in Garcia and
Gordon (1992), and TA∘ was estimated based on historical
near-surface TA data in the Pacific (Eq. 3 in Sabine et al., 2002).
Phosphate concentrations necessary to estimate TA∘ were not
directly measured but instead estimated from a regional empirical relationship
using historical data (Supplement); the uncertainty in estimating phosphate
using this approach propagates to an error in TA∘ of
4 µmol kg-1.
Estimation of ΔDICdiseq
Making accurate estimates of ΔDICdiseq is important
because it is a source of large uncertainty for anthropogenic carbon
inventory calculations (Matsumoto and Gruber, 2005). Traditionally, age of
the water mass is quantified using tracers such as CFCs and then the ΔDICdiseq is subsequently calculated (Gruber et al., 1996; Sabine
and Tanhua, 2010). However, such tracer measurements were not made for this
study. Alternatively, we estimated ΔDICdiseq based on
θ and S data to overcome this limitation (Sabine et al., 2002). The
mean θ and S between σθ of 25.5 and
26.5 kg m-3 were 10.0 ∘C and 33.9, respectively, resembling
water type 1e in Sabine et al. (2010) with a corresponding ΔDICdiseq=-6.24 µmol kg-1, the value used
in this study.
Calculation of the age of water parcel
In order to estimate the age of the water mass, we use Eq. (4) to calculate
DICeq2012-age, which is the DIC of the
water parcel that was in equilibrium with the atmosphere when it was last at the
surface (i.e., equal to 2012 – age of the water mass). Therefore the age of
the water mass can be calculated by comparing the atmospheric CO2 record
to the pCO2,atm that is necessary to generate
DICeq2012-age. The latter was calculated
from the fugacity of CO2 of the water mass when it was last in contact
with the atmosphere at the time of subduction
(fCO2,eq2012-age), assuming 100 % relative
humidity and a barometric pressure of 1 atm (Dickson, 2007). The year that
the water parcel subducted was determined by matching the calculated
CO2,atm to the mean annual CO2,atm record (Keeling et
al., 2005); the age is the difference between 2012 and the calculated year
(Fig. 3). A relationship between σθ and the age was
established by fitting a second-order polynomial to the subsurface data
(n = 186, R2= 0.92) and assuming the age of the surface
water (σθ<25.2) to be 0 (Fig. 3). The nonzero age of the
water that appears around σθ= 24.4 kg m-3
corresponds to the shallow oxygen maximum layer that formed during the
summer. However, since this density range is still shallower than the
seasonal mixed layer, its age was considered to be 0. The age of the water
ranged between 0 and 50 years between σθ of 25.2 and
26.5 kg m-3.
fCO2,eq2012-age calculated from hydrographic
data (left) and the calculated age–σθ relationship (right) is
shown. Good agreement (R2 = 0.87) between the data (black circles)
and the fit (age = 8.852 (σθ-25.2)2+23.132
(σθ-25.2); red line) is observed (right). Age of surface
waters (σθ<25.2 kg m-3) was assumed to be 0.
Estimation of preindustrial DIC
In order to calculate the preindustrial DIC, Eq. (3) is written as
DIC∘=DICeqprein+DICanth+ΔDICdiseq,
where DICanth represents the anthropogenic carbon present in the
water parcel in 2012, and DICeqprein is the DIC
of the water parcel if it were in equilibrium with
pCO2,atm= 280 µatm. Combining Eqs. (2) and (7) and
rearranging gives
DICanth=DIC-ΔDICbio-DICeqprein+ΔDICdiseq.
Calculated DICanth as a function of σθ is shown
in Fig. 4. Note that the values calculated here are in good agreement with
published values using age tracers (Feely et al., 2008; Sabine et al., 2002)
for higher σθ but are significantly higher at lower
densities. This is because the literature values were quantified using
offshore subsurface waters, whereas our study region is near the coast along
an upwelling margin, where subsurface waters are brought near the surface and
are thus affected by surface processes. The agreement at higher density where
surface influence is minimal demonstrates that the model presented here is
capable of making accurate estimates of anthropogenic CO2. Furthermore,
the comparison illustrates the importance of incorporating surface influence
when making acidification projections in shallow, coastal ecosystems.
Preindustrial DIC (DICprein) was calculated by subtracting
DICanth from DIC observed in 2012. Preindustrial pCO2,
ΩAr, and pH were calculated using DICprein and TA
conditions from 2012.
DICanth as a function of σθ. The calculated
values and the fit are represented by black circles and a red line,
respectively. The blue line shows DICanth using the formulations
from Feely et al. (2008).
Results
Carbonate chemistry variability observed in 2012
The results are presented using pH, pCO2, or ΩAr, since
pH was directly measured and pCO2 and ΩAr are commonly
used as stress indicators for respiration (Brewer and Peltzer, 2009) and
calcification (Langdon et al., 2010), respectively. Across all four sites in
2012, pCO2 increased with depth. In 2012, the mean pCO2 in the
surf zone was near atmospheric equilibrium (394 µatm), while the
mean pCO2 at 88 m was 878 µatm (Table 2) and reached a
maximum of 1270 µatm. The variability in pCO2 also increased
with depth (indicated by the SD of the time series), which was only
43 µatm in the surf zone but was 149 µatm at 88 m depth.
The mean (±1 SD) ΩAr decreased with depth; the mean
ΩAr in the surf zone was 2.4 ± 0.25,
whereas it was 1.05 ± 0.18 at 88 m.
Undersaturated conditions (ΩAr<1) were observed 48 %
of the time at 88 m in 2012 but were not observed at other sites. However,
unlike pCO2, the variability in ΩAr, indicated by the
SD, decreased with depth (0.25 at the surface to 0.18 at 88 m) (Table 2; see
Discussion). The mean pH decreased with depth; the mean pH in the surf zone
was 8.05, whereas it
was 7.73 at 88 m. The variability in pH increased with depth until 30 m,
but decreased at 88 m (Table 2).
Distinct, habitat-specific CO2 signatures were observed at the four
deployment sites (Figs. 5, 6 and 7). Here, we define habitat-specific
CO2 signatures as how CO2 conditions varied in that habitat,
regardless of biological or physical origin. In the surf zone, the conditions
were near atmospheric equilibrium, with intrusions of higher pCO2
waters through internal tidal bores, a common feature observed in shallow,
upwelling environments (Booth et al., 2012; Pineda, 1991); temperature and pH
were correlated during these events (Supplement Fig. S2). This leads to a
high, mean daily range of CO2 conditions (e.g., 96 µatm, 0.46,
and 0.085 for pCO2, ΩAr, and pH, respectively)
(Table 3). However, the signature from the internal bores usually only lasted
several hours, and remained at near atmospheric equilibrium for the majority of the time. Higher
occurrence of tidal bores was observed during the spring and summer months
relative to winter (Fig. 5), consistent with previous observations (Pineda,
1991). The mean diel range of pCO2 at the surf zone was significantly
higher than measurements made by a surface mooring located offshore in the
SCB (Leinweber et al., 2009).
Time series of sensor pH between June 2012 and June. The two lower panels
are month-long snapshots. pH is reported at in situ conditions.
Mean daily range of carbonate parameters at in situ conditions for
2012 at each habitat.
pCO2
ΩAr
pH
(µatm)
Surf zone
96
0.46
0.085
Kelp forest
68
0.22
0.054
Canyon edge
167
0.48
0.120
Shelf break
110
0.14
0.053
The mean (±SD) CO2 conditions in the kelp forest and the canyon edge
were similar, and the SDs for ΩAr and pH were the highest among
the sites (Table 2). However, the timescales of the variability were
different, indicating that distinct processes control the CO2 conditions
in these two habitats. For example, the mean daily range for all the
variables was significantly higher at the canyon edge compared to the kelp
forest (Table 3). Submarine canyons are known to amplify tidal energy
(Navarro et al., 2013; Swart et al., 2011), and, in fact, periodic variability
at the canyon edge occurred on semi-diurnal and diurnal cycles, indicative of
tidal forcing. Temperature and pH were correlated on these shorter timescales
(Fig. S2), further supporting the fact that the variability was dominantly
driven by intrusion of cold, deep waters from the canyon.
While tidal forcings and daily biological production are drivers for
carbonate chemistry in the La Jolla kelp forest, the largest variability
occurred on event timescales (Frieder et al., 2012); event timescales are
defined as longer than a day but shorter than several weeks. For example,
pH, pCO2, and ΩAr regularly changed by up to 0.3,
250 µatm, and 1.3 on event timescales, more than 3 times the
mean daily range. Variability on event timescales is due to a combination of
changing water mass, stratification, and biological respiration (Frieder et
al., 2012). In addition, a clear seasonal pattern was observed at the canyon
edge, where higher pCO2 and lower pH and ΩAr were
observed during the spring and summer months (upwelling season) and lower
pCO2 and higher pH and ΩAr were observed during the
fall and winter (relaxation season). Due to incomplete data coverage, a
seasonal trend at the kelp forest and pier sites could not be discerned. The
largest seasonal change among the four sites for ΩAr
(∼ 1) was observed at the canyon edge; pCO2 differed by roughly
200–300 µatm between the two seasons.
The shelf break experienced the highest mean CO2 conditions and the
highest and lowest SD for pCO2 (149 µatm) and
ΩAr (0.18), respectively (Table 2); the SD of pH (0.070) was
lower than the kelp forest (0.083) or the canyon edge (0.075). Variability on
tidal, event, and seasonal timescales were observed at this site (Figs. 5,
6, and 7), as has been previously reported for oxygen (Send and Nam, 2012).
In general, upwelling on event timescales led to greater changes in
pCO2 and pH than on tidal frequencies (Figs. 5 and 6). The largest
variability for all parameters occurred between the seasons, where changes
in pCO2, pH, and ΩAr were approximately
350 µatm, 0.2, and 0.5, respectively. The close proximity of these
four sites demonstrates the wide variety of habitat-specific CO2
signatures that exist over a small spatial scale, especially in near-shore
environments.
Time series of pCO2 calculated from sensor pH and TAest between
June 2012 and June 2013. The two lower panels are month-long snapshots. pCO2
is reported at in situ conditions.
Time series of ΩAr calculated from sensor pH and
TAest between June 2012 and June 2013. The two lower panels are
month-long snapshots. ΩAr is reported at in situ conditions.
Modeled carbonate chemistry
Each habitat showed distinct trends in both modeled mean and variability in
pCO2, pH, and ΩAr owing to increased levels of
anthropogenic DIC (ΔDICanth) (Table 2). For example, the
mean pCO2 at the surf zone (4 m), canyon edge (30 m), and shelf
break (88 m) increased by 225, 435, and 738 µatm, respectively,
from 2012 to 2100; this drastic difference in increased mean pCO2 is
driven by different buffer factors due to depth differences among the sites.
The increase in variability (i.e., SD) was also larger at 88 m
(97 µatm) compared to the surf zone (37 µatm), although
the largest increase occurred at the canyon edge at 30 m
(126 µatm) (Table 2). Similar trends were observed for mean pH,
where the largest mean decrease in pH occurred at 88 m water depth (0.26).
However, the SD increased into the future for the three shallowest sites,
whereas the SD decreased at the shelf break (88 m). In contrast, the largest
decrease in the mean ΩAr occurred at the surface relative to
the deeper sites, whereas the decrease in range was equivalent across all
depths.
The measured and modeled time series for pCO2 and ΩAr
at the shelf break for the year 2012 and 2100 are shown in Figs. 8 and 9. The
variability in pCO2 increases on both seasonal and tidal timescales;
the seasonal amplitude increases from approximately 350 to 650 µatm
and the mean daily range increases from 110 to 325 µatm by 2100.
This greater variability is in addition to an increase in mean pCO2 of
> 700 µatm. On the other hand, the variability in ΩAr on both seasonal and shorter timescales decreases. Furthermore,
the shelf break is projected to experience undersaturated waters over
90 % of the time by 2060, compared to 48 % in 2012. Similar patterns
were observed in the kelp forest as well, where both the mean conditions and
variability in pCO2 increased and ΩAr decreased
(Fig. 10). The largest variability at the kelp forest occurred on timescales
of days to weeks, and high-frequency (<1 day) variability was
significantly smaller than at the shelf break. Therefore benthic organisms at
the kelp forest would experience elevated CO2 conditions for prolonged
periods of time, with only intermittent exposure to near-atmospheric
conditions.
Observed
pCO2 in 2012 (black) and modeled pCO2 using the IPCC RCP 6.0
scenario for the year 2100 (red) at the Del Mar buoy (88 m) over an annual
cycle (top). A close-up for the month of December is shown in the bottom
panel. Note that the range, but not the absolute values, of the vertical axes
for each figure is the same.
Observed ΩAr in 2012 (black) and modeled
ΩAr using the IPCC RCP6.0 scenario for the year 2100 (red) at
the shelf break (88 m) over an annual cycle (top). A close-up for the month
of December is shown in the bottom panel. Dashed lines represent
ΩAr = 1. Note that the range, but not the absolute values,
of the vertical axes for each figure is the same.
Observed (black) and modeled (red) pCO2 (top) and
ΩAr (bottom) at the La Jolla kelp forest (17 m). Modeled
values correspond to projected values in 2100 using the IPCC RCP6.0 scenario.
Note that the range, but not the absolute values, of the vertical axes for
each figure is the same.
Preindustrial pCO2 and ΩAr were compared to conditions
observed in 2012 (Table 2). At most sites, the observed pCO2, pH, and
ΩAr in 2012 were already outside of their preindustrial
variability envelopes (defined as mean ±1 SD), which is consistent with
results from a previous ROMS simulation in the CCS (Hauri et al., 2013b).
These results suggest that all habitats studied here have left, or are about
to leave, the pCO2, pH, and ΩAr conditions that were
experienced during preindustrial times. This is significant as organisms at
these sites are now surviving in conditions that are significantly different
than the conditions under which their ancestors evolved.
The modeled habitat-specific pCO2 and ΩAr conditions
for preindustrial, 2012, 2060, and 2100 are shown in Fig. 11. The histograms
represent the full range of carbonate conditions at each habitat that was
captured by the sensors, which includes both the seasonal and high-frequency
variability. The shape of each distribution skews towards more “corrosive”
conditions at all sites as the model steps forward into the future. This
translates to not only increases in mean pCO2 but also greater
extremes and amount of time spent in extremes.
Histogram of modeled pCO2 (left) and ΩAr (right)
distribution at the four depths for preindustrial (black), 2012 (green), 2060
(blue), and 2100 (red). Atmospheric pCO2 for the years 2060 and 2100
roughly corresponds to 510 and 670 µatm based on the IPCC RCP6.0
scenario.
The projected pCO2 and ΩAr envelopes (mean ±SD) at
each habitat throughout the modeled period are shown in Fig. 12. An
increasing rate of change in pCO2 (ΔpCO2 yr-1) is
observed, whereas ΩAr tends to decrease at a relatively
constant rate. The rate of increase in pCO2 is higher than the
projected atmospheric CO2 increase at all subsurface sites. This
indicates that as ocean acidification progresses, the effects due to elevated
pCO2 are more likely to become exacerbated with increasing depth. Mean
ΩAr is projected to be <1 at the shelf break by 2020 and
leave the 2012 variability envelope around 2070.
Projected pCO2 (left) and ΩAr (right) between
2013 and 2100 under the IPCC RCP6.0 scenario. The solid line and the shaded
region represent the mean and ±1 SD, respectively.
Discussion
Changes in the buffer factors
The general patterns of the acidification trajectories presented here can be
explained by changing buffer factors of seawater, as deeper sites are more
strongly influenced by CO2-rich upwelled waters. The buffer factors
ΠpCO2, ΠpH, and ΠCO3, are defined
as
ΠpCO2=∂pCO2∂DIC,ΠpH=∂pH∂DIC,ΠCO3=∂CO32-∂DIC,
representing the change in each carbonate parameter with respect to a change
in DIC (Frankignoulle, 1994). The effect of temperature on Π is small
(<10 %) between 0 and 15 ∘C for the DIC and TA values observed
here; thus subsequent values were calculated assuming a temperature of
10 ∘C, TA = 2240 µmol kg-1, and
salinity = 33.5 (Fig. 13). The ability for seawater to buffer changes in
pCO2 diminishes under higher concentrations of DIC. For example,
ΠpCO2 increases from 1.6 to 3.3 at the surface between 2012
and 2100 under the RCP6.0 scenario. However, since deeper waters are
naturally elevated in DIC, this effect is more pronounced at the shelf break:
ΠpCO2 increases from 6.2 to 12.3 during the same time
interval. This explains why the surf zone had the lowest mean increase in
pCO2 (225 µatm) despite having the highest increase in DIC
(82 µmol kg-1) out of all of the sites. The shelf break, on
the other hand, had the highest increase in pCO2 (737 µatm)
while having the smallest increase in mean DIC (77 µmol kg-1)
during the same time period. Furthermore, the increase in variability with
depth can be explained as well, as the same biological and physical forcings
on tidal to seasonal cycles cause a larger change in pCO2.
Buffer factor Π for pCO2 (a), CO32-
(b), pH (c), and [H+] (d) as a function of DIC.
Note the inverted y axis in panels (b) and (c). Model
parameters to calculate the Π values were
TA = 2240 µmol kg-1, temperature = 10 ∘C,
salinity = 33.5, pressure = 1.013 bar.
Changes in ΠCO3 can explain the patterns for
ΩAr, since [Ca2+] and KSP remain unchanged.
Unlike ΠpCO2, |ΠCO3| decreases at higher
concentrations of DIC (Fig. 13b); |ΠCO3| decreases from 0.62
to 0.57 at the surface, and 0.49 to 0.3 at 88 m between 2012 and 2100. This
change in ΠCO3 explains the
decrease in both the rate and range of ΩAr as anthropogenic
CO2 continues to infiltrate the ocean.
The ΠpH follows a parabolic shape, where there is a maximum
decrease in pH per DIC added (Fig. 13c). In a pure carbonate solution, this
maximum occurs when DIC = TA, but in seawater it occurs at slightly lower
DIC (Frankignoulle, 1994); this maxima occurs at
DIC = 2225 µmol kg-1 using the parameters listed above.
Therefore we would expect to see a similar trend to pCO2 for pH in
which greatest changes occur at depth
relative to the surface as long as the mean DIC is lower than this threshold.
This condition is only met at the shelf break (88 m) near the end of the
century; thus, as expected, a greater decrease in mean pH and increase in
variability (i.e., SD) was observed with depth (Table 2). One exception was
observed where the SD decreased as ocean acidification progressed at the
shelf break. This is because an increased proportion of time is spent at
greater DIC where ΠpH is past its maxima, leading to a smaller
variability in pH under the same changes in DIC. It is important to note that
the buffer factor of H+ (ΠH+) follows a similar pattern to
pCO2, where it continues to increase as DIC increases (Fig. 13d).
Therefore the rate of increase of [H+] will continue to increase as
ocean acidification progresses, and thus biological responses to [H+]
may become exacerbated in the future.
Observed and modeled carbonate chemistry variability
The carbonate conditions presented here are consistent with previous studies.
For example, at the shelf break, ΩAr had a strong seasonal cycle, and undersaturated waters were observed almost continuously throughout the upwelling season (Nam et
al., 2015), and remained supersaturated for the rest of the year. This is in
good agreement with previous hydrographic surveys in this region, where
aragonite-undersaturated waters were observed as shallow as 60 m during the
beginning of the upwelling season (Feely et al., 2008) but were not observed
in the upper 100 m at the end of the upwelling season in this region
(Bednaršek et al., 2014). Furthermore, estimates based on empirical
equations showed a similar seasonal pattern in ΩAr at 88 m,
where undersaturated waters were observed every upwelling season (Alin et
al., 2012). However, undersaturated waters were not observed in the upper
30 m, unlike northern parts of the CCS where undersaturated conditions are
repeatedly observed at the surface during the upwelling season (Bednaršek
et al., 2014; Feely et al., 2008; Harris et al., 2013). Due to these traits,
the southern portion of the CCS is commonly considered less vulnerable to
ocean acidification compared to its northern counterpart. However, our
results demonstrate that ΩAr as low as 1.3 is routinely
observed in the kelp forest (17 m), demonstrating the imminent threat of
anthropogenic ocean acidification to the southern CCS.
The subsurface habitats characterized in this study routinely experience
ΩAr conditions that have been shown to have non-lethal chronic
effects on various bivalve larvae between ΩAr of 1.2 and 2.0
(Barton et al., 2012; Gaylord et al., 2011; Gazeau et al., 2011; Hettinger et
al., 2012; Waldbusser et al., 2015). However, the length of exposure to these
unfavorable conditions varies between habitats. For example, the organisms in
the kelp forest would be exposed to low-ΩAr conditions for
days to weeks, whereas large tidal variability at the canyon edge could
result in periodic exposure to low-ΩAr conditions on the order
of hours. Therefore the effects of low ΩAr will largely depend
on the reproductive timing and environmental variability that occur on event
to seasonal timescales; the effects of exposure on various timescales are
poorly understood. Such events are expected to become more severe in the
future (Hauri et al., 2013a) and thus could lead to an increased rate of
failed recruitment of bivalves and other keystone organisms (Byrne et al.,
2013).
It may be surprising that the mean diel range of pH was the smallest at the
kelp forest (Table 3), as one might expect a large diel cycle driven by
photosynthesis and respiration in a highly productive kelp forest. This is
most likely because the sensor was deployed near the benthos, below the most
productive region of the forest. Frieder et al. (2012) observed significantly
larger diel pH variability closer to the surface (7 m depth) compared to
near the bottom (17 m depth; same as this study), demonstrating that the
biologically driven diel cycle diminishes with increasing depth within the
canopy. Therefore it is important to keep in mind that the results presented
here are not reflecting kelp forest production dynamics but rather conditions that are experienced by benthic dwelling organisms inside the kelp
forest.
The trajectories are sensitive to the choice of the emission scenario
(Fig. 14). Trends are similar at all depths; thus only the mean pCO2
and ΩAr projections at the shelf break are shown in Fig. 14.
The highest emission scenario (RCP8.5) diverges from the two intermediate
scenarios around 2030, while the lowest emission scenario (RCP2.6) diverges
around 2050. The two intermediate scenarios (RCP4.5 and RCP6.0) do not
diverge significantly until 2070. The delayed response to different
atmospheric CO2 trajectories occurs because upwelled waters have spent
several decades since they were last in contact with the atmosphere (Feely et
al., 2008). Therefore the anthropogenic ocean acidification trajectory for
the SCB is already determined for the next several
decades, and any mitigation due to changing CO2 emissions will be
delayed.
Projections of mean pCO2 (left) and ΩAr (right)
at the shelf break (88 m) based on four projections from the Fifth
Assessment of the IPCC.
The results presented here are site-specific, and they do not necessarily reflect
conditions at all kelp forests, canyon edges, and shelf breaks. However, if
sensor pH data and corresponding regional hydrographic surveys are available,
then a ΔDICanth–σθ relationship can be
established for that region and applied to the sensor data. For example, this
approach can potentially expanded to many regions for the CCS, using the
North American Carbon Program West Coast Cruise (Feely et al., 2008) and the
ΔDICdiseq for the Pacific Ocean (Sabine et al., 2002). If
similar data exist, then this approach can be expanded to other upwelling
margins as well.
The SCB experiences a steady but weaker degree of
upwelling compared to the northern regions of the CCS, where upwelling events
are more pronounced (Bograd et al., 2009). These regions could experience
more extreme conditions regularly, as well as significantly higher
variability in carbonate conditions (Harris et al., 2013). However, such
dynamics are poorly understood, and more high-frequency observations of
carbonate parameters along this system are needed. Source water properties
must be characterized through hydrographic surveys. Alternatively, for
regions where such data for source waters are not available, sensor data can
be combined with either global circulation model or ROMS outputs. This
approach will alleviate the cost associated with characterizing source
waters and, to a large degree, incorporate processes such as interannual
variability, decadal changes in source water properties, and reduced
ventilation. It is critical that inorganic carbon sensors (e.g., pH or
pCO2) are colocated with basic physical oceanographic measurements
(e.g., T and S) to determine source water properties especially for
subsurface deployments.
Model assessment
The sensitivity of the projected carbonate conditions to the assumptions made
in the model is explored here. For example, temperatures observed in 2012
were used to parameterize the model. Sea surface temperature has increased
over the past century due to climate change (Smith et al., 2008), and is
expected to continue. This will affect the CO2 equilibrium concentration
(DICeq), but the effects are small and will reduce
DICeq by only several µmol kg-1. Both pCO2
and ΩAr are dependent on in situ temperature; the effects on
ΩAr are negligible (ΔΩ / ΔT<0.01∘ C-1), whereas ΔpCO2 / ΔT
increases at higher pCO2 levels, and can be as large as
60 µatm ∘C-1 at the end of the century, compared to
30 µatm ∘C-1 at present day at the shelf break. These
temperature dependencies will affect the mean conditions, but the magnitude
of the variability will be relatively unaffected. However, it should be noted
that this simple error analysis does not include any biological feedbacks
that increased temperature or CO2 may induce. For example, phase shifts
from kelp-dominated to algal turfs might be an outcome of sea surface warming
and acidification (Connell and Russell, 2010), with implications for
habitat-scale biogeochemical cycling. Likewise, higher temperatures may
increase remineralization rates along the path of the subducted water (Rivkin
and Legendre, 2001), further enhancing acidification.
TA conditions from 2012 were used to calculate pCO2 and ΩAr for all years. Changes in TA affect the buffer factors of
seawater; thus, alterations in TA distribution will either speed up or slow
down the progression of ocean acidification. However, trends in TA along the
CCS on decadal timescales are unknown due to insufficient data. Reduced
ventilation in high-latitude seas, altered precipitation patterns, and
changes in surface calcification and water-column dissolution rates would all
lead to changes in upwelled TA conditions (Fassbender et al., 2011; Lee et
al., 2006). Quantifying these processes is difficult and out of the scope of
this study. Nevertheless, to demonstrate the magnitude of the uncertainty due
to TA, pCO2 was projected for the year 2100 with a
+20 µmol kg-1 bias to TA. The effects were strongly
dependent on depth: mean pCO2 was reduced by approximately 240, 130,
and 70 µatm at 88 m, 30 m, and the surface, respectively.
Finally, the model presented here projects future carbonate conditions by
assuming that the dynamics that control the variability at each habitat (e.g.,
seasonal and episodic upwelling events, internal waves and tides, and
biological production and respiration) remain the same as 2012 conditions,
and it does not account for any variability that occurs on interannual to
decadal timescales. For example, changes in O2 and pH on the continental
shelf associated with interannual climate events, such as El Niño, have
been observed (Nam et al., 2011). However, since 2012 did not correspond with
a strong El Niño or La Niña phase, we believe that it was not
strongly biased by such events. Furthermore, recent evidence suggests that
the proportion of Pacific Equatorial Waters in the California Undercurrent
has been increasing over the past several decades, thus modifying the source
water properties for upwelled waters onto the continental shelf (Bograd et
al., 2015). Since waters of equatorial origin observed between 100 and 500 m
are elevated in DIC and lower in O2 (Bograd et al., 2015), it is
expected that the SCB will experience higher levels of
acidification than predicted from this study if this redistribution of water
masses of equatorial origin continues. However, at this time, we lack
observations with sufficient longevity to predict how climate variability on
interannual to decadal timescales might modify the acidification trajectory
over the course of the next century. Sustained, high-frequency time series of
inorganic carbon parameters are required to elucidate such effects.
Implications for ocean acidification research
In order to properly assess the impacts of anthropogenic ocean acidification
through laboratory manipulation experiments, the control and experimental
conditions should accurately reflect the study organism's present-day and
future habitat conditions (McElhany and Busch, 2013; Reum et al., 2015). The
most common control treatment used in ocean acidification experiments for
organisms found in the CCS was a pCO2 value of
∼ 400 µatm, reflecting atmospheric conditions (compiled by
Reum et al., 2015). However, our sensor data showed that all subsurface
habitats had significantly greater pCO2 relative to the atmosphere
(Table 2). For example, the mean pCO2 at the kelp forest is about
100 µatm greater than the atmosphere and routinely experiences
conditions of more than 300 µatm above atmospheric. Therefore
utilizing atmospheric pCO2 conditions for control treatments will
necessarily underestimate the baseline pCO2 for organisms collected
from subsurface habitats.
Recent studies that incorporate natural variability into ocean acidification
experiments observed modified responses relative to constant conditions
(Dufault et al., 2012; Frieder et al., 2014). However, the effect of natural
variability on organismal response to ocean acidification, especially through
various life stages is still poorly understood. Our model results demonstrate
that variability trajectories are also habitat-specific. For example, in the
kelp forest, the variability, approximated by the SD, was 93 µatm
in 2012, whereas this increased to 202 µatm in 2100 (Table 2).
Furthermore, despite having similar mean CO2 conditions, the largest
variability was observed on event timescales in the kelp forest, whereas the
dominant variability occurred on tidal and seasonal cycles at the canyon
edge. Therefore future ocean acidification studies investigating the effect
of natural variability should not only incorporate increasing magnitude into
their experimental design but also consider variability patterns on
appropriate timescales.
Temperature and O2 were tightly correlated with carbonate parameters
across habitats and various timescales (daily to seasonal) in this study
(Fig. 15); similar correlation has been documented across the CCS in general
(Reum et al., 2014, 2015). These parameters can potentially act as additional
stressors (Padilla-Gamiño et al., 2013) or stress reliefs (Gooding et
al., 2009) for ocean acidification. However, laboratory experiments
incorporating the effects of temperature (Gooding et al., 2009;
Padilla-Gamiño et al., 2013) and O2 (Frieder et al., 2014) have just
started to be explored for the CCS, and no studies have been conducted that
incorporate all three variables in their experimental design. Future studies
investigating the synergistic effects of O2, temperature, and CO2
should establish experimental conditions based on environmental data
(Fig. 15). Although development of systems that can manipulate individual
parameters is challenging, important strides have been made to make such
experimental setups accessible to the community (Bockmon et al., 2013). The
development of habitat-specific ocean acidification models provides a link
between environment and laboratory to facilitate interpretations of
physiological responses to elevated CO2 in the context of current and
future environmental conditions.
pCO2 as a function of O2 (left) and temperature (right) from
the kelp forest and shelf break. Data observed in 2012 and projected for 2100
are plotted.
Discerning habitat-specific CO2 signatures could lead to the discovery
of local populations that are more tolerant of future CO2 conditions.
For example, large high-frequency variability in CO2 could lead to a
greater capacity for physiological and phenotypic plasticity, as organisms
are routinely exposed to a wide range of CO2. The embryos of
Doryteuthis opalescens, an important fishery species in California, can
tolerate low pH and O2, perhaps due to the fact that they routinely
experience a wide range of pH and O2 (Navarro, 2014).
Furthermore, such environmental conditions may be conducive for the
existence of highly CO2-tolerant subpopulations, allowing for adaptation
to buffer some of the negative effects of ocean acidification
(Hofmann and Todgham, 2010). Alternatively, these
populations could be living near critical biological thresholds, as has been
suggested for the thermal stress of some organisms living in the intertidal
(Somero, 2002). A massive failure in an oyster hatchery in Oregon
was linked to upwelling of high-CO2 waters during a critical life stage
of oyster larvae (Barton et al., 2012), indicating the
existence of CO2 thresholds for some marine organisms
(Bednaršek et al., 2014).
However, such thresholds may be dependent on species, life stage, and/or
environmental history. As we begin to realize which populations of species
and life stages are living near acidification thresholds versus those that
exhibit acidification tolerance, implementation of habitat-specific
acidification models can be used as a tool to aid protection, management, and
remediation efforts of critical marine habitats now and in the future.