BGBiogeosciencesBGBiogeosciences1726-4189Copernicus GmbHGöttingen, Germany10.5194/bg-12-6017-2015Quantifying the influence of CO2 seasonality
on future aragonite undersaturation onsetSasseT. P.t.sasse@unsw.edu.auMcNeilB. I.MatearR. J.LentonA.Climate Change Research Centre, Kensington Campus,
University of New South Wales, Sydney, AustraliaCSIRO Oceans and Atmosphere National Research Flagship,
Hobart, AustraliaT. P. Sasse (t.sasse@unsw.edu.au)22October201512206017603131March201522April201518September20152October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/12/6017/2015/bg-12-6017-2015.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/12/6017/2015/bg-12-6017-2015.pdf
Ocean acidification is a predictable consequence of rising atmospheric
carbon dioxide (CO2), and is highly likely to impact the entire marine
ecosystem – from plankton at the base of the food chain to fish at the top.
Factors which are expected to be impacted include reproductive health,
organism growth and species composition and distribution. Predicting when
critical threshold values will be reached is crucial for projecting the
future health of marine ecosystems and for marine resources planning and
management. The impacts of ocean acidification will be first felt at the
seasonal scale, however our understanding how seasonal variability will
influence rates of future ocean acidification remains poorly constrained due
to current model and data limitations. To address this issue, we first
quantified the seasonal cycle of aragonite saturation state utilizing new
data-based estimates of global ocean-surface dissolved inorganic carbon and
alkalinity. This seasonality was then combined with earth system model
projections under different emissions scenarios (representative concentration pathways; RCPs 2.6, 4.5 and 8.5) to
provide new insights into future aragonite undersaturation onset. Under a
high emissions scenario (RCP 8.5), our results suggest accounting for
seasonality will bring forward the initial onset of month-long
undersaturation by 17 ± 10 years compared to annual-mean estimates,
with differences extending up to 35 ± 16 years in the North Pacific due
to strong regional seasonality. This earlier onset will result in
large-scale undersaturation once atmospheric CO2 reaches 496 ppm in the
North Pacific and 511 ppm in the Southern Ocean, independent of emission
scenario. This work suggests accounting for seasonality is critical to
projecting the future impacts of ocean acidification on the marine
environment.
Introduction
The global ocean currently absorbs about 30 % of annual fossil-fuel
CO2 emissions (Le Quéré et al., 2015), and will likely
sequester up to 80 % of all human-derived CO2 emissions over the
coming centuries (Archer et al., 1997). While this ecosystem
service largely mediates the rate of climate change, the immediate impact of
this additional CO2 is a shift in the ocean's chemical composition,
resulting in lower pH and carbonate ion (CO32-) concentrations – commonly
referred to as ocean acidification (OA;
Caldeira and Wickett, 2003).
Of great concern to marine ecosystems is the immediate impact OA is
presenting to multiple organisms. This includes organisms that require an
adequate supply of CO32- to form and preserve
their calcium carbonate (CaCO3) shells and skeletons (e.g. corals,
pteropods and coccolithophorids). Two key parameters for understanding how a
change in CO32- impacts marine calcifiers are
the saturation states for aragonite (ΩAr; Eq. 1) and calcite
(ΩCa; Eq. 2) – the two main CaCO3 minerals formed by
marine calcifiers.
ΩAr=[Ca2+][CO32-]/Ksp(Ar)∗ΩCa=[Ca2+][CO32-]/Ksp(Ca)∗
Here, [Ca2+] and [CO32-] represent the
concentrations of calcium and carbonate ions respectively, while
Ksp(Ar)∗ and
Ksp(Ca)∗ are the apparent stoichiometric
solubility products for aragonite and calcite. Laboratory and mesocosm
experiments suggest production and dissolution of biogenic CaCO3 are
mainly controlled by seawater Ω levels (Secretariat of the Convention on Biological Diversity, 2014; Fabry
et al., 2008). These experiments further indicate significant decreases in
calcification rates when test species are exposed to Ω levels below
their natural range for periods of days to weeks (Chan and
Connolly, 2013). Once seawater Ω levels fall below 1, referred to as
undersaturation, seawater becomes corrosive to CaCO3 and dissolution
can occur. Although experimental studies show detrimental impacts at
seawater Ω levels above 1 (e.g. Bednarsek et al., 2012; Fabry et
al., 2008), undersaturation is widely regarded as a key threshold value
(e.g. Hunt et al., 2008; Orr et al., 2005). Since aragonite is
approximately 50 % more soluble than calcite, resulting in earlier
undersaturation, the focus of this work is on future changes in ΩAr.
Several previous studies have used Earth system models (ESM) to predict
future annual-mean ΩAr levels under different CO2 emission
scenarios (Caldeira and Wickett, 2003, 2005; Cao et al., 2007; Kleypas et
al., 1999; Orr et al., 2005; Ricke et al., 2013). These annual-mean
projections suggest undersaturation will occur in the Southern Ocean and
high northern latitudes within the 21st century
(e.g. Orr et al., 2005). However, strong natural
seasonality in oceanic CO2 has the potential to significantly alter the
onset of future undersaturation, not captured by these approaches.
McNeil and Matear (2008) first demonstrated how strong CO2
seasonality in the Southern Ocean brings forward the initial onset of
month-long aragonite undersaturation conditions by ∼ 30 years
relative to annual-mean projections. More recent studies in Australia's
Great Barrier Reef (Shaw et al., 2013), Californian coast
(Gruber et al., 2012) and Arctic Ocean
(Steinacher et al., 2009) further demonstrate the
importance of accounting for natural CO2 seasonality when evaluating
future OA levels.
Despite significant efforts over recent years to establish a global carbon
measurement network (e.g. the Global Ocean Acidification Observation
Network; www.goa-on.org; (Newton et al., 2014)), such a
large-scale initiative remains limited by spatial and temporal variability
in oceanic CO2 coupled to the high cost of ship time, resulting in only
a limited understanding of CO2 seasonality throughout the global ocean
(Monteiro et al., 2010). This represents a
critical gap in our ability to understand and predict the influence of
natural variability for the future onset and duration of critical OA levels.
It is important to note that ESMs do provide some insights into regional
CO2 seasonality. However, it has been shown that the current generation of
ESMs do not accurately capture the observation-based magnitude and/or phase
of air–sea CO2 fluxes in most ocean regions, including the Southern
Ocean, North Pacific, Indian Ocean and subpolar North Atlantic (Ishii et
al., 2014; Lenton et al., 2013; Pilcher et al., 2015; Sarma et al., 2013;
Schuster et al., 2009). Consequently, these models do not realistically
characterize the seasonality of ΩAr.
Here, we use newly constrained data-based estimates of global ocean-surface
dissolved inorganic carbon (CT) and alkalinity (AT) of Sasse
et al. (2013b) to diagnose monthly ΩAr distributions for
the nominal year of 2000. We then project our monthly observational
baselines through to 2100 using decadal trends from an ensemble of Earth
system climate models (CMIP5) forced under different emissions scenarios
(RCPs 2.6, 4.5 and 8.5). These results provide new insights into the
influence of sea-surface seasonality on the likely onset times for future
aragonite undersaturation in the global ocean.
The work presented here expands on the study of McNeil and Matear (2008)
with several key improvements: (1) the global CO2
climatologies of Sasse et al. (2013b) better reflect the
latest observations and were derived using a more sophisticated method; (2)
we explore the potential for CO2 disequilibrium to evolve into the
future by exploiting CMIP5 model projections; (3) we project our
observational baseline using three different emission scenarios (RCP2.5, 4.5
and 8.5); (4) we apply the approach globally rather than to the Southern Ocean
alone.
Diagnosing monthly carbon system distributions
The ocean's inorganic carbon system can be fully constrained by knowing any
two parameters within its inorganic carbon constituents – partial pressure
of CO2 (pCO2), dissolved inorganic carbon (CT), total
alkalinity (AT) or pH (Dickson et al., 2007). Here we diagnose
monthly ΩAr distributions using the 1∘× 1∘CT and AT monthly climatologies of Sasse et al. (2013b) in combination
with the World Ocean Atlas 2013 (WOA13)
temperature, salinity and nutrient monthly surface distributions
(objectively analysed decadal averages; Garcia et al., 2014a, b;
Locarnini et al., 2013; Zweng et al., 2013). Since the CT climatologies
of Sasse et al. (2013b) were predicted for the nominal year of
2000 (see Sasse et al. (2013b) for details), the ΩAr values calculated here are also representative of this year.
All calculations were conducted using the total pH scale and carbonic acid
dissociation constants of Mehrbach et al. (1973) as refitted by
Dickson and Millero (1987), KSO4 dissociation constant of Dickson (1990b) and boric acid
dissociation constant of Dickson (1990a). Calculations of
ΩAr used the Ksp values of Mucci (1983) and
[Ca]-salinity relationship of Riley and Tongudai (1967).
To evaluate the realism of our global ΩAr predictions, we
compare the network of in situ ΩAr values to our corresponding
1∘× 1∘ predictions for the same month and
location (Fig. 1). In situ ΩAr values were calculated using
measured AT and CT concentrations, where CT values were first
normalized to the year 2000 via observed Revelle factors and assuming
constant equilibrium with the atmospheric CO2 increase (see Sasse et
al. (2013b) for details). Our data-based approach is
consistent with the general pattern of high ΩAr values in the
tropics which decrease poleward. Our approach also captures well the strong
ΩAr gradients at ∼ 40∘ north and south
and local ΩAr minimas in equatorial upwelling regions (see Fig. S1 in
the Supplement for monthly ΩAr distributions).
Statistical analysis finds the correlation between the global in situ values
and our corresponding space/month 1∘× 1∘
predictions to be 0.98, suggesting our approach accurately captures global
open-ocean ΩAr.
(a) In situ ΩAr measurements normalized to
the year 2000; (b) corresponding 1∘× 1∘ΩAr prediction for the same month and location for the nominal
year for 2000 (see Supplement Fig. S1 for our monthly ΩAr
distributions).
We further compare our zonal mean 1∘× 1∘ΩAr predictions for summer and winter to evaluate the ability
of our approach to capture seasonal variability (Fig. 2). The data-based
zonal pattern compares well to our general understanding of a strong
winter-time minimum in the higher latitudes driven by surface cooling and
strong persistent winds that ventilate deep-waters depleted in
CO32- (McNeil and Matear, 2008). The
stronger winter-time minimum in the Northern Hemisphere is consistent with
our findings of larger seasonal amplitudes in the North Pacific and North
Atlantic compared to the Southern Ocean (see Fig. 4).
Zonal mean ΩAr predictions for winter and
summer. Summer and winter months were defined as June through
to August and December through to February for the Northern Hemisphere
respectively, while Southern Hemisphere differed by 6 months.
Our monthly data-based ΩAr distribution also reconfirms the
contemporary ocean surface is supersaturated with respect to aragonite,
showing 99.3 % of monthly ocean-surface waters with ΩAr
levels greater than 1 in the year 2000. The only region where month-long
undersaturation was found is in the Arctic Ocean (see Fig. S2), which is
consistent with previous data-based (e.g. Mathis and
Questel, 2013) and model-based (e.g. Popova et al.,
2014) studies.
An independent data-based climatology for monthly ocean-surface ΩAr was presented by Takahashi et al. (2014;
hereinafter referred to as T14). In their approach, global ΩAr
distributions were calculated for the nominal year of 2005 on a 4∘× 5∘ resolution using a combination of interpolated
ocean-surface pCO2 and predicted AT values via a salinity and
nitrate relationship. Estimates in the equatorial Pacific were however
omitted due to strong interannual variability.
Comparison between T14 and our global ΩAr values (projected to
the year 2005; see Sect. 6) reveals a global correlation of 0.99, with mean
ΩAr values of 2.68 and 2.72 respectively. This good agreement
between two independent data-based approaches provides additional confidence
in our estimated ΩAr values. Several key benefits in using our
ΩAr baseline include: (1) better spatial resolution; (2)
inclusion of the equatorial Pacific; (3) independent uncertainty estimates in
our ΩAr predictions.
Quantifying uncertainties in our ΩAr predictions
The approach used here to diagnose surface ΩAr distributions
includes both systematic and random sources of error. The main source of
random error derives from uncertainties within the global open-ocean
CT and AT distributions, which have been estimated to be ±11.8
and ±10.2 µmol kg-1 respectively (Sasse
et al., 2013b). To quantify the corresponding uncertainty in our calculated
ΩAr values, we applied an independent testing approach using
16,727 mixed-layer CT and AT independent predictions of Sasse et al. (2013b).
In their approach, measurements from each cruise
(N= 470) and time-series station (N= 2) were individually excluded from
the empirical model training phase, and then used as an independent data set
to predict CT and AT concentrations. Here we employed this data set
to calculate ΩAr values using both the in situ CT and
AT measurements and their corresponding independent predictions.
Comparison between these values revealed a global uncertainty in our ΩAr predictions to be ±0.138 (residual standard error (RSE);
Fig. 3a), with summertime and wintertime RSE values of 0.142 and 0.126
respectively (Fig. 3c, e), indicating no strong seasonal biases.
Statistical plots comparing global ΩAr
values calculated via the network of in situ CT and AT measurements
and independently predicted CT and AT values of Sasse et al. (2013b).
(a) Global independent predictions versus in situ values, where the
red line represents y=x relationship. (b) Global distribution of
independent residual errors. (c, e) Summer- and wintertime independent
predictions versus in situ values. (d, f) Summer- and wintertime distribution
of the independent residual errors. Summer and winter months were defined as
May through to September and November through to March for the Northern
Hemisphere respectively, while Southern Hemisphere differed by 6 months.
Seasonal ΩAr amplitudes for the nominal year
of 2000. Seasonal amplitudes were calculated as the maximum minus minimum
monthly ΩAr values in each 1∘× 1∘ cell (see
Fig. S1 for monthly ΩAr distributions).
To evaluate our approach for systematic errors, we analysed the global
distribution of residual errors via the independent testing approach
described above (Fig. 3b). We further partitioned the residuals by season to
evaluate for any temporal bias (see Fig. 3d, f). The global, summertime and
wintertime residual error distributions all followed a near-normal
distribution with mean residual errors of 0.004, 0.001 and 0.007
respectively. This suggests no strong global or temporal biases exist in our
approach.
To assess for spatial biases, we partitioned the global independent
predictions into 14 ocean regions and calculated RSE values (Table 1; see
Fig. S3 for regions). Here we find all regional RSE values lie within
±0.04 units of the global RSE (0.138), with the exception the Arctic
Ocean, where the RSE value was 0.22 (N= 673). In particular, the Southern
Ocean is where our approach excels, predicting ΩAr values to
within ±0.10 units (N= 2923). The small variance in regional RSE
values around the global value indicates no spatial bias.
Regional and global skill evaluation for predicting
ΩAr (see Fig. S3 for map of spatial division).
RegionZoneaRSEbNcArctic Ocean10.22673Subpolar North Atlantic20.132380Subtropical North Atlantic30.111205Equatorial Atlantic40.16565Subtropical South Atlantic50.12527Subpolar North Pacific60.181541Subtropical North Pacific70.151412Equatorial Pacific80.16764Subtropical South Pacific90.151353Subtropical north Indian100.13137Equatorial Indian110.13481Subtropical south Indian120.111340Southern Ocean130.102923Subantarctic waters140.111426Global0.13816 727
a Corresponding geographical region in Fig. S3;
b residual standard error; c number of measurements.
Finally, it is important to acknowledge that uncertainties and biases in the
WOA13 objectively analysed products will influence our data-derived ΩAr distributions. Since error estimates in the WAO13 products remain
uncertain, this source of uncertainty cannot be accounted for at this time.
However, if we assume errors in WOA13 are uncorrelated and much smaller than
errors associated with the carbonate system, then they will not
significantly contribute to uncertainty in our calculated ΩAr
values.
How large is contemporary seasonal variability?
Seasonal amplitudes were calculated here as the difference between the
maximum and minimum monthly ΩAr values in each 1∘× 1∘
grid cell (Fig. 4). From a global open-ocean
perspective, seasonality was found to be 0.46 ± 0.25 (mean ± standard deviation (1σ)),
while strong regional mixing/upwelling regimes and/or biological production
results in large spatial differences. In the high northern latitudes
(45 to 70∘ N) and southern subtropics (20
to 45∘ S) for example, seasonality was found to be strongest at
0.73 ± 0.20 and 0.46 ± 0.14 respectively, while
seasonality in the equatorial region (20∘ N to 20∘ S)
was found to be weakest at 0.34 ± 0.21.
From an OA perspective, regions where seasonality is strongest will have the
largest implications for the future onset of critical ΩAr
levels. In the tropics for example, where aragonite-secreting corals are
abundant (Tupper et al., 2011), the relatively weak
seasonality will result in little difference between month-long and
annual-mean onset for future ΩAr levels. In the higher
latitudes however, where seasonality is largest, the implications for future
ΩAr onset will be much more pronounced.
It must be noted that our seasonal predictions will underestimate some
coastal regions where limited data exist. Along the coastal Antarctic
continent for example, in situ data have shown seasonal ΩAr
variability of up to 1.75 (McNeil et al., 2010), which is not
captured by our approach.
Is seasonality the dominant mode of ΩAr variability?
Variability in the open-ocean CO2 system is driven mainly by seasonal
and interannual variability (IAV), with diurnal variability only playing a
significant role in coastal waters (Secretariat of the Convention on
Biological Diversity, 2014).
To quantify the relative roles of seasonal and IAV in open-ocean waters, we
analysed results from an ensemble of six ESMs participating in the Coupled
Model Intercomparison Project 5 (CMIP5; Table 2). Each model was first
re-gridded to a 1∘× 1∘ resolution and ΩAr values calculated via the standard CO2 dissociation constants
described in Sect. 2. To constrain the total magnitude of natural
variability we combined the seasonal and IAV signals within each
1∘× 1∘ grid cell (Fig. 5a). For IAV, we
de-trended annual-mean projections from 2006 through to 2100 under the
RCP8.5 emission scenario via a third-order polynomial, and then calculated
the second standard deviation (2σ) in the de-trended data (i.e. 95.4 % of the
year-to-year variance). For seasonality, we used the average seasonal
magnitude (maximum minus minimum) between 2006 and 2016. The relative roles
of variability were finally quantified by dividing the individual components
by the total variability. We also multiplied these values by 100 to present
the relative roles of seasonal variability and IAV as a percentage of the
total natural variability (Fig. 5b, c).
Main characteristics of the six ESMs used in this study.
ModelOcean resolutionBiogeochemical modelReferenceCanESM20.9–1.4∘CMOCZahariev et al. (2008)GFDL-ESM2M0.3–1∘TOPAZ2Dunne et al. (2013)HadGEM2-ES0.3–1∘Diat-HadOCCPalmer and Totterdell (2001)IPSL-CM5A-LR0.5–2∘PISCESAumont and Bopp (2006); Séférian et al. (2013)IPSL-CM5A-MR0.5–2∘PISCESAumont and Bopp (2006); Séférian et al. (2013)MPI-ESM-MR0.4∘HAMOCC5.2Ilyina et al. (2013)
Model-based comparison of seasonal and interannual
variability for ocean-surface ΩAr. (a) Total magnitude of
variability as estimated from the ensemble of ESM. Here seasonal variability
was calculated as the mean seasonal amplitude between 2006 and 2016, while
IAV was calculated via the standard deviation in de-trended annual mean
projections between 2006 and 2100; (b) relative contribution of seasonal
variability to the total variability (in percentage); (c) relative
contribution of interannual variability to the total variability (in
percentage).
This model-based analysis revealed seasonality to be the dominant mode of
variability throughout the global open-ocean, accounting for 74 ± 12 %
(mean ± 1σ) of total natural variability. From a regional
perspective, seasonality is the dominant mode in the higher latitudes,
accounting for 84 ± 5 % of total variability in the Southern Ocean
(south of 30∘ S) and North Pacific (30 to 70∘ N). In the eastern equatorial Pacific however, IAV is the
dominant mode of variability, representing up to 70 % of total variability
(Fig. 5c). With the exception of the central equatorial Pacific, seasonality
is the dominant mode of variable across the greater equatorial region
(30∘ S to 30∘ N), accounting for 67 ± 12 % of the
total natural variability within this region (Fig. 5b). These results are
independent of the emission scenario used to calculate the seasonal and
interannual variability components.
Comparison between our data-based ΩAr seasonal amplitudes
(Fig. 4) and model-based total variability (Fig. 5a) reveals a similar spatial
pattern in regions where seasonality is the dominant mode (i.e. North
Pacific, Southern Ocean and west North Atlantic). Despite this general
agreement, we find our data-based seasonal estimates are on average 1.3
times larger than the 2006–2016 model-based mean seasonal amplitudes in the
North Atlantic, North Pacific and Southern Ocean (1σ= 0.5; see Fig. S4). We
further compared our data-based seasonal amplitudes for the year 2000 to the
2006–2016 mean seasonal amplitudes predicted by the six individual ESMs.
Here we found amplification factors for our seasonal ΩAr
amplitudes ranged from 0.8 to 2.3, with a mean and standard deviation of
1.3 ± 0.5. This suggests ESMs on average under-predict the oceans'
seasonal CO2 cycle by a factor of 1.3 (or 30 %).
Future aragonite undersaturation states (ΩAr) at locations in the (a) North Atlantic and (b) Southern Ocean
under the business-as-usual scenario (RCP8.5). The influence of seasonal variability accelerates
undersaturation conditions by 27 and 8 years relative to annual-mean
estimates (black line) in the North Atlantic and Southern Ocean
respectively. The red points a, b, and c denote the time when month-long,
annual-mean and permanent undersaturation occurs respectively.
Projecting future ΩAr levels
Exchange of CO2 between the ocean and atmosphere is driven by the
air–sea gradient in pCO2. Each year, approximately 70 petagrams of
carbon is naturally exchanged at the air–sea interface in both directions
(Sarmiento and Gruber, 2002). Comparison between ocean-surface and
atmospheric pCO2 reveals seasonality in the ocean is the dominant driver
of this large natural CO2 flux (Sasse et al., 2013a; Takahashi et
al., 2009), which in turn is driven by biological and physical-solubility
processes (Sarmiento and Gruber, 2006) – referred to here as the
natural cycling of carbon.
If the natural cycling of carbon remained in a steady state throughout the
last 2 centuries, the rate of increase in regionally integrated ocean-surface pCO2 would have roughly tracked the atmospheric CO2 growth
rate over longer timescales (Lenton et al., 2012; Tjiputra et al., 2014).
Recent studies have however identified shifts in the oceans' natural cycling
of carbon due to climate-related alterations. For example, decadal-scale
trends in ocean-surface temperature (Levitus et al., 2005; Lyman et al.,
2010) and salinity (Durack and Wijffels, 2010) are influencing
both the solubility of CO2 and ocean circulation pathways, while
shifting wind patterns are impacting circulation and seasonal mixing
processes, resulting in either enhanced or diminished ventilation of deep
waters enriched with CT and nutrients (e.g. Le Quéré et al.,
2007; Lenton et al., 2009).
Added to this climate-mediated change in oceanic CO2 uptake, the
air–sea exchange of CO2 is a slow process (approximately 1 year
equilibration time), where local physical and biological processes can cause
the ocean to deviate from atmospheric CO2. This creates a difference
between the atmospheric and ocean-surface pCO2 (disequilibrium).
Further, as atmospheric CO2 increases, ocean processes can cause the
ocean to lag the atmospheric increase and the disequilibrium term to
increase with time (McNeil and Matear, 2013). For example, in
the polar regions, short residence times of surface waters and the
ventilation of old CO2-rich deep waters creates an increasing CO2
disequilibrium, resulting in a growing difference between atmospheric and
surface ocean CO2 over time.
To account for the effects of future climate change and increasing CO2
disequilibrium described above, we projected our data-based CO2
climatologies using results from an ensemble of six ESMs (Table 2). In this
approach, decadal trends in CT, AT, temperature and salinity were
combined with our monthly data-based CT and AT and WOA13 temperature
and salinity products. Monthly ΩAr values were then calculated
using the standard CO2 dissociation constants presented in Sect. 2.
We projected our CO2 baselines using ESM results forced under several
different representative concentration pathways (RCP8.5, 4.5 and 2.6). Here,
RCP8.5 is a business-as-usual scenario with little mitigation and peak CO2 concentrations
at 935 parts per million (ppm) in the year 2100; RCP4.5 is a scenario where
emissions peak mid-century and are then slowly reduced, resulting in a
peak CO2 concentration of 538 ppm by 2100; finally, RCP2.6 is a
best-case scenario were emissions are dramatically reduced in the near future to the
point where more CO2 is absorbed by the ocean and terrestrial biosphere
than emitted by human activities (Meinshausen et al., 2011).
It should be emphasized that the observation-based CO2 climatologies of
Sasse et al. (2013b) have been shown to accurately reconstruct
the global pattern of present-day ocean-surface CO2 variability.
However, for this study we assume constant seasonality from our base-line
CO2 climatologies throughout the 21st century. Although this
assumption is likely adequate for short temporal projections (< 10 years),
a recent evaluation of 10 ESMs suggests large changes in mixing,
biological production and CO2 solubility will occur within the
21st century (Bopp et al., 2013). By
projecting our base-line climatologies using decadal trends from ESM, we
implicitly capture the decadal response to these changes; however, any
potential shift in the phase and magnitude of CO2 seasonality are not
explored in our approach.
Given the limitations in the current generation of ESM in capturing
seasonality in air–sea CO2 flux and/or ocean-surface pCO2 in many
important regions (Ishii et al., 2014; Lenton et al., 2013; Pilcher et
al., 2015; Sarma et al., 2013; Schuster et al., 2009), their ability to
realistically project future changes in CO2 seasonality is
questionable. We therefore do not account for any change in CO2
seasonality in the current study. Once models evolve to a point where
seasonality of the carbon system is well-represented, potential future
changes to seasonality will need to be explored in future studies.
As a first step to assessing the sensitivity of future ΩAr
predictions to shifts in oceanic CO2 seasonality, we applied the
following approach to model output from six ESMs (Table 2). Seasonal cycles in
CT, AT, temperature and salinity were first averaged over the
decades 2006 through to 2015 and 2091 through to 2100 in each 1∘× 1∘
grid cell. Decadal-mean values from the 2091–2100
period were then added to the 2006–2015 mean seasonal cycles, thereby
shifting the earlier seasonal cycle to typical values of the years
2090–2100. Finally, seasonal ΩAr values were computed using
both the mean 2091–2100 and shifted 2006–2015 seasonal CT, AT,
temperature and salinity values. Comparing the seasonal amplitudes in
ΩAr found shifted values were on average 5.4 % larger than
the 2091–2100 period for the global open-ocean (1σ= 48 %), with
individual model differences ranging from -0.4 to 19.1 %. This
suggests our data-based ΩAr amplitudes are on average 5.4 %
larger than expected if changes in CT, AT, temperature and salinity
seasonality were taken into account.
Quantifying the onset of aragonite undersaturation
When strong natural carbon seasonality is combined with a long-term trend,
the onset and exposure times of biological thresholds are influenced. To
illustrate this point, we present ΩAr projections under the
business-as-usual scenario (RCP8.5) at two 1∘× 1∘ sites in the North
Atlantic and Southern Ocean which are somewhat representative of the larger
region (Fig. 6). At the North Atlantic site, strong seasonality was found to
bring forward the initial onset (time a in Fig. 6a) of aragonite
undersaturation by 27 years relative to the annual-mean (time b; Fig. 6a),
while weaker variability at the Southern Ocean site brings forward
undersaturation by 8 years (Fig. 6b). It is important to emphasize that
monthly undersaturation conditions starts at time a, and then eventually
extends to be permanent over all months (time c). As much as seasonality
brings forward the initial onset of undersaturation, it also delays the
permanent onset (Fig. 6). At the Southern Ocean site for example,
seasonality delays the permanent onset by ∼ 15 years compared to the annual mean. In the
context of ocean acidification impacts, monthly exposure times are
important, since laboratory experiments show that even short exposure times
(i.e. hours to days) can result in significant implications to the health
and well-being of the test species (Chan and Connolly, 2013).
Future ΩAr levels under RCP8.5
Under the business-as-usual scenario (RCP8.5), our results show annual-mean aragonite
undersaturation will occur by the year 2086 ± 9 (mean ± 1σ) in the
North Pacific and North Atlantic, 2074 ± 12 in the Southern Ocean,
while tropical and temperate regions (∼ 40∘ S to
∼ 40∘ N) will remain super-saturated beyond the year
2100 (Fig. 7a). When seasonality is considered, the initial month-long onset
precedes annual-mean estimates by a global average of 17 ± 10 years under the RCP8.5 scenario (70∘ N to 70∘ S;
Fig. 7b–c). In the North Pacific and North Atlantic, where seasonality is
strongest, month-long undersaturation is brought forward by 36 ± 16
and 19 ± 6 years respectively (Fig. 7c).
Estimated onset year for aragonite undersaturation under
RCP8.5 for (a) annual-mean and (b) 1-month onset. (c) Time difference (years)
between annual-mean and month-long estimates.
Onset year for month-long ocean-surface aragonite
undersaturation for (a) RCP4.5 and (c) RCP2.6. Time difference (years)
between month-long and annual-mean surface aragonite undersaturation onset
under (b) RCP4.5 and (d) RCP2.6.
In the Southern Ocean (south of 60∘ S), our results show
month-long aragonite undersaturation will first occur as early as the year
2030, or when atmospheric CO2 concentrations reach ∼ 450 ppm.
While this is consistent with projections by McNeil and Matear (2008)
under the IPCC IS92a scenario, our results show seasonality
will delay the onset of annual-mean undersaturation by 14 ± 7 years,
which is half the delay time found by McNeil and Matear (2008).
This difference likely reflects the faster rate of change in atmospheric
CO2 under RCP8.5 compared to IPCC IS92a, while differences in
seasonality found by the two approaches is likely a secondary factor.
Comparison between future aragonite projections under RCP4.5 and 2.6
relative to RCP8.5.
Widespread onset of permanent ΩAr undersaturation is only
found in the Southern Ocean and Arctic Ocean by the year 2100 (see Fig. S5).
In the Southern Ocean, the average time difference between annual-mean and
permanent onset is 13.0 ± 5.3 years, which is similar to the time
difference found between annual-mean and month-long onset at the same
locations (13.0 ± 5.9 years). Despite these similar basin-wide time
difference values, the correlation coefficient was found to be 0.31,
indicating significant spatial differences. This reflects the
non-symmetrical nature of seasonal ΩAr cycles in some regions
of the Southern Ocean, as observed in Fig. 6b, which further highlights the
importance of accounting for seasonal processes.
Early aragonite undersaturation is of particular concern for the many
important calcifying organisms that inhabit the higher latitudes. Pteropods
for example, are a zooplankton species that form aragonite shells to
provide ballast for vertical migration in search of food and breeding. In
the Southern Ocean, pteropods have been found to represent up to 30 % of
total zooplankton (Hunt et al., 2008), and are
themselves important prey for larger zooplankton, as well as many fish and
bird species (Hunt et al., 2008; Karnovsky et al., 2008). From a
biogeochemical perspective, pteropods account for at least 12 % of the
global CaCO3 flux into the ocean interior (Berner and Honjo,
1981). When pteropods sink to depths at which ΩAr= 1, known
as the saturation horizon or lysocline, field studies show significant
dissolution occurs (Hunt et al., 2008). As more
anthropogenic CO2 enters the ocean system, the aragonite saturation
horizon will approach the upper ocean until the surface waters become
permanently under-saturated. Decade(s) before this occurs however,
seasonality will expose calcifying organisms to month-long undersaturation
conditions, causing unknown changes to the health and well-being of the
wider marine ecosystem.
Future ΩAr levels under RCP 4.5 and 2.6
In the previous section we presented results under the RCP8.5 scenario. We
now explore how lower emission scenarios influence the future onset of aragonite
undersaturation. We consider our ΩAr projections under RCP4.5,
2.6 and their behaviour relative to RCP8.5 (Table 3 and Fig. 8). In the
North Pacific, we find month-long aragonite undersaturation occurs by the
year 2057 ± 24 and 2040 ± 15 under RCP4.5 and 8.5 respectively.
Despite this difference in onset year, atmospheric CO2 concentrations
at time of onset are consistent at 492 ± 45 ppm and 501 ± 60 for
RCP4.5 and 8.5 respectively, with a correlation coefficient of 0.75 (Table 3).
As expected, this suggests undersaturation onset is highly dependent on
the atmospheric CO2 concentration, where we find large-scale
undersaturation in the North Pacific once atmospheric CO2 reaches
496 ppm (mean of RCP4.5 and 8.5). Similarly, our results suggest widespread
aragonite undersaturation will occur when atmospheric CO2 reaches
concentrations of 517 ppm in the North Atlantic and 511 ppm in the Southern
Ocean.
Under RCP2.6, whereby emissions are drastically reduced in the near future,
our results show very sparse undersaturation onset in the major ocean
basins by the year 2100 (Fig. 8). When compared to projections under RCP8.5,
we find a 92.6 % (or 83.6 × 106 km2) reduction in global
open-ocean surface waters exposed to at least month-long aragonite
undersaturation within the 21st century. Regionally, this reduction
increases to 98.9 % (62.8 × 106 km2), 92.8 %
(9.16 × 106 km2) and 99.2 % (6.8 × 106 km2)
in the Southern Ocean, North Pacific and North Atlantic
respectively. This result highlights the potential difference humanity can
make by reducing CO2 emissions in the near future.
To further probe the influence of a lower emission scenario on future OA
onset, we compare the time difference between month-long and annual-mean
aragonite undersaturation onset under RCP8.5 and RCP4.5 at 457 1∘× 1∘
grid cell locations in the Southern Ocean (Figs. 8a
and 7b). Here we find the average onset for month-long undersaturation
occurs by the year 2048 under RCP8.5, and by 2073 under RCP4.5. Despite the
lower emission scenario delaying the initial onset, we find that the time
difference between month-long and annual mean onset is 18 years longer under
RCP4.5 compared to RCP8.5 (i.e. 14 years under RCP8.5 and 32 years under
RCP4.5). This longer time delay under RCP4.5 emphasizes that seasonality
becomes even more important when projecting future OA levels under a slower
emissions scenario.
Surface area exposed to at least month-long (blue) and
annual-mean (orange) aragonite undersaturation in the year 2100 under
RCP8.5. The blue region represents ∼ 23 × 106 km2.
The area labelled PEI represents the pteropod study region of Hunt
et al. (2008) around the Prince Edward Islands.
How does seasonality influence the geographical extent of aragonite undersaturation?
Accounting for seasonality also presents significant implications for the
spatial pattern of future aragonite undersaturation. Here we refer to
regions where seasonality induces at least month-long undersaturation
conditions, while annual-mean ΩAr projections remain
super-saturated throughout the 21st century. By the year 2100, the
latitudinal extent of ocean surface exposed to at least month-long aragonite
undersaturation will have shifted equatorward by ∼
3.5∘ relative to the extent of annual-mean estimates under
the RCP8.5 scenario (Fig. 9). This extension translates to ∼ 23 × 106 km2
of open-ocean surface (or 6.8 % of total
open-ocean area) exposed to at least month-long aragonite undersaturation
by 2100 under the business-as-usual scenario (RCP8.5). This expansion of corrosive aragonite
conditions is likely to impact multiple marine calcifiers living within
these regions much earlier than anticipated under previous annual-mean
projections (e.g. Orr et al., 2005). Pteropods for
example, represent up to 30 % of total zooplankton species around the
Prince Edward Islands (PEI; Fig. 9; Hunt et al.,
2008); if these stocks deplete under future OA levels, the many other
animals that rely on pteropods as a source of food will also be
detrimentally impacted.
Conclusion
Ocean acidification is a global issue which is likely to impact the entire
marine ecosystem – from plankton at the base of the food chain to fish at
the top. Of particular concern is the decreasing concentration of
CO32- ions, which lowers the saturation states
of CaCO3 minerals (ΩAr and ΩCa) and results
in detrimental seawater conditions for marine calcifiers (e.g. pteropods
and corals; Aze et al., 2014; Fabry et al., 2008). Predicting when critical
ΩAr threshold values will be reached is crucial for projecting
the future health of marine ecosystems and for marine resources planning and
management. Here we have assessed how seasonality in oceanic CO2 will
influence the future onset of ΩAr undersaturation.
The influence of seasonality was evaluated by comparing the difference in
future month-long and annual-mean ΩAr undersaturation onset.
Our results suggest seasonality brings forward the initial onset of
month-long undersaturation by 17 ± 10 years compared to annual mean
estimates under RCP8.5, with differences extending up to 35 ± 17 years
in the North Pacific due to strong regional seasonality.
Our results also show large-scale undersaturation once atmospheric CO2
reaches 496 ppm in the North Pacific, 517 ppm in the North Atlantic and 511 ppm
in the Southern Ocean, independent of emission scenario. It is important to
note that seasonality in these regions was also found to be the dominate
mode of variability, accounting for 84 ± 5 % of total model-based
variability in the Southern Ocean (south of 30∘ S) and North
Pacific (30 to 70∘ N). This suggests IAV will not
significantly alter onset times found in this study.
Under lower emission scenarios, the average time difference between
month-long and annual-mean aragonite undersaturation onset increased from
14 years under RCP8.5 to 32 years under RCP4.5 in the Southern Ocean. This
larger time difference under a lower emissions scenario emphasizes the
importance of accounting for seasonality when projecting future OA levels
under a slower emissions scenario. The spatial extent of ΩAr
undersaturation is also drastically reduced under a lower emission
scenario. Under RCP2.6 for example, our results show a 92.6 % (or
83.6 × 106 km2) reduction in open-ocean exposure to ΩAr undersaturation compared to projections under RCP8.5, emphasizing
the importance of mitigating CO2 emissions.
Seasonality also presents significant implications for the spatial pattern
of future ΩAr undersaturation. Here we found month-long
undersaturation extended equatorward by a global average of 3.5∘
(or 23 × 106 km2) compared to annual-mean projections
under RCP8.5. From a biogeochemical perspective, this is particularly
concerning given the regions of expansion from the poles (∼ 40
to 50∘ south and north) are known as important
hotspots for CaCO3 export (Sarmiento and Gruber, 2006).
Finally, the implication of our results is not limited to the higher
latitudes; strong ΩAr seasonality in some subtropical regions
(30∘ S to 30∘ N; see Fig. 4) will likely bring forward the
onset of lower ΩAr waters by similar temporal periods. Since
these regions are rich with sensitive calcifying coral reef ecosystems,
considering the influence of seasonality is important when estimating future
OA levels and their impacts in these regions.
The Supplement related to this article is available online at doi:10.5194/bg-12-6017-2015-supplement.
Acknowledgements
T. P. Sasse would like to acknowledge the funding support from the CSIRO
carbon cluster, and the developers of the ocean data view program (Schlitzer et al., 2013). A. Lenton and R. J. Matear would like to acknowledge the
funding support of CSIRO Oceans and Atmosphere and the Australian Climate
Change Science Program.
Edited by: J. Middelburg
ReferencesArcher, D., Kheshgi, H., Maier, and Reimer, E.: Multiple timescales
for neutralization of fossil fuel CO2, Geophys. Res. Lett., 24,
405–408, 10.1029/97gl00168, 1997.Aumont, O. and Bopp, L.: Globalizing results from ocean in situ iron
fertilization studies, Global Biogeochem. Cy., 20, GB2017, 10.1029/2005gb002591, 2006.Bednarsek, N., Tarling, G. A., Bakker, D. C. E., Fielding, S., Jones, E. M.,
Venables, H. J., Ward, P., Kuzirian, A., Leze, B., Feely, R. A., and Murphy,
E. J.: Extensive dissolution of live pteropods in the Southern Ocean,
Nat. Geosci., 5, 881–885, 10.1038/ngeo1635, 2012.Berner, R. A. and Honjo, S.: Pelagic Sedimentation of Aragonite: Its
Geochemical Significance, Science, 211, 940–942, 10.1126/science.211.4485.940, 1981.Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M.,
Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and
Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century:
projections with CMIP5 models, Biogeosciences, 10, 6225–6245,
10.5194/bg-10-6225-2013, 2013.Caldeira, K. and Wickett, M. E.: Oceanography: Anthropogenic carbon and ocean
pH, Nature, 425, 365–365, 10.1038/425365a, 2003.Caldeira, K. and Wickett, M. E.: Ocean model predictions of chemistry changes
from carbon dioxide emissions to the atmosphere and ocean, J. Geophys.
Res.-Oceans, 110, C09S04, 10.1029/2004JC002671, 2005.Cao, L., Caldeira, K., and Jain, A. K.: Effects of carbon dioxide and climate
change on ocean acidification and carbonate mineral saturation, Geophys. Res.
Lett., 34, L05607, 10.1029/2006gl028605, 2007.Chan, N. C. S. and Connolly, S. R.: Sensitivity of coral calcification to
ocean acidification: a meta-analysis, Glob. Change Biol., 19, 282–290,
10.1111/gcb.12011, 2013.Dickson, A. G.: Thermodynamics of the dissociation of boric acid in synthetic
seawater from 273.15 to 318.15 K, Deep-Sea Res., 37, 755–766,
10.1016/0198-0149(90)90004-F, 1990a.Dickson, A. G.: Standard potential of the reaction: AgCl(s) + 1/2H2(g)
= Ag(s) + HCl(aq), and the standard
acidity constant of the ion HSO4- in synthetic sea water from 273.15
to 318.15 K, J. Chem. Thermodyn., 22, 113–127,
10.1016/0021-9614(90)90074-Z, 1990b.Dickson, A. G. and Millero, F. J.: A comparison of the equilibrium constants
for the dissociation of carbonic acid in seawater media, Deep-Sea Res., 34,
1733–1743, 10.1016/0198-0149(87)90021-5, 1987.Dickson, A. G., Sabine, C. L., and Christian, J. R. (Eds.): Guide to best
practices for ocean CO2 measurements, PICES Special Publication 3,
191 pp., 2007.Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P.,
Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke, W.,
Dunne, K. A., Griffies, S. M., Hallberg, R. W., Harrison, M. J., Levy, H.,
Wittenberg, A. T., Phillips, P. J., and Zadeh, N.: GFDL's ESM2 Global Coupled
Climate–Carbon Earth System Models. Part II: Carbon System Formulation and
Baseline Simulation Characteristics, J.
Climate, 26, 2247–2267, 10.1175/JCLI-D-12-00150.1, 2013.Durack, P. J. and Wijffels, S. E.: Fifty-Year Trends in Global Ocean
Salinities and Their Relationship to Broad-Scale Warming, J. Climate, 23,
4342–4362, 10.1175/2010jcli3377.1, 2010.Fabry, V. J., Seibel, B. A., Feely, R. A., and Orr, J. C.: Impacts of ocean
acidification on marine fauna and ecosystem processes, ICES J. M. Sci., 65, 414–432,
10.1093/icesjms/fsn048, 2008.
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O.
K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: World Ocean Atlas 2013,
Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen
Saturation, in, edited by: Levitus, S. and Mishonov, A., NOAA Atlas NESDIS
75, 27 pp., 2014a.
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O.
K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: World Ocean Atlas 2013,
Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate, silicate),
edited by: Levitus, S., and Mishonov, A., NOAA Atlas NESDIS 76, 25, 25 pp.
2014b.Gruber, N., Hauri, C., Lachkar, Z., Loher, D., Frölicher, T. L., and
Plattner, G.-K.: Rapid Progression of Ocean Acidification in the California
Current System, Science, 337, 220–223, 10.1126/science.1216773, 2012.Hunt, B. P. V., Pakhomov, E. A., Hosie, G. W., Siegel, V., Ward, P., and
Bernard, K.: Pteropods in Southern Ocean ecosystems, Prog. Oceanogr., 78,
193–221, 10.1016/j.pocean.2008.06.001, 2008.Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and
Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC: Model
architecture and performance as component of the MPI-Earth system model in
different CMIP5 experimental realizations, Journal of Advances in Modeling
Earth Systems, 5, 287–315, 10.1029/2012MS000178, 2013.Ishii, M., Feely, R. A., Rodgers, K. B., Park, G.-H., Wanninkhof, R., Sasano,
D., Sugimoto, H., Cosca, C. E., Nakaoka, S., Telszewski, M., Nojiri, Y.,
Mikaloff Fletcher, S. E., Niwa, Y., Patra, P. K., Valsala, V., Nakano, H.,
Lima, I., Doney, S. C., Buitenhuis, E. T., Aumont, O., Dunne, J. P., Lenton,
A., and Takahashi, T.: Air–sea CO2 flux in the Pacific Ocean for the
period 1990–2009, Biogeosciences, 11, 709–734, 10.5194/bg-11-709-2014,
2014.Karnovsky, N. J., Hobson, K. A., Iverson, S., and Hunt, G. L. J.: Seasonal
changes in diets of seabirds in the North Water Polynya: a multiple-indicator
approach, Mar. Ecol. Prog.-Ser., 357, 291–299, 10.3354/meps07295, 2008.Kleypas, J. A., Buddemeier, R. W., Archer, D., Gattuso, J.-P., Langdon, C.,
and Opdyke, B. N.: Geochemical consequences of increased atmospheric carbon
dioxide on coral reefs, Science, 284, 118–120,
10.1126/science.284.5411.118, 1999.Lenton, A., Codron, F., Bopp, L., Metzl, N., Cadule, P., Tagliabue, A., and
Le Sommer, J.: Stratospheric ozone depletion reduces ocean carbon uptake and
enhances ocean acidification, Geophys. Res. Lett., 36, L12606,
10.1029/2009gl038227, 2009.Lenton, A., Metzl, N., Takahashi, T., Kuchinke, M., Matear, R. J., Roy, T.,
Sutherland, S. C., Sweeney, C., and Tilbrook, B.: The observed evolution of
oceanic pCO2 and its drivers over the last two decades, Global
Biogeochem. Cy., 26, GB2021, 10.1029/2011gb004095, 2012.Lenton, A., Tilbrook, B., Law, R. M., Bakker, D., Doney, S. C., Gruber, N.,
Ishii, M., Hoppema, M., Lovenduski, N. S., Matear, R. J., McNeil, B. I.,
Metzl, N., Mikaloff Fletcher, S. E., Monteiro, P. M. S., Rödenbeck, C.,
Sweeney, C., and Takahashi, T.: Sea–air CO2 fluxes in the Southern
Ocean for the period 1990–2009, Biogeosciences, 10, 4037–4054,
10.5194/bg-10-4037-2013, 2013.Le Quéré, C., Rödenbeck, C., Buitenhuis, E. T., Conway, T. J.,
Langenfelds, R., Gomez, A., Labuschagne, C., Ramonet, M., Nakazawa, T.,
Metzl, N., Gillett, N., and Heimann, M.: Saturation of the Southern Ocean
CO2 Sink Due to Recent Climate Change, Science, 316, 1735–1738,
10.1126/science.1136188, 2007.Le Quéré, C., Moriarty, R., Andrew, R. M., Peters, G. P., Ciais, P.,
Friedlingstein, P., Jones, S. D., Sitch, S., Tans, P., Arneth, A., Boden, T.
A., Bopp, L., Bozec, Y., Canadell, J. G., Chini, L. P., Chevallier, F.,
Cosca, C. E., Harris, I., Hoppema, M., Houghton, R. A., House, J. I., Jain,
A. K., Johannessen, T., Kato, E., Keeling, R. F., Kitidis, V., Klein
Goldewijk, K., Koven, C., Landa, C. S., Landschützer, P., Lenton, A.,
Lima, I. D., Marland, G., Mathis, J. T., Metzl, N., Nojiri, Y., Olsen, A.,
Ono, T., Peng, S., Peters, W., Pfeil, B., Poulter, B., Raupach, M. R.,
Regnier, P., Rödenbeck, C., Saito, S., Salisbury, J. E., Schuster, U.,
Schwinger, J., Séférian, R., Segschneider, J., Steinhoff, T.,
Stocker, B. D., Sutton, A. J., Takahashi, T., Tilbrook, B., van der Werf, G.
R., Viovy, N., Wang, Y.-P., Wanninkhof, R., Wiltshire, A., and Zeng, N.:
Global carbon budget 2014, Earth Syst. Sci. Data, 7, 47–85,
10.5194/essd-7-47-2015, 2015.Levitus, S., Antonov, J., and Boyer, T.: Warming of the world ocean,
1955–2003, Geophys. Res. Lett., 32, L02604, 10.1029/2004gl021592, 2005.
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H.
E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D.
R., Hamilton, M., and Seidov, D.: World Ocean Atlas 2013, Volume 1:
Temperature, edited by: S. Levitus, E., and A. Mishonov, T. E., NOAA Atlas
NESDIS 73, 40 pp. 2013.Lyman, J. M., Good, S. A., Gouretski, V. V., Ishii, M., Johnson, G. C.,
Palmer, M. D., Smith, D. M., and Willis, J. K.: Robust warming of the global
upper ocean, Nature, 465, 334–337, 10.1038/nature09043, 2010.Mathis, J. T. and Questel, J. M.: Assessing seasonal changes in carbonate
parameters across small spatial gradients in the Northeastern Chukchi Sea,
Cont. Shelf Res., 67, 42–51, 10.1016/j.csr.2013.04.041, 2013.McNeil, B. I. and Matear, R. J.: Southern Ocean acidification: A tipping
point at 450-ppm atmospheric CO2, P. Natl. Acad. Sci. USA, 105,
18860–18864, 10.1073/pnas.0806318105, 2008.McNeil, B. I. and Matear, R. J.: The non-steady state oceanic CO2
signal: its importance, magnitude and a novel way to detect it,
Biogeosciences, 10, 2219–2228, 10.5194/bg-10-2219-2013, 2013.McNeil, B. I., Tagliabue, A., and Sweeney, C.: A multi-decadal delay in the
onset of corrosive “acidified” waters in the Ross Sea of Antarctica due to
strong air-sea CO2 disequilibrium, Geophys. Res. Lett., 37, L19607,
10.1029/2010gl044597, 2010.
Mehrbach, C., Culberson, C. H., Hawley, J. E., and Pytkowicz, R. M.:
Measurement of the Apparent Dissociation Constants of Carbonic Acid in
Seawater at Atmospheric Pressure, Limnol. Oceanogr., 18, 897–907, 1973.Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T.,
Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K.,
Thomson, A., Velders, G. J. M., and Vuuren, D. P. P.: The RCP greenhouse gas
concentrations and their extensions from 1765 to 2300, Climatic Change, 109,
213–241, 10.1007/s10584-011-0156-z, 2011.Monteiro, P., Schuster, U., Hood, M., Lenton, A., Metzl, N., Olsen, A.,
Rogers, K., Sabine, C., Takahashi, T., Tilbrook, B., Yoder, J., Wanninkhof,
R., and Watson, A. J.: A Global Sea Surface Carbon Observing System:
Assessment of Changing Sea Surface CO2 and Air-Sea CO2 Fluxes,
Proceedings of OceanObs'09: Sustained Ocean Observations and Information for
Society (Vol. 2), Venice, Italy, 21–25 September 2009, 2010.Mucci, A.: The solubility of calcite and aragonite in seawater at various
salinities, temperatures, and one atmosphere total pressure, Am. J. Sci.,
283, 780–799, 10.2475/ajs.283.7.780, 1983.Newton, J. A., Feely, R. A., Jewett, E. B., Williamson, P., and Mathis, J.:
Global Ocean Acidification Observing Network: Requirements and
Governance Plan, available at: http://goa-on.org/docs/GOA-ON_plan_print.pdf (last access: August 2015), First Edn., 60 pp., 2014.Orr, J. C., Fabry, V. J., Aumont, O., Bopp, L., Doney, S. C., Feely, R. A.,
Gnanadesikan, A., Gruber, N., Ishida, A., Joos, F., Key, R. M., Lindsay, K.,
Maier-Reimer, E., Matear, R., Monfray, P., Mouchet, A., Najjar, R. G.,
Plattner, G.-K., Rodgers, K. B., Sabine, C. L., Sarmiento, J. L., Schlitzer,
R., Slater, R. D., Totterdell, I. J., Weirig, M.-F., Yamanaka, Y., and Yool,
A.: Anthropogenic ocean acidification over the twenty-first century and its
impact on calcifying organisms, Nature, 437, 681–686,
10.1038/nature04095, 2005.Palmer, J. R. and Totterdell, I. J.: Production and export in a global ocean
ecosystem model, Deep-Sea Res. Pt. I, 48, 1169–1198,
10.1016/S0967-0637(00)00080-7, 2001.Pilcher, D. J., Brody, S. R., Johnson, L., and Bronselaer, B.: Assessing the
abilities of CMIP5 models to represent the seasonal cycle of surface ocean
pCO2, J. Geophys. Res.-Oceans, 120, 4625–4637,
10.1002/2015JC010759, 2015.Popova, E. E., Yool, A., Aksenov, Y., Coward, A. C., and Anderson, T. R.:
Regional variability of acidification in the Arctic: a sea of contrasts,
Biogeosciences, 11, 293–308, 10.5194/bg-11-293-2014, 2014.Ricke, K. L., Orr, J. C., Schneider, K., and Caldeira, K.: Risks to coral
reefs from ocean carbonate chemistry changes in recent earth system model
projections, Environ. Res. Lett., 8, 034003,
10.1088/1748-9326/8/3/034003, 2013.Riley, J. P. and Tongudai, M.: The major cation/chlorinity ratios in sea
water, Chem. Geol., 2, 263–269, 10.1016/0009-2541(67)90026-5, 1967.Sarma, V. V. S. S., Lenton, A., Law, R. M., Metzl, N., Patra, P. K., Doney,
S., Lima, I. D., Dlugokencky, E., Ramonet, M., and Valsala, V.: Sea–air
CO2 fluxes in the Indian Ocean between 1990 and 2009, Biogeosciences,
10, 7035–7052, 10.5194/bg-10-7035-2013, 2013.Sarmiento, J. L. and Gruber, N.: Sinks for Anthropogenic Carbon, Phys. Today,
55, 30–36, 10.1063/1.1510279, 2002.
Sarmiento, J. L. and Gruber, N.: Ocean biogeochemical dynamics, Princeton
University Press, Princeton, New Jersey, 526 pp., 2006.Sasse, T. P., McNeil, B. I., and Abramowitz, G.: A new constraint on global
air-sea CO2 fluxes using bottle carbon data, Geophys. Res. Lett., 40,
1594–1599, 10.1002/grl.50342, 2013a.Sasse, T. P., McNeil, B. I., and Abramowitz, G.: A novel method for
diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle
data using neural networks, Biogeosciences, 10, 4319–4340,
10.5194/bg-10-4319-2013, 2013b.Schlitzer, R.: Ocean Data View, http://odv.awi.de
(last access: February 2014), 2013.Schuster, U., Watson, A. J., Bates, N. R., Corbiere, A., Gonzalez-Davila, M.,
Metzl, N., Pierrot, D., and Santana-Casiano, M.: Trends in North Atlantic
sea-surface fCO2 from 1990 to 2006, Deep-Sea Rs. Pt. II, 56, 620–629,
10.1016/j.dsr2.2008.12.011, 2009.
Secretariat of the Convention on Biological Diversity: An Updated
Synthesis of the Impacts of Ocean Acidification on Marine Biodiversity,
edited by: Hennige, S., Roberts, J. M., and Williamson, P., Technical
Series no. 75, Montreal, 99 pp. 2014.Séférian, R., Bopp, L., Gehlen, M., Orr, J., Ethé, C., Cadule,
P., Aumont, O., Salas y Mélia, D., Voldoire, A., and Madec, G.: Skill
assessment of three earth system models with common marine biogeochemistry,
Clim. Dynam., 40, 2549–2573, 10.1007/s00382-012-1362-8, 2013.Shaw, E. C., Munday, P. L., and McNeil, B. I.: The role of CO2
variability and exposure time for biological impacts of ocean acidification,
Geophys. Res. Lett., 40, 4685–4688, 10.1002/grl.50883,
2013.Steinacher, M., Joos, F., Frölicher, T. L., Plattner, G.-K., and Doney,
S. C.: Imminent ocean acidification in the Arctic projected with the NCAR
global coupled carbon cycle-climate model, Biogeosciences, 6, 515–533,
10.5194/bg-6-515-2009, 2009.
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C. L., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii,
M., Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema,
M., Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R. G. J., Wong, C. S., Delille, B., Bates, N. R., and de Baar, H.
J. W.: Climatological mean and decadal change in surface ocean pCO2,
and net sea-air CO2 flux over the global oceans, Deep-Sea Res. Pt. II,
56, 554–577, 10.1016/j.dsr2.2008.12.009, 2009.Takahashi, T., Sutherland, S. C., Chipman, D. W., Goddard, J. G., Ho, C.,
Newberger, T., Sweeney, C., and Munro, D. R.: Climatological distributions of
pH, pCO2, total CO2, alkalinity, and CaCO3 saturation in the
global surface ocean, and temporal changes at selected locations, Mar. Chem.,
164, 95–125, 10.1016/j.marchem.2014.06.004, 2014.Tjiputra, J. F., Olsen, A., Bopp, L., Lenton, A., Pfeil, B., Roy, T.,
Segschneider, J., Totterdell, I., and Heinze, C.: Long-term surface
pCO2 trends from observations and models, Tellus B, 66, 10.3402/tellusb.v66.23083, 2014.Tupper, M., Tan, M. K., Tan, S. L., Radius, M. J., and Abdullah, S.:
ReefBase: a global information system on coral reefs,
http://www.reefbase.org (last access: October 2013), 2011.Zahariev, K., Christian, J. R., and Denman, K. L.: Preindustrial, historical,
and fertilization simulations using a global ocean carbon model with new
parameterizations of iron limitation, calcification, and N2 fixation,
Prog. Oceanogr., 77, 56–82, 10.1016/j.pocean.2008.01.007, 2008.
Zweng, M. M., Reagan, J. R., Antonov, J. I., Locarnini, R. A., Mishonov, A.
V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D.,
and Biddle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, edited by:
Levitus, S. and Mishonov, A., NOAA Atlas NESDIS 74, 39 pp., 2013.