Introduction
Since the industrial revolution, a steady increase in anthropogenic
CO2 emissions from fossil fuel burning, cement production, and
land-use change have led to an increase in atmospheric CO2
concentration of about 43 % in 2014 relative to its preindustrial
value according to the latest measurements from the Earth System Research
Laboratory in Mauna Loa (www.esrl.noaa.gov). This represents the
highest CO2 concentration for at least the last 800 000 years.
Increasing atmospheric CO2 is one of the most important drivers for
ongoing, and likely future, climate change, and it affects the ocean carbon
reservoir. By taking up about approximately 26 % of the
anthropogenic CO2 emissions annually , the ocean
slows down the growth of the atmospheric CO2 concentration and
therefore the rate of climate change. However, the ocean carbon uptake rate
will decrease in the future owing to the lowered buffer capacity of the
surface waters and the potential weakening of carbon transport from the
surface to the deep ocean, leading to a positive climate feedback
.
The oceanic carbon sink is mainly controlled by the physical and the
biological pumps, which are both affected by the changing climate
. The physical pump depends mainly on two processes:
dissolution of CO2 gas in seawater and transportation of dissolved
inorganic carbon into the deep ocean by mixing and circulation processes. The
biological pump is predominantly governed by the population of marine
phytoplankton, which consumes the dissolved inorganic carbon (DIC) in the
seawater to produce organic matter or soft tissue via photosynthesis. Through
gravitational forcing, this organic matter sinks into the ocean interior
where it is remineralized back into DIC. The biological pump is also affected
by the global increase in temperature and by changes in circulation. The
solubility of atmospheric CO2 in the ocean's surface is expected to
be negatively impacted by global warming since the solubility of
CO2 gas in seawater decreases with warmer temperatures
. Additionally, the oceanic circulation that links the low DIC
in the ocean surface to the CO2-rich deep ocean could be altered in
the next few decades through weaker upwelling and a slowdown in the Atlantic
meridional overturning circulation (AMOC) . Both of these carbon
pump processes are represented in the latest earth system model (ESM)
simulations from CMIP5, which include for the first time a coupling between
the atmosphere–ocean global climate models (AOGCMs, as in previous CMIPs)
and the biogeochemical fluxes between the ocean, atmosphere, and terrestrial
biosphere reservoirs .
It has been shown that the trend in current anthropogenic CO2
emissions closely follows the Representative Concentration Pathway (RCP) 8.5
scenario , which is the most pessimistic future
scenario, with high atmospheric CO2 concentrations leading to
8.5 Wm-2 additional radiative forcing by 2100. The estimated
emissions reached 37.0±1.3 Gt CO2 yr-1 in 2014
, matching the RCP8.5 scenario leading to the highest increase
in global mean temperature from 3.2 to 5.4 ∘C at the end of
this century relative to 1850–1900. This study focuses on analyzing the
fully interactive ESM simulations from CMIP5 for the 2001 to 2099 period from
the experiments “esmHistorical” and “esmrcp8.5”. We compare the
2001–2010 period from the simulations with observational data.
Figure 1 presents the time series of the global annual CO2 uptake
by the ocean computed from nine different CMIP5 models (Sect. 2.2) from the
years 2001 to 2099, including the observation-based estimate of carbon flux
for the period 1998–2011 . In addition to the large
present-day inter-model spread, the figure also highlights the increase in
the inter-model spread projected into the future. The magnitude of the
standard deviation (i.e., of the inter-model variation) increases by a factor
of 2 from 2001 (±0.3 PgCyr-1) to 2099 (±0.6 PgCyr-1). During this period, the projected cumulative
oceanic carbon sink ranges from 340.4 to 488.5 Pg C. The
148 Pg C difference in the size of the ocean carbon sink translates
into roughly a 70 ppm difference in atmospheric CO2
concentration by the end of the 21st century. In order to improve the
fidelity of future projections provided by the climate modeling community, it
is necessary to identify and attribute the mechanisms responsible for the
growth in the inter-model spread of ocean CO2 uptake and to
determine methods to constrain this. Motivated by these growing uncertainties
and the need to constrain them, our study focuses on analyzing relationships
between regional and global uncertainties in ocean carbon uptakes as
simulated in CMIP5 models. This is necessary because the strength and
variability of the ocean carbon sinks vary considerably from one region to
another and are attributed to region-specific mechanisms. For instance, in
the equatorial Pacific, the long-term trend in CO2 uptake is
strongly influenced by the El Niño variability . In other
regions, such as the Southern Ocean, the variability is related to the
Southern Annular Mode see, e.g.,.
Time series of annual ocean carbon uptake as simulated by nine ESMs
for the 2001–2100 period. Thick gray line indicates the multi-model mean,
shaded areas represent ± one standard deviation of the inter-model
variations. The observation-based estimate (black circle) is from
.
The need to reduce this inter-model spread is imperative to reduce
uncertainty in future climate projections and enable policy makers to make
the most informed decisions. The simulated uncertainty in ocean carbon uptake
could arise from different factors. Feedbacks from ocean and terrestrial
biospheres on the CO2 concentration are expected but highly
uncertain and thus difficult to predict . Differences in
(i) basin-scale ocean evolution of ocean carbon uptake rates, (ii) timing and
amplitude of physical and biogeochemical processes driving the regional
pCO2 seasonal cycle, as well as (iii) responses to transient future
climate change are among potential contributors to future uncertainty in
ocean carbon uptake, and addressing these points will be the main focus of
this study.
The paper is organized as follows: in the next section we describe the
observations and models used in this study as well as the terms and metrics
used to investigate the relationships between present-day and future carbon
uptake and the regional boundaries. Section 3 discusses the results of the
analyses. Additional discussions and comparison with previous studies are
presented in Sect. 4. Finally, the study is summarized in Sect. 5.
Methods
Observation-based estimates
We used the monthly data set documented by for the 2001–2010
period. It includes the surface ocean partial pressure of CO2
(pCO2) and the sea–air CO2 flux (fgCO2) gridded
with a 1∘×1∘ horizontal resolution corresponding to
360 by 180 points in longitude and latitude, respectively. The pCO2
data set is originally extrapolated in space and time from SOCAT (Surface
Ocean CO2 Atlas) version 2 by a two-step
neural-network approach as described in . The sea–air
CO2 flux is computed based on this pCO2 field, applying
a standard bulk formulation and high-resolution Cross-Calibrated
Multi-Platform (CCMP) wind speeds . The monthly averaged sea
surface salinity (SSS) was downloaded from the Simple Ocean Data Assimilation
(SODA; ) and has been regridded to the pCO2 data set.
The sea surface temperature (SST) is from the National Oceanic and
Atmospheric Administration (NOAA) Optimum Interpolation (OI) sea surface v.2
.
The climatology of net primary production (NPP) used for the seasonal
model–data assessment in the Southern Ocean (Sect. 3.2) is documented by
and is computed over the 1997–2010 period using data derived
from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). It uses an
empirical chlorophyll (Chl) algorithm for the Southern Hemisphere that was
tuned to in situ Chl in the Southern Ocean and spatially blended with the
standard SeaWiFS Ocean Color (OC) 4 algorithm .
pCO2 decomposition
In order to allow decomposition of pCO2 variability into its physical
and biogeochemical components, we estimated the alkalinity (ALK) and DIC at
the same resolution as the pCO2 data. The alkalinity was computed from
the SST and SSS estimates depending on the region using the
formulation. When temperatures are out of range in the selected region, the
computation returns a missing value (NaN). The DIC was computed using the
CO2 inorganic carbon chemistry program CO2SYS developed in Matlab
using the gridded SST, SSS, pCO2, and alkalinity as
input parameters. The global average surface silicate and the phosphate
concentrations were used: 10 and 0.75 µmolkgSW-1,
respectively. We expect that this choice has a relatively small influence on
our results since a shift in the concentration of silicate and phosphate by 4
and more than 6 times to their original mean values would generate only
a 0.05 and 0.2 % change in the DIC computation. To complete the
CO2SYS input, we applied the dissociation constants K1 and K2 introduced by
and refitted by .
Decomposition of the total pCO2 seasonal variability was based on the
following approximation :
dpCO2totdt≈dpCO2DICdt+dpCO2ALKdt+dpCO2SSTdt+dpCO2SSSdt,
where the pCO2tot variation in time
(dpCO2tot / dt) is approximately equal to the sum of
the four decomposed pCO2x variations in time, where
x is DIC, ALK, SST, or SSS. The pCO2x terms represent a set of
thermodynamic equations that relates to the inorganic carbon species, taking
into account variation in x, while the other components are kept at their
long-term local average values. In this way,
dpCO2DIC/dt is an estimate of the temporal
variability of the local pCO2 field as a result of changing DIC only.
The same estimates were applied for the other three parameters (i.e., ALK,
SST, and SSS).
Model descriptions and post processing
The nine participating CMIP5 ESMs in alphabetical order, are the (1) Beijing
Climate Center Climate System Model (BCC-CSM1.1(m)), (2) Canadian Centre for
Climate Modelling and Analysis ESM (CanESM2), (3) Community Earth System
Model (CESM1-BGC), (4) Geophysical Fluid Dynamics Laboratory ESM
(GFDL-ESM2G), (5) Hadley Global Environment Model 2 (HadGEM2-ES), (6) Japan
Agency for Marine-Earth Science and Technology (MIROC-ESM), (7) Max Planck
Institute for Meteorology ESM (MPI-ESM-LR), (8) Japanese Meteorology Research
Institute ESM (MRI-ESM1), and (9) Norwegian Climate Centre ESM (NorESM1-ME).
These models have also contributed to the last Intergovernmental Panel on
Climate Change Assessment Report (IPCC-AR5). All outputs were downloaded
directly from the Earth System Grid Federation (ESGF;
http://esgf.llnl.gov), and we analyzed the fully interactive
CO2 emissions-based “esm” simulations. These esm simulations take
into account carbon fluxes between the land–atmosphere and ocean–atmosphere
interfaces to prognostically simulate the atmospheric CO2
concentration; thus, they include more realistic spatially varying
atmospheric CO2 concentration. The selection of these models is
based on the availability of all variables necessary to discuss the impact of
the pCO2 seasonal cycle on the carbon uptake: fgCO2,
pCO2, SST, SSS, DIC, ALK, and NPP. However, the BCC-CSM1.1 and the
MIROC-ESM models do not provide some of these variables (ALK and DIC) and
therefore were only analyzed for the contemporary and future uptake
relationship (Sect. 3.1). In order to compare the global and regional
fgCO2 between the models presented in Sect. 3.1, fgCO2
outputs were also interpolated to the observational grid of 360×180
points. Model outputs from the historical (esmHistorical) experiments were
added to the RCP8.5 (esmrcp8.5) to complete the 2001–2099 period of study.
We used the same “r1i1p1” realization from each model. Tables 1 and 2
summarize the physical and marine biogeochemical components and features of
each model.
Description of the models analyzed in this study, indicating ocean
model resolutions (levels in the vertical and horizontal resolution in degrees), the
thickness of the surface layer, and the marine biogeochemical components
included in the ESM.
Model
Ocean
First layer
Marine biogeochemical
References
(level, zonal, meridional)
thickness (m)
components
BCC-CSM1.1(m)
40 lev, 0.3–1, 1∘
10
MOM4-L40
CanESM2
40 lev, 1.41, 0.94∘
10
CMOC
CESM1-BGC
60 lev, 1.25, 0.27–0.54∘
10
BEC
GFDL-ESM2G
63 lev, 0.3–1∘
2
TOPAZ2
HadGEM2-ES
40 lev, 0.3–1∘
10
Diat–HadOCC
MIROC-ESM
44 lev, 1.4, 0.5–1.7∘
2.5
NPZD
MPI-ESM-LR
40 lev, 1.5∘
10
HAMOCC5.2
MRI-ESM1
51 lev, 0.5–1∘
4
NPZD
NorESM1-ME
53 lev, 1.125∘
10
HAMOCC5.1
Main characteristics of the marine biogeochemical components of the
nine ESMs used in this study: list of nutrients limiting the phytoplankton
growth and the number of explicitly represented phytoplankton and zooplankton
groups.
Model
Nutrients
Phytoplankton
Zooplankton
BEC
5(NO3, NH4, PO4, SiO4, Fe)
3(diatoms, nanophyto-, diazotrophs)
1
CMOC
NO3
1
1
Diat–HadOCC
4(NO3, NH4, SiO4, Fe)
2(diatoms, non-diatoms)
1
HAMOCC5.1
4(NO3, PO4, SiO4, Fe)
1
1
HAMOCC5.2
3(PO4, NO3, Fe)
1
1
MOM4-L40
PO4
–
–
NPZD
NO3
1
1
TOPAZ2
5(NO3, NH4, PO4,SiO4, Fe)
3(diatoms, other eukaryotes, diazotrophs)
1
The marine primary productivity is one of the key components that governs the
carbon cycle in the ocean, impacting the oceanic pump through alteration of
the buffering capacity and the CO2 remaining in the atmosphere. The
ocean NPP is controlled by nutrient availability and other physical factors
such as temperature and light. As presented in Table 2, the CMIP5 models use
different representations of multiple nutrient limitations, varying from one
to five explicit nutrients in CMOC-NPZD and BEC-TOPAZ2 and from one to three
phytoplankton species, NPZD-HAMOCC-CMOC and BEC-TOPAZ2. This highlights the
wide range of biogeochemistry complexity, which can also contribute to the
inter-model spread in their respective outputs. We note that the MOM4-L40
uses the OCMIP2 biogeochemistry module, which does not include an explicit
marine ecosystem; therefore, in this case the primary production is simulated
only as a function of surface phosphate concentration.
Uptake efficiency
In Sect. 3.2 we compute for each model (at the original model resolution) the
“uptake efficiency” (uptakeeffy) of carbon in the ocean,
where y represents the different basin regions as defined in Sect. 2.6 as
well as the global (glb) and other ocean region excluding the SO (eSO). The
uptakeeff measures the efficiency of a specific water mass in
taking up carbon for a given change in atmospheric pCO2. A high
uptakeeff value represents a good capacity of the ocean to
contain DIC for a certain change in atmospheric pCO2 and vice versa.
This term is computed as follows:
uptakeeffy=∂[CTy]∂[pCO2y]=DICypCO2yRFy.
The DICy and pCO2y in Eq. (2) represent the
respective area-weighted mean surface concentration of DIC and
pCO2y within the domain y. RFy is the regional mean
Revelle Factor (RF) , also known as the inverse of the ocean's
buffering capacity for atmospheric CO2 uptake, i.e., to convert
CO2,aq into different carbon species (carbonate and bicarbonate)
within domain y. Water masses with a lower Revelle Factor are more
efficient at taking up anthropogenic carbon . The increase in
atmospheric CO2 has pushed the surface CO2,aq
concentration to a higher level, resulting in an increase in the Revelle
Factor and thus a decrease in ocean's buffering capacity. This
mechanism represents a positive climate feedback, which reduces the uptake
rate of atmospheric CO2 in the future . The DIC and
pCO2 fields were taken directly from the model outputs, whereas the
RF were computed with CO2SYS.
Inter-model correlation
In order to assess qualitatively the existence of any patterns or
consistencies between the simulated CO2 uptakes among the different
models, two metrics of inter-model correlation coefficients have been
computed: Rmeany and Rcumy. A high correlation
coefficient indicates a strong relationship between the contemporary and the
projected inter-model spread. Moreover, a statistically significant positive
correlation coefficient denotes that models that project weak (high) uptake
in the contemporary period tend to project weak (high) future
uptake. The two
correlation coefficients were computed as follows:
Rmeany=CorrCoef110∑t=20012010fgCO2y(t),110∑t=20902099fgCO2glb(t),Rcumy=CorrCoeff110∑t=20012010fgCO2y(t),∑t=20012099fgCO2glb(t).
Rmeany represents the inter-model correlation coefficient
between contemporary annual mean CO2 uptake in the different y
regions and global uptake rate in the last decade of the 21st century (i.e.,
2090–2099). Then, Rcumy represents the inter-model
correlation coefficient between the contemporary annual mean uptake rate and
the cumulative global carbon uptake over the 2001–2099 period.
Regional boundaries
Regional characteristics of anthropogenic-carbon uptake can be assessed
through division of the global ocean into eight basin-scale regions. The
regional distribution is defined according to the low, mid- and high
latitudes, motivated by the large-scale difference in carbon uptake
mechanisms occurring in these regions see, e.g.,. The
regions are as follows: Southern Ocean (SO; 45–70∘ S),
midlatitude Southern Ocean (mSO; 15–45∘ S), tropical Pacific (TPa;
15∘ N–15∘ S), tropical Atlantic (TAt;
15∘ N–15∘ S), Indian Ocean (Ind; north of 15∘ S),
North Pacific (NPa; 15–60∘ N), North Atlantic (NAt;
15–60∘ N), and Arctic Ocean (Arc; > 60∘ N). Computation
for the analysis pertaining to the relationships between contemporary and
future CO2 uptake (Sect. 3.1) is made at the resolution of the
observational data.
We note that the selection of 45∘ S as a boundary between the mid-
and high-latitude SO could pose problems since the SO region has
sophisticated dynamics, and, dependent on the models, the 45∘ S
latitude could cut into regions of dominant carbon sources or sinks. To
address this issue, we also perform additional analyses where we use a
dynamic boundary separating the mid- and high-latitude Southern Ocean
applying a surface density of 26.5 kg m-3. For instance,
apply this density line to separate the subtropical mode water
(TMW; region of weak increase in future CO2 uptake)
and the subantarctic model water (MW; region of strong increase in future
CO2 uptake).
Results
Inter-model contemporary and future CO2 uptake relationships
The relationship between contemporary and future global CO2 uptake
(Rmeanglb) as simulated by the CMIP5 models is shown in
the Fig. 2a, and the relationships relating to Rcumglb
are shown in Fig. 2b. Figure 2a shows that the models have positive
correlation but weak linear relationships between the present and future
CO2 uptake rate. However, the linear relationships become more
pronounced for cumulative carbon sinks (Fig. 2b), as shown by a
Rcumglb value of 0.77. This is also consistent with
Fig. 1, which shows that the inter-model spread in CO2 uptake evolves
in a relatively similar manner into the future.
Global annual contemporary carbon uptake rate vs.
(a) global future (2090–2099) carbon uptake and
(b) cumulative global carbon uptake over 2001–2099. The straight
gray lines show the best-fit linear regression across all models.
Panel (a): the eight regional ocean basins adopted in this
study, defined according to different ocean basins and latitudinal lines of
70, 45, 15∘ S, 15, and 60∘ N, and
(b) correlation coefficient between regional contemporary
CO2 uptake rate with (red bars) future uptake rate in the 2090s and
(blue bars) cumulative carbon uptake in the 21st century. The numbers over
the x axis on panel (b) represent the area of each region (in
108 km2).
Annual contemporary carbon uptake rate vs. global uptake rate
projected for the last decade of the 21st century by CMIP5 models in the
(a) high-latitude Southern Ocean (SO) and (b) other ocean
regions excluding the Southern Ocean (eSO). Annual contemporary carbon uptake
rate vs. cumulative 21st-century carbon uptake projected by CMIP5 models in
the (c) SO and (d) eSO. Panel (e): time series of
correlation coefficient computed in a similar way as that in Fig. 3b (blue
bars) for every decade between 2001 and 2099. The straight gray lines in
panels (a–d) show the best-fit linear regression across all models.
Vertical shades on panels (a, c) depict observation-based estimates
.
Annual contemporary carbon uptake vs. global uptake rate projected
for the last decade of the 21st century by CMIP5 models. Here the SO is
defined using dynamic boundaries separated by a surface water density of
26.5 kg m-3. Panels (a) and (b) show the
contemporary SO carbon uptake on the x axes in Pg C yr-1 and
mol C m-2 yr-1, respectively. Panel (c) illustrates the
26.5 kg m-3 density lines that separate the MW from the TMW for the
month of August 2005 as simulated by the different models (same color
convention as in a and b).
Next, for each region defined in Sect. 2.6 (as depicted in Fig. 3a), we
computed the inter-model correlation coefficient metrics following Eqs. (3)
and (4). The SO region yields the highest correlation coefficient as depicted
in Fig. 3 with RcumSO=0.65 and
RmeanSO=0.76, in blue and red bars, respectively. Other
regions remain weakly correlated with a correlation coefficient close to zero
or under 0.40 for both correlation fields (TPa, TAt, Ind, NPa, NAt, and Arc
regions). Only the mSO region reveals quite a strong negative correlation
coefficient, with RmeanmSO=-0.55. Nevertheless, in all
regions except the SO, the correlation coefficients are statistically
insignificant at the 90 % confidence level, while SO shows a statistical
significance of over 99 %. For the remainder of the analysis, we
therefore combined the seven regions with an insignificant correlation
coefficient into one new region: global ocean excluding SO (eSO).
Time series of anomalies (relative to the year 2001) of annual mean
(a) carbon uptake, (c) net primary production, and
(e) carbon uptake efficiency in the Southern Ocean. Panels
(b), (d), and (f) also show the same fields as the
panels on the left but for the ocean region outside of SO. Thick gray lines
represent the inter-model mean, with gray shadings representing ±1
standard deviation of inter-model variations.
Seasonal cycle of (a–b) carbon flux, (c–d) NPP, and (e–f) SST anomalies for the Southern Ocean region
simulated by CMIP5 models for the 2001–2010 (left panels) and 2090–2099
(right panels) decades. The red (blue) shadings represent the range of G1
(G2) models that simulate anomalously strong (weak) carbon uptake (see also
text in Sect. 3.3). The colored lines represent the mean of the respective
model groups, black lines represent the observational estimate adopted from
for CO2 uptake and SST anomaly, and the NPP was taken from SeaWiFS data as described in .
Globally, for the annual oceanic CO2 uptake during the 2001–2010
period, all models except the NorESM1-ME are well within the range of the
observation-derived estimate from of about
1.99±0.50 PgCyr-1. The two highest uptake estimates are
simulated by the BCC-CSM1.1(m) and NorESM1-ME models (2.47±0.15 and
2.63±0.07 PgCyr-1, respectively), and the lowest uptake
estimates come from the MPI-ESM-LR and MRI-ESM1 models (1.88±0.10 and
1.78±0.13 PgCyr-1, respectively).
Figure 4 depicts the inter-model relationships in the SO and eSO domains for
RmeanSO, RmeaneSO,
RcumSO, and RcumeSO. In SO, four
models (BCC-CSM1.1, NorESM1-ME, MPI-ESM-LR, and CESM1-BGC) overestimate the
carbon uptake flux from the atmosphere to the ocean when compared with two
independent observationally based estimates of about
0.15±0.12 PgCyr-1 and
0.27±0.13 PgCyr-1 ; the latter is derived
from data sets. The highest estimates are simulated by the
BCC-CSM1 and NorESM1-ME models at 1.03±0.09 and
0.64±0.11 PgCyr-1, respectively. Two models (CanESM2 and
MRI-ESM1) underestimate the flux, with the lowest estimate simulated by the
CanESM2 model, which is also the only model to simulate the Southern Ocean as
a source of carbon to the atmosphere at about
-0.64±0.09 PgCyr-1 (Fig. 4a). Three other models
(GFDL-ESM2G, MIROC-ESM, and HadGEM2-ES) project CO2 uptake within
the two observationally based estimates.
Figure 4a and c illustrate the strong inter-model linear relationships
between the contemporary CO2 uptake rate in the SO and the projected
future uptake rate (Fig. 4a) and cumulated carbon uptake over the 21st
century (Fig. 4c). In the last decade of the 21st century, the CMIP5 models
project ocean carbon uptake rates ranging from 4.30 to
5.92 PgCyr-1. The cumulative oceanic CO2 uptake during
the 21st century is projected to be between 340.4 and 488.5 Pg C.
Figure 4 also demonstrates the peculiarity of the Southern Ocean as compared
to the rest of the world's ocean (eSO), where the contemporary CO2
uptake rate in the latter has a relatively strong negative correlation with
RmeaneSO=-0.52 and RcumeSO=-0.24.
To investigate the robustness of this correlation coefficient with time, we
computed Rcumy for all of the original eight regional basins for all 10-year windows between 2001 and 2099 (shown
in Fig. 4e). The SO region is shown to have a consistently strong positive
correlation coefficient across time with RcumSO values
of 0.69±0.04. Other regions have more pronounced temporal variations,
particularly the North Atlantic (NAt), which goes from a negative correlation
in the early 21st century (-0.39) to a positive correlation after 2020; the
latter increases to 0.59. However, the correlation in the NAt remains statistically insignificant at 90 % of
confidence level.
As stated in Sect. 2.6, we also computed the correlation coefficient metrics
for the SO region using a dynamic boundary (instead of a fixed
45∘ S latitude) along the surface water with a density of
26.5 kg m-3. Figure 5c illustrates the model-dependent dynamic
boundaries as simulated for August 2005. Figure 5a and b show that the linear
inter-model relationships remain strong (correlation coefficient of at least
0.76) when the dynamic boundary is used, suggesting that the inter-model
relationships in the SO are relatively robust.
Carbon uptake evolution in the Southern Ocean
In this section, we examine why the SO has the highest
RmeanSO and RcumSO relative to
the other regions. Only seven out of the nine models previously used to
establish the correlations are used; the BCC-CSM1 and MIROC-ESM models are
excluded because they do not provide the monthly ALK and DIC fields needed
for the uptakeeff analysis. The remaining models are CanESM2,
CESM1-BGC, GFDL-ESM2G, HadGEM2-ES, MPI-ESM-LR, MRI-ESM1, and NorESM1-ME.
Figure 6 shows the time series anomalies (relative to the year 2001) of
CO2 uptake, net primary production (NPP), and uptake efficiency
(uptakeeff) in SO and eSO as simulated by the CMIP5 models.
There is a general increase in CO2 uptake for both SO and eSO, as
would be expected from the increasing atmospheric CO2
concentrations under the RCP8.5 scenario. However, in SO (except for the
CESM1-BGC model) the simulated uptake rates steadily increase towards the end
of the 21st century, and the multi-model mean increases to
1.2±0.3 PgCyr-1 higher than in the present day (Fig. 6a).
The CESM1-BGC model simulates stabilization of CO2 uptake during the
last 2 decades of the 21st century. In the other regions (Fig. 6b), the
multi-model mean reaches a saturation point of
1.9±0.4 PgCyr-1 in the 2070s before the uptake strengths
go down to 1.5±0.4 PgCyr-1 in 2100. This “peak and
decline” pattern is consistently shown in all models analyzed here.
The unique SO region benefits from the strong link between deep
and surface ocean through the southern upwelling . Earlier
studies analyzing the previous generation of ESMs also demonstrated that this
region will be an important sink of future atmospheric CO2 although
the efficiency of the sink may decrease . The increasing
CO2 sink in the SO was shown to be associated with a reduction in the
fractional ice coverage which alleviates the light limitation on
photosynthesis and increases in surface ocean temperature, both of which
would increase the phytoplankton growing season.
A global mean decrease in NPP of about -3.12±3.54 PgCyr-1
is projected by the CMIP5 models, predominantly attributed to the increase in
surface temperature leading to stronger stratification and hence reducing the
nutrient supply to the surface ocean through vertical mixing .
The large differences between the structure of the ecosystem models of the
CMIP5 models no doubt contribute to the large inter-model uncertainty. For
example, the GFDL-ESM2G and MRI-ESM1 models simulate global annual NPP
estimates which differ by more than a factor of 2 at 66.7 and
25.9 PgCyr-1, respectively (not shown), and are outside of the
multi-model standard deviation for the NPP estimates. The MRI-ESM1 model
considers only one nutrient limitation and simulates only one type of
phytoplankton, while the GFDL-ESM2G model uses a more sophisticated ecosystem
module with five types of nutrients and three classes of phytoplankton
(Table 2). However, this alone is insufficient to determine the reason why
the GFDL-ESM2G NPP is so strong. On the other hand, the MPI-ESM-LR NPP is at
the low end of the inter-model range. Inter-model variations in the physical
and biogeochemical interaction important for the surface primary
productivity, such as irradiance and upwelling, should be analyzed further to
seek to address this question. This, however, is beyond the scope of the
present study.
Figure 6c–d show the NPP anomalies relative to 2001 in the SO and eSO
regions through the 21st century, highlighting that surface primary
production in SO is either stable or weakly increasing by roughly
0.5±0.3 PgCyr-1 at the end of this century, while it is
clearly decreasing in the other regions (by
-2.6±0.1 PgCyr-1). This is consistent with findings by
, who show that the NPP increase in the SO is predominantly
attributed to the weakening temperature limitation for phytoplankton growth
projected in the future. They also indicate that, despite the inter-model
agreement in long-term trend, the regional inter-model variation is
substantial.
In SO, steady biological production may also be responsible for maintaining
low pCO2 in the summer and keeping a higher buffer capacity than in
the other regions . The two last panels of Fig. 6 show the
anomaly of uptakeeff (Sect 2.4 for definition), which is expected
to decrease as the Revelle Factor increases under future high ambient
atmospheric CO2 concentrations . Nevertheless, the
decreasing trend is weaker in the SO than the eSO region at -0.28±0.01
vs. -0.37±0.02 µmol kg-1ppm-1 respectively by
the year 2099. Indeed, for the same change in pCO2 and roughly the
same Revelle Factor change, the SO experiences a smaller change in DIC (not
shown), indicating a unique process occurring in the high-latitude SO. Deep
winter mixing at polar regions is very efficient in transporting the
anthropogenic carbon from surface to depth, resulting in an increase in
uptake efficiency . Moreover, the increase in the meridional
temperature gradient from the tropic, to the high latitudes projected in the
future could lead to an enhancement of the Antarctic Circumpolar Current via
a stronger wind stress at the surface . As a result, this could
translate into enhancement in intermediate water formation and more efficient
transport of anthropogenic carbon from the surface into depth.
The steady increase in carbon uptake in the SO region could be the reason for
the strong correlation of this region with future global sinks (Sect. 3.1 and
Fig. 4a). In the next subsections, we therefore focus on analyzing the
mechanism for ocean uptake in the SO region as simulated by the different
models.
Inter-model division in the Southern Ocean carbon uptake
Figure 4a–b show that the CMIP5 models exhibit diverse contemporary carbon
uptake sinks in SO, from an outgassing of
-0.64±0.09 PgCyr-1 (CanESM2) to an uptake of
1.03±0.09 PgCyr-1 (BCC-CSM1.1). To investigate the
mechanisms driving the inter-model heterogeneity, we compute the carbon
uptake on a seasonal timescale and compare it to estimates derived from
observations ().
We divided the seven CMIP5 models into two groups. The first group (hereafter
referred to as “G1”) represents those that simulate an anomalously stronger
annual CO2 uptake rate in the SO as compared to the observationally
based estimates and consists of the CESM1-BGC, HadGEM2-ES, MPI-ESM-LR, and
NorESM1-ME models. The second group (hereafter referred as “G2”) comprises
models that simulate anomalously weaker CO2 uptake, consisting of
the CanESM2, GFDL-ESM2G, and MRI-ESM1 models. Figure 7 shows the inter-model
mean and spread of these two groups in their projections of the seasonal
cycle of carbon fluxes, NPP, and anomalies of SST for both the contemporary
(2001–2010) and future (2090–2099) periods.
Contemporary (2001–2010) seasonal mean of air–sea CO2
fluxes from (first column) observations-based estimates of ,
(second column) G1 models, and (third column) G2 models. Each row of panels
depicts values for different seasons (JFM, AMJ, JAS, and OND).
Anomalies of decomposed pCO2 components in the Southern Ocean
for the 2001–2010 (left panels) and future 2090–2099 (right panels)
periods: (a, b) overestimating models;
(c, d) underestimating models. Values shown are from multi-model
mean. The gray lines and markers are estimates derived from observations
according to .
Figure 7 illustrates that the G1 models have nearly the opposite seasonal
cycle to the G2 models. The G1 models (Fig. 7a, red lines) simulate a strong
mean ocean CO2 uptake in December–January of about
0.30 molCm-2month-1, which has the same direction as, but
is more than 4 times stronger in magnitude than, the observation-based
estimate of 0.07 molCm-2month-1. This overestimation
corresponds to the period of the highest NPP, where the G1 model means
simulate an NPP maximum more than 3 times stronger than the observations: 35
compared to 11 gCm-2month-1, respectively (Fig. 7c, black
and red lines). Moreover, the SST anomaly during this 2-month period has
a negligible effect on the CO2 flux; there is no significant change
in carbon flux occurring when the SST anomaly increases from +0.3 to
+1.5 ∘C (Fig. 7e, red line). This highlights that the
biological activity in G1 models is the primary driver for the CO2
flux seasonal cycle in SO during the high-productivity season, while the
impact of the seasonal temperature on the surface pCO2 appears to play
only a secondary role .
In contrast to the G1 models, G2 models (Fig. 7, blue lines) simulate strong
outgassing during the summertime with a negative CO2 flux of nearly
-0.10 molCm-2month-1. This is in disagreement with the
observation-based estimates and is predominantly driven by the SST changes.
The magnitude of the SST anomaly from the G2 models is 2 times stronger than
the observations, whereas the NPP cycle is similar in amplitude. The rapid
warming and cooling of SST simulated in the G2 models during the spring and
fall seasons lead to a higher and lower surface pCO2, respectively. As
a result, the G2 models simulate strong CO2 uptake during the fall
season, which also implies that the solubility pump is a primary driver.
During the austral winter, in August, observation-based estimates show
a maximum outgassing, roughly -0.03 molCm-2month-1
(Fig. 7a, black line). G1 models simulate the same mechanism at this period
but the maximum outgassing is reached earlier in May–June instead of August.
Concurrently, G1 models simulate a minimum of NPP at this time, pushing up
the pCO2 at the surface. Thus, the SO turns into a source of
CO2 for the atmosphere despite the SST anomaly of about
-0.6∘C, which would tend to push the pCO2 in the opposite direction. The same
shift appears for G2 models but for an opposite CO2 flux seasonal
cycle, with a maximum uptake in May of nearly
0.10 molCm-2month-1. The NPP is twice as strong in
May–June for the G2 models than for the G1 models at 1.5 vs.
0.6 gCm-2month-1, respectively. This, in addition to
a stronger magnitude of SST anomaly, leads to a CO2 uptake in
April–June being projected by the G2 models as depicted in Fig. 7a (blue
line).
Figure 8 illustrates the mean spatial variation in CO2 uptake for
each season from and as simulated by the G1 and G2 models
during the contemporary period. In the G1 models, the seasonal cycle that is
too strong as compared to the observation-based estimates occurs throughout
most parts of the SO, especially during October–December and January–March,
with considerably stronger carbon sinks found in the south of the circumpolar current. Between 50 and 60∘ S, the
outgassing in G1 models is noticeably stronger during April–June and
July–September. In the G2 models, the largest source of bias during
October–December and January–March when compared to the
estimates is found in the Atlantic and Indian sectors of the SO where the
models simulate a relatively uniformly strong outgassing.
Future period simulations (Fig. 7, right panels) show that the seasonal phase
in the carbon flux will be relatively similar, but the amplitude will grow
considerably as compared, to the current seasonality. The distinctions in NPP
and SST seasonal cycle between G1 and G2 models are also maintained.
Therefore, the bias in the present-day seasonal phase of CO2 fluxes
is projected to persist toward the end of the 21st century. We note that
there is a 1-month shift in simulated SST anomaly seasonal cycle where a
maximum SST anomaly appears in March instead of February.
Drivers for the Southern Ocean carbon uptake
Following Eq. (1) in the methods section, we decomposed the pCO2
seasonal cycle anomalies into four drivers: DIC, ALK, SST, and SSS for both
the contemporary and future periods (shown in Fig. 9). The SSS-induced
variations are not shown because the magnitude is negligible relative to the
other variables. As with the previous subsection, we focused our analysis on
the two contrasting model groups (G1 and G2). The amplitude of pCO2
from the G1 (Fig. 9a, red line) overestimates the estimations.
Nevertheless, it fits closer with later measurements from ,
though they estimated the sea–air carbon flux only from a section of the
Southern Ocean where pCO2 anomaly amplitudes can reach roughly
120 µatm. In both groups as well as the observation-based
estimates, the SST- and ALK-induced pCO2 anomalies are generally in
phase with each other and they are the opposite of the DIC-induced
variability.
The DIC-induced pCO2 anomaly from the G1 models simulates a minimum in
January instead of March, a 2-month shift as compared to the values derived
from . The G1 models, which simulate anomalously strong SO
carbon uptake, generally simulate too low a surface pCO2 during
December–January (an anomaly of -40 µatm; Fig. 9a, red line)
due to the NPP that is too strong (Fig. 7c, red line). The driving parameter
seems to be the DIC at the water surface (-38 to -64 µatm of
anomalies in December–January). Thus, DIC consumption by the phytoplankton
via photosynthesis confirms the importance of biological activity for carbon
uptake in this group of models during this high-productivity period. The G2
models project nearly the same amplitude as that derived from observations
but depict an opposite phase for carbon uptake. Figure 9 shows that G2 models
simulate surface pCO2 during December–January that is, anomalously,
too strong, of roughly 7 to 12 µatm (Fig. 9c, blue line). There,
the driving parameters are the SST and alkalinity with anomalies of about 38
and 19 µatm in January, respectively. Indeed, these two
components tend to push up the pCO2 at the surface in the summer
season (December–March) and present also a 1-month shift as compared to the
values derived from observations (Fig. 9c, gray squares and circles).
The future simulations accentuate even more the pCO2 seasonal cycles
for the G1 models (Fig. 9b, red line). The amplitude of this seasonal cycle
approximately doubles in 2090–2099 relative to 2001–2010 (i.e., the
standard deviation increases from 24.5 to 50.3 µatm), mostly due
to the DIC-induced variability (Fig. 9b, red triangles). The amplitudes of
the SST-induced and ALK-induced variability are projected to double as well;
however, their combined magnitude is still weaker than DIC-induced
variability. The G2 models maintain roughly the same amplitude of total
pCO2 through the 21st century (Fig. 9d, blue line), though the
pCO2-induced components also increase by a factor of 2. Nevertheless,
the DIC-induced seasonal cycle of the G2 models, conversely to the G1, is of
about the same order of magnitude as the SST-induced seasonal cycle, thus
balancing the change in the pCO2.
Discussion
The ocean plays an instrumental role in buffering the increasing atmospheric
CO2 concentration and the ongoing climate change. In this study, for
the first time, we evaluate the relationships between present-day regional
ocean carbon sinks with future cumulative carbon sinks over the 21st century
under the high CO2 RCP8.5 scenario as simulated by a suite of
fully interactive CMIP5 ESMs. The SO is found to be a good predictor for
future global carbon uptake. We therefore examined the representation of
oceanic carbon uptake and its future evolution in the SO. Specifically, we
assess the model capability to simulate the observed seasonal pCO2
cycle for the present-day period.
With respect to the annual mean CO2 uptake in the Southern Ocean,
evaluate a set of CMIP5 models but from different simulations,
i.e., with prescribed atmospheric CO2 concentrations, and over
a slightly smaller domain (56–62∘ S). Despite these differences,
our present findings are very much comparable to the prior study, with the
CanESM2 and GFDL-ESM2G models simulating net outgassing and close to neutral
CO2 fluxes. By contrast, the HadGEM2-ES and MPI-ESM-LR were shown to
have relatively stronger carbon sinks, especially during austral summer. For
the 2001–2010 period, only one model (MRI-ESM1, ∼362 µatm)
simulates a mean surface pCO2 in the SO that is lower than the
observed ∼364 µatm; the majority of models simulate
a stronger mean carbon sink than the observational estimate. This indicates
the need to consider the seasonal cycle when evaluating carbon uptake
projections in the SO.
also examine the CMIP5 models' simulated pCO2 seasonal
cycle in the Drake Passage as compared to shipboard measurements. They show
that the pCO2 seasonal cycle in this confined domain is representative
of the broader circumpolar region. Our analysis using data
further indicates that the pCO2 seasonal cycle in the SO region is
reasonably homogeneous in phase, with the maximum pCO2 in austral
winter (August) and the minimum in summer (January), as illustrated in
Figs. 7 and 9. We show that most models simulate a larger seasonal
pCO2 amplitude than the observation-based estimates. Despite
overestimating the mean carbon sinks, both HadGEM2-ES and MPI-ESM-LR
reproduce the seasonal phase of the data. The MPI-ESM-LR simulates carbon
uptake in the early spring period that is too strongly biologically mediated,
consistent with the anomalously strong late winter mixing (August–September)
, which causes the required nutrients to upwell to fuel
biological production. The GFDL-ESM2G, one of the two models that simulate
carbon sinks closest to the data-based estimate, simulates the opposite
pCO2 seasonal phase, which is largely attributed to the bias in
absolute values and amplitude of SST seasonal cycle. None of the CMIP5 models
analyzed here are able to reproduce the observed seasonal cycle and annual
mean carbon sinks within the uncertainty range in the SO. This highlights the
difficulty in simulating the correct variability in hydrography and
biogeochemistry in this region.
Anomalies of nonthermal pCO2 seasonal cycle,
(pCO2DIC+ALK) as simulated by seven ESMs for the 2001–2010
period. The gray dashed line indicates the observation-based estimate of the
pCO2DIC+ALK seasonal cycle. The numbers within the
parentheses represent the amplitude for each model as well as for the
observation-based estimate.
Based on the linear inter-model relationship presented in this study, the
GFDL-ESM2G, MIROC-ESM, and HadGEM2-ES models simulate contemporary
CO2 fluxes in the SO closest to the observationally based estimate
(see, for example, Fig. 5a) and therefore are likely to have more credibility
with regard to their future projections. Nevertheless, from our seasonal
cycle analysis, it is not clear if these models simulate the observed
mechanisms governing the CO2 fluxes. According to , the
nonthermal component of the pCO2 variation is an important driver for
the long-term CO2 fluxes in the SO. Figure 10 shows the seasonal
anomaly of a nonthermal CO2 seasonal cycle in the SO from models and
observation-based estimate. The CanESM2 and GFDL-ESM2G simulate comparable
amplitude and seasonal phase with the observation-based estimate, but the
former model has anomalously high surface pCO2 (i.e., it simulates a
net source of CO2 to the atmosphere in the SO). Taking this as an
additional constraint, our analysis suggests that the GFDL-ESM2G performs
best in capturing the observed CO2 fluxes in the Southern Ocean.
In their model study applying an ad hoc parameterization of wind stirring,
demonstrate that changes in wind stirring have a large impact
on the mean carbon uptake and seasonal cycle phasing in the SO (south of
45∘ S). They show that resultant changes in the seasonal onset of
stratification influence both entrainment and the biological pump.
Furthermore, identify the annual mean freshwater flux as the
primary source of error for the SO mixed layer depth in CMIP5 models. This
uncertainty arises from the lack of accurate estimates of buoyancy fluxes
from observations in the region.
In the SO, the CO2 flux and its evolution in response to climate
change also depend critically on the spatial and temporal variation of
convection processes see, e.g.,. Due to the coarse spatial
resolution in CMIP5 models, convection processes along the continental margin
that form the AABW (Antarctic Bottom Water) are not well reproduced
. Similarly, suggest that the anthropogenic
CO2 uptake in the Weddell Sea is closely linked to the size and
timing of deep-water convection. It remains to be investigated how these
uncertainties contribute to the inter-model spread of the projected
CO2 uptake in the SO shown here, especially with the next round of
CMIP6, which includes models with a higher resolution
According to the analysis preformed in this study, improving the
representation of amplitude and seasonal phase of contemporary surface
pCO2 in SO has the potential to reduce the uncertainty of the future
ocean carbon uptake in CMIP5 models. Bias in amplitude is identified to be
associated with the magnitude of primary production in the spring–summer
seasons, whereas bias in the seasonal phase is attributed by poor
representation of SST seasonal cycle. Seasonally varying surface primary
production data along with relevant biogeochemical and ecosystem state
variables (e.g., nutrients and oxygen) would help constrain process
parameterization in the model. In order to improve the SST simulation,
improvements in the representation of physical processes across the air–sea
interface and between mixed layer and ocean interior supported by
high-quality observation would be needed.
Despite a steady increase in surface pCO2 observations in the SO
region over recent decades, it remains markedly undersampled, both spatially
and temporally. Presently, there are only very few locations where the full
annual cycle of observations is available . The SO region also
has the largest differences in the net CO2 fluxes as estimated from
different methods involving observations and models . Recently,
found that DIC-induced small spatial scale pCO2
structures existing in the SO are non-negligible. Such small-scale processes
are generally missing in the coarse-resolution CMIP5 models and sparse
observations. In addition, the strong interannual variations in the air–sea
CO2 fluxes identified in this region see, e.g., could also contribute to the discrepancies in the observed and
model-simulated pCO2 seasonality presented in this study see
also.
Beyond surface processes, uncertainties in the subsurface circulation
patterns could also contribute to the surface biases simulated in the SO.
Here, regions critical for biological production and carbon uptake are
associated with mode and intermediate water formation locations
. Despite the fact that the simulated net uptake rates
of atmospheric CO2 in the SO are mostly overestimated compared to the
values derived from observations (as shown in this study),
show that the CMIP5 models underestimate the anthropogenic-carbon storage in
the Southern Ocean. This indicates either a shortcoming in the simulated
large-scale overturning circulation or too strong a sink of non-anthropogenic
carbon simulated here. Therefore, a better constraint of the former mechanism
should be prioritized in order to improve the projection of the long-term
evolution of air–sea CO2 uptake.
New US-led initiatives that aim to enhance our understanding of the Southern
Ocean processes are emerging, for instance, the Southern Ocean Carbon and
Climate Observation and Modeling project (SOCCOM;
http://soccom.princeton.edu). The biogeochemical Argo floats planned to
be deployed will provide novel measurements that will help identify how the
changing physical processes influence the biogeochemistry dynamics, and vice
versa. The EXport Processes in the Ocean from RemoTe Sensing (EXPORT;
http://cce.nasa.gov/cce/ocean.htm) campaign, which studies the export
and fate of ocean NPP using remote sensing observations, will also provide a
better constraint for ecosystem process parameterization in the model. In
addition, a multi-model intercomparison involving observational data such as
this study is useful to elucidate the complex interplay among physical and
biogeochemical processes, which ultimately would reduce uncertainties in
climate projections.
Conclusions
The latest-generation ESMs project ocean carbon uptake with considerable
uncertainty, and this uncertainty is projected to grow 2-fold by the end of
the 21st century under a high future CO2 emissions scenario. In
this study, the evaluation of the CMIP5 ESMs was focused on assessing the
ability of the models to project the future CO2 fluxes between
ocean and atmosphere by looking at the correlation coefficient of each region
with the global future ocean CO2 uptake.
We found that the highest inter-model correlation is in the Southern Ocean
(SO) region (RmeanSO=0.76,
RcumSO=0.65), meaning that most models agree regarding
the evolution of their CO2 uptake behavior through the 21st century.
The majority of models simulate a steady increase in CO2 sink rate
due to a weaker decrease in buffer capacity and due to a relatively stable
NPP throughout the 21st century. We show that models that take up anomalously
low CO2 in the SO today would project low cumulative CO2
uptake throughout the 21st century and vice versa. This suggests that the
carbon uptake in the SO can be used to constrain future global uptake
uncertainty. We highlighted that in other regions, the models simulate
a decrease in CO2 uptake during the second half of this century but
with a large inter-model spread in the timing of the decreasing trend, thus
affecting the multi-model correlation in these areas.
We have identified a strong bias in the amplitude of carbon uptake simulated
by the CMIP5 models for the period 2001–2010 in the Southern Ocean, ranging
from a source to the atmosphere to a sink almost 3 times more powerful than
has been observed (1.03± 0.09 vs.
0.27± 0.13 PgCyr-1; ). Inter-model
spread in the SO carbon sink arises from variations in the surface
pCO2 seasonality, which is attributed by the bias in the simulated
timing and amplitude of primary production and SST. By analyzing the
differences in the simulated pCO2 seasonalities, we classified two
groups of models according to two different behaviors. Models that simulate
anomalously strong 2001–2010 CO2 uptake in SO (Fig. 7a, red color)
reproduce the observed pCO2 seasonal cycle but its amplitude is 2.5
times stronger than the observation-based estimates. This is because of the
strong surface DIC variations, which push down the pCO2 by simulating
too strong a biological production. The effect on the projected simulations
is an increase in CO2 uptake due to the weaker decrease in seawater
CO2 buffering capacity. Other models that simulate anomalously low
2001–2010 CO2 uptake in SO (Fig. 7a, blue color) simulate a
comparable seasonal pCO2 amplitude to that estimated from the
observations but a wrong seasonal phase. This is due to
the bias in the SST seasonal cycle with stronger amplitude, which tends to
push up the pCO2 in January–March, therefore simulating the SO as
evolving towards a source of carbon for the atmosphere.
These biases in time and magnitude show the difficulty in simulating the
observed marine ecosystem and the biogeochemical processes that contribute
and govern the ocean surface pCO2. Consequently, simulating the right
contemporary seasonal cycles of biological processes NPP and SST in the
Southern Ocean would allow us to constrain the bias in terms of future
oceanic carbon flux.
Seasonal timing and amplitude in pCO2 are shown to be critical in
order to accurately simulate the present and future CO2 uptake, and
therefore accurate monitoring of these biogeochemical processes in the SO is
critical in order to constrain the assessment of the contemporary and future
ocean carbon sink and subsequently the uncertainty in future climate change.
However, the Southern Ocean remains one of the most poorly sampled ocean
regions with respect to biogeochemistry. The observational data analyzed here
were generated through extrapolations from the limited direct measurements;
this could add an extra uncertainty to the analysis. This emphasizes the
urgent need for a sustained and comprehensive observational campaign in this
region, which is emerging as the key region to better constrain the evolution
of future ocean carbon sinks.