Over the last decades, sea surface temperature (SST) reconstructions based
on the Mg/Ca of foraminiferal calcite have frequently been used in
combination with the δ18O signal from the same material to
provide estimates of the δ18O of water (δ18Ow), a proxy for global ice volume and sea surface salinity
(SSS). However, because of error propagation from one step to the next,
better calibrations are required to increase the accuracy and robustness of
existing isotope and element to temperature proxy relationships. Towards
that goal, we determined Mg/Ca, Sr/Ca and the oxygen isotopic composition of
Trilobatus sacculifer (previously referenced as Globigerinoides sacculifer) collected from surface waters (0–10 m) along a
north–south transect in the eastern basin of the tropical and subtropical
Atlantic Ocean. We established a new paleotemperature calibration based on
Mg/Ca and on the combination of Mg/Ca and Sr/Ca. Subsequently, a
sensitivity analysis was performed in which one, two or three different
equations were considered. Results indicate that foraminiferal Mg/Ca allows
for an accurate reconstruction of surface water temperature. Combining
equations, δ18Ow can be reconstructed with a precision of about
± 0.5 ‰. However, the best possible salinity
reconstruction based on locally calibrated equations only allowed for a
reconstruction with an uncertainty of ± 2.49. This was confirmed by a
Monte Carlo simulation, applied to test successive reconstructions in an
“ideal case” in which explanatory variables are known. This simulation shows
that from a purely statistical point of view, successive reconstructions
involving Mg/Ca and δ18Oc preclude salinity reconstructions with
a precision better than ± 1.69 and hardly better than ± 2.65
due to error propagation. Nevertheless, a direct linear fit to reconstruct
salinity based on the same measured variables (Mg/Ca and δ18Oc)
was established. This direct reconstruction of salinity led to a much
better estimation of salinity (± 0.26) than the successive
reconstructions.
Introduction
Since Emiliani's pioneering work (1954), oxygen isotope compositions
recorded in fossil foraminiferal shells became a major tool to reconstruct
past sea surface temperatures (SSTs). After Shackleton's seminal studies (1967, 1968
and 1974), it became clear that part of the signal reflected
glacial–interglacial changes in continental ice volume and hence sea level
variations. The oxygen isotope composition of foraminiferal calcite (δ18Oc) is thus controlled by the temperature of calcification
(Urey, 1947; Epstein et al., 1953) but also by the oxygen isotope
composition of seawater (δ18Ow). The relative
contribution of these two factors cannot be deconvolved without an
independent measure of the temperature at the time of calcification, such as
e.g., Mg/Ca (e.g., Nürnberg et al., 1996; Rosenthal et al., 1997; Rathburn
and DeDeckker, 1997; Hastings et al., 1998; Lea et al., 1999; Lear et al.,
2002; Toyofuku et al., 2000; Anand et al., 2003; Kisakurek et al.,
2008; Dueñas-Bohórquez et al., 2009, 2011; Honisch et al., 2013; Kontakiotis
et al., 2016; Jentzen et al., 2018). The sea surface temperature
reconstructed from the Mg/Ca of foraminiferal calcite has, therefore,
increasingly been used in combination with the δ18O signal
measured from the same material to estimate δ18Ow and global ice
volume and to infer past sea surface salinity (SSS) (e.g., Rohling 2000;
Elderfield and Ganssen, 2000; Schmidt et al., 2004; Weldeab et al., 2005;
2007). These studies also showed that, because of error propagation,
inaccuracies in the different proxies combined for the reconstruction of
past sea water δ18O and salinity obstruct meaningful
interpretations. Hence, while there is an understandable desire to apply
empirical proxy relationships downcore, additional calibrations appear
necessary to make reconstructions more robust. Calibrations using
foraminifera sampled from surface seawater (0–10 m deep) provide the best
possibility to avoid most of the artifacts usually seen when using specimens
from core tops or culture experiments for calibration purposes. Here, we
report a calibration based on Globigerinoides sacculifer, which should now and will be referenced in
this paper as Trilobatus sacculifer (Spezzaferri et al., 2015), from the Atlantic Ocean. Mg
and Sr concentrations were measured on the last chamber of individual
specimens with laser ablation inductively coupled plasma mass spectrometry
(LA-ICP-MS), while the oxygen isotope composition of the same tests as used
for the elemental analyses was subsequently measured by isotope ratio mass
spectrometry (IRMS). Environmental parameters (temperature, T, salinity, S,
dissolved inorganic carbon, DIC, and alkalinity, ALK) but also the isotopic
composition (O18w) of the seawater that the foraminifera were growing
in were measured. The primary objectives of this study are (1) to test and
improve the calibration of both the Mg/Ca and oxygen isotope
paleothermometer for the paleoceanographic relevant species T. sacculifer; (2) to test
whether the incorporation of Sr into the Mg–T reconstruction equation
improves temperature reconstruction by accounting for the impact of
salinity; (3) to evaluate the agreement between observed and predicted δ18Ow, and (4) to test the potential for SSS reconstructions of the
Atlantic Ocean. Our results indicate that the best possible salinity
reconstruction based on locally calibrated equations from the present study
only allowed reconstructions with an uncertainty of ± 2.49. Such an
uncertainty does not allow for viable (paleo)salinity data. This is
subsequently confirmed by a Monte Carlo simulation applied to test
successive reconstructions in an “ideal case”, for which explanatory variables
are known. This simulation shows that from a purely statistical point of view,
successive reconstructions involving Mg/Ca and δ18Oc preclude
salinity reconstructions with a precision better than ± 1.69 and hardly
better than ± 2.65 due to error propagation. Nevertheless, a direct
linear fit based on the same measured variables (Mg/Ca and δ18Oc), and leading to a much better estimation of salinity (± 0.26),
could be established.
Measured temperature, salinity, DIC, ALK and δ18Ow of the stations selected for this study (October/November 2005).
Stations used in this study plotted on gridded data set (Reynolds et
al., 2002) (a). Setup for planktonic foraminifera collections (b).
Material and methodsCollection procedure
Foraminifera were collected between October and November 2005 on board
the research vessel Polarstern (ANT XXIII/1) during a meridional transect of
the Atlantic Ocean (Bremerhaven, Germany, to Cape Town, South Africa; Fig. 1a). Foraminifera were continuously collected from a depth of ca. 10 m using
the ship's membrane pump (3 m3 h-1). The water flowed into a plankton net
(125 µm) that was fixed in a 1000 L plastic tank with an overflow
(Fig. 1b). Every 8 h, the plankton accumulated in the net was
collected. Temperature and salinity of surface seawaters were continuously
recorded by the ship's systems, and discrete water samples were collected
for later analyses of total ALK, DIC and δ18Ow (see Table 1). Plankton and water samples were poisoned with buffered
formaldehyde solution (20 %) and HgCl2 (1.5 mL with 70 gL-1
HgCl2 for 1 L samples), respectively. In total, more than 70
plankton samples were collected during the transect, covering a large range
in both temperature and salinity. Specimens of T. sacculifer from 13 selected
stations, selected as to maximize temperature and salinity ranges, were
picked and prepared for analyses. Salinity, temperature, DIC, ALK and
δ18Ow data reported in this paper represent October/November
values for the selected stations.
(a)Mg/Ca and (b)Sr/Ca (mmol mol-1) and 95 % confidence intervals
plotted versus measured surface temperature (∘C). Each point
represents an average of the Mg/Ca and Sr/Ca per station.
Description of species
Trilobatus sacculifer is a spinose species with endosymbiotic dinoflagellates inhabiting the
shallow (0–80 m deep) tropical and subtropical regions of the world's oceans.
This species displays a large tolerance to temperature (14–32 ∘C)
and salinity (24–47) (Hemleben et al., 1989; Bijma et al., 1990). Based on
differences in the shape of the last chamber of adult specimens, various
morphotypes can be distinguished. Among others the last chamber can be
smaller than the penultimate chamber, in which case it is called kummerform
(kf). This species shows an ontogenetic depth migration and predominantly
reproduces at depth around full moon (Bijma and Hemleben, 1994). Just prior
to reproduction, a secondary calcite layer, called gametogenic (GAM) calcite,
is added (Bé et al., 1982; Bijma and Hemleben, 1994; Bijma et al.,
1994). Juveniles (< 100 µm) ascend in the water column and reach the
surface after less than approximately 2 weeks. Pre-adult stages then slowly
descend within 9–10 d to the reproductive depth. In our samples
(collected between 0 and 10 m depth), T. sacculifer specimens have not yet added the
Mg-enriched gametogenic calcite which generally occurs deeper in the water
column just prior to reproduction. Therefore, only the trilobus morphotype
without GAM calcite is considered in this study, which limits the
environmental, ontogenetic and physiological variability between samples
even if a rather wide size fraction (230 to 500 µm) was selected due
to sample size limitation. This should be taken into account when compared
with other calibrations based on core top and/or sediment-trap-collected
specimens.
Seawater analysis
The DIC and ALK analyses of the sea water were carried out at the Leibniz
Institute of Marine Sciences at the Christian-Albrechts University of Kiel
(IFM-GEOMAR), Germany. Analyses were performed by extraction and subsequent
coulometric titration of evolved CO2 for DIC (Johnson et al., 1993) and by open-cell
potentiometric seawater titration for ALK (Mintrop et al., 2000). Precision/accuracy of
DIC and ALK measurements are 1 µmol kg-1/2 µmol kg-1 and 1.5 µmol kg-1/3 µmol kg-1,
respectively. Accuracy of both DIC and ALK was assured by the analyses of
certified reference material (CRM) provided by Andrew Dickson from Scripps
Institution of Oceanography, La Jolla, USA. Measurements of δ18Ow were carried out at the Faculty of Geosciences, Utrecht
University, the Netherlands. Samples were measured using a GasBench II – Delta
plus XP combination. Results were corrected for drift with an in-house
standard (RMW) and are reported on V-SMOW scale with a precision of
0.1 ‰ and accuracy verified against NBS-19 of
0.2 ‰, respectively. For reconstruction calculations,
δ18Ow data were corrected to the PDB scale by
subtracting 0.27 ‰ (Hut, 1987).
Under a binocular microscope, the maximum test diameter of each specimen was
measured, and individual tests were weighed on a microbalance (METTLER
TOLEDO, precision ± 0.1 µg). Since the foraminifera were never in
contact with sediments, the rigorous cleaning procedure required for
specimens collected from sediment cores was not necessary. Prior to
analysis, the tests were cleaned following a simplified cleaning procedure:
all specimens were soaked for 30 min in a 3 %–7 % NaOCl solution (Gaffey and
Brönniman, 1993). A stereomicroscope was used during cleaning, and
specimens were removed from the reagent directly after complete bleaching.
The samples were immediately and thoroughly rinsed with deionized water to
ensure complete removal of the reagent. After cleaning, specimens were
inspected with scanning electron microscopy and showed no visible signs of
dissolution. This cleaning procedure preserves original shell thickness and
thus maximizes data acquisition during laser ablation. Foraminifera were
fixed on a double-sided adhesive tape and mounted on plastic stubs for
LA-ICP-MS analyses.
Mean elemental (Mg/Ca and Sr/Ca) and isotopic (δ18Oc) composition per station measured in foraminiferal calcite in millimoles per mole (mmol mol-1) and per mille PDB (‰ PDB), respectively. Elemental and isotopic compositions were determined on the same material (n varying from 5 to 13 specimens per station). Isotopic analyses were done in duplicate for each station. Mean δ18Oc-δ18Ow were measured per stations in per mille PDB (‰ PDB).
For each station, 5–13 specimens were analyzed. Their last chambers were
ablated using an excimer 193 nm deep ultraviolet laser (Lambda Physik) with
GeoLas 200Q optics (Reichart et al., 2003), creating 80 µm diameter
craters. The pulse repetition rate was set at 6 Hz with an energy density at
the sample surface of 1 J cm-2. The ablated material was transported on
a continuous helium flow into the argon plasma of a quadrupole ICP-MS
instrument (Micromass Platform) and analyzed with respect to time. The ablation
of calcite requires ultraviolet wavelengths as an uncontrolled disruption
would result from higher wavelengths. By using a collision and reaction cell
spectral interferences on the minor isotopes of Ca (42Ca, 43Ca and
44Ca) were reduced and interferences of clusters like
12C16O16O were prevented. Analyses were calibrated against
NIST 610 glass using the
concentration data of Jochum et al. (2011) with Ca as the internal standard. For
Ca quantification, mass 44 was used while monitoring masses 42 and 43 as an
internal check. In the calcite, the Ca concentration was set at 40 %,
allowing direct comparison to trace metal/Ca from traditional wet-chemical
studies. Mg concentrations were calculated using masses 24 and 26; Sr
concentrations were calculated with mass 88. One big advantage in using
LA-ICP-MS measurements is that single laser pulses remove only a few
nanometers of material, which allows high-resolution trace element profiles
to be acquired (e.g., Reichart et al., 2003; Regenberg et al., 2006;
Dueñas-Bohórquez et al., 2009, 2011; Hathorne et al., 2009; Munsel
et al., 2010; Dissard et al., 2009, 2010a, b; Evans et al., 2013, 2015;
Steinhardt, 2014, 2015; Fehrenbacher et al., 2015; Langer et al., 2016; Koho
et al., 2015, 2017; Fontanier et al., 2018; de Nooijer et al., 2007, 2014,
2017a, b; Jentzen et al., 2018; Schmitt et al., 2019; Levi et al., 2019).
Element concentrations were calculated for the individual ablation profiles
integrating the different isotopes (glitter software). Even though the use
of a single or very few specimens can be criticized when determining
foraminifera Mg/Ca and δ18O in order to perform paleoclimate
reconstructions instead of more traditional measurements, Groeneveld et al. (2019) recently demonstrated that for both proxies, single specimen
variability is dominated by seawater temperatures during calcification, even
if the presence of an ecological effect leading to site-specific seasonal
and depth habitat changes is also noticeable.
Stable isotope analysis
The specimens used for elemental composition analyses using LA-ICP-MS were
subsequently carefully removed from the plastic stubs and rinsed with
deionized water before measuring their stable isotope composition. Depending
on shell weight, two to three foraminifera were necessary to obtain the minimum of
20 µg of material required for each analysis. Analyses were carried
out in duplicate for each station. The results, compiled in Table 2,
represent average measurements. The analyses were carried out at the
Department of Earth Sciences of Utrecht University (the Netherlands) using
a Kiel III and Finnigan MAT 253 mass spectrometer combination. The δ18Oc results are reported in per mille PDB (‰ PDB).
Calibration was made with NBS-19 (precision of 0.06 ‰–0.08 ‰ for sample sizes of 20–100 µg, accuracy better than
0.2 ‰).
Statistical analysis
Within this paper, all statistical analyses with regards to elemental
and isotopic data were carried out using the program R with default values
(R Development Core Team, 2019).
ResultsElemental composition
Overall values of the Mg/Ca and Sr/Ca ratios in the tests of T. sacculifer varied from
1.78 to 5.86 mmol mol-1 (Fig. 2a) and 1.41 to 1.52 mmol mol-1 (Fig. 2b),
respectively (Table 2). These Mg/Ca concentrations compare well with results
found in the literature for this species from either culture experiments,
plankton tow or surface sediment, growing at the same temperatures (e.g., Nürnberg et al., 1996; Anand et al., 2003; Regenberg et al., 2009; Fig. 3). Similarly, the overall variation in Sr/Ca values reported in this study
is comparable to that observed in core top and cultured G. ruber and T. sacculifer combined for
comparable salinity and temperature conditions, (varying between 1.27 to
1.51 mmol mol-1; e.g., Cleroux et al., 2008; Kisakürek et al., 2008;
Dueñas-Bohórquez et al., 2009).
Calibration equations for T. sacculifer.
SourceR2p valuesMg/Ca relationship with temperature This studyMg/Ca= 0.42(±0.13)eT×0.083(±0.001)Eq. (1)0.862.9×10-6Nürnberg et al. (1996)Mg/Ca= 0.37(±0.065)eT×0.091(±0.007)0.93Anand et al. (2003)Mg/Ca= 1.06(±0.021)eT×0.048(±0.012)Regenberg et al. (2009)Mg/Ca= 0.6(±0.16)eT×0.075(±0.006)Sr/Ca relationship with temperature This studySr/Ca=(0.0094±0.002)×T+(1.29±0.05)Eq. (2)0.675×10-4Mg/Ca and Sr/Ca relationship with temperature This studyT=(-27±15)+(8±1)×ln(Mg/Ca)+(28±11)×Sr/CaEq. (3)0.932×10-4Me/Ca relationship with temperature and salinity This study (Mg/Ca)Mg/Ca= exp((-5.10±2)+(0.09±0.009)×T+(0.11±0.05)×S)0.915×10-6This study (Sr/Ca)Sr/Ca= (1.81±0.5)+(0.008±0.002)T-(0.01±0.01)×S0.710.002Relationship of δ18O with temperature This studyT=12.08(±1.46)-4.73(±0.51)×(δ18Oc-δ18Ow)Eq. (4)0.881.6×10-6Erez and Luz (1983)T=16.06(±0.549)-5.08(±0.32)×(δ18Oc-δ18Ow)Mulitza et al. (2003)T=15.35(±0.71)-4.22(±0.25)×(δ18Oc-δ18Ow)Spero et al. (2003)T=12-5.67×(δ18Oc-δ18Ow)Measured δ18O versus measuredδ18Ow=(0.171±0.04)×S-(4.93±1.66)Eq. (5)0.381.2×10-3salinity (this study)Direct linear fit to reconstructS=-0.16(±0.02)e-δ18Oc+0.28(±0.1)Mg/Ca+35.80(±0.33)Eq. (6)0.82<2×10-4salinity based on measuredvariables (Mg/Ca and δ18Oc)
(a) Mg/paleotemperature equations established in this study (Eq. 1) (black dots and full line); based on the data of Nürnberg et al. (1996) (orange diamonds and large, full orange line), Anand et al. (2003)
(small dotted green lines), and Regenberg et al. (2009) (large dotted blue
line). (b) Reconstructed Mg temperatures (October/November 2005) plotted versus
measured temperatures (∘C) presented in Table 1. For each station,
mean measured Mg/Ca was inserted into the equation of Nürnberg et al. (1996) (only cultured specimens of T. sacculifer) (orange dots, full line), the equation
of Anand et al. (2003) (green crosses, small dashed line), and the equation
of Regenberg et al. (2009) (blue triangles, large dashed line).
The relationship between both Mg/Ca and Sr/Ca ratios and measured
temperatures were calculated using least square differences. Both show a
good correlation with surface water temperature (Fig. 2, Table 3). The Mg/Ca
ratio increases exponentially by 8.3 % per degree Celsius (best fit) (Mg/Ca and
Sr/Ca ratios given in mmol mol-1):
Mg/Ca=(0.42±0.13)exp((0.083±0.001)×T),R2=0.86p value=2.9×10-6,
whereas Sr/Ca ratio increases linearly by 0.6 % per degree Celsius (Fig. 2a and
b) (best fit):
Sr/Ca=(0.009±0.002)×T+(1.24±0.05),R2=0.67p value=5×10-4.
Concerning the temperature reconstruction, by inverting the approach,
univariate regressions yields the following:
T=(12.3±1.5)+(10.5±1.2)×log(Mg/Ca),R2=0.86p value=2.9×10-6,
and
T=(-84.1±22.9)+((71.7±15)×Sr/Ca,R2=0.67p value=5×10-4.
Combining Mg and Sr data for a nonlinear multivariate regression allows for the
improvement of the correlation with temperature (best fit):
T=(-27±15)+(8±1)×ln(Mg/Ca)+(28±11)×Sr/Ca,R2=0.93p value=2×10-4.
For comparison with regression found in the literature, Mg/Ca is estimated
below as a function of temperature and Sr/Ca:
Mg/Ca=exp((0.98±1.89)+(0.09±0.02)×T+(-1.43±1.45)×Sr/Ca),R2=0.86p value=2.05×10-5.
Regression for the relationship between salinity and Mg/Ca ratios does not
show any clear correlation (R2=0.09, p value = 0.32). This is in good
agreement with previous culture experiment studies which only report a
minor sensitivity of Mg/Ca to salinity in planktonic foraminifera (e.g., Dueñas-Bohórquez et al., 2009; Hönisch et al., 2013;
Kisakürek et al., 2008; Nürnberg et al., 1996). The correlation
observed between Sr/Ca ratios and salinity (R2= 0.29, p value = 0.053)
is better compared to that between Mg/Ca and salinity, but it remains
relatively weak. Nevertheless, recalculated regressions of Mg/Ca
incorporating salinity show an improvement in the correlation with
temperature (best fit):
Mg/Ca=exp((-5.02±2)+(0.09±0.009)×T+(0.11±0.05)×S),R2=0.91p value=5×10-6.
This result is in good agreement with the recent study of Gray and Evans
(2019), who reported the minor Mg/Ca sensitivity of Trilobatus sacculifer to salinity (3.6 ± 0.01 % increase per salinity unit) and described, based on
previously published culture experiments' data (Dueñas-Bohórquez et
al., 2009; Hönisch et al., 2013; Kisakürek et al., 2008; Lea et al.,
1999; Nürnberg et al., 1996), a similar fit allowing the assessment of the
sensitivity of foraminiferal Mg/Ca of T. sacculifer to temperature and salinity combined.
Mg/Ca=exp(0.054(S-35)+0.062T-0.24).
RSE = 0.51 (Gray and Evans, 2019).
Applying the equation of Gray and Evans (2019) to our data leads to a
correlation of 0.90, which is identical to our findings. In order to
further compare both equations, Mg/Ca values from our study were used to
reconstruct temperature and salinity using the fit established per Gray and
Evans (2019) versus reconstructed temperature and salinity using our fit.
The observed R2 values are then 0.99 and 0.48 for temperature and salinity,
respectively. We can conclude that if the equation of Gray and Evans
(2019) is in perfect agreement with our equation with regards to the
temperature parameter, this is not the case for salinity, which shows a
strong difference between the two equations and is most probably explained by the
weak correlation of Mg/Ca to salinity in our data. Subsequently, the
Bayesian model of Tierney et al. (2019) considering the group-specific
core-top model for T. sacculifer was applied to our data. With that aim, Ω-2
and pH were calculated using ALK and DIC data presented in Table 1. Because
foraminifera in our studies were not submitted to cleaning protocols with a
reductive step, the clean parameter was set to 0. It led to the following
correlation:
Mg/Ca=exp(-11.66+0.06×T-0.21Ω-2+1.40pH),R2=0.82.
Here we can conclude that despite the difference in sampling strategy and
samples' geographical distribution, our regression models are in line with
the previous work of Gray and Evans (2019) and Tierney et al. (2019).
Stable isotope concentration
The δ18O (PDB) values of the tests (δ18Oc) and of
the seawater (δ18Ow) vary from -0.70 ‰ to
-2.98 ‰ and from 0.74 ‰ to 1.25 ‰,
respectively (Tables 1 and 2). The relationship between temperature and the
foraminiferal δ18O (expressed as a difference to the δ18Ow of the ambient seawater) was estimated with a linear least
squares regression:
T=(12.08±1.46)-(4.73±0.51)×(δ18Oc-δ18Ow)[‰],R2=0.88.
The oxygen isotope fractionation (δ18Oc-δ18Ow)
shows a strong correlation with in situ surface water temperature (linear increase
of 0.17 ‰ per degree Celsius).
Comparison with previously established <italic>T. sacculifer</italic> temperature reconstruction
equations
As mentioned above, average juvenile and pre-adult T. sacculifer specimens only spend
between 9 to 10 d in surface waters. Therefore, measured in situ temperature is
representative of the calcification temperatures. This is supported by the
strong correlation between measured temperature and δ18O
analyses (R2=0.90; Eq. 4) and measured temperature versus Mg/Ca
(R2=0.87; Eq. 1). Nevertheless, diurnal variations in
temperatures cannot be discarded and may induce a slight offset between
measured average temperature and mean calcification temperature.
For comparison, three Mg/Ca temperature calibrations for T. sacculifer were considered in
this paper: the equation of Nürnberg et al. (1996) based on
laboratory cultures, the equation established by Anand et al. (2003)
based on sediment trap samples, and the equation derived by Regenberg et al. (2009) based on surface sediment samples.
In each of these studies, only T. sacculifer without SAC chamber were considered (Table 3).
Similarly, in addition to Eq. (4) established in this study, three δ18O-based paleotemperature equations for T. sacculifer were used for comparison
with our data set: (1) Erez and Luz (1983) and (2) Spero et al. (2003),
both based on cultured specimens, and (3) Mulitza et al. (2003), based on
surface water samples (Fig. 4; Table 3).
Reconstruction of δ18Oc-δ18Ow by inserting
the measured temperature into three δ18O-based
paleotemperature equations: the equation of Spero et al. (2003) (light blue squares,
large light dashed blue line), the equation of Mulitza et al. (2003) (pink
dots, small dashed pink line), and the equation sorted by Erez and Luz (1983)
(green triangles, dashed green line) plotted versus measured δ18Oc-δ18Ow (‰ PDB). The diagonal line
represents the 1:1 regression.
Temperature, salinity and δ18Ow of the stations used to determine the salinity–δ18Ow relationship (Eq. 5).
Measured surface δ18Ow (‰ SMOW)
plotted versus measured surface salinity (stations listed in Table 4) (black
dots and full line). Regression lines of the δ18Ow–salinity
relationship calculated by Paul et al. (1999) for the tropical Atlantic
Ocean (from 25∘S to 25∘ N), based on GEOSECS data (green
line), and by Regenberg et al. (2009) (dashed blue line), based on Schmidt
(1999) data for the Atlantic Ocean for the water depth interval of 0–100 m.
Correlation between measured <inline-formula><mml:math id="M511" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O and salinity
Salinity and the oxygen isotope composition of surface seawater were
measured at 23 stations located between 33∘ N and 27∘ S
of the East Atlantic Ocean (Table 4), including the 13 stations
represented in Fig. 1, where foraminifera were sampled. The δ18Ow–salinity relationship (Eq. 5) is plotted in Fig. 5.
δ18Ow=(0.171±0.04)×S-(4.93±1.66),R2=0.38.
For comparison, the δ18Ow–salinity relationships for the
tropical Atlantic Ocean calculated by Paul et al. (1999) (from 25∘ S to 25∘ N), based on Geochemical Ocean Sections Study (GEOSECS) data, and by Regenberg et al. (2009),
based on data from Schmidt (1999) (from 30∘ N–30∘ S), are
plotted in the same figure. Temporal, geographical and depth differences in
sampling, as well as analytical noise, are most probably responsible for the
observed variations.
DiscussionIntra-test variability
The Mg/Ca and Sr/Ca composition of foraminiferal calcium carbonate was
determined using LA-ICP-MS of the final (F) chamber of
size-selected specimen. Eggins et al. (2003) report that the Mg/Ca
composition of sequentially precipitated chambers of different species
(including T. sacculifer) is consistent with temperature changes following habitat
migration towards adult life-cycle stages. As described for T. sacculifer in the Red Sea
(Bijma and Hemleben, 1994), juvenile specimens (< 100 µm) migrate to
the surface where they stay for about 9–10 d before descending to the
reproductive depth (80 m). The addition of GAM calcite proceeds immediately
prior to gamete release (Hamilton et al., 2008). The specimens considered in
this study were collected between 0 and 10 m depth, and in agreement
with measurements on specimens from culture experiments
(Dueñas-Bohórquez, 2009), Mg-rich external surfaces (GAM calcite)
were not observed in our samples. This indicates limited vertical migration
(see Sect. 2.2. for reproduction cycle), reducing therewith potential
ontogenic vital effects responsible for inter-chamber elemental variations
(Dueñas-Bohórquez, 2011) and limited, if any, GAM calcite
precipitation (Nürnberg et al., 1996). If the exact calcification depth
of the last chambers of our T. sacculifer specimen can still be questioned, the lack of
GAM calcite, together with the strong correlation observed between measured
surface temperature versus Mg/Ca-reconstructed temperature, supports the idea
that calcification of the last chamber of our specimen occurred at around 10 m depth. It should be noted that Lessa et al. (2020) recently confirmed
that T. sacculifer calcifies in the upper 30 m. Because the diameter of the laser beam
used in this study was 80 µm, it represents a reliable mean value of
elemental concentration of the last chamber wall; for every analysis of a
single shell, a full ablation of the wall chamber was performed (until
perforation was completed). For comparison, results from traditional ICP-OES (optical emission spectrometry)
Mg/Ca analyses (Regenberg et al., 2009), electron microprobe (Nürnberg et
al., 1996) and LA-ICP-MS (this study) are plotted in Fig. 3a
and suggest comparable foraminiferal Mg/Ca ratios for T. sacculifer at similar
temperatures.
Incorporation of Sr into <inline-formula><mml:math id="M532" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">Mg</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">Ca</mml:mi></mml:mrow></mml:math></inline-formula> temperature calibrations
Combining Mg and Sr data to compute temperature was first suggested by
Reichart et al. (2003) for the aragonitic species Hoeglundina elegans. It has been demonstrated
that variables other than temperature, such as salinity and carbonate
chemistry (possibly via their impact on growth rate), are factors influencing
Sr incorporation into calcite (e.g., Lea et al., 1999;
Dueñas-Bohórquez et al., 2009; Dissard et al., 2010a, b). The good correlation of Sr/Ca with temperature in our results
(R2=0.67, p value =5×10-4) (Fig. 2b) also suggests that temperature
exerts a major control on the amount of Sr incorporated into T. sacculifer tests.
However, Sr/Ca concentration also shows a correlation with salinity
(R2=0.29, p value = 0.053) which is not observed for Mg
(R2=0.09, p value = 0.32). Therefore, the incorporation of Sr into
the Mg–T reconstruction equation might improve temperature reconstruction by
accounting for the impact of salinity. It has recently been suggested that
the Sr incorporation in benthic foraminiferal tests is affected by their Mg
contents (Mewes et al., 2015; Langer et al.; 2016). However, as pointed out
in Mewes et al. (2015), calcite's Mg/Ca needs to be over 30–50 mmol in order
to noticeably affect Sr partitioning. There is no obvious reason to assume
that planktonic foraminifera should have a different Mg/Ca threshold.
Therefore, with a concentration between 2 to 6 mmol mol-1 (Sadekov et al.,
2008), the observed variation in Sr concentration in T. sacculifer tests can be safely
considered to be independent of the Mg/Ca concentrations. Hence, other
environmental parameters such as temperature, salinity and/or carbonate
chemistry, potentially via an impact on calcification rates, must control
Sr/Ca values.
The standard deviation of measured temperatures versus reconstructed
temperature was calculated for each of the three Mg temperature equations
established in this study: for Eq. (1), based on Mg/Ca only, SD = 1.37; for Eq. (3), based on both Mg/Ca and Sr/Ca, SD = 0.98; and for
Eq. (4), based on Mg/Ca ratio and salinity, SD =1.03. The incorporation of
Sr into the Mg temperature reconstruction equation resulted in the standard
deviation the closest to 1 (SD = 0.98), indicating that this statistically
improved reconstructions possibly by attenuating the salinity effect, as well
as potentially other environmental parameters such as variations in
carbonate chemistry or the effect of temperature itself. Therefore, the
combination of Mg/Ca and Sr/Ca should be considered to improve temperature
reconstructions (Table 3). For the remainder of this discussion and in order
to compare our data with previously established calibrations for T. sacculifer, the
equation based on Mg/Ca alone (Eq. 1) will be considered.
Comparison with previous <italic>T. sacculifer</italic> <inline-formula><mml:math id="M562" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">Mg</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">Ca</mml:mi></mml:mrow></mml:math></inline-formula> temperature calibrations
Mg/Ca ratios measured on T. sacculifer from our study show a strong correlation with
measured surface water temperature (R2=0.86, p value =2.9×10-6) (Fig. 2a), increasing exponentially by 8.3 % per degree Celsius. The relation
with temperature (Eq. 1) is comparable to the one published by
Nürnberg et al. (1996) and within the standard error of the calibration
(Fig. 3a). This implies that the temperature-controlled Mg incorporation
into T. sacculifer tests is similar under culture conditions as it is in natural surface
waters. The equation established by Duenas-Bohorquez et al. (2011) based on
T. sacculifer specimens from culture experiments integrates ontogenetic (chamber stage)
effects. Even though incorporating the ontogenetic impact may improve
temperature reconstructions based on Mg/Ca ratios, this is not routinely
done for paleotemperature reconstruction using T. sacculifer. Therefore, the equation of
Nürnberg et al. (1996) is used in our study for the comparison of various
reconstruction scenarios.
A comparable regression (similar slope) has been established for T. sacculifer from
tropical Atlantic and Caribbean surface sediment samples by Regenberg et al. (2009) (Fig. 3a). This regression predicts Mg concentrations that are about
0.15 mmol mol-1 higher compared to our study. Because the Mg–T calibration
from Regenberg et al. (2009) is based on surface sediment samples, Mg
concentrations were correlated with reconstructed mean annual temperatures.
This potentially leads to an over- or underestimation of temperatures
depending on the seasonality of the growth period and might explain the
observed difference between the two regressions. Due to sample limitation,
we analyzed foraminifera from a wider size fraction (230 to
500 µm) compared to Regenberg et al. (2009) (355 to 400 µm),
introducing an additional bias between the two data sets (Duenas-Bohorquez et
al., 2011; Friedrich et al., 2012). Finally, Regenberg et al. (2009),
compiled data of samples from the tropical Atlantic and Caribbean oceans,
while we collected samples from the eastern tropical Atlantic. All of these
potential biases can easily explain the small discrepancy observed between
our regression and the one from Regenberg et al. (2009). Interestingly,
Jentzen et al. (2018) were able to compare Mg/Ca ratios measured on T. sacculifer from
both surface sediment samples of the Caribbean sea and specimen sampled with
a plankton net nearby. They observed a similar systematic increased Mg/Ca
ratio in fossil tests of T. sacculifer (+0.7 mmol mol-1) compared to living specimens,
arguing that different seasonal signals were responsible for the observed
difference. However, it is interesting to note that the Mg/Ca differences
observed between living T. sacculifer (e.g., this study and Jentzen et al., 2018) and
fossil specimens (e.g., Regenberg et al., 2009; Jentzen et al., 2018)
could also be explained by the presence of GAM calcite on T. sacculifer from sediment
samples as GAM calcite is enriched with Mg compared to pre-gametogenic
calcite precipitated at the same temperature (Nürnberg et al., 1996). If
Jentzen et al. (2018) and Regenberg et al. (2009) do not describe the
presence or absence of GAM calcite on T. sacculifer specimens analyzed in their studies,
a study on the population dynamics of T. sacculifer from the central Red Sea (Bijma and
Hemleben, 1994) concluded that the rate of gametogenesis increased
exponentially between 300 and 400 µm to reach a maximum of more than
80 % at 355 µm (sieve size =500µm real test length). It can
therefore safely be assumed that the Mg/Ca difference between living
specimens from the plankton and empty shells from the sediment is due to GAM
calcite.
The Mg temperature data obtained by Jentzen et al. (2018) are, however, in good
agreement with the equation established by Regenberg et al. (2009) and
will therefore not be considered separately in this study. The overall
strong similarity observed between our regression and the one from Regenberg
et al. (2009) indicates nevertheless that Mg temperature calibrations established
on T. sacculifer specimens from plankton tow can be applied to T. sacculifer (without SAC chamber) from the
surface sediment even if these applications have to be considered with care
and only on sediment samples showing no sign of dissolution.
In contrast, the equation of Anand et al. (2003) based on sediment trap
samples is appreciably different (Fig. 3b). This may be due to (1) difference in cleaning and analytical procedures, (2) addition of GAM
calcite at greater depth, and (3) uncertainty in estimated temperature, as indeed mentioned in Gray et al. (2018): “Note the calibration line of
Dekens et al. (2002) and Anand et al. (2003) does not fit the data of Anand
et al. (2003) when climatological temperature, rather than the δ18Ocalcite-δ18Owater temperature, is used.
As shown by Gray et al. (2018), we show the calibrations of Anand et al. (2003) are inaccurate due to seasonal changes in the δ18O of
sea water at that site”.
Anand et al. (2003) fixed the intercept of the exponential regression for
T. sacculifer to the value obtained for a multispecies regression and subsequently
recalculated for each species the pre-exponential coefficients. Using this
approach, their new equation for T. sacculifer is Mg/Ca=0.35exp(0.09×T), which is
identical to Nürnberg et al. (1996) and Eq. (1) from our study.
Still, this implicitly assumes a common temperature dependence exists for
all species, which is not realistic. To avoid a priori assumptions, only the primary
equation of Anand et al. (2003) (see Table 3) is considered in this study.
Comparison with previous <inline-formula><mml:math id="M590" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O temperature calibrations
As for Mg/Ca, the oxygen isotope composition also shows a strong correlation
with measured surface water temperature (R2=0.90). The T. sacculiferδ18O temperature equation of Spero et al. (2003), based on a culture
experiment, is very similar to Eq. (4) in our study. However, sensitivity
(slope) differs within the uncertainties calculated for Eq. (4). As no
uncertainties are given for the Spero et al. (2003) equation, it is
difficult to determine whether these equations are statistically different
or not. In contrast, the equation of Mulitza et al. (2003) has a similar
slope (within uncertainties) but a higher intercept (Fig. 4a). The equation
of Erez and Luz (1983) differs considerably from Eq. (4) for both slope
and intercept parameters. Bemis et al. (1998) suggested a bias in the
calibration due to uncontrolled carbonate chemistry during the experiments
of Erez and Luz (1983) (a decrease in pH, e.g., due to bacterial growth in
the culture medium or to a higher CO2 concentration in the lab – air
conditioners, numerous people working in the same room, etc. – would quickly
lead to an increase in δ18O of culture-grown foraminifera).
This could explain the observed effect between our study (Eq. 4) and
the calibration from Erez and Luz (1983). Although the equation of Mulitza
et al. (2003) is also based on T. sacculifer collected from surface waters, their
equation is significantly different from Eq. (4). This deviation could
possibly be due to a difference in size fractions considered in the two
studies (230 to 500 µm and 150 to 700 µm for this study and
Mulitza et al., 2003, respectively). Berger et al. (1979) already
reported that large T. sacculifer tests are enriched in δ18O relative to
smaller ones (variation of 0.5 ‰ between 177 and
590 µm). Similarly, in culture experiments, larger shells of
Globigerina bulloides are isotopically heavier relative to smaller specimens (variation of
approximatively 0.3 ‰ between 300 to 415 µm; Bemis et al., 1998). Jentzen et al. (2018) reported that “Enrichment of the heavier
18O isotope in living specimens below the mixed layer and in fossil
tests is clearly related to lowered in situ temperatures and gametogenic
calcification”. Gametogenic calcite has been shown to enrich δ18O signatures by about 1.0 ‰–1.4 ‰ relative to
pre-gametogenic T. sacculifer (Wyceh et al., 2018). Finally, variation in light intensity
(e.g., due to a different sampling period and/or sampling location) may have
influenced the δ18O composition via an impact on symbiont
activity (Spero and DeNiro, 1987). Bemis et al. (1998) demonstrated that in
seawater with ambient [CO32-], Orbulina universa shells grown under high light
levels (>380µEinst m-2 s-1) are depleted in
18O on average by 0.33 ‰ relative to specimens grown
under low light levels (20–30 µEinst m-2 s-1). The
different correlation between δ18O and temperature reported by
Mulitza et al. (2003) may be caused by size fraction differences, different
sampling time, light intensity, differences in calcification depth or
hydrography, or a combination of factors. These are all potential biases
that could explain the steeper intercept observed by Mulitza et al. (2003)
relative to our study.
Reconstructions
A few scenarios are considered in the following section, in which one, two
or three proxy equations are combined to solve for salinity.
Three Mg/Ca–paleotemperature equations (Nürnberg et al., 1996;
Regenberg et al., 2009; Anand et al., 2003) were used to compare
“reconstructed” temperatures to the known in situ surface water temperatures.
The mean foraminiferal Mg/Ca ratio measured at each of our stations was
inserted into each of the three equations and solved for temperature (Fig. 3b). The linear regression of reconstructed temperatures based on
Nürnberg et al. (1996) overlaps almost perfectly with the theoretical
best fit. This confirms that calibrations based on culture experiments (the
primary geochemical signal recorded in the tests) are very well-suited for
reconstructing surface water temperature. The regression from Regenberg et
al. (2009) reconstructed surface temperatures that are too warm. This is in
agreement with the fact that the Mg/Ca ratios from surface sediment
foraminifera are slightly higher than for living specimens (Jentzen et al., 2018). The offset increases with decreasing temperature (0.5
and 1.5 ∘C, respectively, at 30 and 16 ∘C).
Finally, the reconstructed temperature using the equation from Anand et al. (2003) shows a strong systematic offset. Because the equation of
Nürnberg et al. (1996) matched our measured temperatures almost
perfectly, their equation will be used to analyze further reconstruction.
Still, we acknowledge that downcore reconstructions will inevitably also
involve GAM calcite, and hence other calibrations established using specimens
collected deeper in the water column or in the sediment should be better
suitable. Similarly, three δ18O–paleotemperature equations
(Erez and Luz, 1983; Mulitza et al., 2003; Spero et al., 2003) were tested
to reconstruct δ18Oc-δ18Ow. The equation of Erez
and Luz (1983) shows a significant systematic overestimation of δ18Oc-δ18Ow and will therefore not be considered any
further. Measured surface water temperatures at our 13 stations were
inserted into the equations of Mulitza et al. (2003) and Spero et al. (2003) to derive δ18Oc-δ18Ow (Fig. 4). The δ18Oc-δ18Ow reconstructions based on the equation of
Mulitza et al. (2003) and Spero et al. (2003) are both slightly more
positive than the theoretical best fit. In order to test the robustness of
δ18Ow reconstructions from the paleoceanographic literature (e.g., Nürnberg and Groeneveld, 2006; Bahr et al., 2011), we use the
reconstructed temperatures based on the Mg/Ca–paleotemperature equation
from Nürnberg et al. (1996) to predict δ18Ow using
measured δ18Oc and the equations from Mulitza et al. (2003)
and Spero et al. (2003). The reconstructed δ18Oc-δ18Ow from inserting the Mg/Ca temperature into these equations is
slightly overestimated (0.5 ‰), but the offsets remain
small enough to consider these as reasonable reconstructions.
When reconstructing δ18Ow by inserting the Mg/Ca temperature
and measured δ18Oc in both equations, the correlation
coefficients of the linear regressions are weak (R2=0.19
and 0.13 for Spero et al., 2003, and Mulitza et al., 2003, respectively)
demonstrating that the reconstructed δ18Ow is not very
reliable; therefore, no reconstruction of salinity using these equations will
be further tested in this paper.
Nevertheless, to test the robustness of theoretical and empirical salinity
reconstructions, we have the perfect data set at hand as every parameter is
known from in situ measurements or sampling. We will use Eqs. (1), (4) and (5)
established in this study and presented in Table 3 for demonstration
purposes.
Mg/Ca=aebT,
with a=0.42(±0.13) and b=0.083(±0.001),
T=c+d(δ18Oc-δ18Ow),
with c=12.08(±1.46) and d=-4.73(±0.51),
δ18Ow=eS+f,
with e=0.171(±0.04) and f=-4.93(±1.66).
(a) Measured salinity (orange triangles) and reconstructed salinity
based on Eqs. (1Bis), (4Bis) and (5Bis) from the present study (black dots) plotted
versus measured δ18Ow.
(b) Reconstructed salinity based on (1) successive reconstructions using
Eqs. (1Bis), (4Bis) and (5Bis) from the present study (black dots) and (2) direct
linear fit (Eq. 6) based on the same measured variables (Mg/Ca and δ18Oc) (purple crosses) plotted versus measured salinity.
Classically, from those equations, it is possible to extract variables
estimated from the observation Mg/Ca and δ18Oc through the following equations:
1BisT^=1blog(Mg/Ca)-log(a),4Bisδ18Ow^=δ18Oc-1dT^-c,5BisS^=1eδ18Ow^-f.
Given that T^ is estimated from the fit from Eq. (1Bis) (Fig. 3a) and
δ18Ow^ is estimated from Eq. (4Bis), S^ is finally
calculated from Eq. (5Bis) (Fig. 5). Hence, the error in S^ is an
accumulation of errors from successive fits. In this study, the standard
deviation of the fit between S^ and the measured salinity for the 13
stations is ±2.49, and the R2 is 0.33 (p value 0.04)
(Fig. 6a and b). In conclusion, even the best possible salinity
reconstruction based on locally calibrated Eqs. (1), (4) and (5) from the
present study only allows salinity reconstructions with a precision of
±2.49. In the modern Atlantic Ocean, and based on recent sea surface
salinity estimations (Vinogradova et al., 2019), such a variability would not
allow the differentiation of water masses between 60∘ N to 60∘ S. Similarly, on a temporal timescale, given that the regional salinity
variations expected in most of the ocean over glacial–interglacial cycles is
less than ±1, 2σ (Gray and Evans, 2019), such an incertitude
on salinity reconstruction would not even allow the differentiation between modern versus last glacial maximum water masses.
In the following steps, we quantify the error propagation more precisely. In
simple cases, error accumulation in an equation can be assessed by
calculating the partial derivatives and by propagating the uncertainties of
the equation with respect to the predictors (Clifford, 1973). However, for
complex functions, the calculation of partial derivatives can be tedious.
Here, error propagation related to S^ was computed by a Monte Carlo
simulation, which is simple to implement (Anderson, 1976) and in line with
the method applied by Thirumalai et al. (2019) on sediment samples of G. Ruber (W)
specimens. It is important to note that the propagated error with a
reconstructed salinity is a combination of fitting errors and errors
associated with measurement inaccuracies (Mg/Ca and δ18Oc). First, we will only consider the error related
to the fitting procedure, (Eqs. 1Bis, 4Bis and 5Bis, assuming that variables, i.e.,
the data, are perfectly known without uncertainties). For example, the
fitting error related to Eq. (4Bis) is computed by fitting δ18Ow from measured δ18Oc and measured temperature; i.e., the data are
known and not approximated. This is done by adding random Gaussian noise
with standard deviation corresponding to the RMSE (root mean square error)
of each fit (1.32 ∘C for Eq. 1Bis,
0.15 ‰ for Eq. 4Bis and 0.55 for Eq. 5Bis). The resulting
standard deviation error for the reconstructed salinity based on 10 000 fits
following the Monte Carlo approach amounted to ±1.69 (each fit using
sampling from random distributions defined above). Hence, ±1.69 is
the smallest possible error for salinity reconstructions, using the three
steps above, only due to its mathematics. We can also estimate the error
propagation at each step: T^±1.32∘C (Eq. 1Bis),
δ18Ow^±0.45 ‰ (Eq. 4Bis) and
S^±1.69 (Eq. 5Bis). Now we will include the uncertainties related
to estimating the variables using proxy data. Hereto, some Gaussian noises
simulating the uncertainties of measured variables (Mg/Ca and
δ18Oc) were introduced with standard
deviations taken from Table 2. The resulting standard deviation error
increased to ±2.65. Therefore, it can be concluded that statistically
speaking, δ18Ow^ cannot be reconstructed to a precision
better than ±0.45 ‰, while salinity cannot be
reconstructed to a precision better than ±1.69 (fitting errors only)
and, in reality, hardly better than ±2.65 (full to error propagation).
Finally, to complete this analysis, a direct linear fit to estimate salinity
using exp(-δ18Oc) and Mg/Ca was performed and led to
an error of ±0.26 and R2=0.82 (p value
2×10-4):
S^=-0.16(±0.02)e-δ18Oc+0.28(±0.1)MgCa+35.80(±0.33),R2=0.81p value≈2×10-4.
This demonstrates that the direct reconstruction using the exact same
variables as those initially measured (Mg/Ca and δ18Oc) led to a
much better estimation of salinity than the successive reconstruction.
Implications
We analyzed shell Mg/Ca and Sr/Ca ratios and δ18O in T. sacculifer
collected from surface water along a north–south transect of the eastern
tropical Atlantic Ocean. We find a strong correlation between Mg/Ca ratios
and surface water temperature, confirming the robustness of surface water
temperature reconstructions based on T. sacculiferMg/Ca.
Insertion of the Sr/Ca ratio into the paleotemperature equation improves
the temperature reconstruction. We established a new calibration for a
paleotemperature equation based on Mg/Ca and Sr/Ca ratios for live T. sacculifer collected
from surface water (Eq. 3).
T=(-27±15)+(8±1)×ln(Mg/Ca)+(28±11)×Sr/Ca.
Scenarios were tested using previously published reconstructions. Results
were compared to reconstructions performed using local calibrations
established in this study and are therefore supposed to represent the best
possible calibration for this data set.
Mg/Ca ratios measured in T. sacculifer specimens collected from surface water allow
accurate reconstructions of surface water temperature.
In addition, δ18Ow can be reconstructed with an uncertainty of ±0.45 ‰. Such δ18Ow reconstructions remain a
helpful tool for paleo-reconstructions considering the global range of
variation in surface δ18Ow (from about -7 ‰ to
2 ‰; LeGrande and Schmidt 2006).
In contrast, the best possible salinity reconstruction based on locally
calibrated Eqs. (1), (4) and (5) from the present study only allowed
reconstructions with an uncertainty of ±2.49. Such an uncertainty
renders these reconstructions meaningless and does not allow for viable
(paleo)salinity data.
This is confirmed by a Monte Carlo simulation applied to test successive
reconstructions in an “ideal case”, in which explanatory variables are known.
This simulation shows that from a purely statistical point of view, successive
reconstructions involving Mg/Ca and δ18Oc preclude salinity
reconstructions with a precision better than ±1.69 and hardly better
than ±2.65 due to error propagation.
Nevertheless, a direct linear fit to reconstruct salinity based on the same
measured variables (Mg/Ca and δ18Oc) was established (Eq. 6) and
presented in Table 3. This direct reconstruction of salinity should lead to
a much better estimation of salinity (±0.26) than the successive
reconstructions.
Data availability
All data generated or analyzed during this study are included in this published article.
Author contributions
JB, GJR and DD designed the research and initiated the original
project. DD completed the foraminifera sampling, sample processing and data
analysis and served as the primary author of this paper. GJR assisted
DD in LA-ICP-MS analyses. SF assisted DD in statistical treatments
associated with data interpretations. MM and CM completed the Monte Carlo
simulation. All of the authors assisted in interpreting, editing and
discussing the results and wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the captain and crew of the Polarstern cruise ANT XXIII/1,
(Bremerhaven–Cape Town) who have been of great support during this
unforgettable experience. We are grateful to Susann Grobe of the Marine
Biogeochemistry group of the IFM-GEOMAR (Germany) for measuring DIC and ALK
of water samples. We thank Arnold Van Dijk of the Department of Earth
Sciences–Geochemistry of the University of Utrecht (the Netherlands) for
measuring the oxygen isotope composition of water and foraminifera. We are
thankful to Gijs Nobbe and Paul Mason for their support with LA-ICP-MS
analyses. We would like to thank Beate Mueller (formerly Hollmann) for her
technical support when handling foraminifera and Gernot Nehrke, Stephan Mulitza and Aurore Receveur for improving earlier versions of
the paper. We thank Dieter Wolf Gladrow for his support during
the initial draft of this paper.
Financial support
This research has been supported by the DFG German Research Foundation (grant no. BI 432/4-2), the ESF under the EUROCORES Programme EuroCLIMATE (grant no. ERAS-CT-2003-980409 of the European Commission, DG Research, FP6) and the NESSC Netherlands Earth System Science (grant no. 024.002.001).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Markus Kienast and reviewed by two anonymous referees.
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