Mc/Ca, Sr/Ca and stable isotope from planktonic
froaminifera T. sacculifer: testing a multi-proxy
approach for inferring paleo-temperature and
paleo-salinity [preprint]

Delphine Dissard, Gert Jan Reichart, Christophe E. Menkès, Morgan Mangeas, Stephan Frickenhaus, Jelle Bijma

► To cite this version:

Delphine Dissard, Gert Jan Reichart, Christophe E. Menkès, Morgan Mangeas, Stephan Frickenhaus, et al. Mc/Ca, Sr/Ca and stable isotope from planktonic froaminifera T. sacculifer: testing a multi-proxy approach for inferring paleo-temperature and paleo-salinity [preprint]. Biogeosciences Discussions, 2020, 38 p. [en ligne]. 10.5194/bg-2020-208. hal-03114510

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Mg/Ca, Sr/Ca AND STABLE ISOTOPE FROM PLANKTONIC FORAMINIFERA

T. SACCULIFER: TESTING A MULTI-PROXY APPROACH FOR INFERRING
PALEO-TEMPERATURE AND PALEO-SALINITY

Delphine Dissard (1, 2), Gert Jan Reichart (3, 4), Christophe Menkes (5), Morgan Mangeas (5), Stephan Frickenhaus (2) and Jelle Bijma (2)

(1) IRD/UMR LOCEAN (IRD-CNRS-MNHN-Sorbonne Université), Centre IRD de Nouméa 101 Promenade Roger Laroque, Nouméa 98848, New Caledonia.

(2) Alfred-Wegener-Institute, Helmholtz-Zentrum für Polar- und Meeres Forschung, Am Handelshafen 12, 27570 Bremerhaven, Germany

(3) NIOZ Royal Netherlands Inst. Sea Res, Den Burg, Texel, Netherlands.

(4) Univ. Utrecht, Fac Geosci. Dept Earth Sci. Utrecht, Netherlands

(5) IRD/UMR ENTROPIE (IRD, Univ. de la Réunion, CNRS, IFREMER, UNC), Centre IRD de Nouméa 101 Promenade Roger Laroque, Nouméa 98848, New Caledonia.

ABSTRACT

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 δ¹⁸O of the 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 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 (0-10m), along a North-South transect in the eastern basin of the tropical/subtropical Atlantic Ocean. We established a new paleo-temperature 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 allow 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 reconstruction with an uncertainty of ±2.49. This was confirmed by a Monte Carlo simulation, applied to test successive reconstructions in an ‘ideal case’, where explanatory variables are known. This simulation shows that from a pure statistical point of view, successive reconstructions involving Mg/Ca and δ¹⁸Oc preclude salinity
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

reconstruction 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 lead to a much better estimation of salinity (±0.26) than the successive reconstructions.

46

47

48

# I. INTRODUCTION

49

Since Emiliani's pioneering work (1954), oxygen isotope compositions recorded in fossil foraminiferal shells became a major tool to reconstruct past sea surface temperature. 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, al., Kisakurek et al., 2008; Duenas-Bohorquez et al., 2009, 2011; Honisch et al., 2013; Kontakiotis et al., 2016; Jentzen et al., 2018). The sea surface temperature (SST) reconstructed from Mg/Ca of foraminiferal calcite has, therefore, increasingly been used in combination with the δ¹⁸O signal measured on the same material, to estimate δ¹⁸Ow, 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 down-core, additional calibrations appear necessary to make reconstructions more robust. Calibrations using foraminifera sampled from surface seawater (0-10m deep), provide the best possibility to avoid most of the artefacts usually seen when using specimen 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 manuscript as *Trilobatus sacculifer* (Spezzaferri et al., 2015), from the Atlantic Ocean. Mg and Sr concentrations were measured on individual specimens with Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS), while the
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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 (O<sub>18</sub>W) of the seawater the foraminifers were growing in, were measured. The primary objectives of this study are to test and improve the calibration of (1) both the Mg/Ca and oxygen isotope paleothermometer for *T. sacculifer*, a paleoceanographically relevant species; (2) evaluate the agreement between observed and predicted δ<sub>18</sub>O<sub>w</sub> and (3) test 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 reconstruction 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’, where explanatory variables are known. This simulation shows that from a pure statistical point of view, successive reconstructions involving Mg/Ca and δ<sub>18</sub>Oc preclude salinity reconstruction 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 δ<sup>18</sup>Oc), and leading to much better estimation of salinity (±0.26), could be established.

## 2. MATERIAL AND METHODS

### 2.1. Collection procedure

Foraminifera were collected between October and November 2005, on board of the research vessel Polarstern (ANT XXIII/1) during a meridional transect of the Atlantic Ocean (Bremerhaven/Germany - Cape Town/South of Africa; Fig. 1a). Foraminifera were continuously collected from a depth of ca. 10 m using the ship's membrane pump (3 m³/h). The water flowed into a plankton net (125 µm) that was fixed in a 1000 L plastic tank with an overflow (Fig 1b). Every eight hours, 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 δ<sub>18</sub>O<sub>w</sub> (see Tab. 1). Plankton and water samples were poisoned with buffered formaldehyde solution (20%) and HgCl<sub>2</sub> (1.5 ml with 70gL<sup>-1</sup> HgCl<sub>2</sub> for 1 L samples), respectively. In total, more than seventy plankton samples were collected during the transect, covering a large range in both temperature and salinity. Specimens of *T. sacculifer* from thirteen selected stations, selected as to maximize temperature and salinity ranges, were picked and prepared for analyses.
<figure><img src="image_6.png" /></figure>

110 Salinity, temperature, DIC, ALK and δ18Ow data reported in this paper represent
111 October/November values for the selected stations.

112

## 2.2. Description of species

114 *Trilobatus sacculifer* is a spinose species with endosymbiotic dinoflagellates inhabiting the
115 shallow (0-80 m deep) tropical and subtropical regions of the world oceans. This species
116 displays a large tolerance to temperature (14-32°C) and salinity (24-47) (Hemleben et al., 1989;
117 Bijma et al., 1990). Based on differences in the shape of the last chamber of adult specimens,
118 various morphotypes can be distinguished. Among others the last chamber can be smaller than
119 the penultimate chamber, in which case it is called kummerform (kf). This species shows an
120 ontogenetic depth migration and predominantly reproduces at depth around full moon (Bijma
121 and Hemleben, 1993). Just prior to reproduction a secondary calcite layer, called gametogenic
122 (GAM) calcite is added (Bé et al., 1982; Bijma and Hemleben, 1993; Bijma et al., 1994).
123 Juveniles (<100µm) ascend in the water column and reach the surface after less than
124 approximately 2 weeks. Pre-adult stages then slowly descend within 9-10 days to the
125 reproductive depth. In our samples (collected between 0 and 10 m depth), *T. sacculifer*
126 specimens have not yet added the Mg-enriched gametogenic calcite, which generally occurs
127 deeper in the water column just prior to reproduction. Therefore, only the trilobus morphotype
128 without GAM calcite is considered (230µm to 500µm) in this study, which limits the
129 environmental, ontogenetic and physiological variability between samples and should be taken
130 into account when compared to other calibrations based on core top and/or sediment trap
131 collected specimens.

132

## 2.3. Seawater analysis

134 The DIC and ALK analyses of the sea water were carried out at the Leibniz Institute of Marine
135 Sciences at the Christian-Albrechts University of Kiel, (IFM-GEOMAR), Germany. Analyses
136 were performed by extraction and subsequent coulometric titration of evolved CO2 for DIC
137 (*Johnson et al.*, 1993), and by open-cell potentiometric seawater titration for ALK (*Mintrop et
138 al.*, 2000). Precision / accuracy of DIC and ALK measurements are 1 µmol kg⁻¹ / 2 µmol kg⁻¹
139 and 1.5 µmol kg⁻¹ / 3 µmol kg⁻¹, respectively. Accuracy of both DIC and ALK was assured by
140 the analyses of certified reference material (CRM) provided by Andrew Dickson from Scripps
141 Institution of Oceanography, La Jolla, USA. Measurements of δ18Ow were carried out at the
142 Faculty of Geosciences, Utrecht University, Netherlands. Samples were measured using a
143 GasBench II - Delta plus XP combination. Results were corrected for drift with an in-house
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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 δ<sub>18</sub>Ow data were
corrected to the PDB scale by subtracting 0.27‰ (*Hut*, 1987).

147

2.4. Carbonate analysis

149 2.4.1. Foraminiferal sample preparation

150 Under a binocular microscope, maximum test diameter of each specimen was measured and
151 individual tests were weighed on a microbalance (METTLER TOLEDO, precision ±0.1µg).

152 Since the foraminifera were never in contact with sediments, the rigorous cleaning procedure
153 required for specimens collected from sediment cores, was not necessary. Prior to analysis the
154 tests were cleaned following a simplified cleaning procedure: All specimens were soaked for
155 30 min in a 3-7% NaOCl solution (Gaffey and Brönniman, 1993). A stereomicroscope was used
156 during cleaning and specimens were removed from the reagent directly after complete
157 bleaching. The samples were immediately and thoroughly rinsed with deionised water to ensure
158 complete removal of the reagent. After cleaning, specimens were inspected with scanning
159 electron microscopy and showed no visible signs of dissolution. This cleaning procedure
160 preserves original shell thickness and thus maximises data acquisition during laser ablation.
161 Foraminifera were fixed on a double-sided adhesive tape and mounted on plastic stubs for LA-
162 ICP-MS analyses.

163

164 2.4.2. Elemental composition analysis

165 For each station, 5–8 specimens were analysed. Their last chambers were ablated using an
166 Excimer 193 nm deep ultraviolet laser (Lambda Physik) with GeoLas 200Q optics (Reichart et
167 al, 2003) creating 80 µm diameter craters. Pulse repetition rate was set at 6 Hz, with an energy
168 density at the sample surface of 1 J/cm². The ablated material was transported on a continuous
169 helium flow into the argon plasma of a quadrupole ICP-MS instrument (Micromass Platform)
170 and analysed with respect to time. Ablation of calcite requires ultraviolet wavelengths as an
171 uncontrolled disruption would result from higher wavelengths. By using a collision and reaction
172 cell spectral interferences on the minor isotopes of Ca (42Ca, 43Ca and 44Ca) were reduced and
173 interferences of clusters like 12C16O16O were prevented. Analyses were calibrated against NIST
174 (U.S. National Institute of Standards and Technology) 610 glass using the concentration data
175 of Jochum et al. (2011) with Ca as internal standard. For Ca quantification, mass 44 was used
176 while monitoring masses 42 and 43 as internal check. In the calcite, the Ca concentration was
177 set at 40%, allowing direct comparison to trace metal/Ca from traditional wet-chemical studies.
<figure><img src="image_9.png" /></figure>

178 Mg concentrations were calculated using masses 24 and 26; Sr concentrations were calculated
179 with mass 88. One big advantage in using LA-ICP-MS measurements is that single laser pulses
180 remove only a few nanometers of material, which allows high resolution trace elements profiles
181 to be acquired (e.g. Reichart et al., 2003; Regenberg et al., 2006; Dueñas-Bohórquez et al.,
182 2009, 2010, Hathorne et al., 2009; Munsel et al., 2010; Dissard et al., 2009; 2010a and b; Evans
183 et al., 2013; 2015; Steinhardt 2014, 2015; Fehrenbacher et al., 2015; Langer et al., 2016; Koho
184 et al., 2015; 2017; Fontanier et al., 2017; De Nooijer et al., 2007, 2014, 2017a and b; Jentzen et
185 al., 2018, Schmitt et al., 2019; Levi et al., 2019). Element concentrations were calculated for
186 the individual ablation profiles integrating the different isotopes (glitter software). Even though
187 the use of a single or very few specimens, can be criticised when determining foraminifera
188 Mg/Ca and δ18O in order to perform paleoclimate reconstructions instead of more traditional
189 measurements, Groeneveld et al., (2019) recently demonstrated that for both proxies, single
190 specimen variability is dominated by seawater temperatures during calcification, even if the
191 presence of an ecological effect leading to site-specific seasonal and depth habitat changes is
192 also noticeable.
193

194 2.5. Stable isotope analysis

195 The specimens used for elemental composition analyses using LA-ICP-MS were subsequently
196 carefully removed from the plastic stubs and rinsed with deionised water before measuring their
197 stable isotope composition. Depending on shell weight, 2 to 3 foraminifera were necessary to
198 obtain a minimum of 20µg of material, required for each analysis. Analyses were carried out in
199 duplicate for each station. The results, compiled in table 2, represent average measurements.
200 The analyses were carried out at the Department of Earth Sciences of Utrecht University (The
201 Netherlands), using a Kiel-III -Finnigan MAT-253 mass spectrometer combination. The δ18Oc
202 results are reported in ‰ PDB. Calibration was made with NBS-19 (precision of 0.06-0.08 ‰
203 for sample size 20-100 µg, accuracy better than 0.2‰).
204

205 2.6. Statistical analysis

206 Within this manuscript, all statistical analyses with regards to elemental and isotopic data, were
207 carried out using the program R with default values (R Development Core Team (2019).
208

209

210

211

3. RESULTS

**3.1. Elemental composition**

211
<figure><img src="image_10.png" /></figure>

212 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 (Fig. 2a) and 1.41 to 1.52 mmol/mol (fig. 2b), respectively (Tab. 2). These 213 Mg/Ca concentrations compare well with results found in literature for this species from either 214 culture experiments, plankton tow, or surface sediment, growing at the same temperatures (e.g. 215 Nürnberg et al., 1996; Anand et al. 2003, Regenberg et al., 2009, Fig. 3). Similarly, the overall 216 variation in Sr/Ca-values reported in this study is comparable to that observed in core top and 217 cultured *G. ruber* and *T. sacculifer* combined, for comparable salinity and temperature 218 conditions, (varying between 1.27 to 1.51mmol/mol; e.g. Cleroux et al., 2008; Kisakürek et al., 219 220 2008; Dueñas-Bohórquez et al., 2009).

221

222

223

224 The relationship between measured temperatures and both Mg/Ca and Sr/Ca ratios were 225 calculated using least square differences. Both show a good correlation with surface water 226 temperature (Fig. 2, Tab. 3). The Mg/Ca ratio increases exponentially by 8.3%/°C (best fit) 227 (Mg/Ca and Sr/Ca ratios given in mmol/mol):

228 Mg/Ca=(0.42±0.13) exp((0.083±0.001)*T[°C]), R²=0.86 pvalue=2.9e-06 (equation 1)

229

230 whereas Sr/Ca ratio increases linearly by 0.6%/°C (Fig. 2a and b), best fit:

231

232 Sr/Ca=(0.009±0.002)*T+(1.24±0.05), R²=0.67 pvalue=5.e-04 (equation 2)

233

234 Combining Mg and Sr data for a non-linear multivariate regression allows improvement of the 235 correlation with temperature, best fit:

236

237

238 T=(-(27±15)+(8±1)*ln(Mg/Ca)+(28±11)*Sr/Ca), pvalue Mg/Ca: 2.10^-4 (equation 3)

239 R²=0.92 pvalue= 2.e-04

240

241 Regression for the relationship between salinity and Mg/Ca ratios does not show any clear 242 correlation (R²=0.09, p-value=0.32). This is in good agreement with previous culture 243 experiments studies which only report a minor sensitivity of Mg/Ca to salinity in planktonic 244 foraminifera (e.g. Dueñas-Bohórquez et al., 2009; Hönisch et al., 2013; Kisakürek et al., 2008; 245 Nürnberg et al., 1996). The correlation observed between Sr/Ca ratios and salinity (R²=0.29, p-
<figure><img src="image_11.png" /></figure>

value=0.053) is better compared to that between Mg/Ca and salinity, but remains relatively weak. Nevertheless, recalculated regressions of Mg/Ca, incorporating salinity, show an improvement of the correlation with temperature, best fit:

$$
\begin{array}{l}
\ln(\text{Mg/Ca[mmol/mol]}) = (-5.02\pm2)+(0.09\pm0.009)*T+(0.11\pm0.05)*S, \\
R^2=0.91 \quad \text{pvalue} = 5e-06
\end{array}
$$

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 to assess the sensitivity of foraminiferal Mg/Ca of *T. sacculifer* to temperature and salinity combined.

$$
\text{Mg/Ca} = \exp(0.054(\text{S}-35) + 0.062\text{T}-0.24) \quad \text{RSE: 0.51} \quad \text{Gray and Evans (2019)}
$$

In order to 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 are then 0.99 and 0.48 for temperature and salinity, respectively. We can conclude, that if the equation of Evans and Gray 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, most probably explained by the weak correlation of Mg/Ca to salinity.

3.2. Stable isotopes 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 (Tab. 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:

$$
T = (11.82 \pm 1.3) - (4.82 \pm 0.45) * (\delta_{18}\text{Oc} - \delta_{18}\text{Ow}) \text{ [‰]}; R^2=0.90 \quad \text{(equation 4)}
$$

The oxygen isotope fractionation (δ₁₈Oc - δ₁₈Ow) shows a strong correlation with *in situ* surface water temperature (linear increase of 0.17‰/°C).

277
<figure><img src="image_12.png" /></figure>

## 3.3. Comparison with previously established *T. sacculifer* temperature reconstruction equations

As mentioned above, average juvenile and pre-adult *T. sacculifer* specimen only spend between 9 to 10 days 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, equation 4), and measured temperature vs. Mg/Ca, (R2=0.87, equation 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 manuscript. The equation of Nürnberg et al. (1996) based on laboratory cultures, (2) the equation established by Anand et al. (2003) based on sediment trap samples and (3) the equation derived by Regenberg et al. (2009) based on surface sediment samples of the Tropical Atlantic Ocean. In each of these studies only *T. sacculifer* without SAC chamber were considered, (Tab. 3).

Similarly, in addition to equation 4 established in this study, three δ18O based paleo-temperature 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; Tab. 3).

## 3.4. Correlation between measured δ18O/Salinity

Salinity and the oxygen isotope composition of surface seawater were measured for 23 stations located between 33°N and 27°S of the Eastern Atlantic Ocean (Tab. 4), including the thirteen stations represented in figure 1, where foraminifera were sampled. The δ18Ow-salinity relationship (equation 5) is plotted in figure 5.

$$ \delta_{18}Ow = (0.194\pm0.04)\*S - (5.8 \pm 1.5), R^2=0.53 \quad (\text{equation 5}) $$

For comparison, the δ18Ow-salinity relationship for the tropical Atlantic Ocean calculated by Paul et al. (1999) (from 25°S to 25°N) based on GEOSECS data, and by Regenberg et al. (2009), based on data from Schmidt 1999 (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.
<figure><img src="image_13.png" /></figure>

# 4. DISCUSSION

## 4.1. Intra-test variability

The Mg/Ca and Sr/Ca composition of foraminiferal calcium carbonate was determined using laser ablation 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*) are 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 (<100µm) migrate to the surface, where they stay about 9-10 days, before descending to the reproductive depth (80m). 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 meters depth, and GAM calcite was not detected. This limits the impact of variability due to migration, reduces potential ontogenic vital effects responsible for inter-chamber elemental variations (Dueñas-Bohórquez, 2010) and, avoids variability due to variable amounts of GAM calcite precipitated (Nürnberg et al., 1996). This is confirmed by the strong correlation (R²=0.87) observed between our Mg/Ca-reconstructed temperature vs. measured surface temperature. In agreement with measurements on specimens from culture experiments (Dueñas-Bohórquez, 2009), Mg-rich external surfaces (GAM calcite) were not observed in our samples.

Because the diameter of the laser beam used in this study was 80µm, 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 Mg/Ca analyses (Regenberg et al., 2009), electron microprobe (Nurnberg et al., 1996) and laser ablation ICP-MS (this study) are plotted in figure 3a and suggest comparable foraminiferal Mg/Ca ratios for *T. sacculifer* at similar temperatures.

## 4.2. Incorporation of Sr into Mg/Ca-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; Dissard et al., 2010b). The good correlation of Sr/Ca with temperature in our results (R²=0.67, p value= 5.e-04, Fig 2b), also suggests that
<figure><img src="image_14.png" /></figure>

346 temperature exerts a major control on the amount of Sr incorporated into *T. sacculifer*' tests.
347 However, Sr/Ca concentration also shows a correlation with salinity (R<sup>2</sup>=0.29, p-value=0.053),
348 which is not observed for Mg (R<sup>2</sup>=0.09, p-value=0.32). Therefore, the incorporation of Sr into
349 the Mg-T reconstruction equation might improve temperature reconstruction by accounting for
350 the impact of salinity. It has recently been suggested that the Sr incorporation in benthic
351 foraminiferal tests is affected by their Mg contents (Mewes et al., 2015; Langer et al.; 2016).
352 However, as pointed out in Mewes et al., (2015), calcite's Mg/Ca needs to be over 30-50mmol
353 in order to noticeably affect Sr partitioning. There is no obvious reason to assume that
354 planktonic foraminifera should have a different Mg/Ca threshold. Therefore, with a
355 concentration between 2 to 6 mmol/mol (Sadekov et al., 2009), the observed variation in Sr
356 concentration in *T. sacculifer*' tests can be safely considered to be independent of the Mg/Ca
357 concentrations. Hence, other environmental parameters such as temperature, salinity and/or
358 carbonate chemistry, potentially via an impact on calcification rates, must control Sr/Ca values.
359

360 The standard deviation of measured temperatures versus reconstructed temperature was
361 calculated for each of the three Mg-temperature equations established in this study. For
362 equation (1), based on Mg/Ca only, SD= 1.37, for equation (3), based on both Mg/Ca and Sr/Ca,
363 SD=0.98, and for equation (4), based on Mg/Ca ratio and salinity, SD=1.03. Incorporation of
364 Sr into the Mg-Temperature reconstruction equation resulted in the standard deviation the
365 closest to 1 (SD=0.98), indicating that this statistically improved reconstructions possibly by
366 attenuating the salinity effect as well as potentially other environmental parameters such as
367 variations in carbonate chemistry or the effect of temperature itself. Therefore, the combination
368 of Mg/Ca and Sr/Ca should be considered to improve temperature reconstructions (Tab. 3). For
369 the remainder of this discussion, and in order to compare our data with previously established
370 calibrations for *T. sacculifer*, the equation based on Mg/Ca alone (equation 1) will be
371 considered.
372

372

373 **4.3 Comparison with previous T. sacculifer Mg/Ca-Temperature calibrations.**

374 Mg/Ca ratios measured on *T. sacculifer* from our study show a strong correlation with measured
375 surface water temperature (R<sup>2</sup>=0.86, p value=2.9e-06) (Fig. 2a), increasing exponentially by
376 8.3% per °C. The relation with temperature (equation 1) is comparable to the one published by
377 Nürnberg et al., (1996) and within the standard error of the calibration (Fig. 3a). This implies
378 that the temperature controlled-Mg incorporation into *T. sacculifer* tests is similar under culture
379 conditions as it is in natural surface waters. The equation established by Duenas-Bohorquez et
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

al., (2010) based on *T. sacculifer* specimen 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 paleo-temperature reconstruction using *T. sacculifer*. Therefore, the equation of Nürnberg et al., (1996) is used in our study for 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 higher compared to our study. Because the Mg-T calibration from Regenberg et al. (2009) is based on sediment-surface samples, Mg concentrations were correlated with reconstructed mean annual temperatures. This potentially leads to an over or under-estimation 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 analysed foraminifera from a wider size fraction (230µm to 500µm), compared to Regenberg et al. (2009) (355-400µm), introducing an additional bias between the two datasets (Duenas-Bohorquez et al., 2010; Friedrich et al., 2012). Finally, Regenberg et al. (2009), compiled data of samples from the tropical Atlantic and Caribbean Ocean, 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 fossils 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 fossils specimens (e.g. Regenberg et al., 2009 and 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-gametogenetic calcite precipitated at the same temperature (Nurnberg 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 analysed in their studies, a study on the population dynamics of *T. sacculifer* from the central Red Sea Bijma and Hemleben (1990) concluded that the rate of gametogenesis increased exponentially between 300 and 400µm to reach a maximum of more than 80% at 355µm (sieve size =500µm real test length). It can therefore safely be assumed that the Mg/Ca
<figure><img src="image_17.png" /></figure>

413 difference between living specimens from the plankton and empty shells from the sediment is
due to GAM calcite.

415 The Mg-Temp data obtained by Jentzen et al., (2018) is 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
417 Regenberg et al. (2009), indicates nevertheless that Mg-temp calibrations established on *T. sacculifer* specimen from plankton tow, can be applied to *T. sacculifer* (without Sac) from the
418 surface-sediment, even if these applications have to be considered with care and only on
419 sediment samples showing no sign of dissolution.

422 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)
uncertainty in estimated temperature (3) addition of GAM calcite at greater depth. Anand et al.,
424 (2003) fixed the intercept of the exponential regression for *T. sacculifer* to the value obtained
425 for a multispecies regression and subsequently recalculated for each species the pre-exponential
426 coefficients. Using this approach their new equation for *T. sacculifer* is: Mg/Ca= 0.35 exp
427 (0.09*T), which is identical to Nürnberg et al., (1996) and equation 1 from our study. Still, this
428 implicitly assumes a common temperature dependence exists for all species, which is not
429 realistic. To avoid *a priori* assumptions only the primary equation of Anand et al., (2003) (see
430 Tab. 3) is considered in this study.
431

432

433 **4.4. Comparison with previous δ<sub>18</sub>O-Temperature calibrations.**

434 As for Mg/Ca, the oxygen isotope composition also shows a strong correlation with measured
435 surface water temperature (R<sup>2</sup>=0.90). The *T. sacculifer* δ<sub>18</sub>O-temperature equation of Spero et
436 al., (2003), based on a culture experiment, is very similar to equation 4 in our study. However,
sensitivity (slope) differs within the uncertainties calculated for equation 4. As no uncertainties
437 are given for the Spero et al., (2003) equation, it is difficult to determine whether these
438 equations are statistically different or not. In contrast, the equation of Mulitza et al., (2003), has
439 a similar slope (within uncertainties) but a higher intercept (Fig. 4a). The equation of Erez and
440 Luz, (1983) differs considerably from equation 4, for both slope and intercept parameters.
441 Bemis et al., (1998) suggested a bias in the calibration due to uncontrolled carbonate chemistry
442 during the experiments of Erez and Luz (1983) (a decrease in pH, e.g. due to bacterial growth
443 in the culture medium or to a higher CO<sub>2</sub> concentration in the lab (air conditioners, numerous
444 people working in the same room etc), would quickly lead to an increase in δ<sub>18</sub>O of culture-
445 grown foraminifera). This could explain the observed effect between our study (equation 4) and
446
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

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 equation (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 590µm). Similarly, in culture experiments, larger shells of *Globigerina bulloides* are isotopically heavier relative to smaller specimens (variation of approximatively 0.3‰ between 300 to 415µm, 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 pregametogenic *T. sacculifer* (Wyceh et al., 2018). Finally, variation in light intensity (e.g. due to 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 level (> 380 µEinst m⁻² s⁻¹) are depleted in O₁₈ by on average 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.

## 5. 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-paleo-temperature equations (Nürnberg et al., 1996; Regenberg et al., 2009; and Anand et al., 2003) were used to compare “reconstructed” temperatures to the known *in situ* surface waters temperatures. The mean foraminiferal Mg/Ca ratio measured at each of our stations was inserted into each of the three equation 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)
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

reconstructed surface temperature that are too warm. This is in agreement with the fact that the Mg/Ca ratio from surface sediment foraminifera are slightly higher than for living specimen (Jentzen et al. 2018). The offset increases with decreasing temperature (0.5°C and 1.5°C respectively at 30°C 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 analyse 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-paleo temperature 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 paleoceanographic literature (e.g. Nürnberg and Groeneveld, 2006; Bahr et al., 2011), we use the reconstructed temperatures based on the Mg/Ca-paleo-temperature 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.

Nevertheless, 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 manuscript.

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* measurement or sampling. We will use the equations 1, 4 and 5 established in this study and presented in table 3, for demonstration purposes.

514
<figure><img src="image_22.png" /></figure>

$$515 \qquad Mg/Ca = ae^{bT} \qquad \text{Eq. 1}$$

516 with $a=0.42(\pm0.13)$ and $b=0.083(\pm0.001)$

$$517 \qquad T = c + d(\delta^{18}Oc - \delta^{18}Ow) \qquad \text{Eq. 4}$$

519 with $c=12.08(\pm1.46)$ and $d=-4.73(\pm0.51)$

$$520 \qquad \delta^{18}Ow = eS + f \qquad \text{Eq. 5}$$

522 with $e=0.171(\pm0.04)$ and $f=-4.93(\pm1.66)$

523 Classically, from those equations it is possible to extract variables estimated from the
524 observation Mg/Ca and δ¹⁸Oc through the equations:

$$525 \qquad \hat{T} = \frac{1}{b}(\log (Mg/Ca) - \log(a)) \qquad \text{Eq. 1'}$$

$$526 \qquad \delta^{\overline{18}Ow} = \delta^{18}Oc - \frac{1}{d}(\hat{T} - c) \qquad \text{Eq. 4'}$$

$$527 \qquad \hat{S} = \frac{1}{e}(\delta^{\overline{18}Ow} - f) \qquad \text{Eq. 5'}$$

528
529
530 Given that $\hat{T}$ is estimated from the fit from Eq. 1' (fig. 3a) and $\delta^{\overline{18}Ow}$ is estimated from Eq. 4',
531 $\hat{S}$ is finally calculated from Eq. 5' (figure 5). Hence, the error in $\hat{S}$ is an accumulation of errors
532 from successive fits. In this study the standard deviation of the fit between $\hat{S}$ and the measured
533 salinity for the 13 stations is ±2.49 and the R² is 0.33 (p-value 0.04) (Fig. 6a and b). In
534 conclusion, even the best possible salinity reconstruction based on locally calibrated equations
535 1, 4 and 5 from the present study only allows salinity reconstructions with a precision of ±2.49.
536 In the modern Atlantic Ocean, and based on recent sea surface salinity estimation (Vinogradova
537 et al., 2019), such a variability would not allow to distinguish water masses between 60°N to
538 60°S. Similarly, on a temporal timescale, given the regional salinity variations expected in most
539 of the ocean over glacial-interglacial cycles is less than ±1, 2σ (Gray and Evans, 2019), such an
540 incertitude on salinity reconstruction would not even allow to distinguish modern versus last
541 glacial maximum water masses.

542
543 In the following steps, we quantify the error propagation more precisely. In simple cases, error
544 accumulation in an equation can be assessed by calculating the partial derivatives and by
545 propagating the uncertainties of the equation with respect to the predictors (Clifford, 1973).
546 However, for complex functions the calculation of partial derivatives can be tedious. Here, error
<figure><img src="image_23.png" /></figure>

propagation related to Ŝ was computed by a Monte Carlo simulation, which is simple to implement (Anderson, 1976). 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, (Eq. 1', 4' and 5', assuming that variables (i.e. the data) are perfectly known without uncertainties). For example, the fitting error related to Eq. 4' 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 (respectively 1.32°C for Eq.1', 0.15‰ for Eq. 4' and 0.55 for Eq. 5'). The resulting standard deviation error for the reconstructed Salinity based on 10000 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: T̂±1.32°C (Eq.1'), δ¹⁸Ow±0.45‰ (Eq.4') and Ŝ±1.69 (Eq.5'). 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 a R² = 0.82 (p-value 2.10⁻⁴):

$$ Ŝ = -0.16(±0.02) e^{-δ^{18}Oc} + 0.28(±0.1) \frac{Mg}{ca} + 35.80(±0.33) (R^2=0.81, p-value \approx 2.10^{-4}) \quad \text{Eq. 6} $$

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 that the successive reconstruction.
<figure><img src="image_24.png" /></figure>

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 a R² = 0.82 (p-value 2.10⁻⁴):

$$582 \qquad \hat{S} = -0.16(\pm0.02) e^{-\delta^{18}Oc} + 0.28(\pm0.1) \frac{Mg}{ca} + 35.80(\pm0.33) (R^2=0.81, p-value \approx 2.10^{-4}) \quad \textbf{Eq. 6}$$

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 that the successive reconstruction.

6. 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. sacculifer* Mg/Ca.

Insertion of the Sr/Ca ratio into the paleo-temperature equation improves the temperature reconstruction. We established a new calibration for a paleo-temperature equation based on Mg/Ca and Sr/Ca ratios for live *T. sacculifer* collected from surface water:

$$601 \qquad T=(-27\pm15)+(8\pm1)*\ln(Mg/Ca)+(28\pm11)*Sr/Ca$$

Scenarios were tested using previously published reconstructions. Results were compared to reconstructions performed using local calibrations established in this study and therefore supposed to represent the best possible calibration for this data set:

(1) Mg/Ca ratios measured in *T. sacculifer* specimens collected in surface water allow accurate reconstruction of surface water temperature.
<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

611 (2) δ¹⁸Ow can be reconstructed with an uncertainty of ±0.45‰. Such δ¹⁸Ow reconstructions 612 remain a helpful tool for paleo-reconstructions considering the global range of variation of 613 surface δ¹⁸Ow (from about -7 to 2‰, LeGrande and Schmidt 2006; ). 614

615 (3) In contrast, the best possible salinity reconstruction based on locally calibrated equations 1, 616 4 and 5 from the present study, only allowed reconstruction with an uncertainty of ±2.49. Such 617 an uncertainty renders these reconstructions meaningless and does not allow for viable 618 (paleo)salinity data. 619

620 This is confirmed by a Monte Carlo simulation, applied to test successive reconstructions in an 621 ‘ideal case’, where explanatory variables are known. This simulation shows that from a pure 622 statistical point of view, successive reconstructions involving Mg/Ca and δ¹⁸Oc preclude 623 salinity reconstruction with a precision better than ±1.69 and hardly better than ±2.65, due to 624 error propagation. 625

625 Nevertheless, a direct linear fit to reconstruct salinity based on the same measured variables 626 (Mg/Ca and δ¹⁸Oc) was established (Eq. 6) and presented in table 3. This direct reconstruction 627 of salinity should lead to a much better estimation of salinity (±0.26) than the successive 628 reconstructions. 629

629
AKNOWLEDGEMENTS

630 We thank captain and crew of the Polarstern cruise ANT XXIII/1, (Bremerhaven-Cape Town) 631 who have been of great support during this unforgettable experience. We are grateful to Susann 632 Grobe of the Marine Biogeochemistry group of the IFM-GEOMAR (Germany) for measuring 633 DIC and ALK of water samples. We thank Arnold Van Dijk of the Department of Earth 634 Sciences-Geochemistry of the University of Utrecht (The Netherlands) for measuring oxygen 635 isotope composition of water and foraminifera. We are thankful to Gijs Nobbe and Dr. Paul 636 Mason for their support with LA-ICP-MS analyses. We would like to thank Beate Mueller 637 (formaly Hollmann) for her technical support when handling foraminifera, and Dr. Gernot 638 Nehrke, Dr. Stephan Mulitza, and Dr. Aurore Receveur for improving earlier versions of the 639 manuscript. We thank Prof. Dieter Wolf Gladrow for his support during the initial draft of this 640 manuscript. This work was supported by the German research foundation (DFG) under grant 641 no. BI 432/4-2 ("PaleoSalt"), and by the European Science Foundation (ESF) under the 642 EUROCORES Programme EuroCLIMATE through contract No. ERAS-CT-2003-980409 of 643 the European Commission, DG Research, FP6. Gert-Jan Reichart acknowledges funding from
<figure><img src="image_27.png" /></figure>

644 the program of the Netherlands Earth System Science Centre (NESSC), by the Ministry of
645 Education, Culture and Science (OCW; Grant 024.002.001).

646

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<table><caption>**Table 1.** Measured temperature, salinity, DIC, ALK, and δ<sub>18</sub>Ow of the stations selected for this study (October/November 2005). Mean annual temperature per station (World Ocean Atlas, 2005).</caption><thead><tr><th rowspan="3">Stations</th><th rowspan="3">Latitude</th><th rowspan="3">Longitude</th><th>Measured</th><th>Mean annual</th><th rowspan="3">Salinity (±0.05)</th><th>DIC (µmol/kg) precision</th><th>Alkalinity (µmol/kg) precision</th><th>δ<sub>18</sub>Ow (PDB) precision</th></tr><tr><th>T°C (±0.05)</th><th>T°C</th><th>1µm/Kg accuracy</th><th>1.5 µm/Kg accuracy</th><th>0.1 ‰ accuracy</th></tr><tr><th>Oct/Nov.</th><th></th><th>2µm/Kg accuracy</th><th>2 µm/Kg accuracy</th><th>0.2 ‰ accuracy</th></tr></thead><tbody><tr><td>25</td><td>22°38.640′N</td><td>20°23.578′W</td><td>24.91</td><td>21.8222</td><td>36.63</td><td>2069</td><td>2391</td><td>1.1</td></tr><tr><td>29</td><td>18°8.088′N</td><td>20°55.851′W</td><td>26.09</td><td>22.6964</td><td>36.24</td><td>2037</td><td>2369</td><td>0.9</td></tr><tr><td>31</td><td>14°32.128′N</td><td>20°57.251′W</td><td>28.24</td><td>25.0457</td><td>35.78</td><td>2009</td><td>2330</td><td>0.8</td></tr><tr><td>35</td><td>10°23.424′N</td><td>20°4.869′W</td><td>29.73</td><td>26.7731</td><td>35.63</td><td>1982</td><td>2304</td><td>1.2</td></tr><tr><td>38</td><td>7°2.114′N</td><td>17°27.818′W</td><td>29.43</td><td>27.764</td><td>34.67</td><td>1929</td><td>2257</td><td>0.7</td></tr><tr><td>40</td><td>4°22.323′N</td><td>15°16.911′W</td><td>28.47</td><td>27.7331</td><td>34.35</td><td>1915</td><td>2214</td><td>0.8</td></tr><tr><td>42</td><td>2°15.702′N</td><td>13°33.854′W</td><td>27.56</td><td>26.9215</td><td>35.72</td><td>2002</td><td>2332</td><td>1.1</td></tr><tr><td>46</td><td>1°35.741′S</td><td>10°33.846′W</td><td>25.91</td><td>25.6623</td><td>36.13</td><td>2053</td><td>2346</td><td>1.0</td></tr><tr><td>49</td><td>4°44.752′S</td><td>8°6.641′W</td><td>24.59</td><td>25.6229</td><td>36.07</td><td>2057</td><td>2369</td><td>0.9</td></tr><tr><td>52</td><td>8°6.086′S</td><td>5°29.077′W</td><td>23.80</td><td>24.864</td><td>35.99</td><td>2062</td><td>2360</td><td>0.7</td></tr><tr><td>56</td><td>11°51.783′S</td><td>2°30.743′W</td><td>22.18</td><td>23.6074</td><td>36.38</td><td>2071</td><td>2387</td><td>1.0</td></tr><tr><td>62</td><td>17°59.620′S</td><td>2°25.321′E</td><td>19.11</td><td>21.2856</td><td>35.99</td><td>2100</td><td>2369</td><td>1.1</td></tr><tr><td>66</td><td>22°26.998′S</td><td>6°6.922′E</td><td>18.71</td><td>20.1148</td><td>35.68</td><td>2070</td><td>2349</td><td>1.0</td></tr></tbody></table>

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<table><caption>**Table 2.** Mean elemental (Mg/Ca and Sr/Ca) and isotopic (δ¹⁸Oc) composition per station, measured in foraminiferal calcite in mmol/mol and ‰ PDB, respectively. Elemental and isotopic compositions were determined on the same material (n varying from 5 to 9 specimens per station); isotopic analyses were done in duplicate for each station. Mean δ¹⁸Oc-δ¹⁸Ow measured per stations in ‰ PDB.</caption><thead><tr><th rowspan="3">Stations</th><th>Measured</th><th>Measured</th><th>Measured</th><th>Measured</th><th>Recons.</th><th>Recons.</th><th>Recons.</th></tr><tr><th>Mg/Ca</th><th>Sr/Ca</th><th>δ¹⁸Oc ‰ (V-PDB) precision 0.08‰</th><th>δ¹⁸Oc-δ¹⁸Ow ‰ (V-PDB)</th><th>δ¹⁸Ow (Mulitza) ‰ (V-PDB)</th><th>δ¹⁸Ow (Spero) ‰ (V-PDB)</th><th>δ¹⁸Ow (this study) ‰ (V-PDB)</th></tr><tr><th>mmol/mol</th><th>mmol/mol</th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>25</td><td>3.22 ± 0.51</td><td>1.53 ± 0.08</td><td>-1.76</td><td>-2.82</td><td>0.38</td><td>0.40</td><td>0.88</td></tr><tr><td>29</td><td>4.01 ± 0.24</td><td>1.52 ± 0.06</td><td>-1.75</td><td>-2.63</td><td>1.00</td><td>0.87</td><td>1.44</td></tr><tr><td>31</td><td>4.78 ± 0.37</td><td>1.56 ± 0.18</td><td>-2.51</td><td>-3.33</td><td>0.73</td><td>0.49</td><td>1.11</td></tr><tr><td>35</td><td>5.46 ± 0.38</td><td>1.59 ± 0.08</td><td>-2.35</td><td>-3.59</td><td>1.27</td><td>0.94</td><td>1.62</td></tr><tr><td>38</td><td>4.31 ± 1.14</td><td>1.58 ± 0.14</td><td>-2.89</td><td>-3.59</td><td>0.07</td><td>-0.10</td><td>0.49</td></tr><tr><td>40</td><td>4.07 ± 0.64</td><td>1.57 ± 0.07</td><td>-2.98</td><td>-3.78</td><td>-0.18</td><td>-0.32</td><td>0.25</td></tr><tr><td>42</td><td>3.79 ± 0.49</td><td>1.53 ± 0.08</td><td>-2.38</td><td>-3.44</td><td>0.21</td><td>0.12</td><td>0.67</td></tr><tr><td>46</td><td>3.92 ± 1.24</td><td>1.47 ± 0.07</td><td>-1.67</td><td>-2.66</td><td>1.02</td><td>0.91</td><td>1.46</td></tr><tr><td>49</td><td>2.99 ± 0.39</td><td>1.55 ± 0.11</td><td>-1.83</td><td>-2.74</td><td>0.10</td><td>0.16</td><td>0.62</td></tr><tr><td>52</td><td>2.97 ± 0.30</td><td>1.50 ± 0.03</td><td>-1.34</td><td>-2.08</td><td>0.57</td><td>0.64</td><td>1.09</td></tr><tr><td>56</td><td>3.31 ± 0.53</td><td>1.50 ± 0.03</td><td>-1.06</td><td>-2.10</td><td>1.15</td><td>1.15</td><td>1.65</td></tr><tr><td>62</td><td>2.20 ± 0.24</td><td>1.47 ± 0.07</td><td>-0.70</td><td>-1.76</td><td>0.38</td><td>0.64</td><td>0.99</td></tr><tr><td>66</td><td>1.66 ± 0.17</td><td>1.48 ± 0.09</td><td>-0.74</td><td>-1.75</td><td>-0.46</td><td>-0.02</td><td>0.23</td></tr></tbody></table>

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<table><caption>Table 3. Calibration equations for *T. sacculifer*.</caption><thead><tr><th>Source</th><th></th><th></th><th>R<sup>2</sup></th><th>p-values</th></tr></thead><tbody><tr><td colspan="5"><strong>Mg/Ca Relationship with Temperature</strong></td></tr><tr><td><strong>This study</strong></td><td><strong>Mg/Ca=0.42(±0.13)e<sup>^T*0.083(±0.001)</sup></strong></td><td><strong>Eq. 1</strong></td><td><strong>0.86</strong></td><td><strong>2.9e-06</strong></td></tr><tr><td>Nürnberg et al., 1996</td><td>Mg/Ca=0.37(±0.065)e<sup>^T*0.091(±0.007)</sup></td><td></td><td>0.93</td><td></td></tr><tr><td>Anand et al., 2003</td><td>Mg/Ca=1.06(±0.021)e<sup>^T*0.048(±0.012)</sup></td><td></td><td></td><td></td></tr><tr><td>Regenberg et al., 2009</td><td>Mg/Ca=0.6(±0.16)e<sup>^T*0.075(±0.006)</sup></td><td></td><td></td><td></td></tr><tr><td colspan="5"><strong>Sr/Ca Relationship with Temperature</strong></td></tr><tr><td><strong>This study</strong></td><td><strong>Sr/Ca=(0.0094±0.002)*T+(1.29 ± 0.05)</strong></td><td><strong>Eq. 2</strong></td><td><strong>0.67</strong></td><td><strong>5.e-04</strong></td></tr><tr><td colspan="5"><strong>Mg/Ca and Sr/Ca Relationship with Temperature</strong></td></tr><tr><td><strong>This study</strong></td><td><strong>T=(-27±15)+(8±1)*ln(Mg/Ca)+(28±11)*Sr/Ca</strong></td><td><strong>Eq. 3</strong></td><td><strong>0.93</strong></td><td><strong>2 e-04</strong></td></tr><tr><td colspan="5"><strong>Me/Ca Relationship with Temperature and Salinity</strong></td></tr><tr><td>This study (Mg/Ca)</td><td>ln(Mg/Ca)=(-5.10±2)+(0.09±0.009)*T+(0.11±0.05)*S</td><td></td><td>0.91</td><td>5.e-06</td></tr><tr><td>This study (Sr/Ca)</td><td>Sr/Ca = (1.81±0.5) + (0.008±0.002) T - (0.01±0.01)*S</td><td></td><td>0.71</td><td>0.002</td></tr><tr><td colspan="5"><strong>δ<sub>18</sub>O Relationship with Temperature</strong></td></tr><tr><td><strong>This study</strong></td><td><strong>T= 12.08(±1.46)-4.73(±0.51)*(<sup>δ</sup><sub>18</sub>O<sub>c</sub> - <sup>δ</sup><sub>18</sub>O<sub>w</sub>)</strong></td><td><strong>Eq. 4</strong></td><td><strong>0.88</strong></td><td><strong>1.6 e-06</strong></td></tr><tr><td>Erez and Luz, (1983)</td><td>T= 16.06(±0.549)-5.08(±0.32)*(<sup>δ</sup><sub>18</sub>O<sub>c</sub> - <sup>δ</sup><sub>18</sub>O<sub>w</sub>)</td><td></td><td></td><td></td></tr><tr><td>Mulitza et al., (2003)</td><td>T= 15.35(±0.71)-4.22(±0.25)*(<sup>δ</sup><sub>18</sub>O<sub>c</sub> - <sup>δ</sup><sub>18</sub>O<sub>w</sub>)</td><td></td><td></td><td></td></tr><tr><td>Spero et al., (2003)</td><td>T= 12.567*(<sup>δ</sup><sub>18</sub>O<sub>c</sub> - <sup>δ</sup><sub>18</sub>O<sub>w</sub>)</td><td></td><td></td><td></td></tr><tr><td><strong>measured δ<sub>18</sub>O vs. measured Salinity (this study)</strong></td><td><strong>δ<sub>18</sub>O<sub>w</sub> = (0.171±0.04)*S - (4.93 ±1.66)</strong></td><td><strong>Eq. 5</strong></td><td><strong>0.38</strong></td><td><strong>1.2 e-03</strong></td></tr><tr><td><strong>direct linear fit to reconstruct salinity based on measured variables (Mg/Ca and δ<sub>18</sub>O<sub>c</sub>)</strong></td><td><strong>S = -0.16 (±0.02) e<sup>^(-δ<sup>^T</sup><sub>18</sub>O<sub>c</sub>)</sup> + 0.28 (±0.1) Mg/Ca+35.80 (±0.33)</strong></td><td><strong>Eq. 6</strong></td><td><strong>0.82</strong></td><td><strong>&lt; 2e-04</strong></td></tr></tbody></table>

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934 Table 4. Temperature, salinity and δ18Ow of the stations used to determine the salinity/ δ18Ow

935 relationship (equation 5)

<table><thead><tr><th>Stations</th><th>Latitude</th><th>Longitude</th><th>T°C(±0.05)</th><th>Salinity(±0.05)</th><th>δ18Ow (SMOW) precision 0.1% accuracy 0.2%</th></tr></thead><tbody><tr><td>19</td><td>33°20.14′N</td><td>14°38.45′W</td><td>22,09</td><td>36,83</td><td>1,3</td></tr><tr><td>21</td><td>30°23.42′N</td><td>16°24.99′W</td><td>23,01</td><td>36,91</td><td>1,4</td></tr><tr><td>23</td><td>25°20.68′N</td><td>18°4.17′W</td><td>24,87</td><td>37,01</td><td>1,8</td></tr><tr><td>25</td><td>22°38.64′N</td><td>20°23.58′W</td><td>24,91</td><td>36,63</td><td>1,3</td></tr><tr><td>29</td><td>18°8.09′N</td><td>20°55.85′W</td><td>26,09</td><td>36,24</td><td>1,1</td></tr><tr><td>31</td><td>14°32.13′N</td><td>20°57.25′W</td><td>28,24</td><td>35,78</td><td>1,1</td></tr><tr><td>35</td><td>10°23.424′N</td><td>20°4.869′W</td><td>29,73</td><td>35,63</td><td>1,5</td></tr><tr><td>36</td><td>9°5.71′N</td><td>19°14.21′W</td><td>29,29</td><td>35,63</td><td>1,1</td></tr><tr><td>37</td><td>7°43.88′N</td><td>18°5.42′W</td><td>29,25</td><td>34,92</td><td>1,0</td></tr><tr><td>38</td><td>7°2.11′N</td><td>17°27.82′W</td><td>29,43</td><td>34,67</td><td>1,0</td></tr><tr><td>39</td><td>5°49.51′N</td><td>16°29.68′W</td><td>29,34</td><td>34,34</td><td>1,0</td></tr><tr><td>40</td><td>4°22.32′N</td><td>15°16.91′W</td><td>28,47</td><td>33,35</td><td>1,1</td></tr><tr><td>42</td><td>2°15.70′N</td><td>13°33.85′W</td><td>27,56</td><td>35,72</td><td>1,3</td></tr><tr><td>43</td><td>0°57.53′N</td><td>12°33.06′W</td><td>26,48</td><td>36,05</td><td>1,3</td></tr><tr><td>46</td><td>1°35.74′S</td><td>10°33.85′W</td><td>25,91</td><td>36,13</td><td>1,3</td></tr><tr><td>47</td><td>2°17.53′S</td><td>10°1.35′W</td><td>26,16</td><td>36,2</td><td>1,2</td></tr><tr><td>49</td><td>4°44.75′S</td><td>8°6.64′W</td><td>24,59</td><td>36,07</td><td>1,2</td></tr><tr><td>51</td><td>6°55.67′S</td><td>6°24.31′W</td><td>24,28</td><td>36,01</td><td>1,1</td></tr><tr><td>52</td><td>8°6.09′S</td><td>5°29.08′W</td><td>23,8</td><td>35,99</td><td>1,0</td></tr><tr><td>56</td><td>11°51.79′S</td><td>2°30.74′W</td><td>22,18</td><td>36,38</td><td>1,3</td></tr><tr><td>62</td><td>17°59.62′S</td><td>2°25.32′E</td><td>19,11</td><td>35,99</td><td>1,3</td></tr><tr><td>66</td><td>22°26.99′S</td><td>6°6.92′E</td><td>18,71</td><td>35,68</td><td>1,3</td></tr><tr><td>69</td><td>25°0.20′S</td><td>8°17.16′E</td><td>18,19</td><td>35,64</td><td>0,9</td></tr><tr><td>72</td><td>27°2.39′S</td><td>10°35.53′E</td><td>18,5</td><td>35,64</td><td>1,0</td></tr></tbody></table>

936

937

938

939

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<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /><figcaption>Figure 1</figcaption></figure>

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942

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<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /><figcaption>Figure 2</figcaption></figure>

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<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /><figcaption>Figure 3</figcaption></figure>

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<figure><img src="image_50.png" /></figure>

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<figure><img src="image_51.png" /><figcaption>Figure 4</figcaption></figure>
<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /><figcaption>Figure 5</figcaption></figure>

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<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /><figcaption>Figure 6</figcaption></figure>

<figure><img src="image_58.png" /></figure>
<figure><img src="image_59.png" /></figure>

FIGURE LEGENDS

1019

1020

1021 **Fig. 1:** Stations used in this study, plotted on gridded data set described in LeGrande and Schmidt (2006) (a). (http://data.giss.nasa.gov/o18data/grid.html). Set up for planktonic foraminifers collections (b).

1023

1024 **Fig. 2:** (a) Mg/Ca and (b) Sr/Ca (mmol/mol) and 95% confidence intervals plotted versus measured surface temperature (°C). Each point represents an average of the Mg/Ca and Sr/Ca per station.

1027

1028 **Fig. 3 a)** Mg/Paleo-temperature equations established in this study (equation 1) (black dots, and full lines), based on the data of Nürnberg et al., (1996) (Orange diamond and large full orange line); Anand et al., (2003) (small green dotted line) and Regenberg et al., (2009) (large blue dotted line) and **3b)** Reconstructed Mg-temperatures (Oct/Nov. 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 lines).

1036

1037 **Fig. 4:** Reconstruction of δ¹⁸Oc-δ¹⁸Ow by inserting the measured temperature into three δ¹⁸O based paleo-T-equation: The equation of Spero et al., (2003) (light blue squares, large light blue dashed line), the equation of Mulitza et al., (2003) (pink dots, small pink dashed line), the equation sorted by Erez and Luz (1983) (green triangles, green dashed line) plotted versus measured δ¹⁸Oc-δ¹⁸Ow (‰ PDB). The diagonal line represents the 1:1 regression.

1043

1044 **Fig. 5:** Measured surface δ¹⁸Ow (‰ SMOW) plotted versus measured surface salinity (stations listed in Tab. 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) (blue dashed line) based on Schmidt (1999) data for the Atlantic Ocean for the water depth interval of 0–100 m.

1049

1050 **Fig. 6:** a) Measured salinity (orange triangles) and reconstructed salinity based on equations 1, 4 and 5 from the present study (black dots), plotted versus measured δ¹⁸Ow.

1051
<figure><img src="image_60.png" /></figure>

1052 b) Reconstructed salinity based on 1) successive reconstructions using equations 1, 4 and 5

1053 from the present study (black dots) and 2) direct linear fit (Eq. 6) based on the same measured

1054 variables (Mg/Ca and $\delta^{18}$Oc) (purple crosses), plotted versus measured salinity.

1055

1056

1057

1058

1059

1060