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Supplementary Materials: Detailed methodology
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Salinity measurements
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Sediment interstitial waters (IW) were collected by squeezing whole core rounds using
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Manheim squeezers (Manheim and Sayles, 1974). Chloride concentration was determined by
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AgNO3 titration (Site 1225) or with an ion chromatography procedure optimized for chloride
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concentration measurement (all other sites). The measured relative standard deviation, based on
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duplicate analyses, was 0.12% for the titrations and 0.09% for ion chromatography. For all
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analyses, IAPSO Standard Seawater was used as the standard (D’Hondt et al., 2003, 2011).
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Measurements were made within three days of core recovery and samples were stored in the
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refrigerator to minimize evaporation. Outliers are eliminated by a Locally Weighted Scatterplot
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Smoothing (LOWESS) using the function lowess from MATLAB. The fit value is subtracted
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from the measured values and residuals over the 95% quantile are considered outliers.
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Porosity Measurements
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Wet porosity was determined for EQP10 and EQP11, while dry porosity was determined for
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Sites U1365, U1370 and 1225. Porosities were measured by utilizing ODP standard procedures
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(Methods A and C of Blum 1997). For sites EQP10 and EQP11, 2 cm3 samples were taken from
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the split cores with a cut syringe and their wet weight was recorded. Samples were dried at
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105°C during at least 24h and dry weighted afterwards. In the case of samples from sites 1225,
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U1370 and U1365 one extra step was done to measure the dry volume with a gas pycnometer
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that was used to compute the porosity.
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The porosity data were smoothed with LOWESS and interpolated afterwards for the depths
used in the model (each 0.5 m).
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Tortuosity Measurements
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Tortuosity is calculated based on measured formation factors. Our reconstruction included
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formation factor empirical inputs as a proxy for tortuosity that had not been considered by
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Adkins and Schrag (2003) for all sites but 1225. The tortuosity (θ2) is calculated as the formation
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factor (f) multiplied by the porosity (𝜙):
𝜃 2 = 𝑓𝜙 (MacDuff ,1976)
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Conductivity of the sediment (𝐶𝑠𝑒𝑑 ) was measured with a Metrohm 712 conductivity meter,
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platinum electrodes and a temperature probe. Conductivity measurements of standard IAPSO
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(𝐶𝑠𝑡𝑑 ) water were taken at least at the beginning and end of each core section. Measurements
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were taken on split cores with the electrodes entering the sediment on the split surface (x axis).
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The formation factor (𝑓) is calculated as a ratio between the electrical resistivity of the sediment
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sample (𝑅𝑠𝑒𝑑 ) and the electrical resistivity of IAPSO standard water (𝑅𝑠𝑡𝑑 ):
𝑓=
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𝑅𝑠𝑒𝑑
𝐶
⁄𝑅 = 𝑠𝑡𝑑⁄𝐶 (Archie, 1947).
𝑠𝑡𝑑
𝑠𝑒𝑑
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Values are detrended for changes on electrical response of the electrodes over time, which
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were replatinized when necessary. Measurements were also corrected to a standard temperature
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of 20°C and measured chloride concentration (interpolated from the pore water measurements).
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The formation factor data were smoothed with LOWESS and interpolated afterwards for the
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needed depths (each 0.5m).
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The difference in contact resistance between the water standard and sediment is not
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accounted for because the high values of wet porosity (>70%) indicate that this difference would
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be minimal. Core expansion was found to be minimal in the cores recovered, and therefore
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should not affect significantly the tortuosity measurements. Despite that we had no access to the
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preferred wireline logging electrical resistivity measurements (Erickson and Jarrard 1998) for
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these sites, we consider that the estimation of the tortuosity based on electrical resistivity
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measured on recovered cores provides a better estimate than Archie’s law.
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Numerical Model
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This model follows the continuity equation of dissolved chemicals as described by Wang et
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al (2008). The burial rate is assumed to be equivalent to the sedimentation rate, assuming steady-
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state compaction. The small sedimentation rates observed in the sites (~0.3 mm/ka) indicate
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small Peclet numbers:
𝑃𝑒 =
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𝑆𝐿
< 1.6 × 10−3
𝐷⁄
𝜃2
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where S is the sedimentation rate, L s the sediment thickness, D is the molecular diffusion
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coefficient and 𝜃 2 is the tortuosity. Making therefore negligible the advection term of the
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equation and the compaction effect on tortuosity. Adkins and Schrag (2001, 2003) had discussed
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the sensitivities of this model for advection and rate of reaction. The rate of reaction term is not
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included here, as it does not affect the balance in these sites because no volcanic ashes were
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found in the sediment recovered. Therefore the diffusion equation for this problem is,
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𝜙
𝜕𝐶
𝜕 𝜙𝐷 𝜕𝐶
𝜕 𝐷 𝜕𝐶
=
( 2
)=
(
),
𝜕𝑡
𝜕𝑧 𝜃 𝜕𝑧
𝜕𝑧 𝑓 𝜕𝑧
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where C is the chloride concentration, t is time, z is depth below the sediment-water interface,
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(𝜙) is the porosity in the sediment, D is the diffusion coefficient with a value of 10-9 m2/s for
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2.5°C (Boudreau, 1996), and f is the formation factor. This equation was implemented as a one-
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dimensional numerical model in MATLAB by using finite differences and second order Adams-
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Bashforth-Moulton method.
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The no-flux bottom boundary condition is implemented for site U1365:
𝜕𝐶
= 0,
𝜕𝑧
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and the concentration boundary condition for all the other sites:
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𝐶(𝑡) = 𝐶0 (𝑡)
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Basement is known to be not impermeable and flow through it has been demonstrated in multiple
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studies (e.g. Fisher and Becker, 1995; Becker and Fisher, 2000) therefore, a concentration
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boundary condition is closer to reality. On the other hand, chert is known to present nearly
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impermeable properties and therefore it supports a non-flux boundary condition.
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The chloride concentration in the basement was not measured and therefore two different options
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were tested:
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1. The average of the deepest five measurements is used as the concentration boundary (five
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points were taken to avoid calculations based on only one measurement) and we assume that the
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value at the sediment-basement interface is identical
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2. The average of the optimized 115ky Bottom Water Boundary Condition (BWBC).
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Method 2 shows a better agreement with the data.
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The temperature dependence of the diffusion coefficient is given the Stokes-Einstein relation
(Bockris and Reddy, 1970):
𝐷(𝑧) =
𝑇(𝑧) 𝜂(𝑇𝑟 )
𝐷(𝑇𝑟 )
𝜂[𝑇(𝑧)] 𝑇𝑟
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Where 𝐷 is the chloride diffusion coefficient with a value of 10-9 m2/s for 2.5 °C (Boudreau,
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1996), z is the depth, (𝑇𝑟 ) is the reference temperature (20 °C), 𝑇(𝑧) is the temperature at depth
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𝑧, and 𝜂 is the viscosity of the water with a value of 1.00 cP at 20 °C. Measured temperature
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gradient and bottom temperature are used for each site.
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Evaluation of the fitting: Finding the optimal α value
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The reconstruction of the initial conditions from an observed diffusion profile is an
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underconstrained inverse problem. As Adkins and Schrag (2003) assumed, chlorinity followed
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sea level changes. We determine the magnitude of the changes to best fit the data. The optimized
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values of the chloride concentration in the deep ocean over time (𝐶𝑧𝑜𝑝𝑡 𝐵𝑊𝐵𝐶 ) are approximated
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with this reconstruction by increasing the magnitude (α) of the chloride concentration curve
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(𝐶𝑧𝐵𝑊𝐵𝐶 ) based on sea level changes:
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𝐵𝑊𝐵𝐶
𝐵𝑊𝐵𝐶
𝐶𝑧𝑜𝑝𝑡 𝐵𝑊𝐵𝐶 = 𝐶𝑧=0
+ ∝ (𝐶𝑧𝐵𝑊𝐵𝐶 − 𝐶𝑧=0
)
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𝐵𝑊𝐵𝐶
where 𝐶𝑧=0
is the current chloride concentration of the bottom water. The optimized value is
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found by minimizing the mean squared error (MSE) when the reconstructed chloride
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concentration values (𝐶𝑧𝑚𝑜𝑑𝑒𝑙 ) are compared to the pore water measured chloride concentration
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values (𝐶𝑧𝑝𝑤 ) for different ∝ values:
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(𝐶𝑧𝑝𝑤 − 𝐶𝑧𝑚𝑜𝑑𝑒𝑙 )2
𝑀𝑆𝐸 =
𝑛
where (n) is the number of measured points.
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That is, the ∝ value that minimizes the MSE is used for calculating the best estimate of the
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chloride concentration during the LGM. An α value of 0 implies that the salinity difference
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predicted solely from the sea level curve best fits the data, while a value greater than 0 infers that
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the best fit is produced by multiplying the salinity predicted by the sea level curve by the
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constant α so that the magnitude of the curve is increased. In the figures which illustrate the
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model results, we include a variation of the optimal value, α ± 0.1 for all models except EQP10
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and EQP11 where α ± 0.2.
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Extrapolation in depth of the curve
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Sites EQP10 and EQP11 were only drilled to 27 mbsf. However, the sediment column at
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these sites is ~100 mbsf based on seismic data. We assumed that similar physical properties are
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found below this depth based on previous work (GPC3 site is located close to EQP11 and
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presented similar values) (Corliss et al., 1982).
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With the above porosity and tortuosity the post-LGM signal only reached ~40 mbsf depth,
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indicating that the measured cores for EQP10 and EQP11 represent >90% of this signal. A model
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run based on site EQP11 for ∝= 2 presented the maximum signal at 40 mbsf. The value obtained
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at 25 mbsf represented 99.79% of the maximum value. Similar values were found for different
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∝´s indicating that this parameter does not affect the depth reached by the post-LGM signal. In
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these sites, it is necessary to consider a depth to basement for the modeling in order to avoid a
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biased signal from the Basement Boundary Condition (BBC).
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Potential influence of the chert layer on site U1365
In marine sediment, bedded chert forms during the solution of biogenic opal-A and
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reprecipitation of opal-A, opal-CT and quartz (Kastner, 1981). Since the degrees of hydration of
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these minerals differ and are variable, there is the potential that the active transformation of
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biogenic opal to chert can significantly influence the chlorinity of the pore fluid. If this is
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occurring, a chlorinity gradient is expected at the interface between the chert layer and overlying
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sediment. At Site U1365, there is no measurable chlorinity gradient at this interface. This
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implies that, the formation of chert at this site, does not presently have a significant impact on
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the pore fluid chlorinity.
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