# Carbonate dissolution fluxes in deep-sea sediments as determined from in situ porewater profiles in a transect across the saturation horizon

Jaclyn E P Cetiner, William M Berelson, Nick E Rollins, Xuewu Liu, Frank J Pavia, Anna R Waldeck, William Gray, Sijia Dong, Kalla Fleger, Holly A Barnhart, et al.

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## ► To cite this version:

Jaclyn E P Cetiner, William M Berelson, Nick E Rollins, Xuewu Liu, Frank J Pavia, et al. Carbonate dissolution fluxes in deep-sea sediments as determined from in situ porewater profiles in a transect across the saturation horizon. Geochimica et Cosmochimica Acta, 2024, 390, pp.145 - 159. 10.1016/j.gca.2024.11.027. hal-05125222

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# Carbonate dissolution fluxes in deep-sea sediments as determined from in situ porewater profiles in a transect across the saturation horizon

Jaclyn E.P. Cetiner<sup>a, *</sup>, William M. Berelson<sup>a</sup>, Nick E. Rollins<sup>a</sup>, Xuewu Liu<sup>b</sup>, Frank J. Pavia<sup>c</sup>, Anna R. Waldeck<sup>d</sup>, Sijia Dong<sup>c</sup>, Kalla Fleger<sup>b</sup>, Holly A. Barnhart<sup>c</sup>, Matthew Quinan<sup>a</sup>, Rucha P. Wani<sup>a</sup>, Patrick A. Rafter<sup>e</sup>, Andrew D. Jacobson<sup>d</sup>, Robert H. Byrne<sup>b</sup>, Jess F. Adkins<sup>c</sup>

## ARTICLE INFO

Associate editor: Christof Meile

**Keywords:**

Carbonate dissolution

Porewater geochemistry

Benthic fluxes

Saturation state

Carbonate burial

## ABSTRACT

Despite their importance for long-term climate regulation, the rates and mechanisms of seafloor carbonate dissolution are poorly understood, especially with respect to calcite saturation and the role of sedimentary metabolic CO₂ production. Here, we present results from an in situ porewater sampler deployed at the Cocos Ridge in the eastern equatorial Pacific, where we examine seafloor carbonate dissolution in locations with bottom water Ωcalcite ranging from 1.0 to 0.84 (1600–3200 m). With cm-scale resolution from the sediment–water interface to 35 cm, we present porewater profiles of total alkalinity, pH, dissolved inorganic carbon (DIC), δ¹³C of DIC, Ωcalcite, [Mn], [Ca], and [Sr], as well as solid phase porosity, % CaCO₃, and % organic C. These profiles provide evidence that deep-sea sedimentary carbonate dissolution occurs via sediment-side control, wherein dissolution is dominated by sedimentary processes rather than strictly bottom water saturation state. We estimate dissolution fluxes using three independent approaches: alkalinity fluxes, δ¹³C of DIC combined with DIC fluxes, and [Ca] fluxes. We report seafloor dissolution fluxes with uncertainties < 38 %: 40 ± 15, 98 ± 20, 100 ± 32, and 89 ± 27 µmol CaCO₃/m²/day at sites 3200, 2900, 2700, and 1600 m deep, respectively. The magnitude of dissolution fluxes is a function of bottom water saturation state (Ωcalcite), bottom water dissolved oxygen, and sedimentary CaCO₃ content, but not correlated with any of these parameters independently. We observe dissolution occurring at all stations, including where bottom water is saturated with respect to calcite, and present evidence that this occurs through respiration-driven dissolution within the sediment. At all sites, porewater Ωcalcite decreases below bottom water values before increasing toward saturation deeper in the sediment. Using the δ¹³C of DIC, we partition the DIC fluxes across the sediment–water interface and find 21–48 % of DIC is sourced from CaCO₃ dissolution, with the remainder sourced from organic matter respiration. We present a sedimentary mass balance, assembled with dissolution rates and mass accumulation rates obtained through Δ¹⁴C of foraminiferal calcite, and calculate CaCO₃ burial efficiencies between 2 and 67 %, inversely correlating with water depth. Our results also provide evidence that net chemical erosion of 5,000–10,000 year old carbonate is occurring at the deepest site. Aerobic organic C respiration coupled with sedimentary CaCO₃ dissolution, as documented here, will provide more alkalinity to bottom waters than from undersaturation-driven dissolution alone. This process can neutralize anthropogenic CO₂ at the seafloor in a larger range of saturation states than previously estimated.

## 1. Introduction

The ocean has absorbed roughly one third of fossil fuel emissions

since the start of the Industrial Revolution (Doney et al., 2009; Khatri-
wala et al., 2013; Gruber et al., 2019). This influx of CO₂ has led to ocean
acidification with an increase in surface seawater hydrogen ion

<footnote><sup>a</sup> University of Southern California, Los Angeles, CA 90089, USA</footnote>

<footnote><sup>b</sup> University of South Florida, St. Petersburg, FL 33701, USA</footnote>

<footnote><sup>c</sup> California Institute of Technology, Pasadena, CA 91125, USA</footnote>

<footnote><sup>d</sup> Northwestern University, Evanston, IL 60208, USA</footnote>

<footnote><sup>e</sup> University of California, Irvine, Irvine, CA 92697, USA</footnote>

<footnote>* Corresponding author.
E-mail address: jepittma@usc.edu (J.E.P. Cetiner).</footnote>
concentration (Doney et al., 2020). Based on modeling studies, ocean buffering will neutralize this excess acidity from anthropogenic CO₂ on timescales of 10,000 years (Archer et al., 2009) via dissolution of seafloor carbonate sediments. There is already evidence of anthropogenic CO₂ being buffered by deep-sea carbonate dissolution in some areas of the North Atlantic (Sulpis et al., 2018), but it will take on the order of 1000–2000 years, i.e., via global ocean ventilation (Broecker et al., 1984) for anthropogenic CO₂ to penetrate throughout the deep ocean. Carbonate (CaCO₃) dissolution, particularly in the deep ocean, is a natural mechanism by which the planet neutralizes CO₂. When anthropogenic CO₂ arrives at the seafloor, either as dissolved CO₂ or as particulate organic carbon (POC) following photosynthesis, knowledge of the rate of CaCO₃ dissolution and the parameters controlling dissolution will improve understanding of how the ocean is responding to anthropogenic climate change. The amount and location of POC oxidation, whether by oxygen or other oxidants, is another important consideration of the fate and neutralization of anthropogenic CO₂.

Respiration-driven dissolution can enhance sedimentary dissolution through the production of acid via aerobic respiration of organic carbon. The subsequent dissolution of CaCO₃ neutralizes CO₂ via the following coupled reactions (Emerson and Bender, 1981):

$$C_{\text{org}} + O_2 \rightarrow CO_2; CO_2 + H_2O \rightarrow CaCO_3 \rightarrow 2HCO_3 + Ca^{2+} \quad (1)$$

Emerson and Bender (1981) concluded that CaCO₃ dissolution is at least partially controlled by the relative rain ratios of organic C and CaCO₃. Martin and Sayles (1996) found that significant dissolution could be occurring in sediments above the saturation horizon due to metabolic CO₂ production, based on organic carbon oxidation rates and dissolution rates estimated from in situ squeezed porewater measurements of calcium [Ca], alkalinity, and dissolved inorganic carbon (DIC). DIC is not only produced by aerobic respiration, but also by sub-oxic diagenetic reactions occurring within the sediment (Froelich et al., 1979). The depth of oxygen penetration may significantly affect the rate at which respiration-driven dissolution can occur, regardless of the bottom water saturation state (Martin and Sayles, 2006). Additionally, even if the rain rates of POC are higher than those of CaCO₃, CaCO₃ dissolution may not occur if the organic carbon is oxidized very near to the sediment-water interface (SWI), thereby allowing bottom water CO₃²⁻ to neutralize metabolic CO₂ (Sayles et al., 2001). Whether respiration-driven dissolution occurs primarily at the sediment-water interface or within the sediment column is difficult to assess without accurate, high-resolution porewater profiles.

An ongoing debate regarding seafloor dissolution is whether dissolution is under water-side or sediment-side control, i.e., if control of the dissolution rate depends primarily on the diffusive-boundary layer between sediment and overlying water, or the porewater chemistry. Some models and sediment bed reactors indicate that seafloor CaCO₃ dissolution is entirely dependent on boundary layer conditions (Boudreau, 2013; Sulpis et al., 2017). One major implication of water-side control is the prediction that there is no change in porewater pH or calcite saturation state (Ωₐₖcite) below the upper few mm of the sediment column (Boudreau et al., 2020). Because porewater has been difficult to sample and measure accurately in the past, no artifact-free, high-resolution Ωₐₖcite porewater profiles have previously been generated to test this implication. Downcore profiles of pH and other carbonate parameters, below the top few mm, would provide key evidence of water-side or sediment-side control.

Quantifying rates of CaCO₃ dissolution has been a longstanding goal in oceanography, and several methodologies have been employed to constrain the marine carbonate budget, such as benthic landers (e.g., Berelson et al., 1990a; Sayles and Dickinson, 1991; Jahnke et al., 1997), in situ microelectrodes (e.g., Archer et al., 1989; Reimers et al., 1992; Hales and Emerson, 1997), mass balances (e.g., Balch et al., 2007), and modeling efforts (e.g., Broecker and Broecker, 1974; Archer and Maier-Reimer, 1994). In addition to dissolution rate, the location in the

sediment column where dissolution occurs is also of interest. While the parameters driving dissolution, such as saturation state, bottom water oxygen concentration, CaCO₃ mineralogy, and organic carbon supply have been the topic of many studies, uncertainty remains about the relative importance of these factors and their control on sedimentary dissolution.

Recently, dissolution kinetics have been well studied in bench-top experiments and the water column to help refine parameterization of the dissolution rate law relating CaCO₃ dissolution rate to saturation state (e.g., Subhas et al., 2015; Dong et al., 2018; Dong et al., 2019; Naviaux et al., 2019a; Adkins et al., 2021). An outstanding question is whether these kinetic rate laws apply to dissolution within the sediment (Boudreau et al., 2020). To independently constrain this question, bulk sediment dissolution fluxes must be defined, and here we summarize how this has been done previously.

Much work has been done to determine seafloor CaCO₃ dissolution fluxes by direct measurements using benthic flux chamber incubations. Near our study site, in situ seafloor dissolution fluxes were determined using chambers on landers in a transect across the equator in the central Pacific (Berelson et al., 1990a, 1990b, 1994). A study by Jahnke et al. (1997) off the coast of California found that modeled rates of carbonate dissolution underestimated rates measured in chambers, indicating that metabolic CO₂ must significantly contribute to the observed dissolution rates. The ROLAI²D lander captured fluxes of alkalinity and DIC in the North Atlantic, and their ratio was used as a proxy for CaCO₃ dissolution and respiration (Sayles and Dickinson, 1991).

Microelectrodes can provide high resolution profiles that can be modeled for estimates of CaCO₃ dissolution within sediments; the major benefits of microelectrodes include mm-scale depth precision and in situ measurement capability. Oxygen and pH microelectrodes, in conjunction with numerical models, have been used to calculate dissolution rates in the top few cm of sediment (Archer et al., 1989; Reimers et al., 1992) and to measure oxygen flux into sediment, a proxy for aerobic respiration that could drive dissolution (Hales et al., 1994). However, there is uncertainty in the interpretation of microelectrode pH data given the multiple reactions that can affect pH. The use of in situ pCO₂ microelectrodes also provides evidence that a fraction of sedimentary dissolution occurs through respiration-driven processes (Hales and Emerson, 1997).

The flux of dissolved calcium may be considered a more definitive proxy for CaCO₃ dissolution (Jahnke et al., 1997) given the many sources and sinks of alkalinity and DIC, but fewer major reactions involving Ca in the shallow sediment column. However, the measurement precision needed to observe slight changes in [Ca] can be difficult to achieve. Measurements of excess Ca relative to Na in porewater has also been considered a signal of sedimentary dissolution in coastal sediments (Green et al., 1998). Ca to alkalinity ratios have been used as a metric of CaCO₃ production and dissolution and other cation exchange processes, with analyses in porewater (Steiner et al., 2022), benthic flux chambers (Jahnke and Jahnke, 2004), and the water column (Steiner et al., 2021).

In situ filtration of porewater is necessary to avoid sampling artifacts associated with extreme pressure and temperature changes occurring when cores travel from the deep sea to the surface (Bischoff et al., 1970; Bischoff and Sayles, 1972; Sayles et al., 1973; Sayles et al., 1976; Murray et al., 1980; Sauvage et al., 2014; Cetiner et al., 2023). Here, we present the first high-resolution porewater profiles (0–35 cm depth range) obtained from porewater extracted in situ at sites in the Cocos Ridge in the eastern equatorial Pacific. We report pH, total alkalinity, DIC, δ¹³C of DIC, and [Ca] measurements on in situ filtered porewater and interpret these profiles via Fick's first law to estimate seafloor carbonate dissolution fluxes using three approaches: alkalinity fluxes, δ¹³C and DIC fluxes, and [Ca] fluxes. We also present a budget for CaCO₃ rain, burial, and dissolution on the Cocos Ridge seafloor.
## 2. Study area and methods

### 2.1. Field site

In December 2021, we conducted a cruise on the R/V Sally Ride on and adjacent to the Cocos Ridge in the eastern equatorial Pacific (Fig. 1). This region contains a wide range of surface sediment carbonate contents: <1–93 wt% CaCO₃ (Moore et al., 1973; Cetiner et al., 2023). Previous studies have found sedimentation rates of 1–3 cm/kyr in this region (Lea et al., 2000; Liao and Lyle, 2014). In a depth transect of 1600–3200 m, we encounter changes in calcite saturation state ranging from 1.0 to 0.84 (Table 1).

Water depth and bottom water properties are summarized in Table 1. Station 1 was located furthest from the ridge and deepest; Stations 5 and 2 were a similar distance from the ridge; Station 3 was on the ridge and shallowest (Table 1). Stations will be presented in this paper in order of deepest to shallowest: 1, 5, 2, and 3. Weight % CaCO₃ correlates with depth and distance to ridge; stations that were shallower and closer to the ridge had the highest sedimentary carbonate content (Table 1). This transect can be thought of as a section down the modern sedimentary lysocline.

At each station, two CTD casts measured dissolved oxygen (Fig. 2a) and Niskin samples were collected for analyses of DIC, δ¹³C of DIC,

alkalinity, and pH. These parameters were all measured to overcome the water column carbonate chemistry system and determine the saturation state of calcite (Ωcalcite) in bottom waters (Fig. 2b) using CO2SYS (v3.1.1). The depth range of the four stations allows us to separately analyze the effects of bottom water saturation state and oxygen content on carbonate dissolution fluxes.

### 2.2. Porewater sampling

Porewater samples were collected using SIPR, a device we built capable of filtering porewater in situ from deep-sea sediments; its design, field validation, and comparison to traditional methods are described in detail in Cetiner et al. (2023). Briefly, two types of filtering mechanisms, termed “needles” and “blades,” are attached to a standard MC-800 multicorer and deployed off a ship in the traditional manner except that, once landed, the multicorer remains stationary on the seafloor for 6–10h. The needles and blades have “windows” that act as sampling ports, holding a Rhizon filter (needles) or covered with filter paper (blades). The needles and blades penetrate the sediment with sampling ports distributed between 0–35 cm, with high resolution (0.5 cm spacing) near the sediment-water interface. Upon computer action via burn wires, spring-loaded syringes draw porewater through the filters and samples are stored in 30 mL glass quartz coils designed to

<figure><img src="image_5.png" /><figcaption>Fig. 1. Map of station locations on the Cocos Ridge. Bold contours denote every 1000 m; thin contours denote every 200 m.</figcaption></figure>
<table><caption>**Table 1**

Station information of relevant bottom water and sediment properties.</caption><thead><tr><th>Station</th><th>Station coordinates</th><th>Water depth (m)</th><th>Bottom water temperature (°C)</th><th>Bottom water Ω<sub>calcite</sub></th><th>Bottom water dissolved O<sub>2</sub> (µM)</th><th>Core top wt% CaCO<sub>3</sub></th></tr></thead><tbody><tr><td rowspan="2">1</td><td>6° 47.08' N</td><td rowspan="2">3223</td><td rowspan="2">1.9</td><td rowspan="2">0.84</td><td rowspan="2">105</td><td rowspan="2">0.8</td></tr><tr><td>88° 15.65' W</td></tr><tr><td rowspan="2">5</td><td>6° 35.99' N</td><td rowspan="2">2911</td><td rowspan="2">1.8</td><td rowspan="2">0.89</td><td rowspan="2">100</td><td rowspan="2">13</td></tr><tr><td>86° 41.75' W</td></tr><tr><td rowspan="2">2</td><td>5° 57.48' N</td><td rowspan="2">2650</td><td rowspan="2">1.8</td><td rowspan="2">0.94</td><td rowspan="2">100</td><td rowspan="2">63</td></tr><tr><td>87° 57.40' W</td></tr><tr><td rowspan="2">3</td><td>5° 10.17' N</td><td rowspan="2">1630</td><td rowspan="2">2.8</td><td rowspan="2">1.0</td><td rowspan="2">75</td><td rowspan="2">78</td></tr><tr><td>86° 35.59' W</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Water column profiles of (a) dissolved oxygen, and (b) carbonate saturation state with respect to calcite, calculated from alkalinity and pH, with inset focused near Ω$_{calcite}$ = 1.</figcaption></figure>

prevent gas loss and minimize mixing with filtered surface seawater, which acts as “fill water” in the tubes. This fill water is added pre-deployment with a fluoride spike which allows quantification of mixing between porewater and fill water as a sample is drawn. In this paper, we report porewater samples that had <1% fill water; dilution corrections to measured parameters are therefore trivial. Fill water is also treated with mercuric chloride to inhibit biological modification once the sample is collected. Total sample volume drawn by the syringe was 30–60 mL for each sampling port. Before the multicorer is retrieved, another mechanism pinches the tubing to prevent further water draw during recovery. To assess penetration depth, a plate resting on the sediment is locked in place to provide an estimate of the sediment-water interface location relative to the needles and blades. Aside from the deepest site, a video camera also records placement of the samplers and sample draw rate. Visual determination of sampler penetration depth was useful, but ultimately, porewater depths were determined based on dissolved [Si] profiles by matching in situ collected porewater to rhizon-processed cores at each site; see Cetiner et al., 2023 for full methodology on how porewater depth was determined. Each blade or set of needles collects eight samples. They are spaced around the multicorer such that one set of samples could be at most 1 m distant from another set. Once on board the ship, porewater samples were analyzed for pH, alkalinity, and DIC within 4h of recovery. The needles and blades collected porewater from slightly different depths, so we have combined samples from all needles and blades from two deployments per station to create full porewater profiles from 0 to 35 cm at each station. Given the porosity of the sediment, we estimate that the porewater collected comes from a diameter of 2–4 cm around the center of each window, the exact diameter being a function of water volume collected per sample. We

have plotted porewater data points assuming they are centered about a 2 cm depth horizon. The total number of in situ porewater samples recovered were 53, 54, 60, and 59, for Stations 1, 5, 2, and 3, respectively.

## 2.3. Chemical analyses

DIC and δ¹³C of DIC were analyzed with a Picarro Cavity Ring-Down Spectrometer (G2131-i) with Liaison; the detailed methodology is described in Subhas et al. (2015). These measurements were made on board the ship. Dickson seawater CRM was used as the standard for DIC; pre-weighed optical calcite (Iceland spar) powder was used as the standard for δ¹³C. Exetainer vials were pre-acidified and pre-weighed in the lab prior to the cruise. After the vials were filled with 3–5 mL of porewater and analyzed on the cruise, the stored vials were weighed again in the lab to obtain the sample mass. Results using this methodology were corrected by normalizing to measured values of Dickson seawater CRM. Uncertainty (1σ) for replicate DIC and δ¹³C measurements were ±23 µmol/kg and ±0.15‰ (VPDB), respectively. DIC uncertainty was higher than reported in our previous studies (e.g., Subhas et al., 2015) likely due to mass determination: ship-board analysis necessitated weighing after analysis, but there was uncertainty in precisely how much sample mass may be removed during the analysis. Additionally, lower sample volume (5 mLs, instead of 7–8 mLs in our previous studies) may have added error.

Total alkalinity and pH were measured spectrophotometrically with the mvMICA system, developed by the Byrne group at University of South Florida and based on methods described in Liu et al. (2006) and (2011). mvMICA is uniquely suitable for SIPR samples due to its small
volume requirement and high precision. The system has one channel each for alkalinity and pH. For alkalinity, 1.5 mL was delivered to a semi-micro disposable cuvette with 10 µL of 2 mM bromocresol purple indicator, which was covered and equilibrated in a 20°C water bath. For pH, 1.5 mL was directly collected in gas-tight syringes made of borosilicate glass and a triple seal plunger; 3 µL of 2 mM pure *meta*-cresol purple dye was injected into the sample using a thin Teflon needle. The samples were thermostated in a custom-made cell warmer for the temperature to stabilize at 20°C (Liu et al., 2006, 2011, 2013). pH was calculated using equations from Muller and Rehder (2018). The pH values and profiles reported here are calculated at in situ pressures and temperatures using CO2SYS (MATLAB v3.1.1) (Lewis and Wallace, 1998), using the total pH scale and K1, K2 constants from Hansson (1972, 1973) and Mehrbach et al. (1973), refit by Dickson and Millero (1987). Uncertainties (1σ) for duplicate alkalinity and pH measurements were ±2 µM and ±0.001, respectively.

Porewater dissolved manganese [Mn] was analyzed at Caltech by ICP-MS (inductively coupled plasma – mass spectrometry) using an Agilent 8800 triple-quadrupole instrument. In situ porewater samples were stored in acid-cleaned HDPE bottles and acidified using 6M distilled HCl. After at least one week, 1 mL aliquots were diluted 10:1 in distilled 5% HNO₃ for ICP-MS analysis. Concentrations of [Mn] were determined via calibration to multi-element commercial ICP-MS standards, matrix-matched to samples by addition to artificial seawater and diluted in 5% HNO₃, identically to the samples. Blanks were assessed by analysis of both pure 5% nitric acid and artificial seawater with no metal standard added. ICP-MS measurements were made in MS/MS mode using He as a carrier gas in the collision cell. The detection limit was 0.03 µmol/kg [Mn].

Porewater calcium, strontium, and sodium were measured at Northwestern University using a Thermo iCap7600 ICP-OES (inductively coupled plasma – optical emission spectrometer). In order to capture signals from high- to low-abundance cations ([Na] and [Sr], respectively), samples were diluted 1:100, by mass, with trace metal clean 3% HNO₃. Each sample was weighed, diluted, and analyzed in five replicates. It was determined that five replicates per sample were needed to resolve the small differences in [Ca] relative to the large background signal. Samples were run in randomized order to account for any instrumental drift not corrected through standard bracketing. Cation concentrations were based on counts per second using two wavelengths from each cation (Ca393.366, Ca396.847, Sr407.771, Sr421.552, Na588.995, and Na589.592 nm). The two wavelengths for each element produced virtually identical results but were averaged, nonetheless. Calibration standards were synthetic mixtures of 1000 ppm cation solutions (Mg, Sr, Na, Ca, and K) diluted in 5% HNO₃ to match expected sample concentrations. Calibration standards were used to correct for instrumental drift within a single run by interpolating between standards run at the beginning and end of the run. Blank measurements were made on HNO₃ and were included in the calibration curve to account for instrumental blank. IAPSO standard seawater was used to adjust for drift across multiple days. Between 3–8 IAPSO standards were measured every run; the IAPSO average in a single run was normalized to the IAPSO average for all runs (Figure S1). This normalization was applied to all porewater samples. In this manner, samples analyzed on different days were normalized to a single measured IAPSO average. [Ca] and [Sr] were normalized to [Na] to preclude any evaporation artifacts (Steiner et al., 2021) that could have occurred during 14 months of sample storage. All stations had a CTD measured salinity of 34.6 ppt; salinity was converted to [Na] using a seawater Na:salinity ratio of 10.781 g/kg (Pilson, 1998). IAPSO seawater measurements had the following one standard error spreads: ± 0.026 mmol/kg Ca, ± 0.56 µmol/kg Sr, and 1.5 mmol/kg Na.

Multicores obtained during SIPR deployments provided downcore sediment intervals that were sampled for porosity, PIC. Total Carbon (TC), mineralogy, and radiocarbon content of foraminifera. Porosity was obtained by mass of water loss upon drying and assigned a standard

salinity (34.6 ppt) and sediment grain density (2.6 g/cm³). Bulk sediment samples were dried in an oven at 50°C for 72h, ground, and homogenized for TC and mineralogical analyses. 2–10 mg of the samples were used for PIC (% CaCO₃) and δ¹³C of PIC, which were analyzed on a Picarro Cavity Ring-Down Spectrometer (G2131-i) with Liaison autosampler, following acidification. TC and δ¹³C of TC measurements were conducted using an Elemental Analyzer (EA, Costech) coupled to a Picarro Cavity Ring-Down Spectrometer (G2131-i). Uncertainties in PIC and TC concentrations were evaluated based on the average offset between duplicate sample measurements and were identified to be ± 0.04 % PIC (7 pairs of duplicates) and ± 0.03 % TC (5 pairs of duplicates). Uncertainty in δ¹³C of PIC was evaluated based on the standard error of the measured optical calcite standard, which was 0.02 ‰. Mineralogy of the sediment was measured with a Bruker D8 Advance X-Ray Diffraction (XRD) at the Los Angeles County Museum of Natural History, Mineralogy Department. Roughly 200 mg of each ground sediment sample was mounted onto a rotating disc and scanned for ~1h. Data analysis was conducted using the software Jade.

Radiocarbon values were measured by accelerator mass spectrometry (AMS) at the University of California, Irvine Keck Carbon Cycle Accelerator Mass Spectrometry (KCCAMS) laboratory. The sediment was dried and washed over a 63 µm sieve with DI water, where pelagic forams were selected from the >250 µm fraction at different sediment horizons. Forams were pre-weighed and then leached with 10% HCl to dissolve ~10% of the test, thereby removing any post-depositional carbonate, then hydrolyzed under vacuum using H₃PO₄. The CO₂ released from this hydrolysis was then graphitized and the ¹⁴C/C of this CO₂ was counted on the AMS. The average percent error of calendar ages using this method was 1.5% for 23 foram measurements.

### 3. Curve fits, calibration, and uncertainty

#### 3.1. Calibrating carbonate system measurements

We over-constrained the carbonate system by measuring pH, total alkalinity, and DIC, providing an opportunity to compare different pairs and assess the overall accuracy of our measurements. We also compared our water column alkalinity measurements to those in the GLO-DAPv2.2021 data product (Figure S2) (Olsen et al., 2020a; Olsen et al., 2020b; Lauvset et al., 2021), where we observe good agreement: R² values for Stations 1, 5, 2, and 3 are 0.94, 0.91, 0.96, and 0.90, respectively. Water column alkalinity and pH measurements were used to calculate water column DIC via CO2SYS (MATLAB v3.1.1) (Lewis and

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Cross plot of DIC calculated from alkalinity and pH vs. measured DIC. Water column (“WC” as open squares and blue fit) and porewater (“PW” as solid circles and red fit). Dashed line indicates 1:1 line.</figcaption></figure>
Wallace, 1998), using the total pH scale and K1, K2 constants from Hansson (1972, 1973) and Mehrbach et al. (1973), refit by Dickson and Millero (1987). Comparing this calculated DIC to our measured DIC, we found our measurements were 1.0051 times higher, so we applied this correction to our measured values for both water column and porewater samples and found good agreement: water column has an R² of 0.99; porewater has an R² of 0.90 (Fig. 3). Hereafter, we use measured DIC (with this correction) in profiles and calculations because our sampling procedure provided more DIC measurements than pH measurements.

## 3.2. Curve fitting

Alkalinity, DIC, δ¹³C of DIC, and [Ca] pore water profiles were fit with an exponential function in the following form:

$$C_z = C_d - (C_d - C_0)^{-\beta z} \quad (2)$$

where z is depth into the sediment (cm), C_z is the concentration (µmol kg⁻¹) at each depth as defined by the curve fit; C_d is the concentration when z approaches infinity, i.e., where the profile approaches an asymptote, C_0 is the bottom water concentration, and β is an attenuation coefficient (cm⁻¹) (McManus et al., 1995, Hou et al., 2019). C_d and β are free fitting parameters. Values for C_0 come from samples collected from Niskin bottles triggered within 10 m of the bottom and assumed to be equivalent to water at the SWI, from which profiles are “anchored.” Needle and blade samples collected ~3 cm above the SWI confirmed that the Niskin-obtained values accurately represented water concentrations very close to the interface.

Although Eq. (2) was initially formatted for porewater dissolved silicate (Hurd, 1972; McManus et al., 1995), the similarity in profile shapes of silicate and carbonate species, specifically the steep gradient in the shallow sediments followed by an approach toward an asymptote, suggests that this equation may also be appropriate for porewater carbonate species. However, it is an empirical fitting equation, not intended to imply any mechanism. While we do not assert that the exponential is necessarily the proper formulation, there are two reasons that imply it is appropriate: 1) the exponential equation results in curvature that we observe in the profiles and curvature is indicative of both diffusion and reaction occurring in that region. Reaction is likely happening in the top 5–10 cm of sediment due to undersaturation with respect to calcite (Fig. 11). 2) The exponential equation results in the profile approaching an asymptotic value deeper in the sediment, which we also observe in the profiles. This shape implies the deeper sediment column is

approaching steady state with respect to carbonate dissolution, which is supported by the saturation state approaching omega = 1 (Fig. 11). For comparison, several other equations were tested to fit the data; no other equations had R² fits as good as the exponential fit (see Table S1 and more discussion in Supplementary Material). Equation (2) was therefore used to establish the gradient ∂C/∂z at the SWI and other depths. The two adjustable parameters, β and C_d, provide adequate degrees of freedom to fit data with R² >0.92. The following profiles were fit with this method: alkalinity, DIC, δ¹³C of DIC, and [Ca] (Figs. 4, 5, 6, and 7, respectively).

## 3.3. Flux calculations

The increasing concentration and concave-down curvature in the top few cm of each profile indicate that reactions are occurring in the shallow sediment column, yielding fluxes across any chosen depth horizon; our focus was on fluxes across the SWI. Profile gradients were converted into fluxes of alkalinity, DIC, and Ca via Fick’s first law:

$$F_z = -D_0 \phi^{2.6} \frac{\partial C}{\partial z} \quad (3)$$

where F is flux (µmol cm⁻² s⁻¹), D_0 is the diffusivity coefficient of the ion of interest (cm² s⁻¹), φ is porosity defined as a function of depth raised to 2.6 to account for tortuosity and porosity in diffusive transport (McManus et al., 1995), and ∂C/∂z is the concentration gradient evaluated at depth z (µmol kg⁻¹ cm⁻¹). Water density, assumed to be 1.025 g cm⁻³, is used to convert ∂C/∂z from units of µmol kg⁻¹ cm⁻¹ to µmol cm⁻⁴. In this manner, the flux can be calculated by evaluating ∂C/∂z at any chosen depth horizon. By using Fick’s first law, we assume transport is dominated by diffusion, rather than by advection, which is an acceptable assumption in slowly accumulating deep-sea sediments (McManus et al., 1995; Berelson et al., 2005). Bioirrigation is not included in the flux calculations for the following reasons: deep-sea sediments are generally considered to be diffusion-dominated (Hammond et al., 1996), there were no macrofauna observed below the SWI in any cores that were collected and processed, and porewater profiles exhibit smooth gradients, which may not be the case if bioirrigation was causing non-local advection. These points of evidence strongly suggest that diffusion is the predominant transport mechanism, but if bioirrigation was present, that would likely skew the fluxes reported to higher values.

Diffusivity of alkalinity and DIC was assigned to that of HCO₃ (the predominant species composing alkalinity and DIC, given the prevailing

<figure><img src="image_8.png" /><figcaption>Fig. 4. Porewater alkalinity profiles. Arrows denote bottom water values. Solid black lines denote best fit; dashed lines are 1σ of fit. Error bars (1σ of duplicate sample analyses) are smaller than the size of the symbols.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Porewater dissolved inorganic carbon (DIC) profiles. Arrows denote bottom water values. Solid black lines denote best fit; dashed lines are 1σ of fit. Error bars (1σ of duplicate sample analyses) are equal to the size of the symbols.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 6. Porewater $\delta^{13}\text{C}$ of DIC profiles. Arrows denote bottom water values. Solid black lines denote best fit; dashed lines are 1σ of fit. Error bars (1σ of duplicate sample analyses) are equal to the size of the symbols.</figcaption></figure>

pH), approximately $5 \times 10^{-6} \text{ cm}^2 \text{ s}^{-1}$; diffusivity of Ca was assigned to approximately $4 \times 10^{-6} \text{ cm}^2 \text{ s}^{-1}$, with exact diffusion coefficients dependent on in situ bottom water temperature (Boudreau, 1997). Including diffusion coefficients for both $\text{HCO}_3^-\text{ and } \text{CO}_3^{2-}$ would vary the overall diffusivity by 0.8% at the most, which is much smaller than the error on the $\partial C/\partial z$ term and therefore is not necessary to include.

### 3.4. Error propagation

Error was propagated through flux calculations using two statistical methods: bootstrapping and Monte Carlo iterations. Because we combined data from different sampling devices and from two deployments, we wanted to test the variability in profile fit. To do this, the profiles were bootstrapped, meaning a random subsample (20% of the data for each station) was fit to Eq. (2) using a non-linear least squares method. The random subsampling was repeated over 1000 iterations; the range of exponential fitting parameters did not change when more iterations

were completed. Each of the 1000 fits resulted in $C_d$ and $\beta$ values. The fit applied to our observations was calculated using the average of $C_d$ and $\beta$; the overall uncertainty of the curve was calculated by combining the uncertainties (1σ) for $C_d$ and $\beta$. Also included in the curve fitting was a depth uncertainty term. Porewater depths were determined by matching dissolved silica profiles from shipboard-processed sediment cores to in situ porewater (Cetiner et al., 2023). Some error in depth assignment is possible, so the depth term was allowed to vary ±1 cm in the curve fitting, normally distributed, as 1 cm was the average offset from the silica correction.

Once the profiles were fit, a Monte Carlo error propagation was used to estimate the final uncertainty on flux calculations. Monte Carlo error propagation involved assigning error to each variable, randomly within the normally distributed standard deviation, over 10,000 iterations to obtain a single uncertainty from an equation that incorporates uncertainties for each term (Equation (4)). Error was propagated through Equation (2) by including the uncertainty of porosity ($\sigma\phi = 1\%$) and $\partial C/\partial z$.
<figure><img src="image_11.png" /><figcaption>Fig. 7. Porewater [Ca], following normalization to [Na]. Arrows denote bottom water values. Solid black lines denote best fit; dashed lines are 1σ of fit. Error bars are 1 standard error of five replicates.</figcaption></figure>

$\partial z$. Error on flux comes from propagating the error through Equation (3) and including the uncertainties of $C_d$ and $\beta$ that were obtained through bootstrapping. In this approach, we assume no uncertainty in the diffusion coefficient. The uncertainty on flux can be propagated as follows:

$$ \sigma_{\text{Flux}} = D_0 (\phi \pm \sigma_{\phi})^{2.6} \left( \frac{\partial C}{\partial z} \pm \sigma_{\partial C / \partial z} \right) \quad (4) $$

# 4. Results

## 4.1. Porewater profiles

Alkalinity (Fig. 4) and DIC (Fig. 5) porewater profiles increase from bottom water values toward what appears to be a near-asymptotic value deeper in the sediment. When sample depth resolution is very high in the top few cm (namely Stations 2 and 3), values in the top 0.5 cm approach the bottom water value. The profile curvature changes with depth: the gradient is much steeper near the SWI, and $\partial C / \partial z$ has a much smaller

slope below 10 cm. The change in slope occurs at progressively shallower depths in the sediment for the sites shallower in the water column. $\delta^{13}$C of DIC profiles also exhibit steeper curvature in the top 0–10 cm, before approaching a near-asymptotic value (Fig. 6). Bottom water for all stations starts near 0‰, and porewater DIC becomes lighter with depth, approaching −1.5 to −2.5‰.

Calcium was measured on porewater from all stations, resulting in the profiles in Fig. 7 showing a general increase of 0.1–0.2 mmol/kg from the bottom water value and a curvature similar to that found in DIC and alkalinity. Because of the necessity of measuring five replicates per sample and the water volume required to make this many measurements, there are fewer data defining these profiles.

At all stations, pH decreases in porewater below overlying water values (Fig. 8), indicating a process that lowers pH is occurring in the shallow sediments. At Station 5, pH increases after the initial drop, surpassing the overlying water value. Profiles of pH were not fit with a curve because interpolated pH profiles were not needed for further calculations.

<figure><img src="image_12.png" /><figcaption>Fig. 8. In situ collected porewater pH. Arrows denote bottom water values. Error bars (1σ of duplicates) are smaller than the size of the symbols.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Fig. 9. Porewater manganese profiles with depth of oxygen penetration shown by arrows, interpreted as approximately the depth where Mn appears. Note difference in x-axes.</figcaption></figure>

Dissolved manganese [Mn] concentration is below detection (<0.03 µmol/kg) at the SWI and remains low until a depth where it increases substantially (Fig. 9). The depth at which [Mn] increases is interpreted as the depth of oxygen penetration (Froelich et al., 1979; Burdige, 1993; Chong et al., 2018), based on the sequence of redox reactions that occur in sediments. Assigning this depth at Station 3 is less definitive, as this porewater contains low [Mn] throughout.

## 4.2. Solid phase

Weight % CaCO₃, % Organic C, and porosity were measured throughout multicores at each station (Fig. 10). % CaCO₃ (PIC) increases downcore at Stations 1 and 5 but remains constant with depth at Stations 2 and 3. Organic C content is higher at the deeper Stations 1 and 5 (0.7–1.5%) than at the shallower Stations 2 and 3 (0.2–0.5 %). Porosity profiles show canonical decreases with depth, although some profiles display stepwise changes. At Station 1, the stepwise porosity decrease coincides with an increase in % CaCO₃. Average 0–1 cm porosity was 94, 93, 88, and 83% for Stations 1, 5, 2, and 3, respectively. Core top % TC, % PIC, % POC, δ¹³C of TC, δ¹³C of PIC, and δ¹³C of Organic C are listed in Table S2.

# 5. Discussion

## 5.1. Defining flux across the sediment-water interface

The flux across the SWI represents an integral of reactions and diffusive transport from the sediment column below. The curvature in

the data indicates the production of alkalinity and DIC occurs primarily in the upper 10 cm, yet there is some flux of these constituents that enter the 0–10 cm zone from deeper in the sediment. By defining fluxes at both horizons, SWI and 10 cm, we examine how much of the net SWI flux is generated at depth. The 10 cm horizon generally corresponds to the depth of oxygen penetration at most stations (Fig. 9) and is also the depth where wt % CaCO₃ content changes dramatically at Stations 1 and 5 (Fig. 10.a). While we are not assigning a change in diagenetic processes strictly at this 10 cm horizon, abrupt or gradual changes in gradients may indicate a transition from higher DIC production via aerobic processes to lower production rates via suboxic processes at depth. The goal of sections 5.2 to 5.5 is to define CaCO₃ dissolution fluxes as a net flux across the SWI and distinguish what portion of the net flux is driven by deeper reaction and transport. Three independent approaches are discussed.

## 5.2. Dissolution flux via DIC and C isotopes

The chemical fingerprints of C isotopes in CaCO₃ and organic C provide additional constraints on the relative importance of each reaction to the total DIC flux. Using Fick's first law, we define the flux of DIC across the SWI and across the 10 cm horizon (Table 2). We use measured δ¹³C of DIC to calculate the relative sources of DIC, analyzed using Keeling plots with porewater data (Keeling, 1958). The curve-fit δ¹³C values were plotted against the inverse of the curve-fit DIC concentrations, with the y-intercept from points in sub-cm intervals indicating the isotopic value of the added DIC (Figure S3). This is a similar approach to that of Martin et al. (2000), in which the Keeling plot was segmented by

<figure><img src="image_14.png" /><figcaption>Fig. 10. Solid phase properties: (a) wt% CaCO₃, (b) wt% organic C, and (c) porosity.</figcaption></figure>
<caption>**Table 2**</caption>

Three dissolution fluxes (µmol/m²/day), across the SWI and 10 cm, determined by alkalinity fluxes, δ¹³C of DIC and DIC fluxes, and Ca fluxes. CaCO₃ dissolution flux via alkalinity is corrected to account for alkalinity production/consumption from other processes (see Section 5.4). 1σ in parentheses.

<table><caption>Fluxes Across the Sediment-Water Interface</caption><thead><tr><th rowspan="2">Station<br>(water<br>depth)</th><th rowspan="2">Alkalinity</th><th rowspan="2">DIC</th><th rowspan="2">Calcium</th><th colspan="3">CaCO<sub>3</sub> Dissolution Fluxes</th></tr><tr><th>Via<br>Alkalinity</th><th>Via<br>δ¹³C of<br>DIC</th><th>Via<br>Ca</th></tr></thead><tbody><tr><td>1<br>(3223 m)</td><td>150<br>(82)</td><td>180<br>(68)</td><td>41<br>(26)</td><td>36<br>(22)</td><td>38<br>(15)</td><td>41<br>(26)</td></tr><tr><td>5<br>(2911 m)</td><td>250<br>(69)</td><td>240<br>(81)</td><td>110<br>(27)</td><td>69<br>(17)</td><td>83<br>(29)</td><td>110<br>(27)</td></tr><tr><td>2<br>(2650 m)</td><td>260<br>(100)</td><td>300<br>(120)</td><td>67<br>(33)</td><td>76<br>(26)</td><td>130<br>(56)</td><td>67<br>(33)</td></tr><tr><td>3<br>(1630 m)</td><td>210<br>(77)</td><td>240<br>(97)</td><td>63<br>(22)</td><td>61<br>(20)</td><td>110<br>(48)</td><td>63<br>(22)</td></tr></tbody></table>

<table><caption>Fluxes Across 10 cm</caption><thead><tr><th rowspan="2">Station<br>(water<br>depth)</th><th rowspan="2">Alkalinity</th><th rowspan="2">DIC</th><th rowspan="2">Calcium</th><th colspan="2">CaCO<sub>3</sub> Dissolution Fluxes</th></tr><tr><th>Via δ¹³C of DIC</th><th>Via Ca</th></tr></thead><tbody><tr><td>1<br>(3223 m)</td><td>56<br>(24)</td><td>46<br>(9.4)</td><td>9.9<br>(4.8)</td><td>14<br>(5.2)</td><td>9.9<br>(4.8)</td></tr><tr><td>5<br>(2911 m)</td><td>55<br>(5.6)</td><td>44<br>(7.2)</td><td>18<br>(2.7)</td><td>7.0<br>(2.5)</td><td>18<br>(2.7)</td></tr><tr><td>2<br>(2650 m)</td><td>30<br>(8.7)</td><td>23<br>(11)</td><td>11<br>(4.1)</td><td>3.4<br>(1.4)</td><td>11<br>(4.1)</td></tr><tr><td>3<br>(1630 m)</td><td>24<br>(6.4)</td><td>21<br>(8.3)</td><td>9.7<br>(2.7)</td><td>9.5<br>(4.0)</td><td>9.7<br>(2.7)</td></tr></tbody></table>

depth horizon. This isotopic mass balance was used to calculate the relative contributions to DIC from two-members:

$$ \delta^{13}C_{\text{DIC}} = (\delta^{13}C_{\text{CaCO}_3})f_{\text{CaCO}_3} + (\delta^{13}C_{\text{org}})f_{\text{org}} \quad (5) $$

where the fractions *f* of DIC contribution sum to 1. End-member δ¹³C values are assigned as the mean δ¹³C values of CaCO₃ and organic C from all stations (Table S2): δ¹³C of CaCO₃ is 0.86 ‰; δ¹³C of organic C is -19.5 ‰. The sensitivity of Equation (5) to the chosen end-member values of δ¹³C for CaCO₃ and organic C is small: a δ¹³C change of 0.15‰ (our analytical uncertainty) yields an 8% change in flux, which is smaller than the overall flux uncertainty. The Keeling intercept does not change much in the top 5 cm (Figure S3), so this calculation is not sensitive to the chosen depth interval; here we have solved for the Keeling intercept in the top 2 cm. Solving Equation (5) for *f*<sub>CaCO₃</sub> results in values of 21, 34, 45, and 48% of the DIC produced from 0 to 2 cm is added through CaCO₃ dissolution, at Stations 1, 5, 2, and 3, respectively. We then multiply these fractions to the DIC fluxes at the SWI, resulting in CaCO₃ dissolution fluxes at the SWI. The same calculation was made for the 10 cm sediment horizon, with the DIC flux evaluated at 10 cm and the Keeling intercept for the 10–12 cm interval. The resultant dissolution fluxes at the SWI and 10 cm are reported in Table 2.

### 5.3. Dissolution flux via calcium

The flux of Ca across the SWI is another estimate of net CaCO₃ dissolution, assuming the only source of Ca is from dissolution, a reasonable assumption in the top 35 cm of sediment. Ca flux across the SWI may be sourced from various depths and will be an integral of net dissolution. By calculating the flux of Ca across the SWI and at 10 cm, we establish an estimate of net dissolution fluxes, as reported in Table 2.

### 5.4. Dissolution flux via alkalinity

The alkalinity flux across the SWI can be converted to a CaCO₃ dissolution flux, assuming all porewater alkalinity derives from

dissolution (Emerson et al., 1982; Berelson et al., 1990a). However, this will be an over-estimate of dissolution as there are organic C respiration reactions that produce alkalinity below the oxygenated zone and this alkalinity will diffuse upward. Additionally, alkalinity can be consumed in the oxic zone due to oxidation of reduced organic species. Because of the numerous diagenetic reactions impacting alkalinity, the alkalinity flux alone may not be the best approach to estimate dissolution fluxes. Several corrections were applied to the alkalinity flux to account for these added sources and sinks. First, the deep Ca flux was subtracted from the deep alkalinity flux; as the Ca flux can be reasonably assumed to be sourced only from dissolution, this subtraction results in the alkalinity flux due to organic C respiration occurring below 10 cm. Then, this deep alkalinity flux due to organic C respiration was subtracted from the SWI alkalinity flux. Second, the Keeling approach was used to estimate the amount of DIC produced from oxic respiration, which can then be converted to alkalinity consumed by assuming Redfield stoichiometry (Redfield et al., 1963). This consumed alkalinity was then added into the SWI alkalinity flux (Table S3, Table S4). This results in the SWI alkalinity flux due to dissolution, having accounted for the alkalinity produced and consumed by other sources. The total alkalinity fluxes across the SWI and 10 cm are listed in Table 2. We include the dissolution flux via alkalinity with its corrections in order to show its general agreement with the two other approaches (Table 2), but do not include it in the final dissolution estimates because its numerous sources and sinks preclude alkalinity from being the most reliable estimate of dissolution. However, comparing the dissolution flux via alkalinity flux after these corrections, it is shown that this approach yields a flux estimate comparable to those of the Keeling and Ca approaches (Table 2).

### 5.5. Comparison of three approaches to calculating dissolution fluxes

Table 3 summarizes net CaCO₃ dissolution in Cocos Ridge sediments. The net CaCO₃ dissolution fluxes across the SWI are reported, as averaged from the δ¹³C of DIC and Ca approaches to calculation dissolution fluxes. The simple mean of these two approaches was calculated, and a Monte Carlo simulation was run to calculate standard deviation of the two approaches, considering the standard deviations of the individual fluxes, resulting in mean dissolution fluxes with standard deviations <38% (Table 3).

Station 1 exhibits the lowest dissolution fluxes across the SWI, while Stations 5, 2, and 3 exhibit larger and comparable dissolution fluxes. Our results indicate that dissolution is occurring at all sites, including where bottom water is saturated for calcite at Station 3. The four stations range in bottom water Ω<sub>calcite</sub> from 0.84 to 1.0, yet there is no correlation between net dissolution flux and bottom water calcite saturation state, nor water column depth. Stations 1, 5, and 2 have comparable bottom water O₂ (100–105 µM), while Station 3 has 75 µM O₂, but dissolution flux also does not correlate with bottom water O₂. Sedimentary POC content is higher at the deeper sites (Stations 1 and 5, Fig. 10.b), which could be due to lateral transport (F. Pavia, pers. comm.), and could assist in CaCO₃ dissolution at these sites.

<caption>**Table 3**</caption>

Net dissolution flux across the SWI and fraction of net flux occurring below 10 cm. Both metrics are defined as the average of the δ¹³C of DIC and Ca approaches to calculating dissolution fluxes. 1σ in parentheses.

<table><thead><tr><th>Station (water depth)</th><th>Net CaCO<sub>3</sub> dissolution flux across SWI (µmol/m<sup>2</sup>/day)</th><th>Fraction of net CaCO<sub>3</sub> dissolution below 10 cm</th></tr></thead><tbody><tr><td>1<br>(3223 m)</td><td>40<br>(15)</td><td>0.30<br>(0.14)</td></tr><tr><td>5<br>(2911 m)</td><td>98<br>(20)</td><td>0.12<br>(0.033)</td></tr><tr><td>2<br>(2650 m)</td><td>100<br>(32)</td><td>0.092<br>(0.039)</td></tr><tr><td>3<br>(1630 m)</td><td>89<br>(27)</td><td>0.12<br>(0.043)</td></tr></tbody></table>
Included in Table 3 is the proportion of dissolution occurring below 10 cm, as averaged from the δ¹³C of DIC and Ca approaches. The δ¹³C of DIC and the calcium gradient approaches are more representative of the deep dissolution fluxes because the Keeling analysis of δ¹³C of DIC considers only the isotopic value of CaCO₃, and there are few reactions occurring in the top 35 cm that add or remove Ca other than CaCO₃ dissolution. A possible caveat is that recrystallization between calcite, aragonite, or biogenic carbonates would complicate the Keeling isotope and mass balance approach by exchanging ¹³C between solids and porewater, while the ratio of alkalinity to DIC would remain the same. However, the agreement between the Keeling and Ca approaches to calculating dissolution flux leads us to believe that there is no significant recrystallization occurring, or if there is, then it does not significantly alter the δ¹³C of DIC mass balance.

The proportion of dissolution occurring below 10 cm illustrates that at Station 1, 30% of the net dissolution flux at the SWI is coming from dissolution deeper in the sediment. The increase in CaCO₃ around 15 cm at Station 1 (Fig. 10a) could provide the CaCO₃ for deep dissolution. At the other three sites, dissolution below 10 cm is also occurring, but it provides about only 10% to the flux across the SWI.

## 5.6. Porewater saturation state

The saturation state (Ω) of a particular mineral phase, in this case calcite or aragonite, is a thermodynamic threshold controlling the onset of dissolution. However, there are examples of dissolution occurring where Ω ≥ 1, for both aragonite and calcite in both the water column and in sediments (Milliman et al., 1999; Dong et al., 2019; Lunstrum and Berelson, 2022; Subhas et al., 2022). We believe we report the first porewater Ω$_{calcite}$ at cm-scale resolution in deep-sea sediments that spans overlying water to 35 cm depth (Fig. 11).

At all stations, Ω$_{calcite}$ initially decreases from bottom water values before increasing deeper in the sediment. Minimum Ω$_{calcite}$ values are 0.48, 0.72, 0.87, and 0.89 for Stations 1, 5, 2, and 3, respectively, and appear 3–8 cm below the SWI. Ω$_{calcite}$ at two sites (Stations 1 and 5) dips below a critical omega threshold (0.75–0.80) where the mechanism of dissolution is known to change from step retreat to homogeneous etch pit formation (Naviaux et al., 2019a). These changes in dissolution mechanisms may be responsible for promoting higher rates of dissolution within the sediment column.

Ω$_{calcite}$ values at 15–35 cm at Stations 1, 2, and 3 approach and appear to stabilize around Ω$_{calcite}$ = 1, indicating the dominant phase

controlling carbonate equilibria in these sediments is likely calcite (with thermodynamic properties as defined by CO2SYS). Station 5, however, approaches higher Ω$_{calcite}$ values that surpass calcite saturation.

To further investigate the mineral solubility control on CaCO₃ and the reason for porewater Ω$_{calcite}$ increasing >1 at Station 5, we explored the possibility of aragonite presence in the sediment. Although all stations are deeper than the aragonite saturation horizon, there has been evidence of aragonite present below 3000 m in the North Pacific water column, likely due to the fast-sinking aragonite particles that are relatively large (Dong et al., 2019). We used two methods to determine the presence of aragonite in sediment. X-ray diffraction (XRD) was used to identify sediment mineralogy. Samples from the top 1 cm of sediment were measured at each station, plus four additional depth horizons at Station 5 up to 35 cm. No aragonite was detected in the sediment samples; only calcite and quartz were detected (clay minerals were not assessed). It is possible that aragonite was present below XRD detection limits (<1%), or that aragonite is present below 35 cm, the maximum depth of the collected sediment core. Such a case could occur if an extreme sedimentation event delivered aragonite to the seafloor that was buried and preserved faster than it could dissolve. We do not dismiss the possibility that a carbonate mineral with a solubility other than calcite or aragonite as defined by CO2SYS (Mucci, 1983) is present in these sediments, such as biogenic carbonates, although it would be unlikely that it was only present at Station 5.

A second method to detect the presence of aragonite was through measurements of porewater dissolved strontium, given aragonite contains much more Sr than calcite (Dodd, 1967). Stations 1, 2, and 3 show minimal to no increase in [Sr] from bottom water to 35 cm (Fig. 12). However, Station 5 shows increasing [Sr] with depth and does not appear to reach an asymptotic value. This [Sr] profile is consistent with the interpretation that aragonite is present >35 cm, potentially supplied by coral and platform carbonate input from Cocos Ridge and Cocos Island. It is also possible that neomorphism/recrystallization has occurred at Station 5, in which aragonite is converted to calcite during diagenesis, or vice versa, and subsequently expels Sr (Katz et al., 1972). Based on recrystallization rates as high as 40%/Mya (Fantle & DePaolo, 2007), our deepest sediment column samples (~20,000 years old, Figure S4) could have been subjected to recrystallization on the order of 1%. Even this small amount of calcite recrystallization could provide the [Sr] increase we measure in porewater. However, the fact that Ω$_{calcite}$ increases to >1 at Station 5, coupled with the increased [Sr], still points toward aragonite dissolution as the cause, as recrystallization will not change Ω$_{calcite}$. Regarding the proposal that aragonite buffers calcite dissolution in sediments (Sulpis et al, 2022), we do not see evidence of this at

<figure><img src="image_15.png" /></figure>

**Fig. 11.** Porewater profiles of calcite saturation state, calculated from alkalinity and pH. Arrows denote bottom water Ω$_{calcite}$ at each station. Vertical lines show Ω = 1 for both calcite and aragonite. Error bars are smaller than the size of the symbols; 1σ of duplicate measurements range from 0.001 to 0.003 Ω$_{calcite}$.

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

**Fig. 12.** Porewater [Sr], following normalization to [Na]. Arrows denote bottom water values. Error bars are 1 standard error of five replicate analyses.
Stations 1, 2, or 3, but perhaps see this phenomenon at Station 5 where [Sr] increases deeper in the sediment.

## 5.7. Respiration-driven dissolution

Our results confirm the hypothesis of Emerson and Bender (1981), that sedimentary carbonate dissolution occurs when $\Omega = 1$ through acid production during aerobic respiration (Equation (1)). The evidence presented here includes:

1. Dissolution observed at all four stations (Table 2), including at Station 3 where bottom water $\Omega_{calcite} = 1$ (Fig. 11)

2. At all stations, porewater pH and $\Omega_{calcite}$ decrease from bottom water values (Figs. 8 and 11, respectively), indicating that diagenetic reactions are occurring in the top 5 cm to lower pH and saturation state. Because sediments are oxygenated in this zone, undersaturation here is most likely driven by acid production via aerobic respiration.

3. The Keeling isotopic analysis indicates addition of DIC with contributions from both organic C and CaCO$_3$ at depths between 0–10 cm (Figure S3).

4. Dissolved Ca profiles show gradients deeper in the sediment (Fig. 7), indicating dissolution is occurring below the SWI.

These observations and our interpretation suggest that bottom water oxygen, organic C, and inorganic C delivery to the seafloor control dissolution fluxes and that dissolution within the sediment can occur, even where bottom water is saturated or very close to saturation with respect to calcite. Our results speak to the debate regarding precisely where seafloor dissolution is occurring, whether within the sediment or in the boundary layer between the water column and sediment (Boudreau, 2013). We show, using well-constrained carbonate chemistry profiles, that there is alkalinity, DIC, and Ca production occurring below the SWI to at least 10 cm and likely deeper, indicating carbonate dissolution is occurring within sediments. The profiles of porewater $\Omega_{calcite}$ also point to a driving force for dissolution occurring within the sediment. This evidence shows that organic carbon respiration within the sediment, not just at the SWI, drives carbonate dissolution.

## 5.8. Sedimentary mass balance

Mass accumulation rates were determined using $\Delta^{14}$C calendar ages of three planktonic foraminifera: *G. ruber*, *T. sacculifer*, and *N. dutertrei* measured at 2–3 depths in each core (Figure S4). Assuming linear sedimentation rates, a line was fit through the $\Delta^{14}$C ages in the top 35 cm to establish sedimentation rates. Dry bulk density was calculated via core-top porosity using the equation defined in Snoeckx and Rea (1994). Dry bulk density of the sediment (g/cm$^3$) multiplied by the linear sedimentation rates (cm/kyr) results in mass accumulation rates (g/cm$^2$/kyr). From these mass accumulation rates and the dissolution fluxes described above, we built a sedimentary CaCO$_3$ budget for the Cocos

Ridge region (Table 4). CaCO$_3$ accumulation rate is calculated by applying the core-top wt% CaCO$_3$ to the mass accumulation rate. CaCO$_3$ rain rate is calculated as the sum of CaCO$_3$ accumulation rate and CaCO$_3$ dissolution flux, which assumes a steady state flux balance. CaCO$_3$ burial efficiency is defined as the fraction of CaCO$_3$ accumulation rate to the CaCO$_3$ rain rate. Because these rates are based on only 2–3 points, the uncertainty of the burial efficiency will be largely dominated by the uncertainty in the sedimentation rate.

While dissolution flux is not correlated with water depth or saturation state, CaCO$_3$ burial efficiency is well-correlated with water depth. Burial efficiency increases ten-fold when moving only 300 m shallower from Station 1 to 5. This implies the sensitivity of the sedimentary lysocline depends more on aerobic respiration and CaCO$_3$ delivery to the seafloor than to bottom water $\Omega_{calcite}$. We also show that 33% of the carbonate falling to the seafloor at 1600 m dissolves before burial, even though this station sits at the calcite saturation horizon.

Surface ocean biological productivity can impact deep-sea dissolution, as the relative rain rates of CaCO$_3$ and organic C impact dissolution and preservation (Emerson and Bender, 1981; Archer and Maier-Reimer, 1994). However, biological productivity at these four stations appears equally low, as suggested by satellite-derived mean ocean chlorophyll (~0.1 mg Chl/m$^3$) in this region (Pennington et al., 2006). The input rate of organic C into these sediments is not part of the scope of this study, but it is clearly coupled to the overall dissolution fluxes we have established based on the carbon isotope evidence. Sediment mixing, incorporation of organic C into the sediment column, and organic C reactivity are also likely factors that affect the preservation of CaCO$_3$.

The mass balance described in Table 4 assumes that the system is balanced, i.e., rain to the seafloor = burial + dissolution. However, if net chemical erosion of deeper carbonate sediments is occurring at Station 1 or elsewhere, then this part of the ocean is not a balanced system. Our interpretation is that Station 1 is out of balance and is undergoing net erosion of deeper CaCO$_3$ sediment. Supporting this statement are 1) 30% of the net dissolution flux is occurring below 10 cm (Table 3), 2) the lack of CaCO$_3$ in the upper 10 cm (< 2%) (Fig. 10), and 3) low $\Omega_{calcite}$ porewater values persist to at least 20 cm (Fig. 11), below the depth where wt% CaCO$_3$ increases substantially (Fig. 10). Based on foraminiferal $\Delta^{14}$C ages (Figure S4), this implies that 5,000 to 10,000 year old carbonates are undergoing net dissolution at least 10 cm deep into the sediment column.

## 5.9. Broader geochemical significance

By filtering porewater in situ, we eliminate the pressure and temperature artifacts that diminish the quality of the carbonate system parameters and here provide the first set of accurate, cm-scale resolution, deep-sea porewater profiles of DIC, alkalinity, pH, Ca and $\delta^{13}$C. Interpreting these data in terms of CaCO$_3$ dissolution yields estimates based on different measured profiles and calculations that all agree to $\pm$38%, and thus a robust value of deep-sea CaCO$_3$ dissolution is herein provided. Benthic CaCO$_3$ dissolution fluxes in other regions of the ocean at

<caption>**Table 4**</caption>

Mass balance of Cocos Ridge shallow sediment, including sedimentation rate, mass accumulation rates via $\Delta^{14}$C, CaCO$_3$ accumulation rate, CaCO$_3$ rain rate, and CaCO$_3$ burial efficiency, assuming steady state conditions. Aside from sedimentation rate, rates and fluxes are in g/cm$^2$/kyr. Dissolution fluxes (units converted from Table 3) and subsequent calculations are based on the net dissolution flux across the SWI.

<table><thead><tr><th>Station (water depth)</th><th>Sedimentation rate (cm/kyr)</th><th>Mass accumulation rate</th><th>0–10 cm Wt% CaCO<sub>3</sub></th><th>CaCO<sub>3</sub> accumulation rate</th><th>CaCO<sub>3</sub> dissolution flux</th><th>CaCO<sub>3</sub> rain rate</th><th>CaCO<sub>3</sub> burial efficiency (%)</th></tr></thead><tbody><tr><td>1 (3223 m)</td><td>1.3</td><td>0.42</td><td>0.72</td><td>0.0030</td><td>0.15</td><td>0.15</td><td>2.0</td></tr><tr><td>5 (2911 m)</td><td>1.3</td><td>0.49</td><td>18</td><td>0.089</td><td>0.36</td><td>0.45</td><td>20</td></tr><tr><td>2 (2650 m)</td><td>1.7</td><td>0.90</td><td>61</td><td>0.54</td><td>0.37</td><td>0.91</td><td>60</td></tr><tr><td>3 (1630 m)</td><td>1.3</td><td>0.85</td><td>78</td><td>0.66</td><td>0.33</td><td>0.99</td><td>67</td></tr></tbody></table>
similar water depths and saturation states range from 0.15 to 0.7 mmol CaCO₃/m²/day (Berelson et al., 1990a; Berelson et al., 1994; Martin and Sayles, 1996; Berelson et al., 2007). The dissolution fluxes presented in this study, 0.040 – 0.100 mmol CaCO₃/m²/day, are on the lower boundary of these previously published flux estimates but are overall comparable. The buffering capacity provided by deep-sea dissolution of CaCO₃ is not primarily controlled by bottom water carbonate saturation, but by acid generated by organic carbon respiration within the sediment column. We show that respiration via oxygen consumption is driving dissolution from sediments buried as deep as 10 cm, thus, at some sites, mid-Holocene to early glacial sediments are dissolving today.

## 6. Conclusions

We present the results and interpretations of in situ collected pore-water profiles from the Cocos Ridge in the eastern equatorial Pacific on a transect spanning the saturation horizon. We measured alkalinity, pH, DIC, δ¹³C of DIC, [Ca], [Sr], and [Mn], and calculated Ωcalcite in pore-water from 0 to 35 cm and solid phase porosity, % CaCO₃, δ¹³C of PIC, % organic C, and δ¹³C of organic C. Through profile curve fitting and application of Fick's first law, we calculated fluxes of alkalinity, DIC, and [Ca] from the sediment to the water column. From those gradients, we derived two independent estimates for CaCO₃ dissolution fluxes using stable C isotopes of DIC and Ca fluxes. We report seafloor dissolution fluxes with uncertainties <38%: 40 ± 15, 98 ± 20, 100 ± 32, and 89 ± 27 µmol CaCO₃/m²/day at sites 3200, 2900, 2700, and 1600 m deep, respectively. We observe the lowest CaCO₃ dissolution flux at the deepest and most undersaturated site (Ω= 0.84), and higher but comparable dissolution fluxes at the remaining sites ranging from 2900 to 1600 m with Ωcalcite values of 0.89–1. Respiration-driven CaCO₃ dissolution occurs at all sites, including at Station 3 where bottom water is saturated with respect to calcite. These dissolution fluxes are not a simple function of water depth, bottom water omega, bottom water oxygen, or surface ocean biological productivity. Therefore, dissolution must be attributed to diagenetic reactions occurring in the sediments rather than simply reflecting bottom water properties.

At our deepest site, 30% of the net CaCO₃ dissolution flux is from dissolution that occurs below 10 cm, providing evidence of the carbonate burn-down effect, i.e., net erosion of older sedimentary CaCO₃ (Broecker et al., 1991). Dissolution of deeply buried carbonate sediment requires a non-steady-state interpretation of deep-sea carbonate budgets. Dissolution of these older carbonates will also impact the Δ¹⁴C of DIC in the deep ocean (Druffel et al., 2008).

Bottom water oxygen content and the lability and distribution of organic C within the sediments may be important co-factors of dissolution. Based on the approaches above that provide estimates of the relative fractions of aerobic respiration and dissolution contributions to DIC, we attribute 21–48% of the observed DIC flux across the SWI to respiration-driven dissolution. When making global estimates of the potential for seafloor dissolution, and in interpreting the shape of the lysocline, bottom water carbonate saturation state alone is not sufficient to predict the presence or magnitude of CaCO₃ dissolution in a given region.

### CRediT authorship contribution statement

**Jaclyn E.P. Cetiner:** Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **William M. Berelson:** Writing – review & editing, Supervision, Resources, Methodology, Investigation, Funding acquisition, Conceptualization. **Nick E. Rollins:** Writing – review & editing, Methodology, Data curation. **Xuewu Liu:** Writing – review & editing, Methodology, Data curation. **Frank J. Pavia:** Writing – review & editing, Methodology, Investigation, Data curation. **Anna R. Waldeck:** Writing – review & editing, Methodology, Data curation. **Sijia Dong:** Writing – review & editing, Methodology, Investigation, Data curation. **Kalla Fleger:**

Writing – review & editing, Methodology, Data curation. **Holly A. Barnhart:** Writing – review & editing, Methodology, Data curation. **Matthew Quinan:** Writing – review & editing, Data curation. **Rucha P. Wani:** Writing – review & editing, Visualization. **Patrick A. Rafter:** Writing – review & editing, Methodology, Data curation. **Andrew D. Jacobson:** Writing – review & editing, Supervision, Resources. **Robert H. Byrne:** Writing – review & editing, Supervision, Resources. **Jess F. Adkins:** Writing – review & editing, Supervision, Resources, Investigation, Funding acquisition, Conceptualization.

### Data availability

Data are stored in the BCO-DMO database in the following four datasets:

**Porewater:** Cetiner, J. E., Berelson, W. M., Rollins, N. E., Liu, X., Pavia, F. J., Waldeck, A., Dong, S., Fleger, K., Barnhart, H., Quinan, M., Wani, R., Jacobson, A., Byrne, R., Adkins, J. F. (2024) In situ porewater data from the Cocos Ridge (Eastern Equatorial Pacific) acquired during cruise SR2113 between November - December 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-06-21. https://demo.bco-dmo.org/dataset/925487

**Solid phase:** Cetiner, J. E., Berelson, W. M., Rollins, N. E., Dong, S., Adkins, J. F. (2024) Solid phase measurements of sediment cores from the Cocos Ridge (Eastern Equatorial Pacific) acquired during cruise SR2113 between November - December 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-06-20. https://demo.bco-dmo.org/dataset/925132

**Water column:** Cetiner, J. E., Berelson, W. M., Rollins, N. E., Liu, X., Dong, S., Fleger, K., Barnhart, H., Byrne, R., Adkins, J. F., Sanchez Noguera, C. (2024) Water column data from the Cocos Ridge (Eastern Equatorial Pacific) acquired during cruise SR2113 between November - December 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-06-20. https://demo.bco-dmo.org/dataset/925367

**C14:** Cetiner, J. E., Berelson, W. M., Pavia, F. J., Quinan, M., Rafter, P., Adkins, J. F. (2024) Radiocarbon (C14) calendar ages measured on pelagic foraminifera species collected from sediment cores from the Cocos Ridge (Eastern Equatorial Pacific) acquired during cruise SR2113 between November - December 2021. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-06-20. https://demo.bco-dmo.org/dataset/925313

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

This work was supported by NSF Ocean Acidification (OCE-1834475 to WMB and JFA). JEPC thanks USC Earth Sciences for additional PhD funding for this work. We thank Aaron Celestian (Natural History Museum of Los Angeles) and Menglong Zhang (Nanjing University) for assistance with XRD mineralogy. We thank Per Hall (University of Gothenburg) and his group for help with design questions regarding syringe type, and the USC Machine Shop, specifically Seth Wieman, for their design and construction efforts on the porewater sampler. Metal analysis was performed at the Northwestern University Quantitative Bio-element Imaging Center. We thank Doug Hammond and Naomi Levine (USC) for helpful comments and insights on the manuscript. We thank the editors of GCA, as well as Andy Dale and two anonymous reviewers, whose thoughtful questions and comments greatly improved the manuscript. Thank you to Emma Johnson for assistance with the porewater sampler during the CDSIP 2021 cruise. Thank you to the
captain and crew of the R/V Yellowfin at the Southern California Marine Institute for helping us test our equipment many, many times. The science party, captain, and crew of R/V Sally Ride SR2113 cruise were indispensable in the collection of the data presented here, and we sincerely thank them all.

## Appendix A. Supplementary material

The Supplementary Material includes ICP-OES IAPSO seawater consistency standards used to normalize porewater calcium and strontium (Figure S1), a comparison of our measured water column alkalinity versus GLODAP data to validate our measurements (Figure S2), alkalinity and DIC fluxes calculated with additional fitting equations, including discussion (Table S1), core top solid phase with % TC, % PIC, % POC, and the δ¹³C values of each, some of which were used as end-members in the Keeling analyses (Table S2), DIC fluxes including DIC produced from oxic respiration (Table S3), alkalinity fluxes including alkalinity consumed by oxic respiration and how that impacts the dissolution flux via alkalinity approach (Table S4), Keeling analyses with δ¹³C of DIC used to calculate CaCO₃ dissolution fluxes (Figure S3), and planktonic foraminifera ages determined by Δ¹⁴C to calculate sedimentation rate for the sedimentary mass balance (Figure S4).

Supplementary material to this article can be found online at https://doi.org/10.1016/j.gca.2024.11.027.

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