# Biological Responses to Ocean Acidification Are Changing the Global Ocean Carbon Cycle

R C Barrett, B R Carter, A J Fassbender, B. Tilbrook, R J Woosley, K

Azetsu-scott, R A Feely, Goyet C., M. Ishii, A. Murata, et al.

► To cite this version:

R C Barrett, B R Carter, A J Fassbender, B. Tilbrook, R J Woosley, et al.. Biological Responses to Ocean Acidification Are Changing the Global Ocean Carbon Cycle. Global Biogeochemical Cycles, 2025, 39 (3), 10.1029/2024gb008358. [https://doi.org/10.1029/2024gb008358](https://doi.org/10.1029/2024gb008358). [arXiv:2403.00835](https://arxiv.org/abs/2403.00835)

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Distributed under a Creative Commons Attribution 4.0 International License
# Global Biogeochemical Cycles®

## RESEARCH ARTICLE

10.1029/2024GB008358

**Key Points:**

* We find evidence of a long-term trend in alkalinity in the surface ocean using measurements and seawater property prediction algorithms

* We determine this trend is likely driven by the CO₂-biotic calcification feedback, a hypothesized negative feedback on atmospheric CO₂

* Ocean chemistry is changing globally due to shifting patterns in the ecology of calcifying organisms caused by anthropogenic CO₂ emissions

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

R. C. Barrett,  
rcbarr1@uw.edu

**Citation:**

Barrett, R. C., Carter, B. R., Fassbender, A. J., Tilbrook, B., Woosley, R. J., Azetsu-Scott, K., et al. (2025). Biological responses to ocean acidification are changing the global ocean carbon cycle. *Global Biogeochemical Cycles*, *39*, e2024GB008358. https://doi.org/10.1029/2024GB008358

Received 13 SEP 2024

Accepted 24 FEB 2025

**Author Contributions:**

**Conceptualization:** R. C. Barrett, B. R. Carter, A. J. Fassbender, B. Tilbrook, R. J. Woosley

**Data curation:** R. C. Barrett

**Formal analysis:** R. C. Barrett, B. R. Carter

**Funding acquisition:** B. R. Carter

**Investigation:** R. C. Barrett

**Methodology:** R. C. Barrett, B. R. Carter

**Project administration:** R. C. Barrett, B. R. Carter

**Resources:** B. R. Carter

**Software:** R. C. Barrett, B. R. Carter

**Supervision:** B. R. Carter

**Validation:** R. C. Barrett

**Visualization:** R. C. Barrett

© 2025. The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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## Biological Responses to Ocean Acidification Are Changing the Global Ocean Carbon Cycle

R. C. Barrett¹ ID, B. R. Carter²,³ ID, A. J. Fassbender² ID, B. Tilbrook⁴,⁵ ID, R. J. Woosley⁶ ID, K. Azetsu-Scott⁷ ID, R. A. Feely² ID, C. Goyet⁸, M. Ishii⁹ ID, A. Murata¹⁰ ID, and F. F. Pérez¹¹ ID

¹School of Oceanography, University of Washington, Seattle, WA, USA, ²Pacific Marine Environmental Laboratory, Seattle, WA, USA, ³Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA, USA, ⁴Environment Research Unit, Commonwealth Scientific and Research Organisation (CSIRO), Hobart, TAS, Australia, ⁵Australian Antarctic Program Partnership, University of Tasmania, Hobart, TAS, Australia, ⁶Department of Earth, Atmospheric, and Planetary Sciences, Center for Sustainability Science and Policy, Massachusetts Institute for Technology, Cambridge, MA, USA, ⁷Department of Fisheries and Oceans, Bedford Institute of Oceanography, Dartmouth, NS, Canada, ⁸Espace-Dev UMR 228, Université de Perpignan Via Domitia, IRD, UM, UA, UG, Perpignan, France, ⁹Climate and Geochemistry Research Department, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan, ¹⁰Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan, ¹¹Instituto de Investigaciones Marinas (IIM), CSIC, Vigo, Spain

**Abstract** Increased oceanic uptake of CO₂ due to rising anthropogenic emissions has caused lowered pH levels (ocean acidification) that are hypothesized to diminish biotic calcification and reduce the export of total alkalinity (A_T) as carbonate minerals from the surface ocean or their burial in coastal sediments. This “CO₂-biotic calcification feedback” is a negative feedback on atmospheric CO₂, as elevated levels of surface A_T increase the ocean's capacity to uptake CO₂. We detect signatures of this feedback in the global ocean for the first time using repeat hydrographic measurements and seawater property prediction algorithms. Over the course of the past 30 years, we find an increase in global surface A_T of 0.072 ± 0.023 µmol kg⁻¹ yr⁻¹, which would have caused approximately 20 Tmol of additional A_T to accumulate in the surface ocean. This finding suggests that anthropogenic CO₂ emissions are measurably perturbing the cycling of carbon on a planetary scale by disrupting biological patterns. More observations of A_T would be required to understand the effects of this feedback on a regional basis and to fully characterize its potential to reduce the efficiency of marine carbon dioxide removal technology.

**Plain Language Summary** Human activities are causing more carbon dioxide (CO₂) to be absorbed by the oceans from the atmosphere, leading to decreasing ocean pH levels (ocean acidification). Acidification slows down biotic calcification, the process by which many marine organisms build their shells and skeletons. Lowered biotic calcification is hypothesized to reduce the carbon moving from the ocean's surface to the deep when these organisms die and sink. This decrease in the amount of sinking shells leads to a buildup in total alkalinity (A_T) in surface waters, which helps the ocean absorb more CO₂—a natural feedback mechanism that could limit the rise of atmospheric CO₂. We have identified signs of this feedback in the global ocean. Our findings show that the A_T in the ocean's surface is increasing by 0.072 ± 0.023 µmol per kilogram per year, which would have caused the amount of human-emitted carbon in the ocean to increase by about 0.20 PgC since the 1990s. This shows that the chemistry of the oceans is changing as human-produced CO₂ emissions cause shifts in the patterns of life and death of some marine organisms. More data on A_T is needed to better quantify this feedback and its impacts.

## 1. Introduction

From the 1960s to the present day, the Earth's oceans have absorbed about 25% of anthropogenic carbon dioxide (CO₂) emissions, reducing atmospheric CO₂ concentrations and slowing the rate of climate change (Friedlingstein et al., 2023; Gruber et al., 2023). The resulting increased levels of CO₂ in the ocean have caused a global pH reduction in seawater, termed ocean acidification, which has begun to alter marine ecosystems and put the human communities that rely on them at risk (Doney et al., 2009, 2020; Gattuso et al., 2015). Biotic calcification by organisms such as coccolithophores, pteropods, foraminifera, and corals remove carbon from the surface ocean in the form of calcium carbonate (CaCO₃) and other carbonate minerals and export it to the deep ocean (Berelson et al., 2007; Boudreau et al., 1999, 2018; Subhas et al., 2022; Milliman, 1993; Milliman et al., 1999). However, it
**Writing – original draft:** R. C. Barrett,
B. R. Carter

**Writing – review & editing:** R. C. Barrett,
B. R. Carter, A. J. Fassbender, B. Tilbrook,
R. J. Woosley, K. Azetsu-Scott,
R. A. Feely, C. Goyet, M. Ishii, A. Murata,
F. F. Pérez

has been hypothesized that ocean acidification could reduce this “CaCO₃ pump” by damaging calcifying organisms, disfavoring CaCO₃ production, and speeding CaCO₃ dissolution (Heinze, 2004; Ilyina et al., 2009; Zondervan et al., 2001).

Total alkalinity ($A_T$), or the excess of hydrogen ion acceptors over hydrogen ion donors in seawater, is a parameter that describes the carbonate system of seawater and does not change with air-sea $CO_2$ exchange (Dickson, 1981). However, the concentration of $A_T$ does shift with changes in biotic calcification and carbonate mineral export. If carbonate mineral export rates are decreasing, less $A_T$ is transported to the depth, which would cause a relative buildup of $A_T$ in the surface ocean. Trends in $A_T$, therefore, can be used to assess whether patterns in biotic calcification and export are changing over time.

Theoretically, an increase in surface $A_T$ due to lowered calcification rates would enhance the ocean's buffering capacity and increase the drawdown of $CO_2$, dampening atmospheric $CO_2$ rise. This “$CO_2$-biotic calcification feedback” would cause greater uptake of $CO_2$ into surface waters and the ocean interior (Ridgwell et al., 2007; Zondervan et al., 2001). However, the magnitude of the response is uncertain and would vary regionally based on the background buffering capacity. Models show that the $CO_2$-biotic calcification feedback may even play a larger role in regulating ocean $CO_2$ content than the well-established $CO_2$-ocean warming feedback, where increased seawater temperatures reduce the amount of inorganic carbon in the ocean present at air-sea $CO_2$ equilibrium (Zhang & Cao, 2016). Furthermore, Earth System Model outputs show differences in both magnitude and sign of the trend in surface alkalinity over time, meaning that existing parameterizations may not adequately represent this feedback (Jiang et al., 2023).

In this work, repeat hydrographic sections of total alkalinity measurements ($A_T$) are used in conjunction with seawater property prediction algorithms to detect trends in $A_T$ consistent with the $CO_2$-biotic calcification feedback in observational data sets. This phenomenon is hypothesized to have emerged, or become statistically significant above natural variability, only in this decade (Schlunegger et al., 2019). In this study, we observe a long-term (>30 years) trend in surface ocean $A_T$, which we find can likely be attributed to a global negative feedback in biotic calcification or carbonate mineral export. This indicates that anthropogenic $CO_2$ emissions are changing the marine carbon cycle's controls on Earth's climate through biological mechanisms.

## 2. Data and Methods

### 2.1. Data Used: GLODAPv2.2023

Data made available by the Global Ocean Data Analysis Project (GLODAP) version 2.2023 were used for this analysis (Lauvset et al., 2022, 2023; Olsen et al., 2019, 2020). GLODAP provides a collection of ship-based ocean biogeochemical data, including marine carbonate system data, that has undergone secondary quality control to improve data product consistency. Cruise records from the current version GLODAPv2.2023 were filtered to include only data from cruises on transects occupied by the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP). All data used in this analysis were collected from cruises associated with GO-SHIP, the World Ocean Circulation Experiment (WOCE)/the Joint Global Ocean Flux Study (JGOFS), Climate and Ocean: Variability, Predictability, and Change (CLIVAR), and Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM).

Data were further filtered to ensure only the highest quality observations were included in this analysis. Only $A_T$ data that were flagged by GLODAP as “2,” meaning “acceptable,” were kept. Additionally, any $A_T$ data that were marked by GLODAP as “calculated” instead of “measured” were excluded. To constrain the analysis to only the open ocean, points with associated salinity measurements that fell outside 30–37 (i.e., estuaries) were excluded. Cruises were trimmed to keep only the data points that were located on GO-SHIP transects, typically omitting only “test cast” stations. Following the filtering and trimming processes, 230,572 $A_T$ measurements spread across 201 total cruises were available for analysis, which were collected between 1 September 1991 and 4 October 2021. Figure 1 shows the spatial coverage of data.

The seawater $A_T$ measurements used in this study have been collected by the global oceanographic community following standard operating procedures (Dickson et al., 2007). These measurements have benefitted from the regular use of a community reference material since the early 1990s (Dickson et al., 2003). The exact titration methods vary between research groups (Carter, Sharp, Dickson, et al., 2024), and there is some indication that different methods can return different values for identical seawater when organic or unidentified contributions to
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Map of locations of $A_T$ measurements (rounded to the nearest 4° of latitude and longitude) available from GLODAPv2.2023 that were used in this analysis following all quality control steps. Coloring shows the number of unique years where measurements are available at each location.</figcaption></figure>

$A_T$ with a $pK_a$ close to approximately four are present (Sharp & Byrne, 2020). The GLODAPv2 adjustments may have accounted for these variations to an unknown degree (Olsen et al., 2019). Inter-laboratory calibration studies show variations between measurements across laboratories (Bockmon & Dickson, 2015). The random variability in a subset of open-ocean repeat hydrographic $A_T$ measurements (which includes cruise-to-cruise variations) has recently been estimated as $(1\sigma) 2 \mu\text{mol kg}^{-1}$ before the GLODAPv2 adjustment procedure (Carter, Sharp, García-Ibáñez, et al., 2024). This is smaller than the uncertainty across all $A_T$ measurements, which is estimated to be $2.4 \mu\text{mol kg}^{-1}$ (Carter, Sharp, García-Ibáñez, et al., 2024). We repeat this analysis here and estimate an uncertainty in $A_T$ of $2 \mu\text{mol kg}^{-1}$ for the subset of cruises involved in the present study (Figure 1). However, we caution that this analysis further limits the cruises considered to the subset of the cruises we use that have overdetermined carbonate chemistry measurements.

## 2.2. Algorithms Used: Empirical Seawater Property Estimation Routines (ESPERs)

Empirical Seawater Property Estimation Routines (ESPERs) are algorithms trained on GLODAPv2.2020 data that are able to predict seawater properties, including $A_T$, at a given location. Salinity, with which $A_T$ has the strongest covariance, and optional additional seawater property information are used to make each prediction (Carter et al., 2021). Two independent algorithms are available—ESPER_LIR, which is based on locally interpolated regressions, and ESPER_NN, which uses a neural network approach.

In this study, we quantify the correlation between time and the evolving difference between $A_T$ measurements and co-located ESPER $A_T$ estimates. ESPERs do not include a long-term temporal component in the prediction of $A_T$, which makes it possible for ESPER-generated predictions to serve as a counterfactual that represents conditions in the ocean under a steady-state scenario. The difference between a measured property of seawater and the ESPER estimate of the same property (henceforth defined for $A_T$ as $\Delta A_T$) allows for the presence and magnitude of any long-term climatic changes in seawater to be assessed. It should be noted that a temporal component in these estimates may be implicit due to long-term trends in the predictors used to produce the estimates (Figure S1 in Supporting Information S1), which would contribute to uncertainty in ESPER predictions. We explore this possibility further when alternative hypotheses are presented (see Section 4). Furthermore, the sign of the trend in $A_T$ over time may be different from the sign of $\Delta A_T$ over time, as $A_T$ is the absolute measurement of total alkalinity, while $\Delta A_T$ represents the difference between the measured and ESPER-predicted $A_T$ value.

This same approach to determining trends in a variable of interest in the marine carbonate system was used in fitting a term in the development of the Locally Interpolated pH Regression (LIPHR) algorithm to account for the temporal effect of ocean acidification on pH predictions (Carter et al., 2018). The method found a global surface ocean acidification rate of $-0.0018 \pm 0.0003$ pH units per year, which compares very closely with independent estimates for a similar time period of $-0.0017 \pm 0.001$ pH units per year (Ma et al., 2023).
<table><caption>**Table 1**
Combinations of Input Parameters Used to Calculate A_T in Each ESPER Equation</caption><thead><tr><th>Equation</th><th>Input properties</th></tr></thead><tbody><tr><td>1</td><td>S<sub>p</sub>, T, NO<sub>3</sub><sup>-</sup>, O<sub>2</sub>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>2</td><td>S<sub>p</sub>, T, NO<sub>3</sub><sup>-</sup>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>3</td><td>S<sub>p</sub>, T, O<sub>2</sub>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>4</td><td>S<sub>p</sub>, T, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>5</td><td>S<sub>p</sub>, T, NO<sub>3</sub><sup>-</sup>, O<sub>2</sub></td></tr><tr><td>6</td><td>S<sub>p</sub>, T, NO<sub>3</sub><sup>-</sup></td></tr><tr><td>7</td><td>S<sub>p</sub>, T, O<sub>2</sub></td></tr><tr><td>8</td><td>S<sub>p</sub>, T</td></tr><tr><td>9</td><td>S<sub>p</sub>, NO<sub>3</sub><sup>-</sup>, O<sub>2</sub>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>10</td><td>S<sub>p</sub>, NO<sub>3</sub><sup>-</sup>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>11</td><td>S<sub>p</sub>, O<sub>2</sub>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>12</td><td>S<sub>p</sub>, SiO<sub>4</sub><sup>4-</sup></td></tr><tr><td>13</td><td>S<sub>p</sub>, NO<sub>3</sub><sup>-</sup>, O<sub>2</sub></td></tr><tr><td>14</td><td>S<sub>p</sub>, NO<sub>3</sub><sup>-</sup></td></tr><tr><td>15</td><td>S<sub>p</sub>, O<sub>2</sub></td></tr><tr><td>16</td><td>S<sub>p</sub></td></tr></tbody></table>

Note. “S<sub>p</sub>” represents salinity on the practical scale, “T” represents temperature (°C), “NO<sub>3</sub><sup>-</sup>” represents nitrate (µmol kg<sup>-1</sup>), “O<sub>2</sub>” represents dissolved oxygen (µmol kg<sup>-1</sup>), and “SiO<sub>4</sub><sup>4-</sup>” represents silicate (µmol kg<sup>-1</sup>).

Both ESPER routines (ESPER_LIR and ESPER_NN) were used to predict A_T at the location and time of each A_T measurement used in this study. There are 16 equations that can be used in each ESPER for seawater property predictions, each of which requires a different combination of input seawater property measurements. For A_T, this is outlined below in Table 1. A_T varies most strongly with salinity, but is also correlated with changes in temperature, oxygen, and macronutrient distributions. Salinity is a required input in all 16 equations and the other predictors are each used in half of the 16 equations.

The unweighted ensemble mean of the estimates produced by the 16 equations was used to calculate ΔA_T for each ESPER routine. Plancherel et al. (2013) showed that errors using multiple linear regression (MLR) algorithms tend to be uncorrelated from each other, and therefore benefit somewhat from ensemble mean averaging. While some of the 16 equations we use in our ensemble average produce estimates with higher fidelity than others, Carter et al. (2019) found the benefit of weighting ensemble members by the inverse of their uncertainty to be limited when estimating anthropogenic carbon accumulation rates. For that study and this one, the important quantity is how the estimate varies over time rather than the value of the estimate, so we again average ensemble members without any prior weighting. The standard deviation of the trend in ΔA_T across the 16 equations is also calculated (u<sub>ESPER</sub>) as an estimate of one component of uncertainty in the trend.

## 2.3. Statistical Analyses

Measurements from the surface ocean as well as the full depth of the water column were separately analyzed, as trends in alkalinity due to ocean acidification are expected to be visible first in the surface ocean (Carter et al., 2016). Multiple definitions of “surface ocean” were tested, including 10 m depth, 25 m depth, 100 m depth, average monthly mean mixed layer depth, and maximum monthly maximum mixed layer depth, and the depth selected had little effect on the results. Climatology described by Holte et al., 2017 were used for average monthly mean mixed layer depth and maximum monthly maximum mixed layer depth. For the following analysis, a surface ocean depth of 25 m will be used. “Full depth” indicates that all available depths of measurements were used, which includes all depths from the surface to a maximum of 6,742 m. The average depth of measurement was 1,426 m. 95% of measurements fell between 0 and 4,500 m in depth.

Two linear regression techniques were used to analyze changes in ΔA_T over time. Robust linear regression (RLM), using maximum likelihood type estimation (M-estimation) with Huber's t function, was performed to reduce the sensitivity of the regression results to outliers in A_T measurements (Figure S2 in Supporting Information S1). Robust regression is an established practice used to reduce the influence of outliers (Andersen, 2007). For comparison purposes-and to ensure our results were not overly sensitive to our robust regression weighting choices-ordinary least squares (OLS) regression was also performed. No temporal or spatial patterns in outliers were detected (Figure S3 in Supporting Information S1).

A Monte Carlo analysis was performed to account for uncertainty in GLODAP data by adding offsets to the data randomly selected from a Gaussian distribution centered at zero with a standard deviation of 2 µmol kg<sup>-1</sup>, which represents the accepted uncertainty in open-ocean repeat hydrographic A_T measurements (see Section 2.1; Carter, Sharp, García-Ibáñez, et al., 2024). Two variations of the Monte Carlo analysis were conducted. In the first “per-cruise transect” method, a random offset was selected for each cruise to be applied to every A_T measurement taken on that cruise to represent systematic sampling errors that may have occurred. 1,000 repetitions of the Monte Carlo simulations were performed, and the standard deviation of the resulting trends in ΔA_T is taken to represent sampling uncertainty (u<sub>sample</sub>). This uncertainty differs from the individual measurement uncertainty (2 µmol kg<sup>-1</sup>) in that it does not represent uncertainty in a single measurement of A_T, but rather the uncertainty in the trend in ΔA_T over time possible when all individual measurements are taken together.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Difference between the ESPER-predicted $A_T$ and ensemble mean for each ESPER equation. Each data point represented in the histogram is one ESPER prediction. Subplot A uses ESPER_LIR as the counterfactual; subplot B uses ESPER_NN.</figcaption></figure>

Sampling uncertainty ($u_{sample}$) is summed in quadrature (the square root of the sum of squares) with $u_{ESPER}$ (previously defined in Section 2.2) to estimate total uncertainty ($U$). Results were analyzed on both a global, regional, and per-cruise transect basis. The per-cruise transect analysis was performed only when two or more reoccupations existed in that transect. In the second “per-point” method, a unique random offset was applied to every measurement used in the analysis, but it was found that this style of Monte Carlo simulation had no meaningful impact on the trend in $\Delta A_T$ due to the large number of measurements (Figures S4 and S5 in Supporting Information S1).

A flow chart outlining the methodology utilized in this work is included as Figure S6 in Supporting Information S1.

## 3. Results

Across all equations, the average $\Delta A_T$ calculated using ESPER_LIR was -0.29 µmol kg⁻¹, with a standard deviation of 4.27 µmol kg⁻¹. The average $\Delta A_T$ using ESPER_NN was -0.26 µmol kg⁻¹, with a standard deviation of 4.23 µmol kg⁻¹. Mean $\Delta A_T$ was statistically indistinguishable across all 16 ESPER equations (Figure 2). Adding more predictors decreases the standard deviation in $\Delta A_T$, consistent with a known improvement in the estimate fidelity in the GLODAPv2.2020 training product, which has strong overlap with the subset of GLODAPv2.2023 we use for this study (Carter et al., 2021). The number of viable $\Delta A_T$ values decreases by 6.3% as the number of predictors used increases from 1 to 5, because each ESPER calculation is only performed if all the required predictors were concurrently measured.

A statistically significant positive temporal trend in $\Delta A_T$ in the surface ocean (<25 m) was found using both ordinary least squares (OLS) regression and robust linear model (RLM) regression (Table 2, Figure 3). In other words, the measured $A_T$ values are increasing compared to the ESPER-calculated counterfactual, which indicates that $A_T$ levels have been increasing on a global scale in the surface ocean over the past three decades.

Trends in $\Delta A_T$ in the surface ocean are positive and greater than twice the overall uncertainty in the trends (i.e., >2σ, or >95% confidence). A positive trend is also seen across the full ocean depth, though this trend is only statistically significant when considering the uncertainty in the slope term fit by the linear regression alone. The deep trend is not statistically significant when considering the much larger overall uncertainty ($U$) estimate that accounts for the likely errors in measurements of $A_T$ and ESPER estimates. Lessening the weight of outliers with RLM always results in a trend that is both more positive and significant, but the statements regarding significance remain unchanged for OLS.
<caption>**Table 2**</caption>

Trends in ΔA<sub>T</sub> Over Time (µmol kg<sup>-1</sup> yr<sup>-1</sup>) for Each Region Calculated by Robust Linear Model (RLM) and Ordinary Least Squares (OLS) Regression With Associated Uncertainties

<table><thead><tr><th rowspan="2"></th><th colspan="2">RLM</th><th colspan="2">OLS</th></tr><tr><th>ESPER_LIR</th><th>ESPER_NN</th><th>ESPER_LIR</th><th>ESPER_NN</th></tr></thead><tbody><tr><td>Surface Ocean (&lt;25 m)</td><td>0.072 ± 0.023</td><td>0.070 ± 0.023</td><td>0.054 ± 0.023</td><td>0.056 ± 0.023</td></tr><tr><td>Full Ocean Depth</td><td>0.011 ± 0.022</td><td>0.005 ± 0.022</td><td>0.008 ± 0.022</td><td>0.003 ± 0.022</td></tr></tbody></table>

Note. ESPER_LIR and ESPER_NN were each used as counterfactuals, and the trends in the surface ocean (<25 m) and for the full depth of measurements are shown for each regression type and counterfactual.

The majority of uncertainty *U* in each trend in ΔA<sub>T</sub> is derived from *u*<sub>sample</sub>, which is calculated using the Monte Carlo simulation (Figure 4). Monte Carlo analysis shows high confidence in a positive trend in surface ocean ΔA<sub>T</sub> over time. On average across the set of 1,000 Monte Carlo runs, the standard deviation of the simulated trends in surface ΔA<sub>T</sub> is 0.022 µmol kg<sup>-1</sup> yr<sup>-1</sup>. Even with this uncertainty, 99.3% of simulations result in a positive trend with ESPER_LIR as the counterfactual (99.5% with ESPER_NN). This does not hold for the full-ocean analysis, as much smaller trends in ΔA<sub>T</sub> are found relative to estimated uncertainties (standard deviation of 0.022 µmol kg<sup>-1</sup> yr<sup>-1</sup>). A positive trend is only seen 65.5% of the time (with ESPER_LIR as counterfactual, 55.9% with ESPER_NN).

Uncertainty analysis shows that trends in ΔA<sub>T</sub> over time along individual transects, which are sometimes apparent from regression statistics, are not able to be confidently distinguished from either the global trend or from the null hypothesis that changes are the result of measurement uncertainties (Figure S7 in Supporting Information S1). Most of the OLS-calculated trends in ΔA<sub>T</sub> across 1,000 Monte Carlo runs do not show a clear positive or negative result (Figure S8 in Supporting Information S1). Furthermore, a visual analysis of the spatial distribution of

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Ordinary least squares (OLS) and robust linear regression (RLM) calculated for surface values only (<25 m depth, subplots A,B) and over the full depth of available data (subplots C, D) using the ensemble mean of each ESPER routine (A, C: ESPER_LIR, B, D: ESPER_NN). RLM regression line is overlaid on a two-dimensional histogram showing the difference between the measured A<sub>T</sub> and ESPER-estimated A<sub>T</sub> (ΔA<sub>T</sub>) over time. Slopes (*m*) and *p*-values (*p*) for both regression techniques are shown on each subplot. Positive slopes on all plots indicate that the measured A<sub>T</sub> is becoming larger than the co-located ESPER-estimated A<sub>T</sub>.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Monte Carlo simulation results. During each run of the Monte Carlo simulation, a single offset randomly chosen from a normal distribution centered at zero with standard deviation 2 µmol kg⁻¹ was applied to all of the measurements made on a single cruise transect, such that all measurements on each cruise were offset by the same amount but each cruise was offset by a different random amount. Histogram shows the slope (μ) and standard deviation (σ) of the difference in measured A_T and ESPER-estimated A_T (ΔA_T) over time obtained by ordinary least squares regression for each of the 1,000 Monte Carlo simulation runs. Subplots A-B: surface values (<25 m depth); subplots C-D: full depth; A, C: ESPER_LIR; B, D: ESPER_NN.</figcaption></figure>

surface ΔA_T does not show any clear patterns (Figure S3 in Supporting Information S1). Combining individual transects into groups of regions (see definition of regions in Figure S9 in Supporting Information S1) reduces uncertainties below the calculated trend for only some regions (Figure 5). Trends and uncertainties on a per-region basis are given in Tables S1 and S2 in Supporting Information S1.

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Trend in the difference in measured A_T and ESPER-estimated A_T (ΔA_T) over time for each region. Dark purple points were calculated using ESPER_LIR as counterfactual, and light blue with ESPER_NN. Trends were calculated with robust linear model regression. Subplot A: surface values only (<25 m); subplot B: full depth of measurements available; subplot C: trend in ΔA_T calculated for full depth of measurements subtracted from trend in ΔA_T calculated for surface values only. Error bars represent calculated uncertainties (U) in subplots A and B. In subplot C, the error is calculated by summation in quadrature of the individual uncertainties U of the corresponding surface and full depth ΔA_T trends.</figcaption></figure>
Three regions, the North Atlantic, South Pacific, and Indian Oceans show positive trends in ΔA_T in the surface ocean above the bounds of uncertainty, but only the Indian Ocean shows a non-zero surface ocean trend above the 2σ confidence level (Figure 5a). The North Pacific shows a decreasing trend in ΔA_T that strengthens across the full ocean depth. The Arctic Ocean especially has a high degree of uncertainty, which can be attributed to both the lower frequency of hydrographic measurements in this basin and the higher uncertainty in ESPER predictions, as the Arctic is generally a challenging area for ESPER algorithms (Carter et al., 2021). It should be noted that these trends do not reflect changes in regional A_T caused by shifts in salinity due to the intensification of the water cycle (Fine et al., 2017), but instead represent changes in A_T due to shifts in biological processes, as ESPER algorithms are designed to account for salinity changes (see Section 4.3 for further discussion).

Potential trends in ΔA_T were also examined across different depth ranges (Table S3 in Supporting Information S1). Between 500 and 4,000 m, the vast majority of depth ranges showed no significant trend in ΔA_T, and none showed significance at the 2σ confidence level. At depths below 4,000 m, a negative trend (generally at 1σ confidence) was detected using both ESPER_LIR and ESPER_NN. This may indicate that diminished biotic calcification at the surface or increased near-surface dissolution has resulted in a decreasing export of carbonates to depth, but the low statistical confidence of the trend implies that ESPER or measurement uncertainties are also plausible explanations, given that measurement uncertainties are nearly constant with depth while changes in A_T distributions are generally smaller.

Differences in trends in ΔA_T on a seasonal basis were observed (Tables S4 and S5 in Supporting Information S1). Positive trends in surface ΔA_T at the 2σ confidence level were found in the winter, spring, and autumn months, while no statistically significant trend was detected in summer. No statistically significant seasonal trends at the 2σ confidence level were detected across the full ocean depth. One plausible explanation for the lack of a significant positive trend in surface ΔA_T during summer months could be a decrease in net community production due to shoaling of the mixed layer brought on by enhanced stratification under a changing climate (Fu et al., 2016). Reduced nitrate uptake would result in a lower production of A_T (Brewer et al., 1975; Kanamori & Ikegami, 1982; Wolf-Gladrow et al., 2007), potentially masking decreases in CaCO₃ production; however, more research is needed to investigate this hypothesis.

Furthermore, ESPERs are trained on seasonally biased data. 40.1% of the measurements are made in summer, while 18.1%, 13.5%, and 28.3% of the measurements are made in spring, winter, and autumn, respectively. This makes extracting seasonal trends with high confidence challenging. It is also possible that ESPERs have implicitly captured the summertime trend in A_T due to a fairly consistent seasonal distribution of measurements over time (Figure S10 in Supporting Information S1), though this trend would fall within the calculated uncertainties.

# 4. Discussion

## 4.1. Regional Trends in ΔA_T

Unlike in the surface ocean, high uncertainties in the underlying processes make it unclear how the deep and intermediate ocean will respond to the CO₂-biotic calcification and export feedback. Subsurface A_T could decrease due to diminished surface export (and thus diminished dissolution) or increase due to higher rates of shelf or water column dissolution. Regardless, models suggest that the deep response should be sluggish in comparison with the surface response (Carter et al., 2016). In contrast, spurious apparent A_T trends from measurement uncertainties or shifts in methods over time could result in trends that span both the deep and surface ocean. Therefore, the difference between the surface trend and the full water column trend might provide a more reliable metric for the impacts of the CO₂-biotic calcification and export feedback.

Indeed, the regional variability, which our Monte Carlo analysis shows to be meaningfully impacted by measurement uncertainties, is diminished when considering this difference metric (Figure 5c). In particular, the larger-than-normal trend in the Indian Ocean and the smaller-than-normal trend in the North Pacific are brought in line with the global trend when this metric is applied. The difference between the surface and full water column trends in ΔA_T is 0.146 ± 0.072 µmol kg⁻¹ yr⁻¹ in the Indian Ocean and 0.035 ± 0.059 µmol kg⁻¹ yr⁻¹ in the North Pacific using ESPER_LIR as the counterfactual (Table S1 in Supporting Information S1). Both the Indian (Müller et al., 2023) and the North Pacific (Carter, Sharp, Dickson, et al., 2024) are regions where there have been shifts in A_T measurement and adjustment practices over time, so we contend the surface-to-deep trend difference is likely a
more reliable metric for these regions. Furthermore, this supports our assertion that much of the regional vari-
ations we see can be attributed to measurement uncertainties. There may be additional interesting regional pat-
terns to examine during this global signal, but we leave this to future work and maintain that this will remain
statistically challenging until more decades of repeated hydrographic A_T measurements are available.

**4.2. Sources of Error**

Errors in this analysis could arise from long-term trends in predictor variables, biases potentially inherent to ESPER_LIR or ESPER_NN, or biases in GLODAP data. One explanation for the trend in ΔA_T is that the strategy for removing the impacts of natural variations by using ESPERs as counterfactuals relies on predictor information that may also be influenced by separate long-term trends. However, we have used many predictor combinations and shown that variations across regression equations, which variably omit each of the predictors besides S_p, are comparatively small (Figure 2). We have included these variations in our overall trend uncertainty estimate U, and we consider long-term trends in S_p (which is common to all ESPER variants) separately below.

It is also possible that the apparent trend is due to a bias inherent to the structure of ESPER_LIR or ESPER_NN.
However, these are independent algorithms trained on nearly identical training data, which indicates that the trend
is a product of A_T measurements and not the algorithm used to account for natural variability. It is also possible
that the data used to train the algorithms, which includes data from many hydrographic cruises that were never
occupied more than once and thus never appear directly within this study, contain a regional or measurement bias.
To assess this, the analysis was repeated with a version of ESPER_LIR (ESPER_LIR_GO-SHIP) trained using
identical training scripts to those used for ESPER_LIR after limiting the training data to the data used for the ΔA_T
calculations in this study (Figure S11 in Supporting Information S1). Statistically equivalent trends in ΔA_T were
found with this variant.

Collectively, these observations provide some support for the statement that the results we find are not an artifact of the algorithm used as a counterfactual or its training data. We note that the GLODAPv2 adjustments are made on the basis of reducing apparent mismatches between measurements made by repeated cruises in the deep and interior oceans. It is possible that adjustments made on this basis could reduce or eliminate the deep ocean A_T trend.

**4.3. Attribution to CO₂-Biotic Calcification and Export Feedback**

To discern if the observed trend in surface ΔA_T can be attributed to the CO₂-biotic calcification and export feedback, other processes affecting the distribution of surface A_T were considered (Fry et al., 2015). These include (a) changes in the rate of riverine discharge of A_T (Cai et al., 2008), (b) increases in A_T input from increased sea ice melt (Rysgaard et al., 2012), (c) dilution of surface A_T by increased land and sea ice melt inputs of freshwater to the surface, (d) changes in the stratification of the ocean and therefore shifts in upwelling patterns of subsurface A_T (Lee et al., 2006), (e) changes in organic matter production and export (Wolf-Gladrow et al., 2007), and (f) trends in A_T distributions due to fluctuations in the ocean's state caused by glacial/interglacial cycles (Yu et al., 2015). For each of these processes, we consider both how this process would change the distribution of alkalinity and the degree to which the empirical ESPER relationships may or may not be expected to counter those changes.

Simple A_T budgets were performed to address processes (a) and (b) and are outlined in Texts S1 in Supporting Information S1 (Cai et al., 2008; Gudmundsson et al., 2021; Stets et al., 2014) and S2 (Dieckmann et al., 2008; Ji et al., 2021; Perovich et al., 2020; Rysgaard et al., 2012; Smith et al., 2020). As a result of increases in the rate of riverine discharge of A_T (process 1), we estimate that the rate of surface A_T increase could be up to 0.028 µmol kg⁻¹ yr⁻¹, approximately 3 times less than the observed trend in ΔA_T, indicating that this process is not sufficient to explain the observed trend. Increases in the rate of sea ice melt (process 2) are similarly less than the observed trend, as we estimate that the rate of surface A_T increase from this process is 0.010 µmol kg⁻¹ yr⁻¹, approximately 7 times less than the observed trend.

Process (3), the effect of dilution by freshwater on surface A_T due to increases in melting of both land and sea ice, is addressed with a salinity budget (Text S3; Carter et al., 2014; Cox & Weeks, 1974; Hugonnet et al., 2021; Smith et al., 2020). It is estimated that surface ocean S_p could be decreasing at a rate of up to -0.03 dec⁻¹, which would cause a decrease in surface A_T over time due to dilution with freshwater (Cox & Weeks, 1974; Hugonnet
et al., 2021). ESPERs predict a positive correlation between $A_T$ and $S_p$, as the average surface ocean coefficient reflecting the sensitivity of $A_T$ to $S_p$ ($\Delta A_T: \Delta S_p$) calculated for use in ESPER_LIR is 55 µmol $A_T$ kg$^{-1}$. This coefficient implies that ESPER-predicted surface $A_T$ could be changing by -0.165 µmol kg$^{-1}$ yr$^{-1}$ (55 µmol $A_T$ kg$^{-1}$ * -0.03 dec$^{-1}$) due to salinity changes. The global surface ocean $A_T: S_p$ ratio, which can also be considered a slope due to the assumption of a freshwater end-member with zero $A_T$, is approximately 66.4 µmol $A_T$ kg$^{-1}$ (mean surface ocean $A_T$ and $S_p$ of 2305 µmol kg$^{-1}$ yr$^{-1}$ and 34.71, respectively, are taken from Carter et al., 2014). Therefore, this freshwater would be expected to decrease measured surface ocean $A_T$ by approximately 1.2 (66.4 µmol $A_T$ kg$^{-1}$/55 µmol $A_T$ kg$^{-1}$) times as much as it would decrease ESPER-estimated $A_T$. Thus, this process might be expected to lead to a negative trend in $\Delta A_T$ (-0.033 µmol kg$^{-1}$ yr$^{-1}$).

Intensification of the hydrological cycle is projected to occur under climate change and would influence sea surface salinity distributions (Durack & Wijffels, 2010). Though shifts in evaporation and precipitation patterns locally would not change the global mean value of $A_T$, this process could affect trends in $\Delta A_T$ on a regional scale (Fine et al., 2017). This possibility is explored in Text S4 in Supporting Information S1 (Carter et al., 2014; Durack & Wijffels, 2010). Applying the same methodology used to quantify the effects from potential long-term salinity changes (process 3), we find that in the Atlantic, where salinity has increased by 0.078 ± 0.095 from 1950 to 2000 (Durack & Wijffels, 2010), a salinity increase could account for a trend in $\Delta A_T$ of +0.022 µmol kg$^{-1}$ yr$^{-1}$. In the Pacific, which freshened by 0.044 ± 0.064 over the same period (Durack & Wijffels, 2010), there could be a trend in $\Delta A_T$ of -0.010 µmol kg$^{-1}$ yr$^{-1}$. In the Indian Ocean, no significant trend was detected in salinity over this period. Even the most extreme changes in salinity reported by Durack and Wijffels (2010), found only in localized regions, of ±0.2 from 1950 to 2000 would only be expected to produce a localized $\Delta A_T$ trend of ±0.05 µmol kg$^{-1}$ yr$^{-1}$, a number that is still less than the globally averaged trend in $\Delta A_T$.

Changes in stratification could also change the supply of $A_T$ from below the surface mixed layer (process 4). However, a related study for nitrate estimated that increases in stratification are leading to a decrease in surface ocean nitrate of -0.18 µmol L$^{-1}$ dec$^{-1}$ (-0.018 µmol kg$^{-1}$ yr$^{-1}$) as the supply from below declines (Gregg & Rousseaux, 2019). As $A_T$ tends to increase with depth in the ocean (on average), an increase in stratification would be expected to lead to a decrease in surface $A_T$ rather than an increase, and thus this change seems unlikely to explain our observations.

If, however, the decline in surface nitrate is due instead to increased organic matter production and export (process 5) with a constant supply of nitrate and $A_T$ and an unchanged carbonate mineral cycle (or a concentration of biological production in a shallower mixed layer), this would be accompanied by an increase in $A_T$ due to charge balance (Brewer et al., 1975; Kanamori & Ikegami, 1982; Wolf-Gladrow et al., 2007). This case represents the extreme other end of the spectrum of the nitrate/$A_T$ relationship. Even when (unrealistically) attributing the surface nitrate decline calculated by Gregg and Rousseaux (2019) entirely to this process, we estimate that the corresponding increase in $A_T$ required to satisfy the charge balance would be 0.024 µmol kg$^{-1}$ yr$^{-1}$, 3 times less than the observed trend in surface $A_T$ increase (Text S5 in Supporting Information S1; Gregg & Rousseaux, 2019; Wolf-Gladrow et al., 2007).

We also consider the possibility that these processes could bias the trend by decreasing the $A_T$ estimated by ESPERs as surface nitrate is decreasing. ESPERs predict a positive correlation between $A_T$ and nitrate (average surface ocean $A_T$:nitrate ratio calculated for use as coefficient in ESPER_LIR is 0.110 µmol µmol$^{-1}$), indicating ESPER-predicted surface $A_T$ could have an implicit trend of -0.0020 µmol kg$^{-1}$ yr$^{-1}$ (0.110 µmol $A_T$ [µmol nitrate]$^{-1}$ * -0.018 µmol kg$^{-1}$ yr$^{-1}$), which is less than 3% of the magnitude of the observed trend (ESPER_LIR, RLM). This sensitivity would only affect predictions from the 50% of ESPER equations that include nitrate as a predictor, so this process would only be expected to change the mean $\Delta A_T$ trend by +0.00099 µmol kg$^{-1}$ yr$^{-1}$ even if the supply of surface ocean $A_T$ were somehow unaffected by the decrease in vertical mixing.

This analysis is repeated to determine the possible effects of a bias in ESPER estimates due to a positive trend in sea surface temperature (SST). Garcia-Soto et al. (2021) quantified a global average SST increase of 0.280 ± 0.068°C dec$^{-1}$ from the years 2010–2019. The average surface ocean $A_T:T$ coefficient calculated by ESPERs is -0.338 µmol kg$^{-1}$ $A_T$ °C$^{-1}$, indicating that ESPER-predicted surface $A_T$ could have an implicit trend of -0.0095 µmol kg$^{-1}$ yr$^{-1}$ (-0.338 µmol kg$^{-1}$ $A_T$ °C$^{-1}$ * 0.280°C dec$^{-1}$). Similar to nitrate, this would only affect 50% of predictions by ESPER equations, therefore biasing the mean $\Delta A_T$ trend by +0.0047 µmol kg$^{-1}$ yr$^{-1}$, which is about 15 times less than the observed trend in $\Delta A_T$.
Due to the effects of glacial/interglacial cycles on the Earth system (process 6), it cannot be assumed that $A_T$ distributions in the ocean were in a steady state prior to the industrial era. However, literature suggests that $A_T$ in the surface ocean was likely to decrease slightly during the pre-industrial Holocene (Brovkin et al., 2019; Elsig et al., 2009; Yu et al., 2015). As implicit biases in the salinity and nitrate predictor variables, which represent abiotic and biotic processes, have already been addressed, it is unlikely that a non-steady state ocean is causing the observed trend in $\Delta A_T$ and instead it seems likely that anthropogenic effects are at the root of this change.

## 4.4. Possible Biological and Chemical Mechanisms

There are four mechanisms related to biotic calcification that could lead to the observed increase in surface ocean alkalinity. First, calcifying organisms could be preferentially decreasing their rates of calcification over time. Alternatively, the number of calcifying organisms in the ocean could be decreasing. Third, it is possible that sedimentary carbonates are being preferentially redissolved instead of buried due to an acidifying ocean. Finally, the rate of dissolution of carbonates in shallow waters could be increasing, and thus the export of alkalinity from the surface ocean could be decreasing or the exported alkalinity could be increasingly redissolved at shallow depth and returned to the surface through mixing processes. We did not find sufficient observational evidence to distinguish between these mechanisms.

Some of these four mechanisms might be expected to result in different signals in alkalinity distributions, as explored to some extent by Liang et al. (2023). These authors assess how well model simulations of various carbonate mineral dissolution mechanisms reconstruct the observed alkalinity distribution. They note that several dissolution processes result in similar preformed and excess alkalinity distributions, and that the best match is found for constant-dissolution with depth or dissolution that is partially mediated by respiration in the upper ocean. Our approach is focused on small changes in the ocean alkalinity distribution over several decades rather than the shape of the global alkalinity distribution, which results from processes acting over the thousand year timescales of ocean overturning. For this reason, our approach is even less well-suited to reveal the exact processes behind the observed trend. We are therefore unable to distinguish between these mechanisms on the basis of our findings.

A review of laboratory studies has found mixed responses of all calcifying species to ocean acidification (Leung et al., 2022), though for calcifying plankton, a decrease in calcification rates is often observed under ocean acidification scenarios (Bednaršek et al., 2019; Kuroyanagi et al., 2021; Meyer & Riebesell, 2015). Furthermore, the magnitude of the observed trend in surface ocean $\Delta A_T$ is relatively small compared to uncertainty in satellite-based measurements of particulate inorganic carbon (PIC), which estimate the average amount of $CaCO_3$ produced annually by coccolithophores to be $1.42 \pm 1.69$ PgC (Hopkins & Balch, 2018). Nevertheless, we believe we have shown that the carbonate system is not in steady state in the anthropocene and that our findings are consistent with the $CO_2$-biotic calcification and export/burial feedback hypothesis.

## 4.5. Applications to Marine Carbon Dioxide Removal

New technologies, referred to as marine carbon dioxide removal (mCDR), have been proposed to harness the ocean's capacity as a carbon sink to store atmospheric $CO_2$ (National Academies of Sciences et al., 2022). One technique, ocean alkalinity enhancement (OAE), involves elevating levels of $A_T$ in the surface ocean such that atmospheric $CO_2$ is drawn down as the surface ocean and atmosphere re-equilibrates via air-sea gas exchange (Kheshgi, 1995; Renforth & Henderson, 2017). By some measures, OAE is a “reverse experiment” for ocean acidification. In an OAE scenario, instead of an accumulation of surface $A_T$, the $CO_2$-biotic calcification feedback would cause a reduction in surface $A_T$ as some of the additional $A_T$ from OAE is diverted to biotic calcification. This would result in a loss of efficiency for an OAE deployment because precipitation of carbonate minerals in seawater drives $CO_2$ from the ocean to the atmosphere in a ratio of about 0.6–0.7 mol $CO_2$ per mole precipitated $CO_3^{2-}$ (Frankignoulle et al., 1994), which would need to be accounted for to accurately determine the amount of $CO_2$ removed from the atmosphere. This study provides an observational constraint on what the magnitude of this inefficiency could be in an OAE operation, indicating that while the $CO_2$-biotic calcification feedback will not likely be a large loss term, it should be counted in the overall budget. Furthermore, the trend in $A_T$ found in this study implies that long-term evaluation of OAE may need to account for non-steady state trends in the “natural” alkalinity cycle.
However, increased rates of calcification due to an influx of $A_T$ could cause other changes in an OAE scenario. Higher levels of $CaCO_3$ could potentially act to increase the amount of ballast material for particulate organic carbon, which could increase the efficiency or remineralization length scale of biological carbon export, which is known as the “more ballast feedback mechanism” (Bach et al., 2019). This could potentially oppose the reduction in available alkalinity due to the $CO_2$-biotic calcification feedback to some degree. Further research must be done to clarify the relative strength of these processes in order to quantify their effects on carbon export during an OAE deployment.

## 5. Conclusions

Algorithms designed to predict seawater properties, including total alkalinity ($A_T$), are used as a counterfactual to extract a trend in surface $A_T$ measurements performed on repeat hydrographic cruises. This >30 years trend can likely be attributed to the hypothesized $CO_2$-biotic calcification feedback, in which increasing oceanic $CO_2$ uptake, worsening ocean acidification, and decreasing rates of biotic calcification and export cause accumulation in surface $A_T$. Over the past 30 years, approximately 20 Tmol of additional $A_T$ has accumulated in the surface ocean due to this buildup, equivalent to over half the annual input of $A_T$ via riverine sources (Cai et al., 2008). Assuming complete equilibration, this would have increased the ocean anthropogenic carbon burden by an estimated 0.2%, or 0.20 PgC (Text S6 in Supporting Information S1, Carter et al., 2014; Humphreys et al., 2022; Lan & Keeling, n.d.). This does not account for the potential impacts of reduced calcification on the biological carbon pump, such as a decreased flux of organic carbon to the deep sea due to lower availability of $CaCO_3$ ballast materials (Riebesell et al., 2009).

It is well understood that marine anthropogenic $CO_2$ accumulation has influenced ocean carbonate chemistry and decreased the ocean's capacity to store additional atmospheric $CO_2$. Our findings here point to a global slowdown in biotic calcification and export and a resulting buildup in surface $A_T$, which increases the surface ocean's ability to uptake $CO_2$. In summary, we contend that anthropogenic $CO_2$ emissions are quantifiably changing the processes by which the marine carbon cycle regulates Earth's climate on a planetary scale, likely by disrupting patterns in the life cycles of calcifying organisms. Ocean acidification is now both a symptom and a cause of measurable anthropogenic perturbations to the global carbon cycle.

Though signatures of the $CO_2$-biotic calcification feedback can be detected on a global scale, we do not have enough repeat hydrographic measurements to assess how the feedback operates on a regional basis or along individual cruise transects. Understanding this phenomenon on a local scale would be necessary to project changes in key ecosystems under climate change and to accurately model the carbon uptake of mCDR deployments. A larger scale of measurements, especially in rapidly changing and relatively under-sampled regions such as the Arctic, would enable us to place further constraints on the effect of this feedback within the marine carbon cycle.

## Data Availability Statement

The GLODAPv2.2023 data used in the study are available at the National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information (NCEI) Ocean Carbon and Acidification Data System (OCADS) via https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0283442/.

* Lauvset et al. (2022, 2023) and Olsen et al. (2019, 2020).

The Empirical Seawater Property Estimation Routines (ESPERs) used for the prediction of alkalinity co-located with GLODAP measurements are preserved at https://github.com/BRCScienceProducts/ESPER and developed by B. R. Carter.

* Carter et al. (2021).

The PyCO2SYS software package used for calculations in Text S6 in Supporting Information S1 is available at https://zenodo.org/records/14506565.

* Humphreys et al. (2022).
Acknowledgments

RCB and BRC thank the National Oceanographic Partnership Program (NOPP) for support through the Inflation Reduction Act under the National Oceanic and Atmospheric Administration's Ocean Acidification Program award NA23OAR0170515 to University of Washington. Analyses used to explore uncertainties in ESPER estimates and measurements were enabled by support from the Global Ocean Monitoring and Observing program of NOAA through funding the Carbon Data Management and Synthesis Program (Fund Ref. I00007298, program officer: Kathy Tedesco). AJF was supported by NOAA's Pacific Marine Environmental Laboratory (PMEL). BT was supported by CSIRO Environment and the Australian Antarctic Program Partnership. RJW was supported by the National Science Foundation Division of Ocean Sciences (OCE-2148468). KAS was supported by the Atlantic Zone Offshore Monitoring Programme (AZOMP) and the Aquatic Climate Change Adaptation Services Program (ACCASP) of Fisheries and Oceans, Canada. MI was supported by the Environmental Research and Technology Development Fund (JPMEERF24S12200) of the Environmental Restoration and Conservation Agency provided by the Ministry of the Environment of Japan. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/5011000111033 and by the European Union under grant agreement no. 101094690 (EuroGO-SHIP). PMEL and CICOES contributions are numbers 5657 and 2024-1405, respectively. The authors would also like to extend their gratitude to the many people who have played a role in the collection of alkalinity data preserved in GLODAP, including Frank Millero, Andrew Dickson, Kazuhiro Nemoto, Christopher Sabine, Douglas Wallace, Hitomi Kamiya, Christopher Winn, Robert Key, E. Peter Jones, Aida Fernández Ríos, and every other scientist, technician, and crew member who contributed countless hours of effort to this data set.

Atmospheric carbon dioxide concentrations used for calculations in Text S6 in Supporting Information S1 are available at https://gml.noaa.gov/ccgg/trends/data.html.

* Lan and Keeling (n.d.).

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