# Linking Surface Phytoplankton Dynamics to Small-Particle Fluxes in the Mesopelagic Zone: Insights From High Latitude Bioregions Using BGC-Argo Floats

Elsa Simon, Léo Lacour, Hervé Claustre, Nicholas Bock, Marin Cornec, Raphaëlle Sauzède, Catherine Schmechtig, Laurent Coppola

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## To cite this version:

Elsa Simon, Léo Lacour, Hervé Claustre, Nicholas Bock, Marin Cornec, et al. Linking Surface Phytoplankton Dynamics to Small-Particle Fluxes in the Mesopelagic Zone: Insights From High Latitude Bioregions Using BGC-Argo Floats. Global Biogeochemical Cycles, 2025, 39 (7), 10.1029/2024GB008447. hal-05164966

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Submitted on 16 Jul 2025

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

## RESEARCH ARTICLE

10.1029/2024GB008447

**Key Points:**

* Six distinct phytoplankton production regimes and their associated dynamics in small particle export are analyzed from a global BGC-Argo database

* Small particles account for 10% of the total organic carbon fluxes down to 1,000 m in high-latitude oceans

* Export and transfer efficiencies of small particles are related to phytoplankton phenology and particle assemblage in the productive layer

**Supporting Information:**

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

**Correspondence to:**

E. Simon
elsa.simon@imev-mer.fr

**Citation:**

Simon, E., Lacour, L., Claustre, H., Bock, N., Cornec, M., Sauzède, R., et al. (2025). Linking surface phytoplankton dynamics to small-particle fluxes in the mesopelagic zone: Insights from high latitude bioregions using BGC-Argo floats. *Global Biogeochemical Cycles*, *39*, e2024GB008447. https://doi.org/10.1029/2024GB008447

Received 4 DEC 2024
Accepted 18 JUN 2025

**Author Contributions:**

**Conceptualization:** Elsa Simon, Léo Lacour, Hervé Claustre, Nicholas Bock, Marin Cornec, Laurent Coppola
**Data curation:** Elsa Simon, Raphaëlle Sauzède, Catherine Schmechtig

**Formal analysis:** Elsa Simon

**Funding acquisition:** Hervé Claustre

**Investigation:** Elsa Simon

**Methodology:** Elsa Simon, Léo Lacour, Hervé Claustre, Nicholas Bock, Marin Cornec

**Software:** Elsa Simon, Nicholas Bock, Marin Cornec

**Supervision:** Léo Lacour, Hervé Claustre, Laurent Coppola

© 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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## Linking Surface Phytoplankton Dynamics to Small-Particle Fluxes in the Mesopelagic Zone: Insights From High Latitude Bioregions Using BGC-Argo Floats

Elsa Simon¹, Léo Lacour¹, Hervé Claustre¹, Nicholas Bock¹, Marin Cornec²,³, Raphaëlle Sauzède⁴, Catherine Schmechtig⁵, and Laurent Coppola¹,⁵

¹Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche (LOV), Villefranche-sur-mer, France, ²School of Oceanography University of Washington, Seattle, WA, USA, ³NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA, USA, ⁴Sorbonne Université, CNRS, Institut de la Mer de Villefranche (IMEV), Villefranche-sur-mer, France, ⁵OSU Ecce Terra, UMS 3455, CNRS and Université Pierre et Marie Curie, Paris, France

**Abstract** Understanding factors controlling the biological carbon pump (BCP) at the regional scale is of major interest for better characterizing carbon sequestration into the deep ocean and, therefore, the ocean's role in climate regulation. This study focuses on high-latitude marine regions, which are responsible for the majority of marine CO₂ absorption. Using data from Biogeochemical-Argo floats, a bioregionalization method was performed on 335 annual time series of chlorophyll *a* concentration and particulate backscattering coefficient, variables from which particulate organic carbon (POC) could be estimated. This analysis highlighted six regimes characterized by distinct seasonality in productivity, export, and transfer of small POC (<100 µm). Both hemispheres exhibited regimes with strong summer blooms and others with deep chlorophyll maxima. Across these regimes, variations in phytoplankton phenology and particle assemblages drove three distinct systems of BCP strength and efficiency for small particles. Despite these differences, processes such as gravitational sinking, the mixed layer pump, or particle fragmentation facilitated the export of small particles down to ~1,000 m across all regions. This resulted in an average annual contribution of ~10% of small particles to total organic carbon fluxes at depth, highlighting the role of small particles in long-term carbon sequestration. These findings emphasize the need for future investigations into processes driving small-particle carbon export and transfer in the mesopelagic zone at annual and seasonal scales.

**Plain Language Summary** Understanding how carbon is transferred from the surface to the deep ocean is essential for studying the ocean's role in regulating the climate. This study focuses on high-latitude regions where most carbon dioxide (CO₂) absorption occurs. Using data from 406 Biogeochemical-Argo floats, we analyzed 335 yearly time series of phytoplankton biomass and small particle (<100 µm) concentration as a function of time and depth within the first kilometer of the ocean. We found six distinct regional and seasonal dynamics based on the timing of phytoplankton blooms and small particle buildup in the mesopelagic zone. Although both hemispheres show strong summer blooms as well as deep layers of phytoplankton, known as deep chlorophyll maxima (DCM), the amount of small particles exported to and transferred across the mesopelagic zone varies due to differences in bloom timing, duration, and types of particles involved. In these six contrasting systems, small particles make up about 10% of the total organic carbon that sinks to ~1,000 m depth. Although often overlooked in global carbon budgets, carbon export by small particles can contribute to the long-term storage of carbon in the ocean.

## 1. Introduction

The oceanic carbon cycle plays a major role in regulating climate, reducing the atmospheric carbon dioxide (CO₂) concentration leading to a dissolved inorganic carbon (DIC) gradient from surface to depth (Sarmiento & Gruber, 2006). Despite their smaller surface extents, high-latitude regions sequester a significant part of anthropogenic CO₂. The Southern Ocean alone represents 40% of the total oceanic uptake of CO₂ (Deppeler & Davidson, 2017), while the high-latitude oceans in the Northern Hemisphere account for more than 20% of the total uptake (Takahashi et al., 2009). High-latitude environments, therefore, play a major role in the global CO₂ sink and require particular attention, especially considering their vulnerability to future climate change (Bopp et al., 2001; Matear & Hirst, 1999; Sarmiento et al., 1998).
Writing – original draft: Elsa Simon

Writing – review & editing: Léo Lacour,
Hervé Claustre, Nicholas Bock,
Marin Cornec, Raphaëlle Sauzède,
Laurent Coppola

The biological carbon pump (BCP) is responsible for two-thirds of the ocean's surface-to-depth DIC gradient (Sarmiento & Gruber, 2006). This pump involves the production of organic carbon by phytoplankton at the ocean surface and its subsequent transfer through various physical and/or biological processes in the mesopelagic (100–1,000 m) domain, where it can be sequestered for >100–1,000 years, with sequestration time increasing with the depth reached. To characterize the BCP, two metrics are widely used: (a) the strength or magnitude, which is the magnitude of the particulate organic carbon (POC) flux at a reference export depth (e.g., 100 m, base of the euphotic zone ($z_{EU}$)) and the productive zone ($z_p$)); and (b) the transfer efficiency, which is the fraction of POC flux transferred at a given depth from the reference export depth (Buesseler et al., 2020). Both of these metrics help describe the amount of carbon sequestered over long periods in the ocean. These metrics are largely determined by the bloom phenology (i.e., timings and magnitude) (Lutz et al., 2007) and the nature and size structure of the particles entering the mesopelagic region, which depends on the composition of phytoplankton and zooplankton communities growing in the upper ocean (Durkin et al., 2016).

To better understand the processes controlling organic carbon sequestration, a particular interest in the study of small, slow-sinking and suspended particles has grown in the last decade. Small particles (<100 µm) are often assumed to be remineralized before reaching depths where carbon is considered sequestrated (Giering et al., 2014; Riley et al., 2012) and are subject to higher respiration/remineralization rates in the upper ocean (Bressac et al., 2024; Cavan et al., 2017). However, other processes have been shown to increase their export to depth, either through the effect of the mixed layer pump (Dall'Olmo & Mork, 2014; Giering et al., 2016; Dall'Olmo et al., 2016), by fragmentation of large, fast-sinking particles (Briggs et al., 2020; Giering et al., 2014, 2016; Wang & Fennel, 2022), and/or by aggregation/disaggregation processes (Richardson & Jackson, 2007; Stemmann et al., 2004). Thus, small POC could be of major importance regarding organic carbon sequestration.

Multi-sensor autonomous platforms such as Biogeochemical Argo (BGC-Argo) floats have proved effective in bringing new perspectives about these small particle dynamics (Dall'Olmo & Mork, 2014; Briggs et al., 2020; Wang & Fennel, 2022; Terrats et al., 2023) with the benefit of providing high-resolution (both on the vertical and temporal dimension), long-term measurements down to at least 1,000 m depth (Claustre et al., 2020). However, these studies have been conducted on a limited number of floats that do not represent global scale dynamics. A recent bioregionalization approach proposed by Bock et al. (2022) has taken advantage of the data coverage provided by the growing fleet of BGC-Argo floats to characterize annual dynamics in phytoplankton biomass (from chlorophyll *a* concentration [Chla]) and suspended particles (from particulate optical backscattering coefficient ($b_{bp}$)) down to 1,000 m depth, at sub-basin scale and across all oceans.

Among the six Essential Ocean Variables measured by BGC-Argo floats, $b_{bp}$ and [Chla] allow for estimating small-particle (<100 µm) POC concentrations (Briggs et al., 2011, 2013; Koestner et al., 2024). In addition, oxygen concentration ([O₂]) time series from BGC-Argo floats can be used to derive annual net community production (ANCP) (B. Yang et al., 2017; Bushinsky & Emerson, 2018; Su et al., 2022), which can be interpreted as total organic carbon (TOC) when integrated over a full annual cycle and a large enough geographical domain. Simultaneous bio-optical and [O₂] measurements allow for the evaluation of small particle fluxes in the context of TOC fluxes.

This study leverages the BGC-Argo network to provide an overview of small particle export and transfer at high latitudes while providing insight into their potential drivers on annual and seasonal scales. Using 335 time series of $b_{bp}$ and [Chla] measurements along with 265 time series of [O₂] measurements from BGC-Argo floats in high-latitude regions, this study characterizes phytoplankton phenology and particle assemblage in the productive zone at the bioregional scale, as well as annual vertical carbon export in the mesopelagic zone. This bioregionalization approach aims to (a) identify patterns in the control of phytoplankton phenology and particle assemblage on the strength of export and the transfer efficiency of small particles in the mesopelagic zone and (b) assess the global contribution of small particles to the TOC fluxes down to almost 1,000 m.

## 2. Methods

In this study, a global BGC-Argo data set of yearly time series of [Chla] and $b_{bp}$ was constructed (Section 2.1) and clustered into bioregions (Section 2.3) that then provided the basis for evaluating phytoplankton phenology (Section 2.4) and particle assemblages (Section 2.5). Small-POC fluxes (Section 2.6) and TOC fluxes (Section 2.7) were estimated to compute BCP strength and efficiency for small particles in each bioregion and to evaluate
<caption>**Table 1**</caption>

Variables Used in This Study, Accompanied by Their Symbols, the Layer of the Water Column in Which They Were Calculated, and the Float Variables From Which They Were Computed

<table><thead><tr><th>Depth layer</th><th>Variable</th><th>Symbol</th><th>Unit</th><th>Float parameter</th><th>Section</th><th>References</th></tr></thead><tbody><tr><td>Entire water column</td><td>Particulate organic carbon</td><td>POC</td><td>mg m<sup>-3</sup></td><td>b<sub>bp</sub>, [Chla]</td><td>2.1</td><td>Koestner et al. (2024)</td></tr><tr><td rowspan="3">Productive layer</td><td>Phytoplankton carbon</td><td>C<sub>phyto</sub></td><td>g C m<sup>-3</sup></td><td>b<sub>bp</sub></td><td>2.1</td><td>Graff et al. (2015)</td></tr><tr><td>Bulk phytoplankton carbon</td><td>iC<sub>phyto</sub></td><td>g C m<sup>-2</sup></td><td>b<sub>bp</sub></td><td>2.4</td><td>Uchida et al. (2019)</td></tr><tr><td>Maximum accumulation rate</td><td>μ<sub>max</sub></td><td>d<sup>-1</sup></td><td>b<sub>bp</sub></td><td>2.4</td><td>Uchida et al. (2019)</td></tr><tr><td rowspan="5">Mesopelagic zone</td><td>Integrated POC stocks</td><td>iPOC<sup>zmax</sup><sub>zP + zi</sub></td><td>g C m<sup>-2</sup></td><td>b<sub>bp</sub>, [Chla]</td><td>2.6</td><td>Dall'Olmo and Mork (2014)</td></tr><tr><td>Small-POC fluxes</td><td>F<sub>POC</sub></td><td>mol C m<sup>-2</sup> y<sup>-1</sup></td><td>b<sub>bp</sub>, [Chla]</td><td>2.6</td><td>Dall'Olmo and Mork (2014)</td></tr><tr><td>Transfer efficiency below z<sub>P</sub></td><td>T<sub>eff</sub></td><td>%</td><td>b<sub>bp</sub>, [Chla]</td><td>2.6</td><td>Buesseler and Boyd (2009)</td></tr><tr><td>Remineralization fluxes</td><td>R</td><td>mol C m<sup>-3</sup> d<sup>-1</sup></td><td>[O<sub>2</sub>]</td><td>2.7</td><td>Su et al. (2022)</td></tr><tr><td>Total organic carbon fluxes</td><td>TOC</td><td>mol C m<sup>-2</sup> y<sup>-1</sup></td><td>[O<sub>2</sub>]</td><td>2.7</td><td>Su et al. (2022)</td></tr></tbody></table>

Note. Further details on their computation can be found in the sections and references indicated.

contrasts across bioregions. All the variables defined in this section are synthesized in Table 1, with their associated symbols, units, sections, and references.

## 2.1. Data Selection and Treatment

### 2.1.1. Data Set and Data Qualification

The data set consists of 406 BGC-Argo floats distributed in the global ocean (Argo, 2023). These floats completed profiles every 5–10 days, from the surface down to 1,000 or 2,000 m. Floats were equipped with Seabird SBE41 Conductivity Temperature Depth sensors and Seabird WetLabs sensors (3 types: FLBB, ECO-Triplet, MCOMS), which all measure chlorophyll *a* fluorescence (excitation at 470 nm and emission at 695 nm) as well as particle backscattering coefficient at 700 nm (b<sub>bp</sub> in m<sup>-1</sup>). Conductivity Temperature Depth data were quality-controlled following Wong et al. (2021). Chlorophyll *a* fluorescence was processed, converted into [Chla] (mg m<sup>-3</sup>), and quality-controlled according to Schmechtig et al. (2015) and Schmechtig, Claustre et al. (2018) (note that the data set was downloaded before the most recent official quality control procedure for the Chla was published by Schmechtig et al. (2023)). b<sub>bp</sub> data were processed and quality-controlled according to Schmechtig, Poteau et al. (2018). A part of this fleet (303 floats) was equipped with optodes estimating the dissolved oxygen concentration ([O<sub>2</sub>] in µmol kg<sup>-1</sup>). [O<sub>2</sub>] measurements were treated and quality-controlled following Thierry, Bittig, Gilbert et al. (2021), Thierry, Bittig, and Team, (2021). Lastly, 141 floats were equipped with radiometers (OCR Sensor Seabird-SATLANTIC) measuring downwelling photosynthetically available radiation (PAR) (Poteau et al., 2019; Schmechtig et al., 2017).

### 2.1.2. Selection of Profiles and Time Series

Based on the Argo Data Management Team's recommended procedures, profiles were first filtered based on treatment mode and quality control flags, with only “good” or “probably good” (or “probably bad” for real time b<sub>bp</sub>, see Table S1 in Supporting Information S1 for details) profiles being retained for analyses. Specific selection criteria for each variable are provided in Table S1 in Supporting Information S1. Besides these standard quality-controlled BGC-Argo procedures, additional checks were performed on b<sub>bp</sub> profiles to ensure a highly qualified data set for this study. Either using a so-called b<sub>bp</sub> audit (ftp://ftp.mbari.org/pub/BGC_argo_audits/BBP700) or visual inspections, the remaining potentially bad b<sub>bp</sub> profiles were removed (Table S2 in Supporting Information S1), providing a data set of 71,670 valid profiles of both b<sub>bp</sub> and [Chla].

After this quality-based selection, another round of data selection was performed, relying on both vertical and temporal resolution of profiles (Bock et al., 2022). Profile dates from the southern hemisphere were shifted forward by 6 months to adjust the seasonal difference between the hemispheres. Dates are further referred to as Julian days (1–365). Profiles were excluded if they displayed fewer than 15 measurements of both [Chla] and b<sub>bp</sub> below 300 m or if the shallowest measurement was below 5 m. The remaining profiles were compiled by year and day of the year to build a one-year time series. In the subsequent analysis, we removed those without profiles in
either the 1–20 or 345–365 Julian day periods to only retain time series spanning nearly a full year. Thus, we ensured that the remaining time series have sufficient temporal and vertical resolution to perform the interpolation needed in the remainder of this study. Following this procedure, 30,169 profiles from 300 floats remained to build the 586 time series analyzed in this study.

### 2.1.3. Signal Treatments

Additional signal treatments were performed on this quality-controlled data set. As in Cornec et al. (2021), a 5-point rolling median filter was applied to [Chla] data, and a 7-point rolling median and a 5-point rolling mean filter to b<sub>bp</sub> data. These procedures removed the positive “spikes” in both [Chla] and b<sub>bp</sub> signals, likely caused by the presence of large particles (approximately greater than 100 µm, commonly fecal pellets or phytoplankton aggregates) passing in front of the detection window of optical sensors (Briggs et al., 2011, 2013).

Converting raw chlorophyll a fluorescence counts to [Chla] requires removing a dark value from the profiles, which accounts for the sensor's background noise. Although the manufacturer provides dark values for each sensor, these may not remain accurate throughout the float's lifetime, as they are estimated at the time of sensor calibration. These values may change after the sensor is integrated into the float and can further drift over time. To address this issue, an in situ dark value is estimated at the beginning of the float's life and applied to profiles during real-time processing. However, this correction may sometimes result in negative [Chla] values at depth potentially due to sensor drift over the float's lifespan. To resolve this issue, an additional offset was calculated for each affected float, based on the noise level of [Chla] between 950 and 1,000 m (Briggs et al., 2020), under the assumption that [Chla] at this depth is effectively zero. This noise level was determined by first computing, for each affected float, the median of all Chla profiles below 300 m. Then, the 50-m bins with a mean exceeding twice this median were removed, and a second median was calculated without these bins. Profiles with negative values were subsequently corrected by setting their minimum Chla value to this newly determined offset and shifting the entire profile accordingly. Lastly, a zooplankton detection algorithm (Haëntjens et al., 2020) was applied to b<sub>bp</sub> profiles to detect and filter out layers of high spike density that correspond to a signal potentially contaminated by the presence of zooplankton, an anomaly not identified by the previous spike removal step.

Finally, POC concentration and phytoplankton carbon biomass (C<sub>phyto</sub>) were computed from the final data set of [Chla] and b<sub>bp</sub> profiles. First, even though b<sub>bp</sub> correlates to first order with particle concentration (Boss et al., 2009; Koestner et al., 2021; Neukermans et al., 2012), it is known to be sensitive to particle assemblage composition (Stramski et al., 2004). This results in difficulties in predicting global scale POC from b<sub>bp</sub> alone. Koestner et al. (2024) recently proposed a new algorithm that includes [Chla] measurements to derive POC and better account for changes in particle composition over time and depth, ultimately enabling global scale estimation of small-POC. Small-POC was then retrieved from b<sub>bp</sub>(700 nm) and [Chla] using the multivariate algorithm proposed by Koestner et al. (2024) in the following two steps:

$$POC^* = 52.82 b_{bp}^{0.1353} \zeta^{0.8849} \zeta^{0.2268 \log b_{bp}} \quad (1)$$

With $\zeta$ [mg m<sup>-2</sup>] calculated as Chla:b<sub>bp</sub>. If POC* <36.8, it was corrected from a bias to compute POC:

$$POC = 10^{-0.734} \times POC^* 1.469 \quad (2)$$

If POC ≥36.8, POC = POC*. They recommend its use for large-scale biogeochemical studies using BGC-Argo float data to better understand and constrain the BCP.

C<sub>phyto</sub> (mg C m<sup>-3</sup>) was calculated from b<sub>bp</sub>(700 nm) by first converting it into b<sub>bp</sub>(470 nm; Uchida et al., 2019):

$$b_{bp}(470) = b_{bp}(700) \times \left(\frac{470}{700}\right)^{-0.78} \quad (3)$$

Then, b<sub>bp</sub>(470 nm) was converted into C<sub>phyto</sub>; Graff et al., 2015:

$$C_{phyto} = 12128 \times b_{bp}(470) + 0.59 \quad (4)$$
## 2.2. Computation of Key Depth Horizons

The euphotic depth ($z_{EU}$), the depth at which the PAR intensity is 1% of the surface value, was retrieved from PAR profiles when available on floats. PAR profiles were estimated following Cornec et al. (2021) for floats not equipped with radiometers and for daytime profiles. First, surface PAR was calculated using a clear-sky model from Gregg and Carder (1990). It was then propagated along the water column using [Chla] profiles and a diffuse attenuation coefficient ($K_d$) adapted from Morel et al. (2007), the latter being computed from empirical bio-optical relationships and varying from region to region according to trophic conditions. Profiles were finally corrected for cloud cover. Locations of PAR time series (modeled or from floats) at the global scale are provided in Figure S1 in Supporting Information S1. Only time series with a complete annual cycle of PAR (modeled or measured by the floats) were used to compute annual variations of $z_{EU}$. Since many floats were profiled at varying times of the day, including nighttime, the $z_{EU}$ time series data set was reduced compared to the Chla and $b_{bp}$ time series due to a lack of sufficient temporal resolution for many $z_{EU}$ time series. After bioregionalization steps (see Section 2.3), 76 $z_{EU}$ time series were available for the remaining analysis, including six estimated from modeled PAR time series (see locations in Figure S2 in Supporting Information S1). Additionally, no significant effect of using modeled PAR versus measured PAR to estimate $z_{EU}$ was evidenced, especially during the productive period and given the final number of modeled $z_{EU}$ time series used in this study (six time series) compared to the global $z_{EU}$ time series data set (Figures S3 and S4 in Supporting Information S1).

The mixed layer depth ($z_{MLD}$) was defined as the depth where density increases by 0.03 kg m⁻³ compared to the one at 10 m (de Boyer Montégut, 2004). The depth of the productive zone ($z_p$), defined as the maximum depth between $z_{EU}$ and $z_{MLD}$ (Buesseler et al., 2020), was considered the depth from which particles are exported in the mesopelagic zone after their production.

## 2.3. Bioregionalization

Time series of [Chla] and $b_{bp}$ needed to be homogenized to account for differences in vertical and temporal sampling resolution between floats. The annual [Chla] and $b_{bp}$ time series were interpolated at 1 m resolution over the vertical dimension (from 1 to 1,001 m) and at 1-day resolution over the 365 annual days. To ensure that the clustering steps account for variations over time and depth down to 1,000 m rather than absolute values of Chla or $b_{bp}$, each time series was centered to mean 0 and scaled to a standard deviation of 1. Time series were transformed into vectors and concatenated to synthesize the temporal and vertical information of each concomitant time series of $b_{bp}$ and [Chla]. A final matrix, containing all the time series data, was then constructed by aggregating concatenated vectors. The number of rows represents the full extent of each time series across depth and time of the different variables (here Chla and $b_{bp}$), resulting in *nrows* = *depths* × *day* × *parameters*, while the number of columns corresponds to the total number of time series in the data set.

Following Bock et al. (2022), an empirical orthogonal function (EOF) was performed on this matrix. This analysis computed the main modes of variability within the data set, along with weightings corresponding to the relative importance of each mode of variability for individual time series. Then, a divisive hierarchical clustering analysis was used to classify the time series based on the EOF weightings. This combination of analysis (EOF and clustering steps) was performed twice, first to segregate high latitude time series from low latitude time series (Figures S5, S6, and S7 in Supporting Information S1) and then to extract clusters within this selection. The first run was performed on 586 yearly time series of [Chla] and $b_{bp}$ originating from the global ocean. This run produced two groups of time series, generally corresponding to a partition in high and low latitudes (Figure S7a in Supporting Information S1). To maintain coherence with our definition of the high latitudes, all the time series below 25° were manually re-assigned as low-latitude ones (Figure S7b in Supporting Information S1). This step removed some time series below 25° that present similarities from the clustering with the high-latitude group. The second run, exclusively performed on the 335 time series classified as high-latitude in the previous step, allowed defining groups based on the spatiotemporal variability of [Chla] and $b_{bp}$ for those environments.

After these two runs, three main clusters were determined (Figures S8, S9, and S10 in Supporting Information S1). While smaller scale patterns could have been highlighted with a higher number of clusters, the choice of
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Average position of individual high-latitude BGC-Argo time series corresponding to the six bioregions (left). SH stands for the southern hemisphere, NH for the northern hemisphere, HCB for high chlorophyll bloom, LCB for low chlorophyll bloom, and DCM for deep chlorophyll maximum. Latitudinal repartition of time series (right). Black lines in the Southern Ocean, from north to south, correspond to the subtropical front (STF), the subantarctic front (SAF), and the polar front (PF) (method from Gray et al. (2018) applied on sea surface temperature climatology from Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing (https://doi.org/10.48670/moi-00052) from 2005 to 2022).</figcaption></figure>

using two vectors to define three clusters at high latitudes was made by maximizing the silhouette score (Rousseeuw, 1987) (a higher score reflects higher intra-cluster similarities), and the number of time series within each cluster. This ultimately allowed the characterization of the small particle fraction of the BCP at the basin/ regional scale robustly captured. To avoid a bias due to the differential distribution of the time series between South and North and to capture specific dynamics in southern and northern hemispheres, clusters were separated between hemispheres, resulting in six final clusters (Figure 1). The number of [Chla] and $b_{bp}$ time series available for each step of the bioregionalization are resumed in Table 2.

Within clusters, time series were considered homogeneous for phytoplankton biomass and small particles. Thus, all individual time series within a cluster were averaged for [Chla] and $b_{bp}$ to obtain climatological time series. Additionally, when available, other BGC-Argo variables (and derivatives, all described in the previous section) were averaged within these clusters. The number of time series averaged for these variables is resumed in Table 3.

Hereafter, we refer to these final clusters as bioregions as they illustrate major production regimes, even though they don't show precise geographical boundaries. In the following Sections (2.4–2.9), the variables and methods described were computed based on the averaged time series of each bioregion.

<caption>**Table 2**</caption>

Number of Time Series of [Chla] and $b_{bp}$ Available at the Different Steps of Bioregionalization

<table><thead><tr><th colspan="6">Global data set (First round)</th></tr><tr><th colspan="6">High latitudes (Second round)</th></tr><tr><th colspan="6">Southern high latitudes</th></tr><tr><th colspan="6">Northern high latitudes</th></tr></thead><tbody><tr><th colspan="6">265</th></tr><tr><th>SH-HCB</th><th>SH-LCB</th><th>SH-DCM</th><th>NH-HCB</th><th>NH-LCB</th><th>NH-DCM</th></tr><tr><td>26</td><td>187</td><td>52</td><td>48</td><td>9</td><td>13</td></tr></tbody></table>
<table><caption>**Table 3**
Number of Time Series Averaged for Each Parameter Within the Six Bioregions</caption><thead><tr><th rowspan="2"></th><th colspan="3">Southern hemisphere</th><th colspan="3">Northern hemisphere</th><th rowspan="2">Total</th><th rowspan="2">Number of floats</th></tr><tr><th>SH-HCB</th><th>SH-LCB</th><th>SH-DCM</th><th>NH-HCB</th><th>NH-LCB</th><th>NH-DCM</th></tr></thead><tbody><tr><td>Chla; b<sub>bp</sub>; C<sub>phyto</sub></td><td>26</td><td>187</td><td>52</td><td>48</td><td>9</td><td>13</td><td>335</td><td>163</td></tr><tr><td>T; S</td><td>24</td><td>172</td><td>48</td><td>37</td><td>8</td><td>11</td><td>300</td><td>158</td></tr><tr><td>PAR</td><td>2</td><td>35</td><td>9</td><td>20</td><td>5</td><td>5</td><td>76</td><td>38</td></tr><tr><td>DOXY</td><td>25</td><td>169</td><td>36</td><td>19</td><td>8</td><td>8</td><td>265</td><td>128</td></tr><tr><td>AOU</td><td>24</td><td>159</td><td>35</td><td>19</td><td>8</td><td>8</td><td>253</td><td>128</td></tr></tbody></table>

## 2.4. Bloom Metrics

The methodology developed by Uchida et al. (2019) was followed to characterize phytoplankton phenology across bioregions. First, the bulk phytoplankton carbon ($iC_{phyto}$) was defined as the integrated $C_{phyto}$ over the water column (here from the surface down to 1,000 m). Before integration, $C_{phyto}$ data were depth-masked using a [Chla] threshold to account for living particulate organic matter only (Uchida et al., 2019). This threshold was calculated using the 90th percentile of [Chla] below 200 m, below which $C_{phyto}$ was set to 0. $iC_{phyto}$ time series were smoothed with a 30-day running mean to remove high-frequency variability. Smoothed $iC_{phyto}$ time series were then used to compute three bloom temporal metrics. The bloom onset was calculated as the time of the annual minimum $iC_{phyto}$. Then, the bloom apex was calculated as the time of annual maximum $iC_{phyto}$. The bloom climax was calculated as the time of maximum accumulation rate of $iC_{phyto}$ ($\mu_{max} = \frac{1}{iC_{phyto}} \times \frac{diC_{phyto}}{dt}$) (Figure 2). Although several methods are used to characterize bloom phenology, this method is robust (Vives et al., 2023) and allows the consideration of phytoplankton biomass accumulation at depth, unlike satellite-based methods.

## 2.5. Chla:b<sub>bp</sub> Ratio as an Optical Index

The Chla:b<sub>bp</sub> ratio was used as an optical index for the composition and size of particle assemblage (Cetinić et al., 2015) and/or for the physiological state of phytoplankton cells (Barbieux et al. (2018); b<sub>bp</sub>:Chla was used instead in this specific study) in the productive layer (Figure 3, top panels). In the mesopelagic zone, the Chla:b<sub>bp</sub>

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Time series of $iC_{phyto}$ smoothed with a 30-day window (green solid lines) and accumulation rates ($\mu$; black solid lines). The green shaded area around the green solid line shows the standard error of $iC_{phyto}$ propagated from $C_{phyto}$ values in each individual time series. Vertical dashed lines show the three bloom temporal metrics (i.e., onset in yellow, climax in orange, apex in red). Shaded light green areas indicate the duration of the bloom from the onset to the apex.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Time series of Chla:b<sub>bp</sub> ratio for each of the six bioregions. Two different ranges of values are shown for surface and mesopelagic values. A mask is applied to data based on the 90th percentile of [Chla] values from 500 to 600 m within the three southern bioregions, below which [Chla] values are considered too low for the ratio to be interpreted.</figcaption></figure>

ratio takes very low values, the nuances of which can be related to the degree of freshness of exported particles (Figure 3, bottom panels). To avoid misinterpretation of this ratio when [Chla] is too low, Chla:b<sub>bp</sub> values were calculated only when [Chla] was above a threshold defined as the 90th percentile of [Chla] values between 500 and 1,000 m in the three northern bioregions, where higher [Chla] occurred in the mesopelagic zone compared to the southern bioregions. Below this [Chla] threshold, values were set to 0.

## 2.6. Annual Estimates of Small-POC Fluxes

The method proposed by Dall’Olmo and Mork (2014) was followed to characterize carbon fluxes in the mesopelagic zone, which estimates particle carbon fluxes from the depth-integrated stocks of POC below $z_P$. The mesopelagic zone was subdivided into iterative layers of decreasing thickness in each bioregion. The lower boundary ($z_{max}$) was estimated for each bioregion as the greatest multiple of 100 below $z_P$ in 100 m increments without exceeding the 1,000 m limit. The upper boundary of these layers changed over the layers, defined as $z_P$ for the thickest layer and then gradually increased by 100 m for each thinner layer. $z_P$ was taken as a reference depth to derive the others as it is the depth below which new production can be considered negligible and from which the sinking particle flux starts to attenuate (Buesseler et al., 2020). As the maximum $z_P$ differed according to bioregions (between 135 and 312 m), the number of layers differed similarly for each bioregion (between 6 and 8 layers). After computing POC concentrations over the water column (Figure 4, middle panels), the daily depth-integrated POC stocks ($\text{iPOC}_{z_p+z_i}^z$ [g C m$^{-2}$]) were calculated over the iteratively deeper layers described above. $\text{iPOC}_{z_p+z_i}^z$ time series were smoothed for each layer using a 30-day window (Figure 5). Annual POC fluxes ($F_{POC}$ [g C m$^{-2}$ y$^{-1}$]) were derived as the slope of the segment between the annual iPOC minimum and the annual iPOC maximum (Figure 5). The segment's start and end points were dynamically adjusted based on the depth layer at which the flux was calculated to account for potentially longer accumulation events in the mesopelagic zone and the time lag between accumulation just below $z_P$ and that at deeper layers. Following Dall’Olmo and Mork (2014), the small-POC flux was assumed to be negligible at $z_{max}$.

Export production, which characterizes the BCP strength for small particles, corresponds to $F_{POC}$ at $z_P$. Transfer efficiency at a certain depth below $z_P$ ($T_{\text{eff}(z_P+t)}$) was defined as the part of the flux arriving at a depth relative to the flux at $z_P$ (Buesseler & Boyd, 2009):
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Averaged one-year time series for each of the six bioregions of (a)–(d) surface [Chla] (0–150 m), (b)–(e) particulate organic carbon (0–1,000 m), and (c)–(f) AOU anomaly rates of change (0–1,000 m). On panels a, b, d and e, solid black, solid pink, and dashed lines show $z_{\text{MLD}}$, $z_{\text{EU}}$, and $z_p$, respectively. Vertical dashed lines show bloom timing metrics (i.e., onset in yellow, climax in orange, apex in red). On panels c–f, the red gradient shows a net $O_2$ input rate, and the blue gradient shows a net $O_2$ consumption rate. Black and gray contours show, respectively, the beginning and the end of the productive period.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Time series of integrated particulate organic carbon stocks ($iPOC_{zp+zi}^{max}$). Gray to black lines show iPOC in iterative deepening layers (decreasing shading intensity corresponds to the deepening of the upper limit of the layer until $z_{\text{max}}$). Blue solid lines show $z_{\text{MLD}}$. Green segments show seasonal net accumulation $iPOC_{zp+zi}^{\text{max}}$ within each layer, from minimum annual $iPOC_{zp+zi}^{\text{max}}$ to maximum annual $iPOC_{zp+zi}^{\text{max}}$. Carbon fluxes are derived from the slopes of these segments. Colored triangles show bloom timing metrics (i.e., onset in yellow, climax in orange, apex in red).</figcaption></figure>
$$T_{\text{eff}(zp+i)} = \frac{\text{FPOC}(zp + i)}{\text{FPOC}(zp)} \qquad (5)$$

## 2.7. Annual Estimates of TOC Fluxes

TOC fluxes were estimated from ANCP using a method adapted from Su et al. (2022). This approach is based on depth-integrated variations in oxygen concentration ([O₂]) over the mesopelagic zone, as measured by BGC-Argo floats. Both physical and biological processes control variations in [O₂]. Assuming that there is no new O₂ production and gas exchange with the atmosphere below the productive layer and that the effects of both vertical and horizontal advection and diffusion of O₂ are negligible at the scale of bioregions, [O₂] drawdowns mainly reflect remineralization processes (Arteaga et al., 2019; Hennon et al., 2016). To compare TOC fluxes with small particle fluxes, the method from Su et al. (2022) was adapted by estimating fluxes on the same depth layers as in Section 2.6. Several modifications were necessary, not least because the iterative layers are of variable depth. The modified method is summarized as follows.

TOC fluxes were computed from depth-integrated AOU-anomaly rates of change at depths relative to zₚ (Figure 4, bottom panels) using the following steps. Every individual O₂ time series (Figure S11a in Supporting Information S1) was first converted into AOU (µmol kg⁻¹) using Garcia and Gordon (1992) (Figure S11b in Supporting Information S1) to remove the temperature's effect on oxygen solubility:

$$\text{AOU} = O_{2,\text{sat}} - O_2 (\mu\text{mol kg}^{-1}) \qquad (6)$$

With O₂, sat being the saturation oxygen concentration (µmol kg⁻¹) depending on potential temperature and salinity, and O₂ the measured oxygen concentration (µmol kg⁻¹).

For each AOU time series, an average AOU annual profile was calculated (Figure S11c in Supporting Information S1). AOU-anomalies time series were then derived by subtracting this mean profile from AOU data at depths from the surface down to 1,000 m (Figure S11d in Supporting Information S1). The mean annual vertical [O₂] gradient varies among time series but particularly between bioregions (Figure S12 in Supporting Information S1). As the iterative depth layers used for flux computation were defined according to zₚ, their depth varies over time across this [O₂] (and consequently AOU) gradient. Without correcting for this gradient, observed AOU drawdowns and increases would primarily reflect depth-related variations rather than the organic matter remineralization. Computing AOU-anomalies time series, therefore, avoided accounting for this gradient in AOU rates of change and subsequently in carbon flux estimations.

After averaging these AOU-anomalies time series for each bioregion, the rates of change in AOU-anomalies were computed at each depth below zₚ and over the productive period (Dₚₐρ). Dₚₐρ was defined as the period in the annual cycle where most of the export production and remineralization occurs (Arteaga et al., 2019). Given the strong stratification of the water column during this period, entrainment and de-entrainment of oxygen between the different layers were considered negligible and unlikely to significantly affect TOC flux estimates. The start and end limits for Dₚₐρ were, respectively, set by the annual smoothed AOU-anomalies (90-day window) minimum and maximum at each depth below zₚ. These limits dynamically change with depth accounting for potential longer or shorter productive periods and for the time lag between remineralization just below zₚ and that in deeper layers. The remineralization fluxes (R, mol C m⁻³ d⁻¹) during Dₚₐρ were determined at each depth as

$$R(z) = \frac{1}{1.45} \times \frac{d[\text{AOU} - \text{anomaly}](z)}{dt} \qquad (7)$$

Where 1/1.45 stands for the respiratory quotient for organic matter remineralization (Anderson & Sarmento, 1994) and d[AOU-anomaly]/dt stands for the slope of linear regression of AOU-anomalies at a given depth below zₚ over Dₚₐρ. Non-significant R(z) values (p > 0.05) were set to zero.

Finally, ANCP (mol C m⁻² y⁻¹) within each layer below zₚ was calculated as the depth-integrated product of R(z) by Dₚₐρ(z):
$$ANCP = \int_{z_p+i}^{z_{max}} R(z) \times D_{prod}(z) \quad (8)$$

ANCP finally represents the remineralized organic matter integrated over a full annual cycle and large geographical and temporal scales. Consequently, ANCP is interpreted and referred to hereafter as TOC flux.

## 2.8. Potential Biases in Carbon Fluxes Estimations

POC flux estimates associated with small particles represented a lower limit over the water column for several reasons. First, a fraction of the small-POC likely underwent remineralization in the mesopelagic zone, meaning it did not contribute to small-POC accumulation and was omitted in our calculations. Nevertheless, this fraction contributed to carbon demand at depth, potentially from gravitational flux, and should have been included in our estimates (Wang & Fennel, 2022). Remineralization rates could have been used to estimate this missing fraction by analyzing periods when oxygen drawdown (AOU increase) and small-POC accumulation coincided in each bioregion (Figure S13 in Supporting Information S1). However, in addition to small particles, DOC and larger particles are also remineralized in the mesopelagic zone with varying proportions across bioregions, making it impossible to specifically isolate and estimate small particle remineralization in this study. On the other hand, the assumption that the flux at $z_{max}$ was negligible may not hold in some bioregions. Even though Terrats et al. (2023) showed that small-POC fluxes at 1,000 m were negligible in the Southern Ocean, in the present study, the flux at $z_{max}$ represented between 1.9% and 2.8% of export at $z_p$ in the Southern Hemisphere but between 5.3% and 6.5% in the northern hemisphere (Table S3 in Supporting Information S1). A non-negligible flux at $z_{max}$ would underestimate export flux over the water column (Dall’Olmo & Mork, 2014). The extent to which this would increase our estimates in each bioregion requires further investigation.

TOC flux estimations relied on the remineralization of organic matter to estimate the exported material. However, not all the exported POCs were remineralized; a fraction likely escaped remineralization, such as fast-sinking large particles (Riley et al., 2012). These particles escaping remineralization down to 900–1,000 m were not accounted for in our calculations, and TOC flux estimates thus represented a lower limit for the mesopelagic zone. Moreover, potential oxygen diffusion at the $z_p$ interface during the productive period would mitigate the oxygen decrease caused by remineralization, resulting in lowered TOC flux estimates. In deeper layers, the $O_2$ gradient is weaker (see Figure S11 in Supporting Information S1) and vertical diffusion considered negligible. Thus, small-POC contribution to TOC fluxes is more likely to be overestimated at $z_p$ than deeper in the mesopelagic domain. Additionally, a constant global respiratory quotient was used to derive carbon fluxes from oxygen measurements, while this value is known to vary regionally (Moreno et al., 2022) due to its sensitivity to the physiological state of heterotrophic communities, ecological pathways, and carbon sources (Berdal et al., 1995; Romero-Kutzner et al., 2015). This quotient is also susceptible to change with depth (Gerace et al., 2023) and may bring bias when comparing values from different ocean regions or over the water column. Finally, the assumption that positive and negative values of horizontal oxygen advection compensate for each other may not be applicable when averaging only a few time series, leading to temporal variations in oxygen that do not solely rely on the biological remineralization process. In such cases, local events may also have a greater influence on final estimates.

## 2.9. Uncertainties Computation

The largest component of the uncertainty resulted from the conversion of $b_{bp}$ and [Chla] into POC, estimated by Koestner et al. (2024), and from averaging the time series within a bioregion. The latter introduces uncertainties from the spatio-temporal variability of individual time series within a bioregion. The resulting POC uncertainty was then propagated throughout the analysis using a Monte Carlo approach (5,000 iterations). For TOC fluxes, the uncertainty associated with each mean vertical profile of AOU was propagated to the mean AOU-anomaly profiles. Then, a Monte Carlo approach propagated this uncertainty to the TOC flux estimation (5,000 iterations). Uncertainties related to sensors were considered negligible with respect to this spatio-temporal variability.
<table><caption>**Table 4**

Metrics of Phytoplankton Dynamics (±Standard Error)</caption><thead><tr><th rowspan="2"></th><th colspan="2">iC<sub>phyto</sub> min (gC.m<sup>-2</sup>)</th><th colspan="2">μ<sub>max</sub> (d<sup>-1</sup>)</th><th colspan="2">iC<sub>phyto</sub> max (gC.m<sup>-2</sup>)</th><th rowspan="2">Bloom duration (onset to apex) (days)</th></tr><tr><th>Value</th><th>Onset (Julian day)</th><th>Value (10<sup>-3</sup>)</th><th>Climax (Julian day)</th><th>Value</th><th>Apex (Julian day)</th></tr></thead><tbody><tr><td>SH-HCB</td><td>1.84 (±0.06)</td><td>365</td><td>12.9</td><td>127</td><td>6.96 (±0.22)</td><td>184</td><td>184</td></tr><tr><td>SH-LCB</td><td>1.99 (±0.05)</td><td>359</td><td>8.5</td><td>80</td><td>4.63 (±0.14)</td><td>176</td><td>182</td></tr><tr><td>SH-DCM</td><td>2.17 (±0.05)</td><td>324</td><td>7.9</td><td>66</td><td>3.54 (±0.14)</td><td>123</td><td>164</td></tr><tr><td>NH-HCB</td><td>1.21 (±0.06)</td><td>365</td><td>18.3</td><td>67</td><td>4.31 (±0.21)</td><td>149</td><td>149</td></tr><tr><td>NH-LCB</td><td>1.82 (±0.06)</td><td>336</td><td>14.3</td><td>72</td><td>3.33 (±0.11)</td><td>104</td><td>133</td></tr><tr><td>NH-DCM</td><td>1.70 (±0.06)</td><td>352</td><td>11.9</td><td>65</td><td>3.19 (±0.10)</td><td>89</td><td>102</td></tr></tbody></table>

## 2.10. Relationships Between Surface Metrics and Small-POC Fluxes in the Mesopelagic Zone

Relationships between surface metrics and small-POC fluxes in the mesopelagic zone were analyzed, focusing on both their export strength and transfer efficiency below $z_p$ ($T_{eff}$). For surface metrics, phenology timings (onset, climax, apex), associated values ($iC_{phyto}$, $μ_{max}$) and duration between timings were considered. Relationships analyzed further in this study exhibited a $R^2$ greater than 0.7. The significance of correlations was assessed using a Spearman's rank correlation test ($p$-value <0.05).

# 3. Results

## 3.1. Bioregionalization at High Latitudes

Following clusterization described in Section 2.3, six bioregions were determined at high latitudes (Figure 1), three in northern high latitudes: Northern Hemisphere High Chlorophyll Bloom (NH-HCB), Northern Hemisphere Low Chlorophyll Bloom (NH-LCB) and Northern Hemisphere Deep Chlorophyll Maximum (NH-DCM); and three in southern high latitudes: Southern Hemisphere High Chlorophyll Bloom (SH-HCB), Southern Hemisphere Low Chlorophyll Bloom (SH-LCB) and Southern Hemisphere Deep Chlorophyll Maximum (SH-DCM).

In the Southern Ocean, our results highlighted a latitudinal and inter-basin separation of bioregions (Figure 1), with SH-DCM time series primarily occurring north of the Subtropical Front (STF), SH-HCB time series south of the Subantarctic Front (SAF) and almost exclusively in the Pacific basin. Contrastingly, SH-LCB time series were found in all three basins, mainly south of the STF. In the Northern Hemisphere, the NH-HCB time series primarily occurred above 50°N and exclusively in the North Atlantic basin, while the NH-LCB and NH-DCM time series were centered on or below 50°N and distributed between the Atlantic and Pacific basins (Figure 1). Each bioregion was characterized by a distinct spatiotemporal dynamic in both phytoplankton biomass (Section 3.2) and particle assemblage (Section 3.3) in the productive zone and mesopelagic organic carbon export and transfer (Section 3.4).

## 3.2. Phytoplankton Phenology in the Productive Zone

In each hemisphere, bioregions exhibited a distinct bloom phenology based on phytoplankton carbon biomass ($C_{phyto}$) (Figure 2, Table 4). In the southern hemisphere, SH-HCB had the highest $iC_{phyto}$ max with the latest onset, climax and apex and the longest bloom duration (duration from onset to apex). In contrast, SH-DCM showed the lowest $iC_{phyto}$ max, the earliest onset, climax and apex and the shortest bloom duration. SH-LCB held between these two bioregions both in timings and values (Table 4). In the northern hemisphere, NH-HCB stood out from NH-LCB and NH-DCM by exhibiting the highest $iC_{phyto}$ max, the latest onset and apex, and the longer bloom duration. NH-LCB and NH-DCM were more similar, but NH-LCB exhibited a longer bloom duration due to an earlier onset and a later apex.

Between hemispheres, the most comparable bioregions in terms of timings were NH-LCB with NH-DCM and SH-DCM, and SH-LCB with NH-HCB (Figure 2, Table 4). However, metrics still spanned a large range of values and timings. First, the timing of the bloom onset exhibited a one-month difference between the earliest
<caption>**Table 5**</caption>

Small-POC and Total Organic Carbon Fluxes at $z_p$ (Strength), $T_{eff}$ at 100 m Below $z_p$ (± Standard Deviation), and $z_p$ and $z_{max}$ Annual Minimum, Maximum and Mean Depths in Each Bioregion

<table><thead><tr><th rowspan="2"></th><th colspan="2">Strength (molC m<sup>-2</sup> y<sup>-1</sup>)</th><th colspan="2">T<sub>eff</sub> (%)</th><th rowspan="2">z<sub>p</sub> min; z<sub>max</sub> min (m)</th><th rowspan="2">z<sub>p</sub> max; z<sub>max</sub> max (m)</th><th rowspan="2">z<sub>p</sub> mean; z<sub>max</sub> mean (m)</th></tr><tr><th>Small-POC</th><th>TOC</th><th>Small-POC</th><th>z<sub>p</sub> min</th></tr></thead><tbody><tr><td>SH-HCB</td><td>0.74 (±0.01)</td><td>6.3 (±2.5)</td><td>35 (±2)</td><td>50; 850</td><td>186; 986</td><td>110; 910</td></tr><tr><td>SH-LCB</td><td>0.52 (±0.01)</td><td>2.6 (±2.4)</td><td>33 (±2)</td><td>54; 754</td><td>205; 905</td><td>113; 805</td></tr><tr><td>SH-DCM</td><td>0.32 (±0.01)</td><td>3.6 (±3.1)</td><td>30 (±2)</td><td>50; 850</td><td>135; 935</td><td>82; 882</td></tr><tr><td>NH-HCB</td><td>0.51 (±0.01)</td><td>5.0 (±1.1)</td><td>38 (±2)</td><td>37; 637</td><td>313; 913</td><td>127; 727</td></tr><tr><td>NH-LCB</td><td>0.32 (±0.01)</td><td>4.2 (±3.4)</td><td>61 (±3)</td><td>40; 840</td><td>198; 998</td><td>85; 885</td></tr><tr><td>NH-DCM</td><td>0.24 (±0.01)</td><td>2.8 (±2.0)</td><td>68 (±4)</td><td>53; 853</td><td>134; 934</td><td>74; 874</td></tr></tbody></table>

(SH-DCM) and the latest (SH-HCB and NH-HCB). Apex showed even greater variation, spanning 3 months between the earliest (SH-DCM) and the latest (SH-HCB). These differences in onset and apex resulted in varying bloom durations ranging from 3.5 months in NH-DCM to 6 months in SH-HCB and SH-LCB. Climax also differed by up to 2 months, with the earliest in NH-DCM and the latest in SH-HCB. Finally, $iC_{phyto max}$ exhibited strong variation across bioregions, reaching its highest in SH-HCB, more than twice that of the lowest value in NH-DCM.

### 3.3. Chla: $b_{bp}$ Ratio: From Surface to Depths

#### 3.3.1. Particles Assemblage in the Productive Zone

Even though detangling the effect of light or nutrient stress from changes in community composition on the Chla: $b_{bp}$ ratio is difficult, the observed trends in the ratio capture contrasted dynamics in particle assemblage in the productive zone across bioregions (Figure 3, top panels).

In SH-HCB, SH-LCB, and NH-HCB, high-latitude environments and productive regions, the Chla: $b_{bp}$ signal likely represents phytoplankton dynamics (Bellacicco et al., 2019) and, more particularly, phytoplankton community composition (Barbieux et al., 2018). Its increase in the productive zone from winter to summer (to 618 ± 73 in SH-HCB, 643 ± 87 mg m⁻² in NH-HCB, 400 mg m⁻² in SH-LCB, Figure 3, Figure S15 in Supporting Information S1) likely reflected a shift of the phytoplankton community from pico-, nano-phytoplankton to micro-phytoplankton (Cetinić et al., 2015).

In NH-LCB and NH-DCM, the ratio increased in summer, respectively, to 461 ± 71 mg m⁻² and 444 ± 101 mg m⁻², but with vertical variability (Figure 3 and Figures S14, S15, S16 in Supporting Information S1). The lower surface ratio suggested a dominance of pico- and nano-phytoplankton communities, while its increase with depth suggested photo-acclimation or nutrient limitation (Cetinić et al., 2015; Cornec et al., 2021). The characterization of the particle assemblage based only on the Chla: $b_{bp}$ ratio remained complex in these two bioregions.

SH-DCM Chla: $b_{bp}$ values within the productive zone showed contrasted dynamics compared to the other bioregions, with lower values (264 ± 57 mg m⁻²) during spring and summer. This difference resulted from a stronger increase in $b_{bp}$ than in [Chla] (Figure S14 in Supporting Information S1), likely reflecting the dominance of pico- and nano-phytoplankton communities during these seasons (Cetinić et al., 2015; Rembauville et al., 2017). However, the [Chla] increased more at the DCM depth (Figures S15 and S16 in Supporting Information S1) compared to upper layers, which could be explained by the photoacclimation process near the euphotic depth during this period.

#### 3.3.2. Particles Freshness in the Mesopelagic Zone

Chla: $b_{bp}$ ratio values in the mesopelagic zone, although very low (below 250 mg m⁻²) compared to those in the productive zone, highlighted injection of relatively fresh material at depth in late winter when the MLD is at its deepest in each bioregion (Figure 3, bottom panels). This phenomenon appeared to be reinforced in the northern
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Mean (solid lines and dots) and standard deviation (shaded area) of (a) small-POC fluxes and (b) T<sub>eff</sub> at depths below z<sub>p</sub> for each bioregion (colors).</figcaption></figure>

hemisphere with even deeper injections reaching at least 400 m to more than 600 m in NH-HCB. By contrast, in summer, such fresh particles reached a depth of 200 m or less in all the bioregions.

**3.4. Organic Carbon Fluxes Over the Mesopelagic Zone**

**3.4.1. Regional Variability of Small-POC Fluxes**

In all bioregions, POC concentrations associated with small particles (<100 µm) in the mesopelagic zone showed strong seasonality, as highlighted by high POC concentration plumes over spring and summer, sometimes reaching 1,000 m depth (Figure 4, middle panels). For small particles, a stronger export at z<sub>p</sub> (i.e., strength) was observed in higher latitudes (SH-HCB, SH-LCB, and NH-HCB) above 0.5 mol C m<sup>-2</sup> y<sup>-1</sup> (Table 5, Figure 6). The strength was about twice as low at lower latitudes (SH-DCM, NH-LCB, and NH-DCM) (Table 5, Figure 6). T<sub>eff</sub> at 100 m below z<sub>p</sub> were similar for four bioregions (30 ± 2% to 38 ± 2% for the HCB, SH-DCM, and SH-LCB), and significantly almost twice stronger in the NH-LCB and NH-DCM (respectively 61 ± 3% and 68 ± 4%) (Table 5, Figure 6).

Based on these results, bioregions were classified into three categories: (a) high strength/low-efficiency regimes for SH-HCB, SH-LCB, and NH-HCB, (b) low strength/low-efficiency regime for SH-DCM, and (c) low strength/high-efficiency regimes for NH-LCB and NH-DCM.

**3.4.2. Regional Variability of TOC Fluxes**

TOC fluxes did not exhibit the same unidirectional latitudinal variations observed in small-POC fluxes (Table 5, Figure 7). Indeed, in the Southern Ocean, fluxes reached the highest values in the SH-HCB but declined in the SH-LCB, while SH-DCM fluxes were intermediate to these two bioregions. In the northern hemisphere, TOC flux estimates were the highest at lower latitudes (NH-LCB and NH-DCM) and lower at higher latitudes in NH-HCB (Table 5, Figure 7). However, given the observed standard deviations, no statistically significant differences can be evidenced between bioregions.

**3.5. Relationships Between Surface Metrics and Small-POC Fluxes in the Mesopelagic**

Positive relationships were observed between the strength of export of small particles and apex, climax, and iC<sub>phyto</sub> at apex (Figure S17 in Supporting Information S1). Conversely, a negative relationship was found between T<sub>eff</sub> of

<figure><img src="image_10.png" /><figcaption>**Figure 7.** Mean total organic carbon fluxes (solid lines and dots) and associated standard deviation (dashed lines) at depths below z<sub>p</sub> for each bioregion (colors).</figcaption></figure>
small particles and duration of the bloom from onset to apex (Figure S17 in Supporting Information S1). The correlation between small particle $T_{\text{eff}}$ and bloom duration showed a *p*-value <0.07. Although this latter result is less statistically significant, it is still considered relevant and discussed herein. The Chla:b<sub>bp</sub> ratio was excluded from these linear analyses as its variations over time and depth are affected by multiple factors and can be interpreted in different ways (see Section 3.3 for results on the trends in the Chla:b<sub>bp</sub> ratio). However, microphytoplankton communities appeared to be dominant in spring/summer in high strength regimes, while more pico-, nano-phytoplankton appeared to be dominant in low strength regimes.

# 4. Discussion

## 4.1. Validation of Organic Carbon Fluxes Estimations

POC fluxes estimated from bio-optical measurements of BGC-Argo floats are difficult to compare with estimates obtained using traditional methods, such as sediment traps or thorium-based estimates, as these methods are complementary rather than equivalent. They operate at different temporal and spatial scales and target distinct carbon fractions to estimate fluxes. Nonetheless, in the Norwegian Sea, Dall’Olmo and Mork (2014) estimated small-POC fluxes from individual floats and two consecutive years within a range of values which falls within our estimates in NH-HCB (Table 6). Additionally, although in low latitudes compared to our studied regions, values reported by Kheireddine et al. (2020) using the same approach in the Red Sea, and Estapa et al. (2019) around Bermuda Atlantic Time-series Study from a different sensor are of the same order of magnitude to our estimates (Table 6), reinforcing the reliability of our results.

TOC flux (from ANCP) estimates in the Southern Ocean (Table 5) are within the same range as reported by previous studies (Hennon et al., 2016; Johnson et al., 2017; Arteaga et al., 2019; Su et al., 2022; X. Yang et al., 2024; see Table 6) using BGC-Argo floats to retrieve ANCP in the Southern Ocean but are determined using different methods (b<sub>bp</sub>, oxygen- or nitrate-based methods, over different depths ranges). These studies unveiled spatial variability in ANCP, encompassing latitudinal and basin-scale variations. Among these variations, results from this study particularly align with the results of Su et al. (2022), enhancing the confidence in adaptations made to their methodology. Indeed, they found patterns such as higher ANCP occurring near 60°S in the Pacific basin (where most of the SH-HCB time series are located) as well as lower ANCP around 50–55°S in all basins of the Southern Ocean (where most of SH-LCB time series are located). To our knowledge, in the northern hemisphere, the only study using ANCP derived from BGC-Argo floats in the mesopelagic zone was carried out by Hennon et al. (2016). In comparison with estimates from the present study (Table 6), they reported higher values in the Western Pacific as well as lower values near the Ocean Station Papa (OSP), however falling within the same order of magnitude considering uncertainties. Other studies (Quay et al. (2020); Emerson (2014)) estimated ANCP from geochemical observations in the subpolar Pacific region around 50°N and from oxygen and carbon mass balance at OSP, with both lower estimates than this study still falling within the range of uncertainties (Table 6). Comparable estimates evidenced here for both hemispheres highlight a robust methodological approach for estimating TOC fluxes at the bioregional scale.

Finally, regarding $T_{\text{eff}}$ of small particles, Dall’Olmo and Mork (2014) estimated values between 53% and 68% at 100 m below $z_p$ in the Norwegian Sea based on BGC-Argo float b<sub>bp</sub> measurements. Our results in NH-HCB are lower, which could be explained by the fact that this bioregion included not only the Norwegian Sea but also times series from other northern seas, where $T_{\text{eff}}$ might be lower. In other studies, $T_{\text{eff}}$ are mostly estimated either from Marine Snow catchers (Cavan et al., 2017), gel traps (Laurenceau-Cornec et al., 2015), Th<sup>234</sup> (M. Estapa et al., 2021), or neutrally buoyant traps (Marsay et al., 2015) often based on instantaneous POC flux profiles. Moreover, variations in season, depth, and POC size fraction make comparisons difficult. Unlike instantaneous estimates, we estimated a seasonal $T_{\text{eff}}$ of small-POC over the mesopelagic zone at the scale of bioregions. While this complicates direct comparisons, it yields valuable estimates of $T_{\text{eff}}$, particularly in non-steady-state regions such as those examined in this study, where instantaneous estimates can differ significantly from seasonal ones.

## 4.2. Dominant Surface Drivers of Small-POC Fluxes in the Mesopelagic Zone

These findings suggest that the strength of the export of small particles may primarily be influenced by particle assemblage composition, annual maximum integrated biomass, and phytoplankton phenology (Figure 8, Figure S18 in Supporting Information S1). Specifically, annual small-POC export is increased by later bloom climax and apex over the season, along with higher bulk phytoplankton carbon biomass at the apex. Previous studies showed
<table><caption>**Table 6**
Small-POC and Total Organic Carbon Flux Estimates From Different Regions and Based on Different Methods for Comparison With This Study's Estimates</caption><thead><tr><th></th><th>Study</th><th>Method</th><th>Region</th><th>TOC fluxes from literature (molC m<sup>-2</sup> y<sup>-1</sup>)</th><th>TOC fluxes from this study (molC m<sup>-2</sup> y<sup>-1</sup>)</th><th>Bioregion</th></tr></thead><tbody><tr><td rowspan="3">Small-POC fluxes</td><td>Dall'Olmo and Mork (2014)</td><td>b<sub>bp</sub> (BGC-Argo floats)</td><td>Norwegian Sea</td><td>0.55 (±0.04); 0.45 (±0.06); 0.48 (±0.07); 0.76 (±0.07)</td><td>0.51 (±0.01)</td><td>NH-HCB</td></tr><tr><td>Kheireddine et al. (2020)</td><td>b<sub>bp</sub> (BGC-Argo floats)</td><td>Red Sea</td><td>~0.24</td><td>–</td><td></td></tr><tr><td>Estapa et al. (2019)</td><td>Optical sediment traps (BGC-Argo floats)</td><td>Bermuda Atlantic Time-series Study</td><td>~0.8</td><td>–</td><td></td></tr><tr><td rowspan="10">TOC fluxes</td><td rowspan="2">Arteaga et al. (2019)</td><td>Oxygen-based and nitrate-based estimates (BGC-Argo floats)</td><td>Subtropical Zone</td><td>0.9 (±0.9)</td><td>3.6 (±3.1)</td><td>SH-DCM</td></tr><tr><td></td><td>Subantarctic Zone–Polar Antarctic Zone</td><td>2 (±1.3); 1.9 (±1.4)</td><td>2.6 (±2.4)</td><td>SH-LCB</td></tr><tr><td rowspan="3">Hennon et al. (2016)</td><td rowspan="3">Oxygen-based estimates (BGC-Argo floats)</td><td>Seasonal Ice Zone</td><td>1.7 (±1.3)</td><td>6.3 (±2.5)</td><td>SH-HCB</td></tr><tr><td>Southern Indian</td><td>3.8 (±1.1)</td><td>2.6 (±2.4)</td><td>SH-LCB</td></tr><tr><td>Southern Pacific</td><td>3.2 (±3.2)</td><td>2.6 (±2.4); 6.3 (±2.5)</td><td>SH-LCB; SH-HCB</td></tr><tr><td rowspan="2">Johnson et al. (2017)</td><td rowspan="2">Nitrate-based estimates (BGC-Argo floats)</td><td>Western Pacific</td><td>3.8 (±1.8)</td><td>2.8 (±2.0)</td><td>NH-DCM</td></tr><tr><td>OSP</td><td>1.3 (±1.5)</td><td>4.2 (±3.4); 2.8 (±2.0)</td><td>NH-LCB; NH-DCM</td></tr><tr><td>Su et al. (2022)</td><td>Oxygen-based estimates (BGC-Argo floats)</td><td>&lt;40°S</td><td>~0.6</td><td>3.6 (±3.1)</td><td>SH-DCM</td></tr><tr><td rowspan="2">Yang et al. (2024)</td><td rowspan="2">Oxygen-based estimates (BGC-Argo floats)</td><td>40°–55°S</td><td>~2.7–4</td><td>2.6 (±2.4)</td><td>SH-DCM</td></tr><tr><td>&gt;60°S</td><td>~2</td><td>6.3 (±2.5)</td><td>SH-HCB</td></tr><tr><td rowspan="2">Quay et al. (2020)</td><td rowspan="2">Geochemical observations</td><td>Southern Ocean Pacific 60°S</td><td>~6.5</td><td>6.3 (±2.5)</td><td>SH-HCB</td></tr><tr><td>50°–60°S</td><td>~2.5–5.5</td><td>2.6 (±2.4)</td><td>SH-DCM</td></tr><tr><td rowspan="2">Emerson (2014)</td><td rowspan="2">Oxygen and carbon mass balance</td><td>Subantarctic Zone</td><td>4.2 (±1.65)</td><td>3.6 (±3.1)</td><td>SH-DCM</td></tr><tr><td>Subpolar Pacific (50°N)</td><td>1.8 (±0.6)</td><td>4.2 (±3.4); 2.8 (±2.0)</td><td>NH-DCM; NH-DCM</td></tr><tr><td></td><td></td><td>OSP</td><td></td><td>2.3 (±0.6)</td><td>4.2 (±3.4); 2.8 (±2.0)</td><td>NH-DCM; NH-DCM</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Schematic of the dominant drivers of the strength and transfer efficiency of small-POC 100 m below $z_p$ for the six bioregions. Arrows show the direction of each driver influencing either export parameter (vertical for strength, horizontal for transfer efficiency). Gray lines show the regional standard deviation of the strength and transfer efficiency.</figcaption></figure>

that the export flux may be influenced by the intensity of net primary production when studied at large temporal scales (Laws & Maiti, 2019; Maiti et al., 2013) or by particle composition and planktonic ecosystem structure (Laurenceau-Cornec et al., 2023). Here, particle assemblage seems to influence export strength, as large phytoplankton (such as diatoms) blooms occur in spring/summer in high-strength regimes (SH-HCB, SH-LCB, NH-HCB). In contrast, in low-strength regimes (SH-DCM, NH-LCB, NH-DCM), the particles correspond to smaller phytoplankton concentrated on seasonal DCMs (Figure 8). High export diatom blooms have already been described in the Southern Ocean (Buesseler, 1998; Smetacek et al., 2012) with high fluxes down to 1,000 m (Terrats et al., 2023) as well as in the North Atlantic (Allen et al., 2005; Martin et al., 2011). Previous studies have linked stronger POC export fluxes with the late development of blooms dominated by large phytoplankton but also argued that increased export can also take place in blooms dominated by smaller phytoplankton exported via zooplankton fecal pellets (Wiedmann et al., 2014), emphasizing zooplankton as a key player in carbon export (Steinberg & Landry, 2017).

In contrast, $T_{eff}$ is likely related to the duration of the bloom (Figure 8, Figure S18 in Supporting Information S1). Shorter blooms are associated with high-efficiency regimes (~70% in NH-LCB and NH-DCM), whereas longer blooms are associated with low-efficiency regimes (~40% in NH-HCB, SH-HCB, SH-LCB, and SH-DCM) (Figure 8, Figure S19 in Supporting Information S1). Temperature and oxygen are known to drive transfer efficiency (Laws et al., 2000; Sanders et al., 2016; Van Mooy et al., 2002), as they influence phytoplankton community composition as well as heterotrophic communities (microbes and zooplankton) carbon-specific respiration rate (Buesseler & Boyd, 2009; Iversen & Ploug, 2013). Here, it can be argued that shorter blooms result in higher $T_{eff}$ by promoting a decoupling between phytoplankton and heterotrophic communities. Indeed, in NH-LCB and NH-DCM, the higher fraction of small-POC transferred at 100 m below $z_p$ suggests less mineralization below $z_p$ than in the other bioregions. Given the expected high turnover rates for suspended particles (Cavan et al., 2017), this may be attributed to a low abundance of heterotrophic communities. A similar phenomenon has been observed for regimes characterized by high export efficiency and low primary production. This configuration is due to a low abundance of zooplankton and bacterial communities, suggesting a decoupling between primary production and mineralization processes (Henson et al., 2019).

Although disentangling the processes operating between productive and mesopelagic zones remains challenging as no direct relationships are established here, our results suggest that at high latitudes, productive zone processes can be important drivers of small-POC export and transfer at depths.
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Comparison of the mean (dots) and standard deviation (horizontal and vertical gray dashed lines) of total organic carbon (TOC) fluxes and small-POC fluxes (molC m⁻² y⁻¹) for each layer of the six bioregions, as a function of the distribution of bioregions (A, colored dots), and of the depth of the layers (B, color gradient). Note that both axes are on a logarithmic scale. Dark green dotted, dash-dotted, and dashed lines represent the 5%, 10%, and 20% thresholds for the contribution of small-POC fluxes to TOC fluxes.</figcaption></figure>

## 4.3. Substantial Contribution of Small Particles to Carbon Sequestration

Our results suggest that the combined effect of processes such as direct sinking, fragmentation of larger particles, and the mixed layer pump (processes detailed below) enables small particles to sink deep enough to contribute to long-term sequestration, with an average contribution of 10% to organic carbon fluxes down to 900–1,000 m at high latitudes (Figure 9). However, some differences are evident between bioregions, with small-POC contribut- ing up to 20% in SH-LCB and down to 2.5% in SH-DCM. Previous studies suggest an even higher seasonal contribution of small particles to POC fluxes (Baker et al., 2017; Cavan et al., 2017; Durkin et al., 2015). Using the Marine Snow Catcher, Baker et al. (2017) show that slow-sinking particles can represent the largest part of sinking POC in the North Atlantic and up to 90% of POC flux in post-bloom periods, below the MLD in the Southern Ocean. They also highlight that the contribution of small particles increases with depth when large particles contribute >10% to the total flux. As turnover rates are higher for slow-sinking particles compared to fast-sinking particles, probably due to their higher surface-to-volume ratio (Baker et al., 2017), this increase with depth is likely due to the fragmentation of large particles, which contributes to the pool of small particles (Baker et al., 2017; Cavan et al., 2017; Durkin et al., 2015). Fragmentation could then contribute significantly to small particle export at depth in the high-strength/low-efficiency regimes, where blooms of micro-phytoplankton were observed. On the other hand, the mixed layer pump could also play a major role in the export and transfer of these particles (Dall’Olmo et al., 2016; Boyd et al., 2019; Lacour et al., 2019) in all strength/efficiency regimes, as it seems to initiate export early in the season (i.e., in mid/late winter) for all the six bioregions (Figure 5). During those periods, the bloom has already begun (at onset), and some POC is susceptible to being isolated from the productive zone and transferred to deeper layers when the MLD shallows, as shown in Figure 5 with the increase in iPOC over the mesopelagic layers from the moment the MLD shallows. This process seems to export fresher particles in late winter compared to spring/summer (Figure 3, lower panels), which could affect their consumption and then transfer on a seasonal basis (Bol et al., 2018). However, further studies are needed to determine the extent to which the seasonality of these processes might affect annual estimates of small-POC fluxes in the mesopelagic zone.

Driven by large particle fragmentation and the mixed layer pump, small-POC are more likely to be sequestered over long timescales than through the gravitational process only. Although defining a precise sequestration depth is challenging due to spatial variability—affected by global circulation, local physical processes, mixed layer depth, and carbon remineralization depth (Baker et al., 2022)—, Cavan et al. (2019) have proposed the maximum winter mixed layer depth as the depth limit for carbon sequestration for at least more than 1 year. Particles reaching this depth are highly likely to escape re-entrainment during future mixing events, increasing the likelihood of long-term storage.
## 5. Conclusion

Major dynamics in small-particle export at high latitudes were investigated using global scale bioregionalization on 335 BGC-Argo floats annual time series of $b_{bp}$ and $Chla$ down to 1,000 m depth. This enabled the study of phytoplankton biomass and carbon export dynamics across highly contrasting environments, including under-sampled regions that are, for example, remote and/or exposed to harsh weather conditions.

Our results indicated that the overall variability of phytoplankton biomass and carbon export at high latitudes can be grouped within six distinct productivity regimes. Diatom blooms and seasonal DCMs of smaller phytoplankton occurred in both hemispheres, with each regime showing specific phytoplankton phenology. Three types of BCP strength and efficiency systems (i.e., two key parameters to constrain the BCP) for small particles were driven by these productivity regimes, and in particular by the combination of both phytoplankton community composition and bloom phenology: (a) high strength/low-efficiency regimes at higher latitudes of the Southern Ocean and the North Atlantic Ocean, (b) low strength/low-efficiency regime at subtropical latitudes of the Southern Ocean, (c) low strength/high-efficiency regimes in lower latitude in both North Atlantic and Pacific Oceans. Small particles strength of export was more related to the phytoplankton community composition in spring/summer, along with the annual maximum depth-integrated biomass, climax (maximum $i_{C_{phyto}}$ accumulation rate), and apex (maximum $i_{C_{phyto}}$) timings, while small particles $T_{eff}$ was related to the duration of the bloom.

This study further highlights the significant contribution of small particles to organic carbon fluxes down to deep waters, accounting for an average of ~10% of total flux over a year across all bioregions. Such contributions likely stem from different mechanisms of export (e.g., mixed layer pump vs. gravitational pump) and processes such as respiration by heterotrophic organisms or fragmentation of larger particles. These findings highlight the need to better account for small particles in carbon budgets and models and to develop a comprehensive understanding of how the annual and seasonal dynamics of small particles impact long-term carbon sequestration. Finally, with the ongoing expansion of the BGC-Argo array (Claustre et al., 2020), this type of analysis could lead to (a) the determination of a higher number of bioregions, with the contribution of current under-sampled or unsampled area, (b) a higher-resolution definition of the geographical boundaries of these bioregions, (c) better constrain estimates of key parameters of the BCP, as the strength and efficiency, (d) a refined estimation of the surface predictors of small particles fluxes in the mesopelagic zone.

## Data Availability Statement

All BGC-Argo data are available at https://doi.org/10.17882/42182#101760 (Argo, 2023) or from the Argo Global Data Assembly Center (ftp://ftp.ifremer.fr/ifremer/argo/dac). All the data used in this study were collected and made freely available by the international Argo Program and the national programs contributing to it (https://argo.ucsd.edu; https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System. All analyses were performed in R4.3.3 using RStudio 2023.12.1 + 402. RStudio is an open-source software freely available through the publisher's website (https://www.rstudio.com/).

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## Acknowledgments

REFINE has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement N° 834177). Argo-2030 has received the support of the French government within the framework of the "Investissements d'avenir" program integrated in France 2030 and managed by the Agence Nationale de la Recherche (ANR) under the reference "ANR-21- ESRE-0019." LL was supported by the European Union's Horizon 2020 Marie Sklodowska-Curie grant (no. 892653). MC was supported by the National Science Foundation (award: OCE 2023274/ 2147809 to A.J. Fassbender), the National Oceanic and Atmospheric Administration (NOAA) Global Ocean Monitoring and Observing program, and the NOAA Pacific Marine Environmental Laboratory. The authors thank the Argo Data Management team (ADMT) and the BGC- Argo Data Management task team (BGC ADMT).
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