# Observing the Global Ocean with Biogeochemical-Argo

Hervé Claustre, Kenneth Johnson, Yuichiro Takeshita

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Hervé Claustre, Kenneth Johnson, Yuichiro Takeshita. Observing the Global Ocean with Biogeochemical-Argo. Annual Review of Marine Science, 2020, 12 (1), pp.23-48. 10.1146/annurev-marine-010419-010956. hal-03137764

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*Annual Review of Marine Science*

# Observing the Global Ocean with Biogeochemical-Argo

Hervé Claustre,<fnref n="1" /> Kenneth S. Johnson,<fnref n="2" /> and Yuichiro Takeshita<fnref n="2" />

Annu. Rev. Mar. Sci. 2020. 12:23–48

First published as a Review in Advance on
August 21, 2019

The *Annual Review of Marine Science* is online at
marine.annualreviews.org

https://doi.org/10.1146/annurev-marine-010419-010956

Copyright © 2020 by Annual Reviews.
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## Keywords

profiling floats, ocean biogeochemistry, marine ecosystems, global observation, climate change

## Abstract

Biogeochemical-Argo (BGC-Argo) is a network of profiling floats carrying sensors that enable observation of as many as six essential biogeochemical and bio-optical variables: oxygen, nitrate, pH, chlorophyll *a*, suspended particles, and downwelling irradiance. This sensor network represents today's most promising strategy for collecting temporally and vertically resolved observations of biogeochemical properties throughout the ocean. All data are freely available within 24 hours of transmission. These data fill large gaps in ocean-observing systems and support three ambitions: gaining a better understanding of biogeochemical processes (e.g., the biological carbon pump and air-sea CO₂ exchanges) and evaluating ongoing changes resulting from increasing anthropogenic pressure (e.g., acidification and deoxygenation); managing the ocean (e.g., improving the global carbon budget and developing sustainable fisheries); and carrying out exploration for potential discoveries. The BGC-Argo network has already delivered extensive high-quality global data sets that have resulted in unique scientific outcomes from regional to global scales. With the proposed expansion of BGC-Argo in the near future, this network has the potential to become a pivotal observation system that links satellite and ship-based observations in a transformative manner.

<footnote n="1">¹Laboratoire d'Océanographie de Villefranche, Institut de la Mer de Villefranche, CNRS, Sorbonne Université, 06230 Villefranche-sur-Mer, France; email: claustre@obs-vlfr.fr</footnote>

<footnote n="2">²Monterey Bay Aquarium Research Institute, Moss Landing, California 95039, USA; email: johnson@mbari.org, yui@mbari.org</footnote>
# INTRODUCTION

The ocean is under increasing anthropogenic pressure, and successfully addressing urgent global challenges such as climate-change mitigation and adaptation will require creative solutions. Not only does direct human overuse risk depleting sea resources (Campbell et al. 2016, Worm et al. 2006), but anthropogenic CO₂ emissions have a wide spectrum of physical, chemical, and biological consequences. These emissions warm the ocean (Wijffels et al. 2016), which leads to sea-level rise (Nerem et al. 2018), declines in sea-ice cover (Polyakov et al. 2017), reduced nutrient fluxes to the upper ocean (Moore et al. 2018), ocean deoxygenation (Keeling et al. 2010), and ocean acidification (Doney et al. 2009). These changes influence marine habitats, affect plankton growth, and have cascading effects across all levels of the ocean ecosystem (Cheung et al. 2013, Plaganyi 2019), all of which may result in significant economic and noneconomic costs to society (Doney et al. 2012, Hoegh-Guldberg & Bruno 2010). However, we still do not fully understand the underlying ocean mechanisms and their reactions to these threats—a significant barrier to making well-founded management decisions.

This lack of understanding is due largely to the paucity of concurrent physical, chemical, biological, and ecosystem measurements throughout the global ocean. The late Walter Munk captured this sentiment in his assertion that “if I were to choose a single phrase to characterize the first century of modern oceanography, it would be a century of undersampling” for physical properties (Munk 2010, p. 2), and this statement is even more applicable to biogeochemical and ecosystem properties. This undersampling problem critically limits our ability to quantitatively describe key processes in the oceanic cycles of carbon, nitrogen, and oxygen as well as overall ecosystem change. Shipboard measurements have contributed significantly to our understanding of basin-scale to global distributions and processes that control essential ocean variables, but their acquisition is highly limited in space and time and requires significant overhead. A new, low-cost observational system is needed to sample the ocean on the spatiotemporal scales required for dissecting the intricacies of global change.

Over the last several decades, the development of robotic technology has demonstrated the potential for a network of autonomous floats to address this undersampling problem. The Argo program comprises a global array of more than 3,900 profiling floats that measure pressure, temperature, and salinity (Riser et al. 2016). Profiling floats are free-drifting, battery-powered platforms that take measurements from 2,000-m depths to the surface every 10 days (Figure 1) and can operate for up to seven years. Their vertical position in the water is controlled by altering the floats' density with a buoyancy engine that changes their volume but not their mass. At the surface, these data are broadcast to shore via satellites and made publicly available almost in real time. Through this groundbreaking program, physical data from the upper half of the world's oceans are now being collected with greatly reduced spatial or temporal biases (Riser et al. 2016). This network of robotic floats has been the only scalable observational strategy for the global ocean interior and has transformed our understanding of ocean heat content and sea-level rise (Cheng et al. 2019, Levitus et al. 2012), enabling observation, understanding, and prediction of the ocean's physical processes and its role in Earth's climate (Riser et al. 2016, Wijffels et al. 2016).

Following Argo's example, biogeochemical oceanographers have adapted profiling-float technology to their own scientific requirements with a goal of reducing the paucity of biogeochemical data on the world's oceans. Through continued efforts to develop and implement biogeochemical and optical sensors on profiling floats, a variety of successful proof-of-concept projects have been launched during the last 10 years. These projects have demonstrated the unique potential of this technology to explore and understand biogeochemical processes at unprecedented scales of observation (e.g., Bishop et al. 2002, 2004; Boss et al. 2008; Johnson et al. 2010;
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

The typical mission of a Biogeochemical-Argo (BGC-Argo) float. From its starting position (●), the float descends to 1,000 m (●) and parks there, typically for about nine and a half days (●). It then dives to 2,000 m (●) before beginning its ascent, during which its sensors acquire data at a preset vertical resolution (●). Once at the surface, the float transmits data through Iridium satellite communication and may receive new commands to modify its sampling strategy for subsequent sampling cycles (●). The cycle then repeats, with the float again diving to 1,000 m and drifting for a preset time period.

Körtzinger et al. 2004). In parallel, working groups (IOCCG 2011) and community papers (Claustre et al. 2010; Gruber et al. 2007, 2010; Johnson et al. 2009) have described the potential for biogeochemical profiling floats to transform our understanding of ocean chemistry and biology.

All of these initiatives set the stage for the 2016 launch of the international Biogeochemical-Argo (BGC-Argo) program (Biogeochem.-Argo Plan. Group 2016, Johnson & Claustre 2016), which currently includes 14 countries. Run in close collaboration with the Argo program, BGC-Argo aims to operate a global array of 1,000 biogeochemical floats measuring six core variables down to a depth of 2,000 m: oxygen, nitrate, pH, chlorophyll *a*, suspended particles, and downwelling irradiance. These essential ocean variables were selected because (*a*) the sensing technology for these measurements has been demonstrated on a large number of floats and (*b*) they support the three main themes of the Global Ocean Observing System: climate, marine ecosystem health, and operational services. The BGC-Argo science plan targets five science topics [ocean acidification, nitrogen cycling in oxygen minimum zones (OMZs), the biological carbon
pump, ocean carbon uptake, and phytoplankton communities] and two ocean management topics (living marine resources and carbon budget verification). Like data from the core Argo program, all BGC-Argo data are freely available almost in real time. This review addresses various aspects of the BGC-Argo program and the associated network of profiling floats as it transitions from a small group of research programs to a long-term, sustained international effort.

FLOATS, SENSORS, AND DATA SYSTEM

Float Technology

Profiling-float technology was initially developed to optimize measurements for temperature, salinity, and pressure (Davis et al. 1992, 2001). The costs and capabilities of the floats, such as the size of the buoyancy engine (and thus its payload capability), were minimized for efficiency, as the floats were not expected to be recovered. The earlier floats were powered with alkaline batteries and could only acquire data with a vertical resolution of 60 measurements over 2,000 m. Data were transmitted through the very-low-bandwidth Argos network, requiring floats to stay at the surface for 12 hours (Davis et al. 1992, Riser et al. 2016). By the late 1990s, profiling floats were routinely deployed for physical oceanography (Roemmich et al. 2009). As the technology matured, the first pilot projects dedicated to biogeochemistry were implemented using oxygen (Körtzinger et al. 2004, Riser & Johnson 2008) or bio-optical sensors (Bishop et al. 2002, Boss et al. 2008, Mitchell 2003).

The development and evolution of the float technology itself were enabling steps for BGC-
Argo. The addition of biogeochemical sensors imposed new constraints, particularly with respect
to energy requirements, data transmission, and vertical stability. There were two notable improve-
ments in float technology, both driven by the core Argo program: the use of lithium batteries and
the Iridium satellite data system. The biogeochemical community greatly benefited from these
upgrades. Lithium batteries provide significantly more power to accommodate biogeochemical
sensors or take measurements at a higher vertical resolution. Iridium increased the rate of data
transmission and the amount of data transmitted, enabling larger and more complex data streams
from biogeochemical sensors. The two-way Iridium transmission also enabled float missions to be
adjusted in situ in response to specific biogeochemical processes or events. Finally, there are now
floats with ice-avoidance algorithms and data-storage capabilities (Wong & Riser 2011), which
allows floats to operate under seasonal sea ice and transmit data later, during ice-free conditions
(Briggs et al. 2018, Chamberlain et al. 2018, Mayot et al. 2018). It is worth recalling that, when
floats cycle at 10-day intervals, their lifetimes are only weakly compromised (if they are compro-
mised at all) by biofouling: The float spends 90% of its life drifting in dark, cold waters at a depth
of 1,000 m; experiences a wide range of pressures during its ascents and descents; and, thanks
to Iridium, spends very little time transmitting at the surface. This is certainly one of the main
reasons why Argo and BGC-Argo are so cost-effective.

While great strides have been made regarding float technology, continued development will allow BGC-Argo to extend its efficiency and influence. For example, as in the core Argo program, success rates and float lifetimes vary among manufacturers and float deployment groups. Bringing all groups to the high level of success obtained by some groups can expand the float array at essentially no cost.

Sensors

Biogeochemical sensors deployed on profiling floats have significant constraints in terms of size,
power, pressure and temperature tolerance, response time, and stability. They must operate for
multiple years with no direct human interaction (Johnson 2017). Despite these challenges, sus-
tained development efforts from research groups and partnerships with industry have resulted
in a small suite of commercially available sensors that are now routinely integrated onto floats.
These include oxygen, nitrate, pH, bio-optical (chlorophyll fluorescence and particle backscat-
tering, to ascertain chlorophyll a concentration and suspended particle abundance, respectively),
and radiometry (downwelling irradiance) sensors. We briefly describe each sensor below. Note
that other biogeochemical sensors have been integrated into profiling floats, such as pCO2 sen-
sors (Fiedler et al. 2013), transmissometers (Estapa et al. 2017), plankton counters (Checkley et al.
2008), and particle imaging systems (Bishop et al. 2016), but we do not include those here as most
of them have not been shown to be scalable to a global array.

**Oxygen sensors.** Oxygen sensors were the first to be widely integrated into profiling floats. They remain the most common ones on floats today (1,070 BGC-Argo floats have been deployed with oxygen sensors), collecting more than 10 times as many oxygen profiles per year compared with profiles of samples analyzed on ship (Johnson et al. 2017a).

Clark-type electrodes were the first type of oxygen sensors to be equipped on profiling
floats (Riser & Johnson 2008), with fluorescence lifetime optode sensors following soon after
(Körtzinger et al. 2005, Tengberg et al. 2006). Both sensor types were deployed on floats in roughly
equal proportions for several years. Experience with Clark-type sensors, particularly drift and ini-
tial calibration issues (Takeshita et al. 2013), has increasingly led the community to rely exclusively
on optodes.

Subsequently, a technique for in situ calibration based on measurements of atmospheric O₂—
so-called air calibration—was developed (Bittig & Körtzinger 2015, 2017; Bushinsky et al. 2016;
Johnson et al. 2015). This approach takes advantage of the facts that optodes respond to the pO₂ in
both water and air with no bias (Bushinsky et al. 2016) and that the zero readings of optodes seem
to be robust and stable over time (Johnson et al. 2015). The sensor is calibrated by applying a gain
correction based on atmospheric measurements. The pO₂ of the atmosphere can be calculated at
the location of the float; thus, a calibration can be conducted each time a float surfaces, and sensor
drift can be assessed and corrected for (Bittig & Körtzinger 2015, 2017; Johnson et al. 2015). This
approach has now become a mainstay for calibrating oxygen measurements on floats, and as a
result, oxygen measurements are now arguably the best-calibrated measurement in BGC-Argo,
with a fleet-wide accuracy of 1% of surface oxygen concentration or better (Johnson et al. 2017a).
Dynamic errors related to slow sensor response remain an unresolved challenge for oxygen floats
(Bittig et al. 2014).

**Nitrate sensors.** Nitrate is measured from the absorbance spectra of seawater in the ultraviolet range (Johnson et al. 2013). In oxic waters, the spectrum is dominated by bromide and nitrate ions; the contribution from bromide is calculated from temperature, salinity, and pressure and removed prior to deconvolving the spectra to calculate nitrate concentration (Johnson & Coletti 2002). There are currently 272 floats equipped with nitrate sensors, annually producing more than four times as many nitrate profiles as are obtained from ships. Large arrays of float nitrate sensors have an average accuracy of ±0.5 µmol kg⁻¹ (Johnson et al. 2017a, Mignot et al. 2019). Nitrate sensors use the most power of the biogeochemical sensors. Early versions required approximately 17% of the overall power budget (Riser et al. 2018), but recent developments in the controlling electronics have significantly decreased their power requirements (Bittig et al. 2019).

**pH sensors.** Profiling floats measure pH using the Deep-Sea DuraFET, a sensor comprising a Honeywell Ion Sensitive Field Effect Transistor (ISFET) and a chloride-ion-selective electrode
as the reference electrode, directly exposed to seawater (Johnson et al. 2016). The ISFET is a small solid-state device with low power requirements and a fast response time, making it an effective sensor for monitoring inorganic carbon dynamics in the ocean. Initially developed for shallow-water applications, ISFETs showed remarkable stability and performance in seawater (Martz et al. 2010, Takeshita et al. 2014) and were quickly adapted for profiling applications (Johnson et al. 2016). Based on the experiences with oxygen sensors, stringent sensor characterization and calibration protocols were implemented early on. Each sensor is individually calibrated over the temperature and pressure ranges expected for the float, resulting in a fleet-wide accuracy of ±0.015 (updated from Johnson et al. 2017a). There are currently 156 floats equipped with pH sensors, producing more than five times as many pH profiles annually as ship-based measurements.

**Postdeployment calibration protocol for nitrate and pH sensors.** Nitrate and pH sensors require detailed postdeployment corrections to obtain the data quality mentioned above. Despite careful predeployment calibration of the sensors, biases from laboratory calibrations are observed for almost all sensors when deployed. Fortunately, these biases manifest as simple offsets either in concentration (for nitrate; Johnson et al. 2015) or in the calibration coefficient (for pH; Johnson et al. 2013). Currently, nitrate and pH can be corrected by referencing sensor values measured at depths below 1,000 m to high-quality climatologies based on shipboard measurements. The Global Ocean Data Analysis Project version 2 (GLODAPv2) data set (Olsen et al. 2016) represents a unique, high-quality global data set in which deep nitrate or pH measurements can be used as the reference values. There are various methods of using GLODAPv2 to produce the reference measurements at the appropriate float geolocation, including techniques using multiple regression (Carter et al. 2016, 2018; Williams et al. 2016) or machine learning through neural networks (Bittig et al. 2018b, Sauzède et al. 2017). Each of these methods requires inputs of pressure, temperature, salinity, oxygen, and geolocation measured by the float to produce the reference fields. Therefore, accurate oxygen measurements from in situ air calibrations are essential for obtaining accurate pH and nitrate measurements.

**Bio-optical sensors.** There are 443 BGC-Argo floats that have been deployed with bio-optical sensors for chlorophyll fluorescence and particle backscattering, which can be used to estimate chlorophyll a concentration and suspended particle concentration, respectively. These sensors are highly energy efficient, using only approximately 3 J per sample, and show little drift over time. The scaling factor used to convert fluorescence response to chlorophyll a concentration can vary due to taxonomy, light, nutrients, and/or physiology. As a result, the measurement accuracy is estimated to be ±50% as an average for large numbers of floats. Individual profiles may have even greater biases (Roesler et al. 2017). The backscattering signal is processed to remove scattering by water, leaving only the signal due to backscattering by particles ($b_{bp}$). In the open ocean, most of the particles are phytoplankton or their detritus (Graff et al. 2015), and $b_{bp}$ can then be used to estimate particulate organic carbon with an uncertainty of ±3 mmol C m⁻³ (Johnson et al. 2017a).

Presently there are no reference climatologies for chlorophyll *a* or $b_{bp}$ that enable sensor calibration or drift correction. However, referencing chlorophyll *a* and $b_{bp}$ to their surface remote-sensing counterpart appears, at first order, to be an appropriate alternative to check the consistency of the measurements (Boss et al. 2008, Haentjens et al. 2017). Alternative solutions have been proposed that use radiometric measurements from floats combined with bio-optical models to address possible change between chlorophyll fluorescence and chlorophyll *a* over a float's lifetime (Roesler et al. 2017, Xing et al. 2011), and these proposals deserve more scrutiny.
**Radiometry sensors.** Radiometric measurements provide key insight into the spectral light field within the ocean environment, which relates directly to the growth and metabolism of complex photosynthetic communities. So far, 181 radiometers have been deployed on biogeochemical profiling floats, all of which are Atlantic OCR-504 multispectral ocean-color radiometers. These instruments measure downward irradiance through four channels, with the typical configuration for biogeochemical floats including three narrow-wavelength (10 or 20 nm) ranges centered on 380, 412, and 490 nm, as well as photosynthetically available radiation integrated over 400-700 nm.

The intrinsic nature of radiometric measurements (which are highly dependent on the time of day and water constituents) prevents the use of any reference database for postdeployment quality control. The comparison of surface measurements with the output of clear-sky optical models is the only way to identify issues with sensor performance (Organelli et al. 2016). Fortunately, many applications of radiometry utilize the relative change in irradiance with depth (Organelli et al. 2017a,b)—the so-called diffuse attenuation coefficient (Xing et al. 2011)—and these results are not strongly sensitive to sensor drift.

## A Unique Data System

One of the strengths of BGC-Argo is its unique data-management system, which makes all data freely available in near real time. This system, which is still evolving, is built largely on the core Argo system and is based on 13 internationally coordinated data-assembly centers that decode raw data transmitted by floats and validate the data with automatic checks before forwarding them to the global data-assembly centers, where the community accesses the information.

It is generally recognized that the raw biogeochemical sensor data need additional adjustment before they are suitable for use in most research or operational analyses (Bittig et al. 2018a; Johnson et al. 2015, 2018, Organelli et al. 2016; Xing et al. 2018). Therefore, the BGC-Argo system automatically applies correction to the data in real time, and this data stream is referred to as adjusted data. After several months to a year of deployment, corrections are reviewed by human operators, after which the data are termed delayed-mode corrected data. However, these processes are evolving, and the automatic adjustment of raw biogeochemical data and the production of delayed-mode corrected data have been implemented by only a few of the data-assembly centers, which were developed primarily to process the physical data streams (pressure, temperature, and salinity). As a result, current data users must pay attention to the data state, and they may find that there are inhomogeneities in the data products. However, the raw data will always be available, and users are free to perform their own quality control for research projects. As the system matures, these inhomogeneities will diminish. Large advances in developing uniform data-processing procedures have been made in recent years.

The long-term goal for this international system is to ensure that the entire data set is interoperable and useful at the global scale, which is of fundamental importance when addressing scientific objectives of global-scale relevance. Even with the inhomogeneities noted above, the amount of quality-controlled data acquired by BGC-Argo (Figure 2) is already greater than any other data collection published so far. This is the case for $b_{bp}$ and radiometric data (Valente et al. 2016), chlorophyll fluorescence profiles (Sauzède et al. 2016), and the chemical variables (Johnson et al. 2017a).

## WHAT HAS BEEN DONE?

### The Biological Carbon Pump

Biological production of organic matter and the subsequent export of this material lowers the $CO_2$ in surface waters and strongly influences the atmospheric $CO_2$ concentration and thus the
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>

Status of the Biogeochemical-Argo array and associated data acquisition. (a) Map of active floats as of February 2019. The color coding indicates the variables acquired by each float's sensor payload. Updated versions of this map are available from the Biogeochemical-Argo website (http://biogeochemical-argo.org/float-map-network-status-maps.php). (b) Maps of data acquired by the network for each variable as of March 2019. The white number on each map indicates the number of profiles available for that variable. Updated versions of these maps are also available from the Biogeochemical-Argo website (http://biogeochemical-argo.org/cloud/document/implementation-status/BGC_summary.pdf).
habitability of the planet. This process, termed the biological carbon pump, reduces atmospheric CO₂ by approximately 200 ppm relative to a modeled abiotic ocean (Parekh et al. 2006, Watson & Orr 2003). The biological carbon pump requires both the production of organic matter in the sunlit ocean and its subsequent removal to deeper waters. The first component, called net community production (NCP), is defined as gross photosynthesis minus respiration at all trophic levels. It is usually expressed as a vertical integral over a suitable depth range and time frame. Annual NCP (ANCP) is the time integral over a full year. The loss of material from the upper ocean by vertical processes is termed export production (EP). Assuming steady state, ANCP and EP should balance when averaged over large enough spatial scales (on the order of 100 km). However, there may also be significant mismatches on shorter spatial scales (Estapa et al. 2015).

**Net community production.** In general, NCP has been determined from profiling floats by con-
structing mass-balance budgets in the euphotic zone over seasonal cycles using oxygen, nitrate,
or dissolved inorganic carbon derived from pH. The first measurements of NCP were made with
oxygen sensors in the subtropical gyre, where positive summer NCP was observed over multiple
years (Riser & Johnson 2008). Their analysis was limited to spring through fall, as NCP could
be calculated only in the subsurface shallow oxygen maximum layer that forms during these sea-
sons, where air-sea exchange could be ignored; oxygen data at the time were not accurate enough
to determine the contributions of air-sea gas exchange. The widespread adoption of air calibra-
tion has since opened the door to extending these calculations to the surface and over seasonal
to interannual timescales, as air-sea fluxes can now be accurately assessed. This has enabled the
determination of ANCP in a variety of locations (Bushinsky & Emerson 2015, Huang et al. 2018,
Yang et al. 2017). Most of these values are within the canonical range of 2–4 mol C m⁻² y⁻¹, found
by Emerson (2014) at many sites and using mass-balance estimates of ANCP from oxygen, carbon
isotopes, and nitrate. However, floats in the subtropical South Pacific have notably lower ANCP
values, near zero (Yang et al. 2017).

Determining NCP using the surface seasonal cycle of nitrate and then converting to carbon using the Redfield relationship has the advantage that it does not require an estimate of air-sea gas exchange. The first measurements of NCP using nitrate measured by profiling floats were made at Ocean Station Papa over six years and demonstrated a clear seasonal cycle in NCP, with net autotrophy during the summer and net heterotrophy during the winter (Plant et al. 2016). More recently, ANCP was determined with profiling-float nitrate observations in the Greenland Sea (Mayot et al. 2018) and in the Southern Ocean and Southern Hemisphere subtropics (Johnson et al. 2017b). The nitrate-based measurements in both the Greenland Sea and the Southern Hemisphere subtropics were consistent with the observations of Emerson (2014).

Nitrate measurements from floats have also illuminated important mechanisms of nutrient
supply to the surface ocean to support production (D'Ortenzio et al. 2014, Johnson et al. 2010,
Pasqueron de Fommervault et al. 2015). These measurements are particularly useful in resolving
the dichotomy between ANCP values determined from the mass balance of carbon or oxygen and
nitrate supply (Karl et al. 2008, Michaels et al. 1994). For example, physical mechanisms of nutrient
supply appear to be insufficient to support observed ANCP in the oligotrophic ocean. Profiling-
float observations near Hawaii (Johnson et al. 2010) and Bermuda (Fawcett et al. 2018) provide
clear evidence of alternate mechanisms that resolve the discrepancies. These results suggest that
two novel mechanisms may operate to resolve the conundrum of large ANCP with no supporting
nitrate: Either nitrate is transported vertically, without inorganic carbon, by biological processes
(Johnson et al. 2010), or the ANCP that is observed produces organic carbon with no associated
nitrogen (Fawcett et al. 2018).
Profiling-float measurements of pH can be used to compute dissolved inorganic carbon con-
centrations (Williams et al. 2018) when they are combined with estimates of total alkalinity derived
from accurate global observations compiled as part of the GLODAPv2 database (Olsen et al. 2016)
using multiregression analysis or machine learning methods (Bittig et al. 2018b, Carter et al. 2018).
The dissolved inorganic carbon concentrations provide an additional path to estimate ANCP that
is just beginning to be utilized (Briggs et al. 2018).

**Export production and remineralization.** As carbon-rich material is exported from the surface (i.e., EP), it is remineralized in the mesopelagic. Since consumption in the water column reduces carbon export at 1,000 m to approximately 10% of the flux leaving the euphotic zone (Martin et al. 1987), the respiration rates should roughly balance EP. Depth-resolved remineralization rates were first measured using profiling floats equipped with oxygen sensors in the Southern Ocean (Martz et al. 2008). The inferred rates of EP from the floats were consistent with independent estimates. This technique has been applied globally to obtain a broad view of carbon export (Hennon et al. 2016).

Bio-optical sensors provide an additional tool to understand carbon export and have helped identify new pathways and mechanisms of EP. In the Norwegian Sea, Dall'Olmo & Mork (2014) demonstrated the impact of mixed-layer shoaling in isolating small, suspended particles within the mesopelagic zone. Dall'Olmo et al. (2016) subsequently addressed the contribution of this so-called mixed-layer pump to carbon export at a global scale through a combination of Argo, BGC-Argo, and satellite backscattering-coefficient products and showed that it is highly significant (23% of the biological carbon pump on average, and sometimes reaching 100%) in areas of very deep winter mixing. BGC-Argo $b_{bp}$ time series in the North Atlantic subpolar gyre revealed a basin-scale average of 55 mg C m$^{-2}$ d$^{-1}$ due to the mixed-layer pump during the winter-spring transition (Lacour et al. 2019). In the Southern Ocean, Llort et al. (2018) identified physical (temperature and salinity) and biological (oxygen and $b_{bp}$) signatures of carbon transport resulting from eddy subduction and estimated that this eddy subduction pump contributes less than 19% of EP in the Southern Ocean but can be more important on a local scale. Through a comparative analysis of vertical gradients in nitrate and particles derived mainly from Southern Ocean float data, Stukel & Ducklow (2017) concluded that approximately 50% of the carbon flux leaving upper layers is due not to gravitational sinking but to physical passive transport of suspended particles. In the Drake passage, Davies et al. (2019) reported that 18% of the particulate carbon in blooms was exported through gravitational sinking and subduction.

Following spring blooms in subpolar areas, spikes in fluorometer or backscattering measure-
ments can be used as proxies for large particles, predominantly composed of phyto-aggregates
or fecal pellets in the mesopelagic realm, which are the main contributors to gravitational flux
(Boyd et al. 2019, Briggs et al. 2011, Rembauville et al. 2017). The progressive disappearance of
these spikes with depth and the concurrent enhancement of a nonspiky signal in fluorescence
and backscattering suggest that the fragmentation of these large particles into smaller suspended
particles is a dominant process of the flux attenuation (N. Briggs, G. Dall'Olmo & H. Claustre,
manuscript in review). Additionally, saturating optical signals are sometimes observed in the
mesopelagic zone. They are attributed to the presence of animals, such as migrating organisms
(Boyd et al. 2019), but this signal is not yet used for any quantification of fluxes linked to these
organisms.

Although transmissometers are not core BGC-Argo sensors and are not amenable to global de-
ployment, they have nevertheless proven to be very informative for characterizing particle sinking
fluxes in terms of magnitude (Bishop 2009; Bishop & Wood 2009; Bishop et al. 2002, 2004, 2016),
type of particle involved (large or small) (Estapa et al. 2013, 2017), and drivers of these fluxes (Estapa et al. 2019).

## Air-Sea Gas Exchange

Exchanges of oxygen and carbon dioxide across the air-sea interface are critical controls on ocean biogeochemistry and Earth's climate. Air-sea exchange removes approximately 25% of the anthropogenic carbon emitted to the atmosphere each year (Sabine et al. 2004). Equilibration of surface waters with atmospheric oxygen, before they descend in winter to form the deep waters of the ocean, is an essential part of maintaining an oxic deep ocean. Most of our understanding of the dynamics of these processes comes from ship-based measurements of oxygen and carbon dioxide in surface waters during benign summer months. Profiling floats provide the ability to observe air-sea exchange in the winter, when high wind speeds accelerate the process.

Profiling floats with oxygen sensors provided one of the first observational data sets for oxygen dynamics during deep-water formation in the Labrador Sea (Körtzinger et al. 2004). The oxygen inventory in this deep-water-formation area increased rapidly and then diminished due to lateral mixing with surrounding waters that had not been ventilated. Using the same floats, Kihm & Körtzinger (2010) constrained gas exchange rates during the time period when deep water forms and confirmed a cubic dependence of gas exchange on wind speed (Wanninkhof 2014). Bushinsky et al. (2017) used surface-ocean oxygen measurements from 150 profiling floats in the Southern Ocean to estimate the air-sea oxygen flux south of 30°S. They found that the total Southern Ocean is a sink for oxygen with a mean flux of −183 Tmol y⁻¹, with most of the uptake occurring in winter. This oxygen sink is approximately twice that of previous estimates (Gruber et al. 2001) and has important implications for the interpretation of the seasonal cycles of atmospheric oxygen (Manning & Keeling 2006).

One of the key concepts in ocean biogeochemistry is apparent oxygen utilization, which is used to estimate the amount of oxygen removed from seawater due to respiration. Calculation of apparent oxygen utilization is based on the assumption that seawater oxygen is at equilibrium with the atmosphere when deep waters are formed. Wolf et al. (2018) used an array of profiling floats equipped with oxygen sensors in the Labrador Sea to demonstrate that surface waters may be significantly undersaturated with respect to oxygen during deep-water formation, consistent with model predictions (Ito et al. 2004).

Profiling-float pH measurements can be used to compute seawater pCO₂ when they are combined with estimated total alkalinity values (Bittig et al. 2018b, Williams et al. 2017). The computed pCO₂ values in surface waters have an uncertainty near 11 µatm (Williams et al. 2017). While this uncertainty is much larger than the uncertainty associated with direct observations of surface-water pCO₂ (±2 µatm; Pierrot et al. 2009), regional CO₂ fluxes should be accurately constrained using an array of floats, given that no systematic biases exist. Estimates of air-sea CO₂ flux based on 35 pH profiling floats in the Southern Ocean revealed an unexpected and significant outgassing of CO₂ during winter in a region where few ship observations exist (Gray et al. 2018). The magnitude of this outgassing was large enough to shift the Southern Ocean from a strong CO₂ sink to near neutral, which has far-reaching implications for the global carbon cycle.

## Oxygen Minimum Zones

Suboxic (O₂ < 4.5 µM) areas of the ocean play an important role in controlling the ocean carbon cycle, particularly by regulating the rates of nitrate loss through denitrification or anaerobic ammonium oxidation (anammox) reactions (Deutsch et al. 2011, Keeling et al. 2009). The factors
that control the volume of suboxic regions of the ocean are not well understood, and there are no extant observing systems that allow us to observe the temporal variability of these systems at large scales. Profiling floats with oxygen, nitrate, and bio-optical sensors allow long-term assessments of these processes, which cannot be made using other approaches. The oxygen sensors on profiling floats have sufficient sensitivity to enable the assessment of variability in concentrations of less than 1 µmol kg⁻¹ (K.S. Johnson et al. 2019, Sarma & Udaya Bhaskar 2018, Wojtasiewicz et al. 2018b), allowing profiling-float data to contribute to regional assessments of OMZ variability in space and time (Czeschel et al. 2012). Profiling floats have also contributed to the identification of anomalous OMZs in the Atlantic (Karstensen et al. 2015). Profiling-float oxygen data in the OMZs of the Arabian Sea and Bay of Bengal have been used to examine the role of eddies and pycnocline variability in ventilating these systems (Prakash et al. 2012, Sarma & Udaya Bhaskar 2018). Lotliker et al. (2018) used profiling-float data to assess hypotheses regarding the expansion of *Noctiluca* blooms in the Arabian Sea and showed that it was not correlated with oxygen concentration but possibly was correlated with increasing stratification.

Profiling-float sensors also provide insights into processes that regulate OMZ biogeochemistry. The backscattering sensors on floats regularly show suspended particle maxima within the OMZs of the eastern tropical South Pacific (Ulloa et al. 2012, Whitmire et al. 2009), the Arabian Sea (Wojtasiewicz et al. 2018b), and the Black Sea (Stanev et al. 2017). These particle maxima are associated with the microbial community that drives redox processes in the OMZ. K.S. Johnson et al. (2019) combined oxygen and nitrate sensor data to deduce the processes that limit denitrification in the Bay of Bengal.

The optical nitrate sensors on profiling floats are also excellent sources of data on dissolved bisulfide ions, which can accumulate in anoxic waters (Guenther et al. 2001, Johnson & Coletti 2002). Stanev et al. (2018) utilized this capability to continuously observe the structure of the anoxic interface in the Black Sea over an 18-month period.

## Filling Observational Gaps: A Unique Exploring Tool

The most significant achievements of BGC-Argo result primarily from its intrinsic capability to collect sustained high-quality data sets for multiple years where previous platforms could not (Figure 3). For example, floats cover open-ocean areas where there is little to no routine ship traffic, such as the middle of the subtropical gyres (Mignot et al. 2014, Riser & Johnson 2008), and they can operate in the harshest sea conditions, such as winter in high-latitude environments (Gray et al. 2018, Lacour et al. 2017, Mignot et al. 2018). Additionally, BGC-Argo extends surface measurements made by ocean-color satellites throughout the euphotic layer (Lavigne et al. 2015) and to critically undersampled domains like the mesopelagic realm (e.g., Dall'Olmo & Mork 2014, Estapa et al. 2017, Rembauville et al. 2017). The floats also allow observations of the water column beneath seasonal sea ice (Briggs et al. 2018, Johnson et al. 2017b, Mayot et al. 2018). Furthermore, the high vertical resolution of float profiles (up to the meter scale) has revealed features that would have been obscured by the lower vertical resolution typical of ship-based measurements (Ravichandran et al. 2012, Stanev et al. 2018, Wojtasiewicz et al. 2018b). Finally biogeochemical floats can operate over a wide range of temporal scales depending on the mission (less than one day to several years), allowing the investigation of a variety of phenomena, from episodic events (Chacko 2017, Udaya Bhaskar et al. 2016, Xu et al. 2019) to interannual variability (Boss et al. 2008, Plant et al. 2016, Westberry et al. 2016).

This diversity of observational scales permitted by a single network of platforms makes possible new investigations, novel exploration, and unanticipated results (Ardyna et al. 2019, Lacour et al. 2017). The interoperability of the global BGC-Argo data set is of unique value when addressing
<figure><img src="image_5.png" /><figcaption>a</figcaption></figure>

Profile
locations

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_8.png" /></figure>

(Caption appears on following page)
<caption>**Figure 3** (*Figure appears on preceding page*)</caption>

Time series of data acquired by Biogeochemical-Argo floats deployed in two remote areas. (a) Data gathered by World Meteorological Organization (WMO) float number 5904468 in the Atlantic sector of the Southern Ocean. Note that the float acquired data under ice (during the periods identified by white sectors in the surface layer) and then transmitted them when the ocean surface became ice free. (b) Data gathered by WMO float number 6901472 in the North Atlantic subtropical gyre. Abbreviations: $b_{bp700}$, particle backscattering at 700 nm; PAR, photosynthetically active radiation.

regional or subregional peculiarities in biogeochemical or bio-optical properties; Organelli et al. (2017b), for example, evaluated regional bio-optical anomalies with respect to mean bio-optical laws. As the BGC-Argo data set grows, opportunities for basin-scale comparisons of biogeochemical processes will also be possible. For example, all of the available particle backscattering data from floats can be used to compare the timing, duration, and magnitude of phytoplankton blooms in the Pacific, Atlantic, and Indian Ocean sectors of the Southern Ocean (Figure 4). Therefore, while many BGC-Argo investigations are driven primarily by science questions, the potential discoveries from exploration are a significant motivation for many scientists engaged in deploying floats and networks.

<figure><img src="image_9.png" /><figcaption>**Figure 4**</figcaption></figure>

A comparison of the seasonal buildup of particulate organic carbon (POC) from the tropics to the Antarctic continent in the (a) Pacific, (b) Atlantic, and (c) Indian Ocean sectors of the Southern Ocean in the upper 30 m. All data acquired by floats so far (Figure 2) are used in this representation. POC was computed from particle backscattering (Johnson et al. 2017a). Additional quality control was applied to remove data from sensors that were excessively noisy or had excessively high mean values below 500 m.
## Biogeochemical-Argo and Satellite Synergies

The growing BGC-Argo data sets can be uniquely used to address the impact of mesoscale physics on biogeochemistry, through the colocation of floats with altimetric fields obtained from satellites. Such approaches have revealed the role of cyclonic or anticyclonic eddies and their impact on the surface nitrate fields in permanently stratified environments (Ascani et al. 2013, Johnson et al. 2010, Pasqueron de Fommervault et al. 2015), the vertical position and magnitude of the deep chlorophyll maximum (Pasqueron de Fommervault et al. 2015), the magnitude in surface chlorophyll (Dufois et al. 2017), and the particle backscattering (Boss et al. 2008). BGC-Argo floats trapped in mesoscale eddies also revealed hot spots of intense remineralization (Karstensen et al. 2015) and of carbon export (Fiedler et al. 2016) below the euphotic layer. Furthermore, it appears that the magnitude of the biogeochemical response to the passage of tropical cyclones is dependent on the presence of mesoscale eddies, which act in preconditioning water masses (Chacko 2017, Girishkumar et al. 2019).

Satellites are increasingly used to define float deployment locations. For example, Mao et al. (2018) used altimetry to guide float deployments within cyclonic or anticyclonic eddies of the Leeuwin Current. Similarly, an ocean-color-based bioregionalization of the Mediterranean Sea (D'Ortenzio & d'Alcala 2009) served as the framework to seed and sustain a long-term BGC-Argo observation of this marginal sea (F. D'Ortenzio, V. Taillandier, H. Claustre, L. Prieur, E. Leymarie, et al., manuscript in review). Furthermore, ocean-color satellite data combined with float nitrate data clearly highlighted the temporal sequence of winter convection affecting the surface-layer nitrate replenishment and subsequent bloom in the northwestern Mediterranean Sea (D'Ortenzio et al. 2014).

Optical variables (chlorophyll *a*, $b_{bp}$, and radiometry) measured by BGC-Argo floats are also measured by ocean-color satellite sensors, which allows for synergetic and complementary use of both types of data for a variety of purposes. Standard BGC-Argo float measurements can be used to cross-compare data from satellites and for colocated matchups (Boss et al. 2008). Floats that also measure upwelling radiances can be used to validate key radiometric quantities measured by satellites (Gerbi et al. 2016, Leymarie et al. 2018, Wojtasiewicz et al. 2018a) and could even contribute to the so-called commission phase of future ocean-color missions. Finally, machine learning opens new avenues in synergistically combining BGC-Argo parameters with ocean-color remote-sensing counterparts (Figure 5), as with the development of three- and four-dimensional global climatologies of the particle backscattering, a proxy for particulate organic carbon (Sauzède et al. 2016).

## Biogeochemical-Argo and Biogeochemical Modeling Synergies

Biogeochemical models, especially those being operated at the global scale, suffer from a lack of observational data, both for initialization and for validation. This represents a major weakness for monitoring and forecasting the state of the ocean (Le Traon et al. 2019). The recent emergence of a high-quality global BGC-Argo data set (and associated uncertainties) can provide new perspectives to mitigate model limitations, especially in regions with high float densities. For example, in the Mediterranean Sea, satisfactory restitution of the seasonal and vertical dynamics of chlorophyll *a* by biogeochemical models can be achieved through either the assimilation of BGC-Argo data (Cossarini et al. 2019) or an accurate parameterization of the irradiance field (Terzić et al. 2019). Similarly, highly resolved physical-biogeochemical coupled models developed for the Bay of Bengal (Chakraborty et al. 2018) and the Gulf of Mexico (Damien et al. 2018) have revealed the importance of vertical in situ fields of key BGC-Argo variables for model assessment. In the Southern Ocean, an ocean state estimate relying on a model-generated best
<figure><img src="image_10.png" /><figcaption>Figure 5</figcaption></figure>

A three-dimensional view of particle backscattering ($b_{bp}$) for the month of June. This representation is an outcome of the neural network developed by Sauzêde et al. (2016), which allowed the merging of remote-sensing ocean-color data (chlorophyll *a* and $b_{bp}$) with Argo-based density profiles to retrieve 0–1,000-m vertical profiles of $b_{bp}$. The southern extension of this representation is limited because of the nonavailability of satellite measurements in this latitudinal zone for this time period. Note that the neural network was trained with Biogeochemical-Argo data at a time when only approximately 8,800 Biogeochemical-Argo vertical profiles of $b_{bp}$ were available. Such representations will be refined and better resolved with the increasing availability of data, especially in the Southern Ocean (see, for example, Figure 2).

fit to BGC-Argo observations (Verdy & Mazloff 2017) illustrates the maturity of such methods in generating a climate baseline and testing hypotheses.

Modeling is becoming an essential part of the deployment strategy of BGC-Argo (Talley et al. 2018). For example, an observation system simulation experiment determined that a network of 150 BGC-Argo floats could reconstruct surface properties and annual-mean 2,000-m inventories (Kamenkovich et al. 2017).

Finally, we should emphasize that machine learning opens new avenues in combining BGC-Argo with other data sources to develop new data sets or products supporting modeling requirements. In this context, Bittig et al. (2018b) introduced the concept of dynamic climatologies, where key biogeochemical variables, such as nutrients and the carbonate system and their associated uncertainties, can be predicted from temperature, salinity, and oxygen, which are routinely measured in BGC-Argo. These predictive capabilities have the potential to fulfill the requirements for model initialization and validation.
## Biogeochemical-Argo and Ship-Based Observation Synergies

The link between BGC-Argo and ship-based observations is key to the ongoing success of this program. Ship-based observations, particularly those conducted as part of the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP) (Talley et al. 2016) and associated repeat hydrography lines, provide significant opportunities for float deployments. Programs such as GO-SHIP provide the reference measurements for validation of BGC-Argo sensor data (Johnson et al. 2017a, Mignot et al. 2019, Taillandier et al. 2018). In addition, the ship-based programs create high-quality climatologies at depths below 1,000 m that are used to assess float sensor performance. In return, BGC-Argo brings the long-term temporal (seasonal and interannual) extension of data acquisition to the spatiotemporally restricted ship-based observations (Johnson et al. 2017b). The development of synergies between both observation systems and the associated communities is only beginning but will undoubtedly continue developing (Roemmich et al. 2019).

## THE WAY FORWARD

### New Applications

Profiling floats are cost-effective platforms with the potential to accommodate a variety of sensors, providing they have low energy demands and are close to neutral buoyancy. Therefore, beyond the long-term objective of a sustained BGC-Argo mission, the community is recurrently engaged in testing and evaluating potential new applications. Imaging sensors already used on CTD (conductivity, temperature, and depth) rosettes (Picheral et al. 2010) have been miniaturized and are beginning to be implemented on profiling floats to cost-effectively measure particle size distributions (from 100 µm to a few millimeters) and zooplankton biomass over extended periods. Passive acoustic listeners are now onboard profiling floats and provide estimates of wind intensity and rainfall (Riser et al. 2008, Yang et al. 2015). These sensors could be upgraded to monitor anthropogenic noises as well as the presence of mammals. Hyperspectral radiometry could replace the present multispectral measurements to refine the estimation of optically significant substances (pigments and phytoplankton functional types, colored dissolved organic matter, and nonalgal particles) and to better support the validation of future hyperspectral satellites [e.g., the NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission]. Finally, ongoing pilot projects are addressing the implementation of turbulence measurements (Roemmich et al. 2019), which, in addition to being of fundamental importance in understanding ocean physical processes, are also key drivers of the availability of nutrients supporting marine life.

## Biogeochemical-Argo and Metrics of Changes in Ocean Chemistry and Ecosystem Health

In addition to a more in-depth understanding of oceanic biogeochemical cycles, the continued expansion of the BGC-Argo data set will likely become a powerful tool to accurately monitor the ocean biogeochemical and ecosystem state and rate of change. Argo has played an essential role in reducing the uncertainties in metrics addressing ocean warming (e.g., ocean heat content) (Cheng et al. 2019, Levitus et al. 2012) and sea-level rise (von Schuckmann et al. 2014) and their regional variability. In the same way, BGC-Argo has the potential to contribute to the development and refinement of metrics of change in ocean chemistry and ecosystem health, which are needed to better inform stakeholders and policy makers. The monitoring of oxygen, nitrate, and pH at seasonal scales (Figure 2) enables direct observations of ocean metabolism (Bushinsky & Emerson 2015, Plant et al. 2016, Riser & Johnson 2008). Long-term observation of
these parameters will transform our ability to assess changing ecosystem processes. Observations
of oxygen concentration and pH by BGC-Argo floats over decadal intervals will document trends
in deoxygenation and ocean acidification in ways that were previously impossible to achieve.
Similarly, water transparency as derived from radiometric measurements could easily be anchored
to historical data (Boyce et al. 2010) to complement the satellite record and address potential
evolutions in phytoplankton biomass in the upper ocean.

Argo: Global, Full-Depth, and Multidisciplinary

After an initial implementation phase relying on research-driven pilot projects, the completion of a sustained global array of 1,000 floats is now the main BGC-Argo goal for the coming decade. Reaching this target will rely on a tight coordination with and within the Argo program. To accompany this evolution, Argo has recently broadened its historical founding goals into a long-term vision summarized as “global, full-depth, and multidisciplinary.” Argo will be reorganized through three main missions encompassing the historical core Argo mission (10-day resolution, 0–2,000 m, 2,500 floats), the Deep Argo mission (0–6,000 m, 1,200 floats) (G.C. Johnson et al. 2019), and the BGC-Argo mission (Biogeochem.-Argo Plan. Group 2016, Roemmich et al. 2019). All missions will work in a fully coordinated and synergetic way, which means that BGC-Argo will have the mandate to contribute to the core Argo mission by providing high-quality temperature and salinity profiles. This new organization represents a cost-effective way to ensure the sustainability of the entire Argo array while strongly diversifying its scientific and operational objectives. The renewal of the overall Argo objectives and vision has great potential not only for constructing a sustainable Argo program but also for strengthening the multidisciplinary science and ocean exploration outcomes that can be expected from a global robotic array.

SUMMARY POINTS

1. Significant progress has been made in both sensor and float technology, enabling high-quality measurements of six biogeochemical properties on profiling floats. These parameters are at the core of Biogeochemical-Argo (BGC-Argo). This technology is ready and proven to be scalable.

2. The flexibility of the community and its willingness to continually adapt to new technology and data quality-control and calibration protocols has been one of the keys to success. A sufficiently flexible data system has been created to continue accommodating these advances.

3. Until recently, most scientific discoveries were based on data from one or a handful of floats and were made by the research group that had deployed them. Data quality and interoperability have improved to a point where regional to basin-scale studies are becoming possible and non-BGC-Argo users are writing papers using the program's data.

4. BGC-Argo is now a fully integrated and official component of Argo, sharing the same long-term vision to implement a cost-effective sustained network through continuous technology improvement, development of common logistics, and free delivery of data.

5. A global BGC-Argo data set will have strong synergies with remote sensing, modeling, and ship-based observational programs.
# DISCLOSURE STATEMENT

The nitrate and pH sensors used in BGC-Argo were developed in K.S.J.'s laboratory at the Monterey Bay Aquarium Research Institute, and the institution receives royalty payments from commercial licenses. K.S.J. has recused himself from receiving any compensation from these licenses related to BGC-Argo.

# ACKNOWLEDGMENTS

H.C. acknowledges funding from the European Research Council for the remOcean project (grant agreement 834177), from the Horizon 2020 research and innovation program for the AtlantOS project (grant agreement 2014-633211), and from the BNP Paribas Foundation for the Southern Ocean and Climate (SOCLIM) project. K.S.J. was supported by the David and Lucile Packard Foundation and by the US National Science Foundation's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project under National Science Foundation award PLR-1425989, with additional support from NOAA and NASA. Logistical support for this project in the Antarctic was provided by the National Science Foundation through the US Antarctic Program. Raphaëlle Sauzède and Thomas Jessin are acknowledged for preparing **Figures 1, 2, and 5**.

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