Oceanic particulate organic carbon (POC) is a small but
dynamic component of the global carbon cycle. Biogeochemical models
historically focused on reproducing the sinking flux of POC driven by large
fast-sinking particles (LPOC). However, suspended and slow-sinking particles
(SPOC, here < 100 µm) dominate the total POC (TPOC) stock,
support a large fraction of microbial respiration, and can make sizable
contributions to vertical fluxes.
Recent developments in the parameterization of POC reactivity in PISCES (Pelagic Interactions Scheme for Carbon and
Ecosystem Studies model; PISCESv2_RC) have improved its ability to capture POC
dynamics. Here we evaluated this model by matching a global 3D simulation
and 1D simulations at 50 different locations with observations made from
biogeochemical (BGC-) Argo floats and satellites. Our evaluation covers
globally representative biomes between 0 and 1000 m depth and relies on (1) a refined scheme for converting particulate backscattering at 700 nm
(bbp700) to POC, based on biome-dependent POC /bbp700 ratios in the
surface layer that decrease to an asymptotic value at depth; (2) a novel
approach for matching annual time series of BGC-Argo vertical profiles to
PISCES 1D simulations forced by pre-computed vertical mixing fields; and (3) a critical evaluation of the correspondence between in situ measurements of
POC fractions, PISCES model tracers, and SPOC and LPOC estimated from high
vertical resolution bbp700 profiles through a separation of the baseline
and spike signals.
We show that PISCES captures the major features of SPOC and LPOC across a
range of spatiotemporal scales, from highly resolved profile time series to
biome-aggregated climatological profiles. Model–observation agreement is
usually better in the epipelagic (0–200 m) than in the mesopelagic
(200–1000 m), with SPOC showing overall higher spatiotemporal correlation
and smaller deviation (typically within a factor of 1.5). Still, annual mean
LPOC stocks estimated from PISCES and BGC-Argo are highly correlated across
biomes, especially in the epipelagic (r=0.78; n=50). Estimates of
the SPOC / TPOC fraction converge around a median of 85 % (range 66 %–92 %)
globally. Distinct patterns of model–observations misfits are found in
subpolar and subtropical gyres, pointing to the need to better resolve the
interplay between sinking, remineralization, and SPOC–LPOC interconversion
in PISCES. Our analysis also indicates that a widely used satellite
algorithm overestimates POC severalfold at high latitudes during the
winter. The approaches proposed here can help constrain the stocks, and
ultimately budgets, of oceanic POC.
Introduction
The biological carbon pump (BCP) is the ensemble of processes that transfer
the organic matter produced by plankton in the sunlit ocean surface to
deeper layers (Volk and Hoffert, 1985). This vertical flux plays a central
role in the Earth's climate, as it influences the oceans' capacity to absorb
and ultimately store atmospheric CO2 over centennial or millennial timescales (Kwon et al., 2009; Passow and Carlson, 2012).
The BCP is also central to biogeochemical functioning of the ocean, as it
determines the quality and quantity of organic matter available to the ocean
interior
(Arístegui et al.,
2009; Hernández-León et al., 2020) and the seafloor ecosystems. The
spatiotemporal patterns of organic matter supply and respiration influence
the distribution of dissolved oxygen, inorganic carbon, remineralized
nutrients and trace metals in the ocean interior
(Bianchi
et al., 2018; Duteil et al., 2012; Hayes et al., 2015; Oschlies et al.,
2018; Weber et al., 2016) and their return pathways to the surface. In
consequence, the BCP is intimately linked to, and feeds back on, upper-ocean
productivity.
Over the last decades, BCP research has placed emphasis on understanding the
ecological and physical factors that control the gravitational export of
particulate organic carbon (POC; see Table 1 for abbreviation definitions). This
process is often represented as the product of export production (the
fraction of net primary production exported below the euphotic layer) and
transfer efficiency (Teff; the fraction of export production that reaches a
given depth below the euphotic layer). Both variables vary widely across
ocean biomes and along the seasonal cycle
(Buesseler
and Boyd, 2009; Passow and Carlson, 2012; Buesseler et al., 2020), and our
understanding of the underlying mechanisms is still limited by the
relatively small amount of in situ measurements (Mouw et al., 2016).
Biogeochemical models have been built, and their parameters tuned, to be
able to reproduce these sparse observations of export production and
vertical flux attenuation, sometimes reaching apparently contradictory
results (Marsay et al., 2015). In
comparison, the models' ability to represent marine particle concentrations
has received less attention (Lam et
al., 2011).
List of abbreviations.
TypeAbbreviationDefinitionRegion nameNASPGNorth Atlantic subpolar gyreSTGSubtropical gyres of the Atlantic and Pacific oceansProcess or conceptBCPBiological carbon pumpCATSCoherent annual time seriesMLDMixed layer depthTeffVertical transfer efficiency of POCOperationally definedPOCParticulate organic carbon (used here as a generic name)chemical compartmentaSPOCSmall particulate organic carbonLPOCLarge particulate organic carbonTPOCTotal particulate organic carbon (here equivalent to SPOC + LPOC)Bio-optical variablebbp700Particulate backscattering coefficient at 700 nm wavelengthChl aChlorophyll a concentration estimated from fluorescencebNumerical model orNEMONucleus for European Modelling of the OceanparameterizationPISCESPelagic Interactions Scheme for Carbon and Ecosystem StudiesRCReactivity continuum parameterization for POC degradationProject nameNAOSNovel Argo Ocean Observing SystemremOCEANRemotely-Sensed Biogeochemical Cycles in the Ocean
a These variables may be estimated directly from seawater sampling,
indirectly from in situ or remote sensors, and from biogeochemical models.
See Table 2.
b See text Sect. 2.2 for details.
Marine particles are mainly composed of living microbial plankton cells,
living metazoans, and detritus (“marine snow” aggregates, fecal pellets,
zooplankton feeding structures, biominerals), whose size ranges from
< 1 µm to several millimeters (Bishop et al., 1980; Mullin
et al., 1965; Stemmann and Boss, 2012). Indeed, these particles feature wide
variations in their physicochemical properties and degree of biological
processing (Kharbush et al., 2020; Lam et
al., 2015; Passow and Carlson, 2012; Stemmann and Boss, 2012), as well as
microbial colonization (Baumas et al., 2021; Duret et al., 2019; Mestre et
al., 2018), all of which change during the particles' lifetime.
The gravitational sinking speed generally increases with particle size, although
observations show a wide scatter around canonical Stokes' law predictions
(Cael et al., 2021; Laurenceau-Cornec et al., 2019). Owing to this general
relationship, particle populations are often partitioned into a few
functional size classes: large particles, typically defined as larger than
50 or 100 µm, which usually sink at several tens or hundreds of meters per day, and small particles, which usually sink slowly (< 10 m d-1) or are suspended in the water column. In this study, POC is
divided into small POC (SPOC) and large POC (LPOC), with a nominal cutoff at
100 µm (Table 2; Sect. 2.2.2).
Match between BGC-Argo observations, PISCES tracers (Aumont et al.,
2017), real-world particulate organic carbon pools, and particle size
rangesa.
POC fractionBGC-Argo observationPISCES tracer (carbon currency)Closest real-world correspondenceSPOCbbp700 vertical profiles,despiked signal. Most sensitive to 0.5–30 µm particles (Dall'Olmo et al., 2009; Organelli et al., 2018). Calibrated as POC with Eqs. (1) and (2).PHY: “nanophytoplankton”, includescalcifiers (default fraction 30 %).Nominally 1–10 µm.Picocyanobacteria and non-diatomphyto-eukaryotes, generally 0.5–20 µm.PHY2: silicifying microphytoplankton. Nominally 10–50 µm.Diatoms, between 1.5 µm cells (Vaulot et al., 2008) and millimeter-scale chains. Mean ESDb generally < 50 µm in seawater (Snoejis et al., 2002; Ciotti et al., 2002; Bricaud et al., 2004).POC: detrital particulate organic carbon. Nominally < 100 µm. In practice, it includes heterotrophicprokaryotes' biomass with currentparameter values (see Sect. 4.3).Detrital particles between ∼ 0.2c and 100 µm. In practice, measurements may include particle-attached and free-living organisms, viruses, colloids and adsorbed DOC.ZOO: microzooplankton.Nominally 10–200 µm.Microzooplankton. Mostly ciliates and flagellates with size similar to their prey, down to around 2 µm (Calbet, 2008).Heterotrophic prokaryotes (BACT): currently not a prognostic tracer in PISCES. Not considered explicitly in this study (see Sect. 4.3).Free-living heterotrophic prokaryotes(bacteria and archaea), < 1 µm.LPOCbbp700 vertical profiles, spikesignal. Particle size between ∼ 100 µm (bbp700 spike of 2.3×10-5 m-1) and ∼ 2 mm (bbp700 spike of 8×10-3 m-1) (Briggs et al., 2020). Calibrated as POC with Eqs. (1) and (2).GOC: detrital particulate organic carbon. Nominally > 100 µm.Detrital particles > 100 µm (aggregates, fecal pellets).Includes attached microbes.ZOO2: mesozooplankton. Includes flux feedersd. Nominally 0.2–2 mm.Mesozooplanktond
a See Stemmann and Boss (2012) for typical seawater particle size
spectra.
b Equivalent spherical diameter.
c Particles in the 0.2–0.8 µm size range and DOC are retained
with variable efficiency by the filters commonly used to determine POC
(Bishop,
1999; Cetinić et al., 2012; Graff et al., 2015; Lee et al., 1995;
Morán et al., 1999; Strubinger Sandoval et al., 2021).
d PISCES mesozooplankton represents mostly copepods in the euphotic
layer and flux feeders below it. The fraction of flux feeders is diagnosed
in PISCES from the proportion between flux feeding rates and total
mesozooplankton ingestion rates. By construction, flux feeding becomes the
dominant mode of mesozooplankton feeding below the euphotic layer under
productive surface waters in PISCES. In reality, a wide variety of feeding
strategies and organisms are found in the mesopelagic
(Ikenoue
et al., 2019; Kiørboe, 2011; Mayor et al., 2020; Stukel et al., 2019).
The traditional BCP paradigm posits that gravitational sinking of LPOC
controls the vertical carbon flux (Sarmiento and Gruber, 2006).
However, it has been known for decades that POC export is shaped by
additional processes, such as the physical transport of particles by
convection and subduction and the “active” particle flux mediated by the
vertical migration of metazoans. Recently, these processes have been collectively
termed “particle injection pumps” (Boyd et al., 2019). In parallel, the
role of dissolved organic carbon in vertical carbon export has been widely
recognized (Jiao et al., 2010; Passow
and Carlson, 2012; Legendre et al., 2015). Therefore, the BCP is
increasingly seen as a diverse array of interconnected mechanisms.
One aspect that has recently received considerable attention is the role of
suspended and slow-sinking particles (Alonso-González et al., 2010;
Baker et al., 2017). Owing to their longer residence time, small particles
usually dominate the POC stock
(Aumont et al.,
2017; Baker et al., 2017) and may support a proportional fraction of the
respiration
(Baltar
et al., 2010a, b; Belcher et al., 2016; García-Martín et al.,
2021). Convective mixing
(Bishop
et al., 1986; Dall'Olmo and Mork, 2014; Lacour et al., 2019) and subduction
(Llort et
al., 2018; Omand et al., 2015; Resplandy et al., 2019) can transport SPOC
into the mesopelagic layer, adding to other export mechanisms and
potentially making large contributions to total POC export
(Alonso-Gonzalez et al., 2010; Henson et
al., 2015). Production of SPOC in the mesopelagic and below also results
from the fragmentation of LPOC, caused by physical disaggregation (Takeuchi
et al., 2019), bacterial solubilization, and zooplankton activity
(Briggs
et al., 2020; Goldthwait et al., 2004; Mayor et al., 2020; Stemmann et al.,
2004b). Moreover, SPOC is also produced through bacterial chemosynthesis in
the dark ocean (Arístegui et al., 2009; Herndl and
Reinthaler, 2013). Altogether, these findings illustrate how our limited
knowledge of POC characteristics and cycling hampers a mechanistic
understanding of the BCP and mesopelagic carbon budgets
(Giering
et al., 2014).
Biogeochemical models designed to capture only gravitational POC sinking
fail to represent POC stocks in the ocean interior. Aumont et al. (2017)
recently showed that the Pelagic Interactions Scheme for Carbon and
Ecosystem Studies model (PISCESv2;
Aumont et al., 2015)
underestimated POC by 1 order of magnitude or more below the epipelagic
layer. This pitfall is likely common to any state-of-the-art model with a similar structure
(Laufkötter
et al., 2016; Séférian et al., 2020). Aumont's work also showed that
the model's fit to observed deep-ocean POC concentrations could be
dramatically improved by treating detrital POC, both small and large, as a
mixture of particles with different reactivity (or lability) towards
bacterial degradation. In this scheme, termed the reactivity continuum (RC)
parameterization, detrital POC degradation is computed after dividing it
into many reactivity classes that approximately follow a continuous gamma
distribution – hence its name. The most labile fractions are rapidly
consumed below the upper mixed layer, such that vertically exported POC
becomes progressively more refractory. This results in enhanced preservation
of SPOC in the model and a much more realistic fraction of SPOC with respect
to total POC (TPOC) in the ocean interior. In addition, the RC scheme does
not appreciably degrade model estimates of the gravitational POC flux.
Despite this breakthrough in the representation of POC fractions in
PISCESv2_RC (hereafter “PISCES”), the new parameterization
was evaluated using only sparse measurements (Druffel et al., 1992; Lam et
al., 2011, 2015) based on large-volume filtration with in situ pumps. This
approach enables an accurate determination of the mass and composition of the
particulate fraction but cannot afford high-frequency sampling over extended
spatiotemporal scales (Bishop, 1999; Boss et al., 2015; Gardner et al.,
2006). During the last decade, the launching of the biogeochemical Argo
(BGC-Argo) program of robotic observations has ended the historical
undersampling of particles in the ocean interior
(Claustre et al., 2020). BGC-Argo floats
provide vertical profiles of temperature, salinity, bio-optical, and chemical
variables between 0–1000 m every 1 to 10 d in near-real time and are
thus well-suited to study particles (∼ 0.5 µm to
∼ 2 mm in size; Table 2) in the mesopelagic layer, where the
strongest POC gradient occurs. The rapidly growing fleet of BGC-Argo floats
equipped with bio-optical sensors enables a comparison between models and
observations at global scales with enhanced spatiotemporal resolution.
Unfortunately, BGC-Argo floats measure only a bio-optical proxy of POC, the
particulate backscattering coefficient (usually at 700 nm, bbp700) and
empirical conversion factors are needed to estimate POC
(Bishop
and Wood, 2008; Cetinić et al., 2012; Stramski et al., 2008). These
conversion factors vary in response to several concurrent processes that
alter particle abundance, size distribution, shape, composition, and
ultimately optical properties (Boss et al., 2015; Giering et al., 2020).
In this study we compare SPOC and LPOC concentrations estimated from
BGC-Argo floats to their PISCES-simulated counterparts, as well as
satellite-retrieved surface POC concentration. The comparison is enabled by
a novel empirical algorithm to convert bbp700 to POC. Observations and
simulations are matched in 3D (biome-wide climatological scale) and 1D (at
defined locations over an annual cycle). These complementary strategies
allow us to evaluate the skill of PISCES at simulating POC stocks and
fractions in globally representative biomes. We conclude with a list of
recommendations to fully exploit the potential of robotic particle
observations combined with biogeochemical modeling.
MethodsDefinition of vertical and horizontal domains
Studies of the BCP usually decompose the ocean into vertical domains: a
surface layer where autotrophic activities dominate and one or several
ocean interior layers where heterotrophic processes dominate. Functional
definitions based on light penetration, peak export production, vertical
mixing, or long-term carbon sequestration are usually the most appropriate
ones for process studies
(Buesseler
and Boyd, 2009; Buesseler et al., 2020; Guidi et al., 2015; Palevsky and
Doney, 2018). Because this paper is mainly descriptive and combines
observations and simulations, we will refer to layers defined by fixed
depths: epipelagic (0–200 m), mesopelagic (200–1000 m), and bathypelagic
(1000–4000 m).
Over the horizontal dimensions, our comparisons between observations and
model results rely on the ocean biomes defined by
Fay and McKinley (2014). These authors
subdivided each ocean basin (Atlantic, Pacific, Indian, and Southern Ocean)
into different biomes based on observed variables, namely sea-surface
temperature, spring–summer chlorophyll a concentration (Chl a), ice fraction,
and maximum mixed layer depth (MLD), all on a 1∘×1∘ grid. This division resulted in 17 regions ascribed to one of
the following five biomes: the ice biome, the subpolar seasonally stratified
biome, the subtropical seasonally stratified biome, the subtropical
permanently stratified biome, and the equatorial biome. The analyses
reported herein focus on the following four biomes (Fig. 1): the seasonally
stratified North Atlantic subpolar gyre (NASPG); the permanently stratified
Atlantic and Pacific subtropical gyres, which were grouped together (STG);
the seasonally stratified Southern Ocean (subantarctic); and the
Mediterranean Sea, which was added here owing to the abundance of BGC-Argo
data and represents a seasonally stratified subtropical biome. Fay and
McKinley's definition allows biome boundaries to change from one year to
another. Here we analyzed only data from the core of each biome, defined as
the grid cells that never changed classification during the 1998–2010
satellite observation period.
Global distribution and abundance of BGC-Argo profiles between
2010 and 2019. Grid cells (1∘×1∘) with at least one profile of the backscattering coefficient at 700 nm (bbp700) are marked
with black dots, and those with at least 20 profiles are marked with
yellow-filled circles. The gray contours indicate the 1000 m isobath. Color
shading indicates ocean biomes (see text), whose names are indicated on top
of the bottom histograms. For each biome, light color indicates its average
extent over 13 years of satellite observations (1998–2010), whereas the
darker color indicates the “core” grid cells that never changed biome
classification during the same period. The bottom panels (b–f) show the number
of BGC-Argo bbp700 profiles per year in the four selected biomes and in the
global ocean.
BGC-Argo observations
The global dataset acquired by the array of BGC-Argo floats was downloaded
from the Global Data Assembly Center hosted by Ifremer
(ftp://ftp.ifremer.fr/ifremer/argo/dac/, last access: 14 January 2020) (Argo,
2000). The selected floats were equipped with a Seabird-Wetlabs ECO-Triplet
sensor package including a Chl a fluorometer (excitation at 470 nm; emission
at 695 nm) and a backscattering sensor at 700 nm (bbp700), in addition
to the conductivity–temperature–depth (CTD) probe. The downloaded
measurements had undergone the standard processing, which includes the
application of calibration equations to raw sensor output and the
performance of near-real-time quality control to both CTD
(Wong et al., 2021) and Chl a measurements
(Schmechtig et al., 2018). Since no specific quality control
procedure has been established yet for bbp700 profiles, the latter were
only quality-controlled according to the general criteria
(Schmechtig et al., 2016). Thus, we used all bbp700
measurements with quality control flag ≤ 3 (equivalent results were
obtained with flag ≤ 2). Two different processing pipelines were
applied to different subsets of the BGC-Argo data, as described below.
Global gridded climatologies (3D approach)
The global dataset acquired between 2010 and 2019 was used to produce global
gridded monthly and seasonal climatologies for bbp700 and Chl a. The
measurements were binned onto the ORCA2_L31 grid used for
NEMO–PISCES simulations (see Sect. 2.4.1), which has a horizontal resolution of
about 2∘ that increases to 0.5∘ in the meridional
direction in the equatorial domain, and 30 oceanic vertical levels between
the surface and the ocean bottom. The thickness of the vertical bins
increases progressively from 10 m at the surface to 339 m in the 22nd
bin (870–1209 m), the deepest one containing BGC-Argo data. In each grid
element, the average, median, range, and data counts were computed. Profiles
from the CSIRO and INCOIS data assembly centers were not used because, at
the time of download, they had not taken into consideration the new
calibration files provided by the manufacturer. A total of 72 460 profiles
were used to calculate the global gridded climatologies.
Profile time series for individual floats sampling at higher
resolution (1D approach)
A subset of the floats, deployed mostly by the projects NAOS, remOCEAN, and
Bio-Argo France (model NKE PROVOR CTS-4), were programmed to sample at
higher temporal and vertical resolution than the Argo defaults (10 d and
10 m). These floats made vertical profiles between 1000 m and the surface
every 2, 5, or 10 d with a vertical resolution of 10 m between 1000 and
250 m (or 350), 1 m between 250 (or 350) and 10 m, and 0.2 m between 10 m
and the sea surface. We processed this dataset with a dedicated pipeline to
extract additional information on POC size fractions and their dynamics.
Along each vertical profile we computed depth, conservative temperature,
absolute salinity, σθ, and spiciness
(Flament, 2002) from the calibrated pressure,
temperature, and salinity using the R package oce (Kelley,
2011). The MLD was calculated as the shallowest depth where σθ exceeded the surface reference value by 0.03 kg m-3
(Bishop and Wood, 2009; Sallée et al., 2021). The surface reference
corresponded to the σθ at 5 m after applying a five-point
running mean to the top 10 m of the profile. The 0.03 kg m-3 criterion
provided sensible results across biomes and was consistent with the
NEMO-simulated turbocline depth (see Sect. 2.5.2). Eleven additional MLD criteria
were also calculated to assess the robustness of the approach (Fig. S1).
Following Briggs et al. (2011, 2020),
each bbp700 vertical profile was smoothed with sequential 11-point
running-minimum and running-maximum filters to separate the baseline from
the spikes. The baseline signal corresponds to the bulk population of small
particles, whose diameter is smaller than 100 µm and mostly between
0.5 and 30 µm
(Dall'Olmo et
al., 2009; Organelli et al., 2018). Each spike reflects the passage of a
particle larger than about 100 µm in front of the sensor window.
Previous studies inferred that backscattering spikes are caused mostly by
phytodetrital aggregates but also by large zooplankton and phytoplankton
(Bishop and Wood, 2008; Briggs et al., 2011; Gardner et al., 2000). Assuming
that backscattering sensors sample a volume of 10 mL (Briggs et al., 2020),
we estimated that backscattering spike concentration was typically between
a few and < 100 L-1, consistent with previous independent
estimates (McDonnell and Buesseler, 2010; Stemmann et al., 2008; Stemmann
and Boss, 2012). Backscattering spikes were on average 4–10 times more
abundant than chlorophyll fluorescence spikes. The bbp700 spikes larger
than 0.008 m-1, associated with particles larger than ∼ 2 mm, were removed, with a negligible impact on the total spike signal (Briggs
et al., 2020). Unlike Briggs et al. (2020), we did not
subtract from the baseline profile the 850–900 m signal, which in that
study was attributed to a background of small refractory particles with
constant concentration. The baseline and spike signals were converted to
SPOC and LPOC, respectively, as described in the next section.
All measurements were subsequently averaged into 18 vertical bins of
progressively increasing thickness, such that the deepest bins contained at
least 10 measurements. Finally, each profile was interpolated onto the L75
vertical grid commonly used in NEMO simulations. This grid has 46 bins
between the surface (0–1 m) and the deepest layer considered here (901–996 m). The profile time series was temporally binned into 5 d periods.
For the comparison to PISCES 1D simulations, BGC-Argo time series were cut
into 1-year periods (shifted by 6 months in the Southern Hemisphere),
which we will call coherent annual time series (CATS) hereafter. The CATS
fulfilled the following conditions: (1) sampling dates spanned at least
between days of year 25 and 340; (2) the float remained in the same region
and did not cross major oceanic fronts according to the vertical–temporal
evolution of temperature, salinity, σθ, and spiciness; (3) bottom depth was > 1000 m for all profiles (bathymetry obtained
from the 15 arcsec GEBCO 2019 product; https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2019/gebco_2019_info.html, last access: 12 May 2019); and (4) the Chl a and bbp700 sensors were stable according
to both the vertical profiles and the continuous measurements acquired
during drift at 1000 m between profiles. A total of 50 CATS from 28
different floats were selected, with 10–16 CATS in each biome and 32 (18) in
the Northern (Southern) Hemisphere (Table S1).
Conversion of bbp700 to POC
To convert the profiles of the backscattering coefficient at 700 nm
(bbp700) to POC we developed an empirical algorithm building on
previous studies
(Bol et al.,
2018; Evers-King et al., 2017). The behavior of this algorithm is
summarized in Fig. 2 and discussed in Sect. 4.2. Further details are
provided in the Appendix. The algorithm estimates the POC /bbp700 ratio
along the vertical profile between 0 and 1000 m and proceeds in two steps.
First, the POC /bbp700 ratio is calculated by prescribing a
POC /bbp700 ratio in the surface layer (zsurf,biome) and an
exponential decrease with depth in the underlying water column, which
converges asymptotically towards a constant deep value (c):
POCbbp700z=c+abiome⋅e-0.001⋅b⋅z-zsurf,biome,z>zsurf,biome.
The abiome coefficient is biome-specific, whereas the asymptote at
depth is fixed at c=1000 mmol C m-3 m. The zsurf,biome
corresponds to the 5 % quantile of the climatological MLD in summer in a
given biome (here ranging between 14 m in the Mediterranean and 41 m in the subantarctic). The POC /bbp,700 ratios at zsurf,biome,
corresponding to abiome+c in Eq. (1), are taken from the literature
and range between 2600 and 4900 mmol C m-3 m (Fig. 2; Table A1).
Second, the POC /bbp,700 profile derived from Eq. (1) is modified by
extrapolating a constant POC /bbp,700 value, taken from a reference
depth, zref, to the sea surface. In each vertical profile, zref is
defined as the deepest of zsurf,biome and the MLD:
POCbbp700z=POCbbp700zref,z≤zref;zref=maxzsurf,biome,MLD.
The exponential decrease prescribed by Eq. (1) is similar to that proposed by
Bol et al. (2018), except for the inclusion of the constant term c that
prevents the ratio from becoming 0 at depth. The slope of the exponential
decrease (b=-6.57) is constant in all biomes and based on our fit to the
Cetinić et al. (2012) dataset, using
the same depth bins as Bol et al. (2018) but additionally forcing the curve
towards c at 1000 m.
Empirical model used to convert the backscattering coefficient at
700 nm (bbp700) to particulate organic carbon (POC). Black dots and error
bars show the dataset of Cetinić et al. (2012) as binned by Bol et al. (2018). The black curve shows the exponential fit of Bol et al. (2018) to
the Cetinić dataset. The dashed light-blue curve shows our fit to the same dataset
(NASPG: North Atlantic subpolar gyre), forced to converge to a nonzero
minimum value at depth. The remaining curves show similar functions with the
same exponential slope (b=-6.57) as the NASPG fit, but with different
surface values derived from the following studies: Loisel et al. (2001) for
the Mediterranean Sea; Stramski et al. (2008) for the ensemble of
subtropical and tropical areas excluding equatorial upwellings (STG); and
Johnson et al. (2017) for the Southern Ocean (subantarctic). The depth of
the homogeneous surface layer, zsurf,biome in Eqs. (1) and (2), corresponds
to the 5 % quantile of the climatological MLD in summer in a given biome:
15 m in the NASPG, 14 m in the MED, 21 m in the STG, and 41 m in the SO. The
dotted green line, shown for the SO case only, illustrates the behavior of
the algorithm for a hypothetical MLD of 150 m.
The uncertainty of regional bbp700–POC conversion factors in the
epipelagic is typically < 10 % according to the standard error of
the POC vs. bbp700 linear regression slopes (Table A1). The few
available measurements in the mesopelagic suggest a POC /bbp700
uncertainty lower than a factor of 2. Through this study, we will assume
that model / observation ratios larger (smaller) than 2 (0.5) can safely be
regarded as model overestimates (underestimates), which possibly is a conservative
criterion for the epipelagic layer.
The conversion of bbp700 to POC was done using different MLD data for
the global climatologies and the CATS. For the global climatologies we used
the Monthly Isopycnal and Mixed-layer Ocean Climatology (MIMOC) of
Schmidtko et al. (2013), downloaded from https://www.pmel.noaa.gov/mimoc/, last access: 24 November 2020, which was reprojected onto the ORCA2
horizontal grid. Although MIMOC is based on an algorithm that evaluates
several MLD criteria, it has been shown to be globally consistent with the
MLD based on a 0.03 kg m-3σθ threshold (Holte and
Talley, 2009; Sallée et al., 2021). For the float time series, we used
the MLD defined by a 0.03 kg m-3 threshold computed for each individual
profile.
Ocean color satellite data
Satellite observations for the 1997–2019 period were downloaded from
GlobColour (https://www.globcolour.info, last access: 2 March 2020), a merged multi-sensor
dataset. Monthly sea-surface POC fields based on the
algorithm of Stramski et al. (2008) were used to compute monthly
climatologies that were subsequently reprojected onto the ORCA2 grid.
PISCES simulations and matching with observations
Simulations were run using the ocean biogeochemistry model PISCESv2
(Aumont et al., 2015) with the RC
parameterization for detrital POC (Aumont et al., 2017). The configuration
of PISCES used here has 24 tracers: two classes of phytoplankton
(“nanophytoplankton” and diatoms), detrital particles (small and big), and
zooplankton (micro- and mesozooplankton), plus 18 additional tracers that
comprise dissolved inorganic macronutrients and iron, inorganic carbon
chemistry variables, dissolved organic carbon (DOC), and different
particulate stocks of iron and silica. Phytoplankton growth depends on
light, inorganic nitrogen, phosphorus, and iron, with an additional silicate
requirement for diatoms. Microzooplankton and mesozooplankton consume the
two classes of phytoplankton and small detrital particles with different
preferences, and mesozooplankton also predate on microzooplankton. Detrital
particles are produced through the mortality of phytoplankton and
zooplankton (which are routed to small and large particles in different
proportions), zooplankton sloppy feeding, and DOC coagulation. Production of
large detritus also results from enhanced diatom mortality upon bloom
collapse, aggregation of small detritus, and zooplankton mortality and fecal
pellet production (the latter two derived from a closure term that accounts
for unresolved higher trophic levels). Small and large detrital particles
are nominally smaller/larger than 100 µm and sink, respectively, at 2
and 50 m d-1. Both small and large detritus are remineralized by
implicit bacterial activity and consumed by flux feeding mesozooplankton.
Remineralization follows first-order kinetics with an initial specific rate
“k” of 0.035 d-1 (at 0 ∘C) for freshly produced detritus in
the upper mixed layer. This k decreases with depth as an emergent result
of the RC parameterization. To account for bacterial solubilization of
aggregate-binding polymers, 10 % of the degraded large detritus is routed
to small detritus (this fraction is hard-coded based on previous
calculations). The flux feeding rate depends on the particles' sinking flux and thus attenuates the flux of large particles more strongly than that of
small particles. Additionally, a variable fraction of the large detritus
intercepted by flux feeders is fragmented into small detritus. Flux feeding attenuates up to around 50 % of the large detritus sinking flux through the top 500 m
during intense export events. Phytoplankton growth rates and
remineralization rates increase with temperature with a Q10 of 1.9,
whereas zooplankton growth rates have a Q10 of 2.14.
To evaluate PISCES simulations against in situ POC measurements or their
proxies, the correspondence between PISCES tracers (in italics) and observed POC
fractions must be established. In this study we assumed that SPOC
corresponds to the sum of PISCES-simulated nanophytoplankton (PHY), diatoms
(PHY2), small detrital particles (POC), and microzooplankton (ZOO), whereas LPOC
corresponds to the sum of PISCES-simulated large detrital particles (GOC) and
mesozooplankton (ZOO2) (Table 2). Total POC (TPOC) corresponds to the sum of
those six PISCES tracers or, which is the same, SPOC + LPOC. Heterotrophic
prokaryotes (BACT) are not a prognostic tracer in PISCES and are not explicitly
included in our analysis. The correspondence between observed and simulated
POC fractions, explicit and implicit, is discussed in Sect. 4.3.
PISCES 3D simulations vs. biome-aggregated observations
For the global-scale comparison between PISCES outputs and observations from
BGC-Argo and satellites, we used the simulation presented by Aumont et al. (2017), which was forced by pre-computed dynamical fields from a
pre-industrial run of the ocean circulation model NEMO. Global monthly
climatological fields of the PISCES tracers were used to compute seasonal
climatologies of modeled SPOC, LPOC, and TPOC. To enable a direct comparison to
the BGC-Argo observations, model output was resampled at locations where
BGC-Argo profiles were available over the 2010–2019 period. Prior to
comparison with modeled fields, BGC-Argo observations were further screened
to remove “outliers” in each biome and season. Outliers were defined as
grid cells where the mean bbp700 in the upper 50 m was above the 95 %
percentile or greater than 0.008 m-1 (Briggs et al., 2020). The same
spatial resampling was applied to satellite-retrieved POC.
PISCES 1D simulations vs. BGC-Argo coherent annual time series (CATS)
A more detailed comparison was undertaken by matching each of the CATS from
individual BGC-Argo floats with a PISCES water-column (“PISCES 1D”)
simulation. The match was based on the coherence between the seasonal cycle
of MLD observed by the float and the turbulent layer simulated by NEMO. The
pre-computed dynamical fields used to evaluate the match-ups were obtained
from an ocean-only historical simulation (NEMO v3.6) at 1∘
resolution with 75 vertical levels (ORCA1_L75 grid) forced
with the JRA-55 atmospheric reanalysis that covered the 1958–2018 period
(Tsujino et al., 2020) following the OMIP2 protocol (Griffies et al., 2016).
The float-observed MLD and the NEMO-simulated turbocline depth (defined by
turbulent vertical diffusivity >5×10-4 m2 s-1) were compared over the entire annual cycle in all the model grid
cells that had been visited by the float on a given year. The best model
grid cell was selected based on model–observation correlation,
root-mean-square error, and time lag in the onset date of summer
stratification. An example of the metrics used to match NEMO dynamical
fields and BGC-Argo MLD is provided in Figs. S1–S4. The associated model configuration and datasets are available in public repositories (Galí et al., 2021a and b).
PISCES 1D offline simulations were forced using the dynamical fields from
the selected model grid cells. The same annual forcing, corresponding to the
year of the BGC-Argo observations, was repeated over 5 simulation years.
After 4 years of spin-up, the output from year 5 at a 5 d resolution was
used for the comparison to the BGC-Argo CATS. Initial conditions
(climatological fields of inorganic nutrients and carbon chemistry
variables) and boundary conditions (atmospheric deposition) were the same as used for PISCES 3D
(Aumont
et al., 2015, 2017). Nutrient fields were restored towards the mean annual
profile below 300 m. This procedure avoided drift in nutrient stocks by
replenishing the upper ocean with the same amount of nutrients each year,
resulting in regular seasonal cycles after 1 year and identical cycles
from year 4 onwards.
ResultsClimatological POC fields
This section describes the comparison among TPOC fields estimated from
BGC-Argo and satellite observations and PISCES simulations across four ocean
biomes. Figure 3 compares monthly climatologies at the sea surface (0–20 m),
Fig. 4 compares seasonal climatologies between 0–1000 m, and Fig. 5
displays skill metrics (Pearson's correlation, model / observations ratio, and
bias) for the vertical profiles shown in Fig. 4, as well as for the 1D
simulations matched to BGC-Argo CATS.
Monthly sea-surface particulate organic carbon (POC) concentration
at the sea surface. Shown are POC estimates based on BGC-Argo (derived from
the backscattering coefficient at 700 nm, bbp700), satellite
(GlobColour), and PISCES. PISCES TPOC results from the addition of the
detritus (D), phytoplankton (P), and zooplankton (Z) tracers (Table 2), which
are shown here as cumulative sums. Satellite data not shown for months when
more than half of the ocean pixels could not be observed because of low
solar elevation at high latitudes.
Seasonal 0–1000 m profiles of particulate organic carbon (POC).
BGC-Argo estimates of TPOC (based on the backscattering coefficient at 700 nm, bbp700) are represented with the median and the 0.025–0.975
quantiles within each biome. PISCES TPOC results from the addition of the
detritus (D), phytoplankton (P), and zooplankton (Z) tracers (Table 2), which
are shown here as cumulative sums.
Seasonally stratified subpolar biomes
In the subpolar biomes, near-surface TPOC ranged between ∼ 1 mmol m-3 in the winter months and around 5 (subantarctic) or 10 (NASPG) mmol m-3 in early summer. In these biomes, PISCES-simulated TPOC was
within the 2.5–97.5 % bounds of BGC-Argo observations for most months
(Fig. 3). During the apex of the bloom (months 5–7), however, median PISCES
estimates exceeded those obtained from BGC-Argo (by ∼ 80 %)
and satellites (by ∼ 15 %). In the subantarctic, this
pattern extended through the fall.
Satellite TPOC was in poor agreement with both PISCES and BGC-Argo TPOC
outside the apex of the bloom. During the winter semester (months 10–12 and
1–3), and considering only the subset of pixels observed by both satellites
and floats, satellite TPOC exceeded BGC-Argo TPOC by a factor of 6.1 (factor of 3.3)
in the NASPG (subantarctic), as discussed in Sect. 4.1.
PISCES reproduced the vertical decrease in TPOC concentration down to 1000 m
(Fig. 4) with generally good skill (Fig. 5), but some misfits were observed.
In the North Atlantic subpolar gyre (NASPG), PISCES underestimated TPOC
through the epipelagic and the upper mesopelagic during the winter by
∼ 40 % (Fig. 4) because of too vigorous convection in the
NEMO dynamical fields that kept phytoplankton under insufficient light
exposure. In the subantarctic biome, simulated TPOC exceeded BGC-Argo
estimates in the upper portion of the mesopelagic layer in spring, and the
overestimation pattern propagated downwards through the summer and fall. A
similar but smaller overestimation pattern was observed in the NASPG in
summer and fall.
Summary of skill metrics for the comparison between PISCES and
BGC-Argo particulate organic carbon (POC) profiles in different biomes and
seasons. Pearson's correlation coefficient, mean model / data quotient, and
mean bias (mmol m-3) are computed in linear space (untransformed data).
Horizontal black lines show the benchmark values for each statistic. Symbols
represent different types of simulations and data aggregation levels. In the
case of the PISCES 3D climatological simulation, the statistics are computed
at two spatial aggregation levels: using all the grid cells with matching
BGC-Argo profiles in a given biome (circles), and after averaging all the
documented grid cells within the biome (empty triangles), as displayed in
Fig. 4. In the case of the PISCES 1D simulations, statistics are computed
for each individual CATS separately (small crosses), and the biome-season
median is also shown (small filled triangles).
Permanently and seasonally stratified subtropical biomes
In the oligotrophic biomes, monthly median surface TPOC displayed low
seasonal amplitude. Total POC concentrations estimated from BGC-Argo data
typically oscillated around 2 mmol m-3, with a maximum / minimum ratio of
around 1.6 in the Mediterranean and 1.3 in the Atlantic and Pacific
subtropical gyres (STG). In the STG, satellite and BGC-Argo TPOC were in
good agreement, which was to be expected because subtropical bbp700–POC
conversion factors and satellite POC are based on the same study (Stramski
et al., 2008). By contrast, PISCES TPOC exceeded BGC-Argo TPOC around
2-fold in the STG. In the Mediterranean, satellite TPOC exceeded BGC-Argo
TPOC by ∼ 80 %, pointing to the differences in the
respective POC estimation algorithms. PISCES TPOC was nearly fourfold higher
than BGC-Argo TPOC at the surface in the Mediterranean, an overestimation
that results from unrealistic physics in that basin caused by the too coarse
(2∘) model grid (Tonani et al., 2008; Lebeaupin Brossier et al.,
2011) (see Sects. 3.3 and 4.3). Vertical TPOC profiles evidenced the shortcomings
of PISCES simulations in the oligotrophic gyres. Compared to BGC-Argo
profiles, PISCES simulations produced too sharp deep POC maxima and
underestimated TPOC in the waters above and below (Fig. 4). These mismatch
patterns prompted us to examine the seasonal cycles of POC
in different biomes in greater detail.
Coherent annual time series of SPOC and LPOC: case studies
In this section we describe two CATS from BGC-Argo floats and their PISCES
1D counterparts (Sect. 3.2.1). The floats, identified by their World
Meteorological Organization (WMO) number, are nos. 6901486 in the NASPG (year
2015) and the 6901660 in the South Pacific STG (year 2017). Float
6901486 represents the most productive conditions of our dataset, with
annual median (maximum) Chl a of 0.60 (10.7) mg m-3 and TPOC of 5.5
(16.7) mmol m-3 in the near-surface layer (0–20 m). By contrast, float
6901660 represents the most oligotrophic waters, with annual median
(maximum) Chl a of 0.011 (0.036) mg m-3 and TPOC of 1.8 (2.5) mmol m-3 in the near-surface layer. In Figs. S5–S10 we provide additional
examples of the CATS–PISCES 1D match-ups in the four biomes and in two
subregions within the NASPG.
Labrador Sea (North Atlantic subpolar gyre)
Float 6901486 was deployed in June 2013 close to the Reykjanes Ridge in the
Irminger Sea, NW Atlantic subpolar gyre. After drifting SW carried by the
East Greenland Current, the float was trapped in the Labrador Sea cyclonic
circulation between 2014 and July 2017, when it stopped communication after
completing 344 profiles (cycles). During its multi-year sampling in the
Labrador Sea (56–60∘ N latitude and 48–54∘ W
longitude), over a bottom depth of around 3500 m, the float showed stable
physical and bio-optical records at 1000 m, broken only by winter convection
events, and recurring annual patterns of spring–summer phytoplankton
blooming and vertical carbon export as depicted by Chl a and POC profiles.
Here we describe the year 2015 (Fig. 6), characterized by deep convection
during February and March, when the MLD generally exceeded 1000 m (Fig. S1).
Epipelagic TPOC increased rapidly upon water-column re-stratification in
mid-April and peaked in mid-May. A secondary bloom peaked in late June after
a transient MLD deepening caused by stormy weather. Epipelagic TPOC
decreased progressively thereafter until a small bloom was observed in
October linked to pycnocline erosion. This bloom terminated rapidly and
epipelagic TPOC reached the baseline level in late December. The SPOC
fraction dominated epipelagic TPOC all year round, and the highest LPOC
fractions of nearly 20 % were recorded at the apex of the spring–summer
blooms. Distinct vertical particle export events were observed in May and
June, matching the surface phytoplankton blooms, and August, when nutrient
limitation likely triggered bloom collapse. These export pulses produced
synchronous increases in SPOC and LPOC through the mesopelagic layer, though
with different magnitudes. After reaching relative minima in October,
mesopelagic SPOC and TPOC increased again in November, but they showed
different vertical patterns.
Temporal evolution of small and large POC concentrations (SPOC and
LPOC, respectively) in the North Atlantic subpolar gyre (NASPG). Panels (a)
and (d) show observations from the BGC-Argo float WMO 6901486, which drifted
in the Labrador Sea during the year 2015. Panels (b) and (e) show the
corresponding PISCES 1D simulation. Panels (c) and (f) show the ratio between
the model and the observations and the corresponding correlation coefficient
and RMSE in log10 scale. Solid lines depict the observed MLD (a and d,
in gray) and the simulated turbocline depth (b and e, in black). In panels a
and d, the dotted lines show the depths of the maximum Chl a fluorescence
gradient and the dashed lines an alternative MLD estimated with a
0.005 ∘C temperature threshold that captures weak stratification.
Panels (g), (h), and (i) show the vertically integrated SPOC and LPOC stocks in
different layers: 0–200 m or epipelagic (g), 200–1000 m or mesopelagic (g), and 0–1000 m (i). On top of panels (g)–(i) we show the correlation and mean
bias between BGC-Argo and PISCES total POC (TPOC).
The matching PISCES 1D simulation captured the patterns of
SPOC and LPOC with good skill in the epipelagic and, to a lesser extent, the mesopelagic
layer (Fig. 6). Excellent correlation (r=0.92) and bias (-0.5 %)
between BGC-Argo and PISCES were found for vertically integrated epipelagic
TPOC (Fig. 6i). A delayed start of the bloom was observed in the simulation,
which can be partly attributed to a delay of around 1 week in the onset of
permanent stratification in the model. It is also plausible that modeled
phytoplankton reacted too weakly to the cessation of deep convection, which
was captured by alternative MLD metrics in BGC-Argo profiles (Fig. 6a and
b). Despite the general good agreement between observed and simulated SPOC,
the model produced a conspicuous plume of SPOC that sank from the surface
spring bloom into the mesopelagic layer, at the prescribed constant rate of
2 m d-1, which was not found in the observations. At the core of this
plume, PISCES POC exceeded BGC-Argo SPOC more than 2-fold. A similar
model–observation mismatch was observed in all the northern and southern
subpolar CATS as well as in some CATS in the Mediterranean (Figs. S5–S8
and S10). On average, simulated LPOC exceeded BGC-Argo LPOC by 36 % and
96 % in the epi- and mesopelagic layers, respectively. The largest
overestimation was observed during the midsummer export event. On the other
hand, LPOC underestimation was found during the May bloom between 0–400 m.
PISCES qualitatively reproduced the late summer peak of mesopelagic LPOC,
which was observed in all the subpolar North Atlantic CATS. By contrast,
it failed to reproduce both the decrease in SPOC and LPOC in fall between
600–800 m and the LPOC increase below 800 m. The latter occurred in 6 out of
11 CATS in the NASPG, all located in the Labrador Sea. The apparent
decoupling of deep mesopelagic LPOC from the overlying water column may be
related to the insufficient temporal resolution of BGC-Argo profiling during
that period compared to LPOC sinking speed or reflect LPOC export events
from surface waters not located vertically over the float
(Siegel and Deuser, 1997).
South Pacific subtropical gyre
Float 6901660 was deployed in March 2015 in the western South Pacific STG
and drifted westwards until it deflected SW while approaching Tahiti. As of
March 2021, the float was still active and had completed 244 cycles with
stable continuous records at the 1000 m drift depth. Between July 2017 and
June 2018 (18–21∘ S latitude and 148–157∘ W longitude),
the period selected for the CATS analysis, the BGC-Argo profiles portrayed a
stably stratified water column typical of the core of the subtropical gyres
(Fig. 7). Vertical mixing events that reached a depth of around 100 m were
observed in July, August, and October. However, their effect on surface SPOC
was hardly noticeable, indicating that turbulent entrainment of nutrients
was too weak to stimulate new production significantly. A deep Chl a maximum
was present all year round between 150 and 200 m as identified by the
maximum Chl a gradient. This Chl a maximum did not translate into a deep POC
maximum. The fraction LPOC / TPOC was consistently around 6 % in the
epipelagic and 12 % in the mesopelagic according to the vertically
integrated stocks. The vertical–temporal distribution of LPOC was patchy in
the lower mesopelagic (500–1000 m), perhaps reflecting the difficulty of
detecting rare aggregates from bbp700 spikes.
Temporal evolution of small and large POC concentrations (SPOC and
LPOC, respectively) in the South Pacific subtropical gyre (STG). Panels (a)
and (d) show observations from the BGC-Argo float WMO 6901660, which drifted
westwards near Tahiti, during the year 2016. Panels (b) and (e) show the
corresponding PISCES 1D simulation. Panels (c) and (f) show the ratio between
the model and the observations and the corresponding correlation coefficient
and RMSE in log10 scale. Solid lines depict the observed MLD (a and d,
in gray) and the simulated turbocline depth (b and e, in black). In panels (a)
and (d), the dotted lines show the depths of the maximum Chl a fluorescence
gradient and the dashed lines an alternative MLD estimated with a
0.005 ∘C temperature threshold that captures weak stratification.
The bottom panels show the vertically integrated SPOC and LPOC stocks in
different layers: 0–200 m or epipelagic (g), 200–1000 m or mesopelagic (g),
and 0–1000 m (i). On top of panels (g)–(i) we show the correlation and mean
bias between BGC-Argo and PISCES total POC (TPOC).
The epipelagic TPOC stock (driven by SPOC) simulated by PISCES matched
the observations well, with a high temporal correlation coefficient (r=0.69;
Fig. 7i) despite the low seasonal variability in this tropical setting.
PISCES overestimated SPOC between the base of the mixed layer and the deep
Chl a maximum and underestimated SPOC below it. Thus, the low mean bias of
epipelagic SPOC (9 %) resulted from these mutually compensating biases. In
the mesopelagic layer, vertically integrated PISCES SPOC was on average
45 % lower than BGC-Argo SPOC and showed low negative temporal correlation
to the observations. Regarding LPOC, the simulated stock was nearly twice as
large as BGC-Argo estimates in the epipelagic, with the largest
overestimation seen in the deep Chl a maximum. In the mesopelagic, PISCES
LPOC exceeded BGC-Argo estimates by 25 % on average. Despite these
deviations, PISCES simulations supported the increase in the LPOC / TPOC
fraction between the epipelagic and the mesopelagic layers deduced from
BGC-Argo profiles.
Coherent annual time series of SPOC and LPOC: generalized approach
Although each of the 50 CATS included in this study has unique features,
some of the misfit patterns between PISCES and BGC-Argo data described in
the previous section are common to most CATS in a given biome or, more
broadly, in subpolar vs. subtropical biomes. In this section we generalize
the quantitative comparison between the 50 BGC-Argo CATS and their PISCES 1D
counterparts across the four biomes. We consider separately the epipelagic
and mesopelagic domains, focusing on mean annual SPOC and LPOC standing
stocks (Fig. 8), the SPOC / TPOC fraction (Fig. 9), and the mesopelagic Teff
of SPOC and LPOC (Fig. 10).
Mean annual POC stocks. BGC-Argo versus PISCES scatterplots are
shown for the standing stocks (vertical integrals) of small POC (SPOC) and
large POC (LPOC) in the epipelagic (0–200 m) and mesopelagic (200–1000 m)
layers. Reference lines indicate a range of model / data ratios, from 1:1
(perfect correspondence) to a factor of 3 or its inverse. Biomes are
distinguished with different colors, and the size of the circles is
proportional to the temporal correlation between BGC-Argo and PISCES stocks
at each location (as shown in the bottom panels of Figs. 6 and 7).
Mean annual SPOC / TPOC fractions. BGC-Argo versus PISCES
scatterplots are shown for the epipelagic (0–200 m) and mesopelagic
(200–1000 m) layers. Reference lines indicate a range of model : data ratios,
from 1:1 (perfect correspondence) to a factor of 1.5 or its inverse. Biomes
are distinguished with different colors, and the size of the circles is
proportional to the annual mean TPOC stock in the epipelagic layer, used as
an indicator of productivity.
Epipelagic SPOC stocks ranged between 193–425 mmol C m-2 (1.0–2.1 mmol C m-3 in concentration units) according to BGC-Argo observations.
The SPOC range in the corresponding PISCES 1D simulations was 282–537 mmol C m-2 (1.4–2.7 mmol C m-3). Although the ranges of the different
biomes overlapped, smaller stocks were usually found in the more
oligotrophic (STG and Mediterranean) biomes. The agreement between simulated
and observed epipelagic SPOC was better in the STG and NASPG biomes, whereas
simulated epipelagic SPOC exceeded observations by around 50 % in the
Mediterranean and subantarctic biomes. Simulated and observed stocks were
positively correlated (r=0.45, p=9×10-4).
In the mesopelagic domain, observed and simulated SPOC stocks ranged between
145–283 and 105–308 mmol C m-2, respectively (corresponding
to concentration ranges of 0.18–0.35 and 0.13–0.39 mmol C m-3). The
correlation between simulated and observed mesopelagic SPOC stocks was not
significant (r=-0.08, p=0.55). PISCES SPOC was similar to or
slightly higher than BGC-Argo SPOC in the subpolar biomes but up to
2-fold lower in the STG and Mediterranean biomes, as previously shown in
Figs. 4 and 7.
Mean annual POC transfer efficiency between 200 and 700 m.
BGC-Argo versus PISCES scatterplots are shown for small POC (SPOC) and large
POC (LPOC). Reference lines indicate a range of model / data ratios, from 1:1
(perfect correspondence) to a factor of 3 or its inverse. Biomes are
distinguished with different colors, and the size of the circles is
proportional to the annual mean TPOC stock in the epipelagic layer (0–200 m), used as an indicator of productivity.
Epipelagic LPOC stocks were smaller and showed wider inter-biome variability
than SPOC stocks, with around 2-fold higher LPOC in subpolar biomes. The
positive correlation between simulated and observed LPOC was highly
significant (r=0.78, p=2×10-11). Yet, PISCES LPOC
(range 19–83 mmol C m-2, 0.10–0.42 mmol C m-3) typically
exceeded BGC-Argo LPOC (range 27–119 mmol C m-2, 0.14–0.60 mmol C m-3) by around 50 % and up to 3-fold for some CATS. A lower but
still highly significant correlation was found in the mesopelagic (r=0.64, p=5×10-7), where PISCES LPOC was usually within a
factor of 1.5 of observations, except for the NASPG biome where it exceeded
BGC-Argo LPOC around 2-fold.
The SPOC / TPOC fraction showed low variability in the epipelagic layer (Fig. 9), with a median (range) of 89 % (84 %–94 %) and 85 % (75 %–91 %) for
BGC-Argo and PISCES, respectively. The simulated SPOC / TPOC fraction was
within ± 10 % of BGC-Argo estimates for all biomes except the NASPG,
where PISCES tended to underestimate the observed SPOC / TPOC fraction. A
significant positive correlation (r=0.56, p=2×10-5)
was found between simulations and observations in the epipelagic layer. The
SPOC / TPOC fraction was lower in the mesopelagic layer according to both
BGC-Argo (75 %–90 %) and PISCES (66 %–85 %) estimates. In this case, CATS
from the subantarctic biome showed excellent model–observation agreement,
whereas PISCES was 20 % lower than BGC-Argo estimates in the other three
biomes. No significant correlation was found between the simulated and
observed SPOC / TPOC fraction in the mesopelagic.
Transfer efficiency (Teff; Fig. 10) was computed as the ratio between the
SPOC or LPOC concentrations in the depth bins centered at 697 m (range
662–734 m) and 200 m (range 190–210 m). The shallowest bin corresponds to
the bottom of the epipelagic layer, and the 500 m interval was chosen
following previous studies (Lam et al., 2011; Dall'Olmo and Mork, 2014).
Different depth ranges between 180 and 800 m gave similar Teff patterns.
The analysis of Teff yielded some important insights:
SPOC and LPOC showed similar Teff, both in the observations (medians of
0.39–0.41) and in the model (medians of 0.27–0.29). Similar patterns were
found when each biome was considered separately.
BGC-Argo Teff usually exceeded PISCES Teff and spanned a wider range. The
best agreement was generally found in the STG and the subantarctic biomes,
and the poorest agreement occurred in the Mediterranean where BGC-Argo Teff
was typically twice the modeled Teff.
Distinct patterns were found for four CATS in the Labrador Sea (NASPG)
characterized by high epipelagic TPOC (> 440 mmol C m-2). In
this subset, PISCES and BGC-Argo Teff were in good agreement for SPOC
(median 0.40 for both), whereas, for LPOC, PISCES Teff (0.36–0.45) doubled
BGC-Argo Teff (0.12–0.28), which had some of the lowest values of the
dataset. These CATS showed a LPOC minimum at 600–800 m in fall, which
PISCES could not reproduce (Sect. 3.2.1).
PISCES and BGC-Argo Teff showed a weak positive correlation for SPOC (r=0.26, p=0.07), with some improvement when the Labrador Sea “outliers”
were removed (r=0.34, p=0.02). A negative correlation between
simulations and observations was found in the case of LPOC Teff (r=-0.29, p=0.04), which became non-significant without the Labrador Sea
outliers (r=0.04, p=0.81).
Our observational estimates are sensitive to the choice of bbp700–POC
conversion factors. These factors are especially uncertain in the
mesopelagic due to data scarcity, which prompted us to use a constant global
value for “c” in Eq. (1) (Fig. 2); c=1000 mmol C m-2 is the
asymptotic POC–bbp700 conversion factor below 1000 m, taken from
Cetinić et al. (2012). To address this uncertainty, we conducted
sensitivity tests where c was halved or doubled. The resulting range of
500–2000 mmol C m-2 is probably generous, as indicated by our indirect
estimates based on the studies of Bishop (1999) and Bishop et al. (1999)
(Appendix A). As expected, changing c had little effect on epipelagic POC, a
larger effect on mesopelagic POC and Teff, and no effect on the SPOC / TPOC
fraction (because the same conversion factor is used to estimate SPOC and
LPOC). Halving c resulted in steeper POC profiles, which brought mesopelagic
SPOC closer to the 1:1 line in the STG and Mediterranean, at the expense of
increasing the SPOC bias in the subpolar biomes and that of LPOC everywhere
(Fig. S11). Doubling c caused a less steep vertical decrease in BGC-Argo
POC, which overall worsened the model–observation agreement in the
mesopelagic (Fig. S12), except for LPOC stocks in the NASPG.
DiscussionTowards a globally consistent picture of POC fields in observations and
models
Global quantification of POC stocks through the water column has been
elusive until recently because of data sparseness and limitations of model
parameterizations, both of which are especially severe below the epipelagic.
Our joint analysis of PISCES simulations, satellite observations, and over
70 000 BGC-Argo vertical profiles reveals a globally consistent picture
across the epi- and mesopelagic layers (Figs. 3–10). The global ocean stock
of TPOC simulated by PISCES amounts to 4 Pg C, shared in a proportion of
39 % (1.6 Pg C), 25 % (1 Pg C), and 36 % (1.4 Pg C) between the epi-,
meso-, and bathypelagic layers (Table 3; the bathypelagic is defined here as
1000–5000 m to include the entire model domain in the stock calculation).
The PISCES-simulated TPOC concentration is on average within a factor of
1.56 (1.42) of BGC-Argo estimates for the median (mean) seasonal biome
profiles shown in Fig. 4 (Mediterranean excluded). Aumont et al. (2017)
reported a similar reliability index of 1.6 for the comparison between
PISCES and in situ chemically determined POC profiles. Thus, our evaluation
lends further confidence to the POC reactivity continuum parameterization
implemented in PISCES, which represents both SPOC and LPOC as a mixture of
fractions with different lability (Aumont et al., 2017), in globally
representative biomes.
The epipelagic TPOC stock simulated by PISCES, 1.6 Pg C, is comparable to
previous observational estimates. Gardner et al. (2006) estimated the global
POC stock within the first optical attenuation depth using a compilation of
in situ POC and cp measurements leveraged by ocean color satellite
data. They obtained a global stock of 0.43 Pg C, and invoked some scaling
arguments to estimate that the total POC stock down to middle-mesopelagic
“background levels” would range from 1 to 2 Pg C. Stramska (2009) obtained a
larger global epipelagic POC stock of 1.8–2.3 Pg C using the satellite
algorithm of Stramski et al. (2008). Our match-up analysis indicates that
satellite TPOC exceeds BGC-Argo estimates by up to 7-fold at high
latitudes outside of the summer season (Fig. 3). This deviation is well
beyond the nominal uncertainty of the satellite POC product (< 30 %) and the range of observed POC /bbp700 ratios at the sea surface
(Fig. 2). Therefore, we conclude that the algorithm of Stramski et al. (2008) overestimates POC at high latitudes in winter, an issue that deserves
further investigation. Potential explanations are the satellite algorithm
being calibrated mostly against samples from lower latitudes (50∘ N–30∘ S), or its sensitivity to the differential atmospheric
attenuation of blue and green wavelengths at low solar elevation.
Recently, Evers-King et al. (2017) calculated the global POC stock in the
upper mixed layer using several satellite algorithms (including that of
Stramski et al., 2008) and indicated that 0.77–1.3 Pg C was a plausible
range. Our PISCES-based estimate for the mixed-layer POC stock, using the
same MLD climatology (Schmidtko et al., 2013), is 0.58 Pg C. The lower
PISCES-derived estimate may arise from the combination of POC overestimation
by some satellite algorithms, discussed above, with PISCES' tendency to
underestimate mixed-layer POC in oligotrophic areas (Figs. 4 and 7). This
negative bias in PISCES is compensated by a positive POC bias in deep Chl a
maxima (below the MLD), resulting in smaller deviations between BGC Argo and
PISCES over the entire epipelagic layer in the STG biome (Figs. 8 and S13).
Given the limited coverage of in situ seawater sampling and satellite
observations in some regions and seasons (Evers-King et al., 2017), further
intercomparison between those observations, BGC-Argo data, and models is
needed to better constrain epipelagic POC stocks.
POC in the lower mesopelagic and below has been traditionally treated as a
“background” signal
(Bellacicco
et al., 2019; Gardner et al., 2006; Loisel and Morel, 1998). This approach
is convenient for studies that focus solely on upper-ocean processes because
POC concentration decreases exponentially with depth. Yet, our global
estimates and several previous studies highlight the need to turn our
attention to the large POC stocks (> 2 Pg C) that reside in the
meso- and bathypelagic layers, whose dynamics are still poorly understood.
In line with previous studies
(Dall'Olmo and Mork,
2014; Poteau et al., 2017), we showed that mesopelagic POC exhibits clear
seasonal cycles in productive regions (Figs. 6, S5–S7, and S10), owing to
their connection with the upper-ocean through numerous biological and
physical processes (Boyd et al., 2019; Briggs et al., 2020). Despite being
less reactive on average than upper-ocean POC, meso- and bathypelagic
organic particles are microbial hotspots that host key biogeochemical
functions, from enzymatic decomposition of macromolecules
(Arnosti
et al., 2012; Baltar et al., 2010a, b) to aerobic and anaerobic
respiration (Bianchi et al., 2018;
Karthäuser et al., 2021) and
chemosynthesis (Arístegui et al.,
2009; Herndl and Reinthaler, 2014; Pachiadaki et al., 2017). Moreover,
mesopelagic particles are consumed by upper trophic levels that sustain
fisheries (Bode et al.,
2021; Woodstock et al., 2021).
Global and regional POC stocks, concentrations, and fractions in
different layers as simulated by PISCESv2_RC.
VariableDepth range (m)Open-ocean biomes GlobaldIceSPSSaSTSSbSTPScEquatorialArea (%) 6.514.312.046.09.2100TPOC0–200732812632631541590(Tg C)200–1000572211733979010061000–5000802742376331441438TPOC0–2001.182.222.571.661.991.85(mmol C m-3)200–10000.290.480.440.250.290.331000–50000.140.180.170.100.130.13SPOCe0–200728179898481(%)200–10007575707971751000–5000818077797579Phytof (%)0–20454541584950Diatomsg (%)0–202821143414Non-phytoh (%)0-20555559425150Detritusi (%)0–201525312631240–200364348445042200–1000626770787870
a Subpolar seasonally stratified.
b Subtropical seasonally stratified.
c Subtropical permanently stratified.
d Larger than the sum of biomes because the latter do not include
coastal areas.
e SPOC / TPOC.
f (PHY+PHY2) / TPOC.
gPHY2/ TPOC.
h (POC+GOC+ZOO+ZOO2) / TPOC.
i PISCES detritus (POC+GOC) divided by TPOC. Heterotrophic prokaryotes are
de facto included in TPOC.
In situ bio-optical measurements are poised to play a key role in monitoring
marine POC stocks in layers that cannot be accessed by remote sensing. For
example, Sauzède et al. (2020) merged BGC-Argo and satellite observations to obtain a dynamic 3D
view of particle backscattering. Using a data-driven machine learning
approach, they were able to predict the profiles of log10bbp700
measured by two BGC-Argo floats in the NASPG and STG biomes (R2 of
∼ 0.85 and mean absolute percentage deviation of
∼ 12 %) from the sole knowledge of physical properties of
the water column and surface ocean color (remote-sensing reflectance). Their
estimates were recently extended to POC (Sauzède et al.,
2021), which can be of great utility for constraining biogeochemical models.
Here we took an entirely different approach, based on converting available
bbp700 data to POC with a simple empirical algorithm (Fig. 2) and then
comparing it to the outputs of the PISCES model. Our PISCES-based estimates
obtained a median R2=0.86 and mean absolute percentage deviation of
38 % (5 d depth-binned log10POC; 50 CATS from 28 globally
distributed floats). This good skill is remarkable because neither the
empirical POC estimates nor PISCES were tuned to maximize their mutual
agreement. Still, our study shows that the comparison of bio-optics-derived
POC measurements and PISCES is affected by different types of uncertainty
that we analyze in the following sections.
Bio-optical underpinnings of POC fields based on BGC-Argo observations
Accurate interconversion between bio-optical variables and concentrations is
key for constraining ocean particle dynamics and their model representation
(Bishop
et al., 2004; Gardner et al., 2006). Here we discuss the processes that
appear to drive, to first order, the variability of the POC /bbp700
ratios embodied in Eqs. (1) and (2) (Fig. 2; Appendix A) and the main strengths
and weaknesses of our scheme.
Changes in the trophic status appear as the primary driver of POC /bbp700
variability in the epipelagic layer (Cetinić et al., 2012; Fig. A1).
Productive waters host greater absolute and relative abundance of diatoms
(Uitz et al., 2006) (see
also Table 3), which have lower POC per cell volume
(Menden-Deuer and Lessard, 2000) and are covered with silica
frustules that may scatter light more efficiently than naked cells
(Twardowski et al., 2001), altogether resulting in lower POC
content per unit bbp700
(Cetinić et al., 2012;
Oubelkheir et al., 2005). The proportions of different autotrophic and
heterotrophic organisms and detritus are also likely to vary with upper-ocean productivity (see Sect. 4.3). If the mass-specific backscattering
coefficients of these components were better known, their systematic
variation patterns could be used to develop a continuous formulation for
POC /bbp700 rather than the regionalized conversion factors used here.
However, POC /bbp700 is influenced by other seawater constituents whose
occurrence is less predictable, foremost, biogenic calcite (e.g., from
coccolithophores) and desert dust
(Claustre, 2002; Loisel et al.,
2011), both of which enhance bbp700. In our dataset, we detected a
CATS (float WMO 6901647, year 2016) that was strongly affected by coccolith
backscattering in the Iceland Basin, an area known for its massive
coccolithophore blooms (Moore et
al., 2012). This CATS was an obvious outlier in our model–observation scatterplots (Fig. S13), likely because the enhancement of bbp700
caused by coccoliths had no translation in TPOC, and was therefore removed
from the analysis.
The decrease in POC /bbp700 along the vertical axis (Fig. 2) reflects
the increase in the particles' index of refraction, hence the backscattering
ratio
(Cetinić
et al., 2012; Nencioli et al., 2010). This change is likely caused by the
remineralization of organic materials (Martin et al.,
1987) that leaves a higher mineral fraction
(Honjo
et al., 2008; Lam et al., 2011) and the increase in the structural
complexity of aggregates with depth (Organelli et
al., 2018). According to our sensitivity analysis (Figs. S11 and S12), the
prescribed exponential decrease in POC /bbp700 towards a constant
POC /bbp700 (c=1000 mmol C m-2) at depths > 1000 m
provides a good compromise globally, given the limited knowledge of
mesopelagic POC /bbp700 and its variability across regions. However, the
comparison between simulated and observed mesopelagic SPOC (and thus TPOC)
is more favorable in subpolar than in subtropical biomes. In the latter,
better model–observation agreement was found when POC /bbp700 was
halved (c=500 mmol C m-2). A lower c would also bring the simulated
Teff for SPOC and LPOC closer to BGC-Argo estimates. It is tempting to
hypothesize that lower latitudes have lower mesopelagic POC /bbp700
owing to the greater proportion of calcite
(Francois
et al., 2002; Honjo et al., 2008; Lam et al., 2011, 2015). These hypotheses
need further verification.
Finally, the decrease in surface POC /bbp700 with deeper vertical
mixing imposed by Eq. (2) partially reflects the dilution of surface particle
assemblages by entrainment of deeper waters
(Lacour et al., 2019) with lower
POC /bbp700 (Bol et al., 2018). Modulation
of POC /bbp700 by vertical mixing improves the agreement between PISCES
and BGC-Argo data in regions with wide seasonal MLD amplitude such as the
NASPG. Still, our approach should be seen as a simplistic first-order
approximation, and alternative formulations should be further evaluated when
more data become available. For example, POC /bbp700 might be kept
constant regardless of the MLD or estimated for each profile as the average
within the mixed layer of the pre-computed POC /bbp700 profile (Eq. 1).
In both cases, however, POC /bbp700 would have to decrease abruptly
below the mixed layer to meet the low POC /bbp700 in the mesopelagic,
producing sharp discontinuities that have not been observed. On the other
hand, the behavior prescribed by Eq. (2) may not be appropriate for situations
when vertical mixing cannot erode the seasonal thermocline or pycnocline because in such cases it will entrain water from the deep Chl a maximum, and
not mesopelagic water, to the upper mixed layer.
Our POC–bbp700 conversion algorithm omits several other sources of
uncertainty. Foremost, it assumes that SPOC and LPOC have the same
POC /bbp700 ratio, which largely corresponds to that of the more
abundant SPOC (Figs. 6–8). In addition, the in situ data used to
parameterize Eqs. (1) and (2) are not free of uncertainty
(Cetinić
et al., 2012; Lam et al., 2011; Morán et al., 1999; Organelli et al.,
2018; Strubinger Sandoval et al., 2021). Despite these limitations, the
POC–bbp700 conversion scheme used here provides POC estimates that are
generally consistent with PISCES (Fig. 8) and with previous assessments
(Aumont et al., 2017). Moreover, this scheme allowed us to identify
potential shortcomings of satellite-based assessments
(Evers-King
et al., 2017; Stramski et al., 2008). The development of more sophisticated
POC–bbp700 interconversion schemes is desirable and would greatly
benefit from the measurement of mass-specific bio-optical properties of
various seawater constituents across different ocean basins and depths.
Correspondence between observed and simulated POC fractions
An additional source of uncertainty in our analysis is the imperfect match
between the PISCES tracers and the observable POC fractions (Table 2). A
positive aspect of our evaluation is the reasonable agreement between the
simulated SPOC / TPOC fraction and the BGC-Argo estimates based on the
bbp700 spike signal (Fig. 9). Our estimates converge to a global median
value of 85 %, with 95 % of the data between 69 %–92 %, fully within
the range of size-fractionated chemical POC determination (Aumont et al.,
2017, and references therein). Our SPOC / TPOC estimates can also be compared
to the suspended POC estimated with the marine snow catcher during spring in
subpolar epipelagic and upper mesopelagic waters (Baker et al., 2017). The
slow-sinking POC measured by the marine snow catcher should not be included
in this comparison because it sinks at around 18 m d-1, a range more
typical of particles > 100 µm (Cael et al., 2021). The
median 94 % of suspended POC reported by Baker et al. (2017) is higher than the
86 % (81 %) SPOC / TPOC estimated here from BGC-Argo (PISCES) in the
subpolar biome, suggesting further comparison between different approaches
is needed. The tendency of SPOC / TPOC to decrease between the epipelagic and
the mesopelagic, found in both PISCES and BGC-Argo data (Fig. 9, Table 3),
was also reported in previous studies (Aumont et al., 2017, and references
therein; Organelli et al., 2020), lending further
confidence to our estimates. On the other hand, the simulated partitioning
of POC into different living and detrital compartments is probably less
realistic, as discussed in greater detail below.
An aspect that deserves further attention is the partitioning between
phytoplanktonic (autotrophic), heterotrophic, and detrital POC in the upper
ocean. Unfortunately, this partitioning is far from being well established
from observations. Using bio-optical data, Oubelkheir et al. (2005)
estimated that detritus accounted for around 60 % of POC across a wide
range in ocean productivity, and 70 %–85 % of POC was assigned to
non-phytoplanktonic material (detritus + heterotrophs). These percentages
accord well with those found by
Claustre et al. (1999) in the tropical Pacific (their Table 2; see also Organelli et al.,
2020). By contrast, Graff et al. (2015) found wide latitudinal variations in the non-phytoplanktonic POC,
increasing between ∼ 20 % and ∼ 80 % from the
tropical to the temperate Atlantic. Bellacicco et al. (2019) analyzed the
covariation between bbp700 and Chl a in the (much larger) BGC-Argo
dataset. They concluded that the background bbp700, defined as the
bbp700 that does not covary with Chl a, decreases with productivity from
> 80 % in subtropical gyres to < 20 % in the NASPG,
with a mean contribution of 65 % in oligotrophic areas. Although the
background bbp700 fraction cannot be entirely assigned to
non-phytoplanktonic material, the biogeographic patterns found by Bellacicco
et al. (2019) are hardly compatible with those found by Graff et al. (2015)
or with a nearly constant detrital fraction across biomes. The detrital and
non-phytoplanktonic POC fractions in PISCES near the sea surface range,
respectively, between 15 %–31 % and 42 %–59 % (annual mean values by
biome; Table 3). Although they are within the full range of observations,
these values suggest that PISCES underestimates the percentage of detrital
POC, especially in oligotrophic waters. A deeper analysis, properly matching
observations and simulations over time and space, should be undertaken to
obtain a mechanistic understanding of upper-ocean POC partitioning, with
potential consequences for remotely sensed POC export estimates (e.g.,
Siegel et al., 2014).
PISCES bias may also arise from the inappropriate representation of some POC
reservoirs, such as heterotrophic prokaryotes (bacteria and archaea, BACT in
PISCES), which have long been recognized as important contributors to the
suspended POC (Morel and Ahn, 1990; Gasol et al., 1997). However, BACT are not
explicitly modeled in PISCES as prognostic tracers, meaning they are not
interacting fully with other tracers. Instead, they are diagnosed in the
productive surface layer from zooplankton biomass, based on an old model
version that had interactive BACT. Below this layer, BACT biomass is propagated
downwards with a power function based on Arístegui et al. (2009), which
resembles a Martin curve (Martin et al., 1987), and is therefore very
sensitive to the reference depth (Z0)
(Buesseler et al., 2020). We find two
main arguments against the inclusion of PISCES-estimated BACT in our POC
estimates with the current model configuration. First, the empirical BACT
estimation in PISCES has not been validated, to our knowledge, and may
introduce noise into the comparisons. Second, PISCES-simulated POC already
includes heterotrophic prokaryotes because their biomass was not removed
from the in situ POC measurements used to adjust the POC parameters in
PISCESv2_RC (Aumont et al., 2017). In consequence, adding
BACT to SPOC causes overestimation of mesopelagic POC (Figs. S14–S16) and can
produce unrealistic temporal patterns (Fig. S15). Nevertheless, we believe
that inclusion of prognostic bacteria would enable a more realistic simulation
of POC stocks, with the potential side effect of improving the simulation of
element fluxes in PISCES.
The comparison between simulated and observed LPOC is a novel contribution
of our study. The method of Briggs et
al. (2011, 2020), originally developed to study intense POC export events,
was here extended to estimate SPOC and LPOC separately over the full annual
cycle through the epi- and mesopelagic domains. Our analysis shows that,
despite the mismatch in terms of concentration, the LPOC derived from the
spikes of high-resolution bio-optical profiles is strongly correlated to the
PISCES-simulated LPOC (r=0.78, r2=0.61) along a wide trophic
gradient (Fig. 8). This result is encouraging and supports the more
widespread deployment of instruments that perform high-resolution
bio-optical sampling to shed light on the spatiotemporal dynamics of large
aggregates and particles
(Briggs
et al., 2020; Lampitt et al., 1993; Stemmann et al., 2008). On the other
hand, it is unclear to what extent the LPOC inferred from the bbp700
spike signal is capturing mesozooplankton biomass, in addition to
aggregates. The exclusion of PISCES mesozooplankton (ZOO2) from the comparison
increases model–observation mismatch, with BGC-Argo LPOC exceeding PISCES
estimates around 2-fold, although the high correlation remains (r=0.76, p< 10-10). Imaging devices mounted on BGC-Argo floats
may provide a more accurate quantification of LPOC, allowing for the
separation of detrital LPOC (Trudnowska et al., 2021)
from mesozooplankton and micronekton (Haëntjens et
al., 2020) and the separation and quantification of particle classes
contributing to flux across the complete particle spectrum (Bourne et al.,
2021).
Importance of realistic physics and model evaluation across scales
In Fig. 5, PISCES simulations and BGC-Argo observations are compared using
an array of skill metrics computed on the seasonal vertical profiles of TPOC
between 0–1000 m. Starting with the 3D seasonal climatology, we observed
that the correlation between the median (aggregated) profiles was generally
better than the correlation between the spatially collocated
(non-aggregated) profiles within a given biome and season (Fig. 5a), whereas
no differences were found in terms of dispersion metrics (Fig. 5b and c).
The difference in correlation was larger in subpolar biomes, suggesting that
the model–observation spatial mismatch was magnified in regions with more
energetic ocean dynamics and sharper physical and biogeochemical gradients,
whose real-world location may not be well reproduced by the ocean
circulation model used to force PISCES. For PISCES 1D simulations, their
correlation coefficients with their CATS counterparts was usually in the
high range of the correlation coefficients obtained by the biome-median
PISCES 3D profiles. In terms of dispersion metrics, the ensemble of PISCES
1D simulations showed wider dispersion, but the best 1D simulations clearly
outperformed the 3D simulation in a given region and season. The better
skill of 1D simulations was more evident during spring, a season
characterized by the onset of stable stratification after deep winter
vertical mixing in middle and high latitudes. The greatest difference
between 3D and 1D simulations was found in the Mediterranean, highlighting
the more realistic vertical mixing and upper-ocean productivity in the 1D
simulations.
Our cross-scale evaluation indicates how crucial it is to evaluate model
physics before extracting conclusions on biogeochemical model performance
(Doney
et al., 2004; Kriest et al., 2020; Löptien and Dietze, 2019). In our 1D
CATS approach, the skill of PISCES simulations was maximized by carefully
matching observed and modeled vertical mixing (Figs. S1–S4), which is a key
driver of upper-ocean ecosystems. This approach has a subjective component and may also suffer from the idealized assumption that BGC-Argo profiles
reflect mostly vertical-scale processes, disregarding horizontal advection
(Alonso-González et al., 2009). Yet,
the similar misfit patterns encountered for different CATS within a given
biome support the robustness of the 1D matching approach (compare Fig. 6
versus Fig. S6 and Fig. 7 versus Fig. S9). To further evaluate this issue, we matched
different neighboring NEMO grid cells to the same in situ CATS. Again, this
exercise indicated that our main conclusions are not sensitive to the
choices made for 1D model–observation matching (compare Figs. 6 and S5). Indeed, alternative matching approaches can be devised, each of which
have advantages and pitfalls, for example (1) sampling the outputs of
biogeochemical models at the locations visited by BGC-Argo floats, which may
require high-resolution models; (2) deploying virtual BGC-Argo floats
(van Sebille et al., 2018) and
comparing them statistically to observations; or (3) forcing 1D
biogeochemical simulations with observed physical fields, e.g., vertical
mixing (Llort et al., 2015) or light
(Terzić et al., 2019). As a general rule, the good skill
of the best PISCES 1D simulations (Fig. 5) indicates that our CATS approach
can be used to tease apart model–observation misfits caused by the physical
and biogeochemical components, opening up new avenues for parameter
optimization (Falls et al., 2022) and model development.
Our cross-scale evaluation is also informative as to the spatiotemporal
scales that can be addressed with a given model setup. This matter was
recently tackled by Bisson et al. (2019) using the
export production model of Siegel et al. (2014), which is forced by satellite-derived primary production. In
particular, they showed that this diagnostic model could be optimized to
reproduce global climatological patterns but exhibited poor skill when faced
with non-climatological datasets, which reflect local snapshots of ecosystem
functioning. Model evaluation at climatological scales provides an
incomplete picture, especially in productive regions where much POC export
can take place during intense but short-lived events (Briggs et
al., 2020). Such events are smoothed out when climatologies are computed,
and their coherence with the physical forcing is lost. Our work shows that a
prognostic model like PISCES can afford both event-scale and climatological
scale predictions. This capability is important to test our process-level
understanding, which underpins climate change projections.
Joint use of BGC-Argo and models for process-level understanding
Properly representing POC stocks is crucial for constraining epi- and
mesopelagic carbon budgets and, ultimately, estimating the strength of the
BCP and predicting its future evolution. The mismatch patterns between
simulated and observed POC profiles (Figs. 4, 6, and 7) indicate different
types of model shortcomings in subpolar and subtropical latitudes. The
poorest agreement between PISCES and BGC-Argo data is found when their
respective estimates of mesopelagic Teff are compared (Fig. 10), which
should prompt further research on this key descriptor of the BCP, both in
terms of POC fluxes (Buesseler et al.,
2020) and stocks (Lam et al., 2011;
this study) and their relationship with the structure and productivity of
the upper-ocean ecosystem.
In the subpolar biomes, we find discrepancies between the patterns of SPOC
and LPOC vertical export triggered by the intense surface phytoplankton
blooms typical of these waters (Figs. 6, S5–S7, and S10). The observed rapid
SPOC increase at depth cannot be explained by the SPOC gravitational
sinking, and fragmentation of rapidly sinking LPOC has to be invoked
(Briggs et al., 2020). This fragmentation may be caused by
zooplankton feeding
(Mayor
et al., 2020; Stemmann et al., 2004a, b; Stukel et al., 2019) and
swimming (Goldthwait et al., 2004),
combined with bacterial hydrolysis of aggregate-binding polymers
(Arnosti
et al., 2012; Baltar et al., 2010a), and turbulence at a high kinetic energy
dissipation rate (Takeuchi et al., 2019). In relative terms, mesopelagic
SPOC increases less strongly than LPOC during blooms (Fig. 6i), which is
consistent with SPOC being a byproduct of the transformation of less
abundant LPOC. Fragmentation processes may supply fresher SPOC to the
mesopelagic, enhancing the coexistence of suspended particles with variable
freshness
(Alonso-Gonzalez et
al., 2010; Aumont et al., 2017) and overall contributing to POC
remineralization (Giering et
al., 2014; Mayor et al., 2020).
In the subtropical gyres and the most oligotrophic Mediterranean waters,
PISCES underestimates TPOC between 0–1000 m except for the deep Chl a
maximum, where it overestimates TPOC. The prominent deep POC maximum
simulated by PISCES is generally not found in observations from the STG
biome (Figs. 7 and S9), where deep Chl a maxima generally reflect
phytoplankton photo-acclimation, not enhanced phytoplankton biomass
(Cornec et al., 2021). Thus,
PISCES possibly overestimates the productivity of deep Chl a maxima globally,
indirectly causing stronger nutrient limitation at the surface. Between the
deep Chl a maximum and 200 m, POC pools decrease more steeply in PISCES than
in BGC-Argo observations. Between 200 and 700 m, by contrast, simulated SPOC
and LPOC Teff are only 10 % lower than observations, well within
observation uncertainty. Thus, the mesopelagic SPOC deficit simulated by
PISCES in the STG originates mostly through the insufficient vertical POC
export at 200 m depth. The Mediterranean represents a different case,
whereby the large disagreement between PISCES and BGC-Argo Teff may result
from either poorly constrained POC /bbp700, simulated POC dynamics, or
both. Thus, this region may provide a good test bed for studying the role of
the mineral fraction in the BCP, including the controversial ballast
hypothesis (François et
al., 2002; Klaas and Archer, 2002; Passow, 2004).
In summary, the mismatch with observations suggests the need to improve the
representation of SPOC–LPOC interconversion in PISCES. The size
distribution of POC along the vertical axis is a key variable for
constraining POC budgets because it reflects the interplay between
gravitational sinking, remineralization, trophic transfer, and 3D dynamics
including horizontal POC advection
(Alonso-González et al., 2009;
Boyd et al., 2019). In many instances (Fig. 9), our analysis suggests that
better model performance in the mesopelagic may be achieved by increasing
the net transfer of LPOC to SPOC, e.g., through LPOC fragmentation, and the
Teff of both fractions. The vertical model–observation mismatch patterns
observed here emphasize that POC budgets have to be computed with the
highest vertical resolution affordable, or otherwise an apparent POC budget
balance may result from compensating imbalances in different horizons
(Giering et al.,
2014; Marsay et al., 2015). The detailed level of information available from
BGC-Argo floats may prove to be extremely valuable to help improve the POC
schemes embedded in models such as PISCES.
Conclusions and outlook
In this study we compared globally distributed POC observations between
0–1000 m made by BGC-Argo floats to the predictions made by the PISCES
model (PISCESv2_RC). A subset of BGC-Argo floats profiling at
high vertical resolution enabled us to analyze small and large POC
separately. The comparisons rely on a proposed new scheme for converting a
bio-optical measurement (bbp700) to POC. Although PISCES recreates the main features observed in subpolar and subtropical biomes with
good skill,
the comparison is still hampered by (1) spatial and temporal variability in
POC /bbp700 conversion factors, (2) mismatches in observed and simulated
physics, and (3) imperfect correspondence between observed and simulated POC
fractions. An evaluation of these uncertainties allowed us to detect
limitations of the biogeochemical model parameterizations. Some limitations
may be tackled by optimizing model parameters (e.g., particle sinking and
remineralization rates), whereas others may require changes in model
structure, for example the representation of zooplankton feeding on, and
transformation of, mesopelagic particles (Mayor et al., 2020). The
descriptive work and model–data matching strategies presented here pave the
way towards the use of BGC-Argo observations for data assimilation,
parameter optimization (Falls et al., 2022), and, ultimately, model development.
Widespread use of BGC-Argo data for understanding POC budgets and the BCP
can complement classical model constraints based on vertical POC fluxes and
ocean-interior nutrient remineralization. Merging of BGC-Argo and satellite
data streams through data-driven approaches – which allow for great
flexibility – and mechanistic models – which provide process-level
understanding – can soon provide us with a high-resolution 4D view of the
oceanic carbon cycle. Further work is granted to investigate POC dynamics
through a combination of PISCES, autonomous observations, and ship-based
observation programs (e.g., GEOTRACES; Lam et al., 2015) and data
compilations (Mouw et al. ,2016; Evers-King et al., 2017).
Below we list several research priorities, whose implementation would
advance the study of the biological carbon pump through the synergies
between BGC-Argo observations and modeling.
Observations
High-resolution bio-optical profiles are indispensable for process-level
understanding. A combination of low- and high-resolution sampling in separate
or individual floats (e.g., burst sampling) may provide a good compromise
between float lifetime and observation capabilities.
More in situ measurements are needed to constrain POC estimation from
bio-optical variables, especially in meso- and bathypelagic waters. The addition
of transmissometers may enable more accurate POC quantification (Bishop,
1999) and particle characterization (Boss et al., 2015).
The inclusion of new types of sensors
(Claustre et al., 2020) and the extension
of measurements into the bathypelagic (Deep-Argo;
Roemmich et al., 2019)
hold high potential for advancing BCP research.
Further developments in BGC-Argo data processing are needed, with the final
goal of supplying a wide public with user-friendly data products in near
real time.
Models
Evaluating model skill at resolving POC stocks, in addition to fluxes, is
key to ensure that models reproduce observed fluxes for the right reasons.
Evaluation against globally consistent datasets is critical to avoid model overtuning towards small, sparse datasets, such as vertical POC fluxes.
Continuous development of schemes representing particle dynamics across the
entire size spectrum is needed to constrain ecologically, climatically, and
economically relevant element fluxes.
Extension of prognostic modeling of bio-optical properties
(Dutkiewicz et al., 2019) into the meso- and
bathypelagic layers would enable direct matching with measurements made from
autonomous platforms, facilitating their assimilation by models.
Joint planning of field observation and modeling projects, from their very
conception and through their entire development, is key to fully exploit the
capabilities of each approach.
Calculation of POC /bbp700 ratios and related optical
considerations
POC /bbp700 ratios at the sea surface were obtained from the slope of
the linear regression between POC and bbp700 (Table A1). Our approach
essentially follows the literature compilation made by Cetinić et al. (2012). The linear regressions generally yielded small and non-significant y intercepts for sea-surface data. Therefore, we assumed the slopes were equal
to the POC /bbp700 ratio. When the bbp measurements were made at
another wavelength, we converted them to bbp700 assuming an exponent
η=0.41 (Cetinić et al., 2012), according to the following equation:
bbp(λ1)=bbp(λ0)⋅(λ1/λ0)η.
Coherent patterns in the conversion factors between POC and
bio-optical variables. Left: relationship between the POC versus cp
slope and the maximum cp in a given dataset, based on the Fig. 8 from
Cetinić et al. (2012) with the addition of Bishop (1999) datasets.
Right: analogous relationship for bbp700, as used in our study. Linear
fits are only illustrative.
Compilation of studies that reported linear regressions between
POC and bbp700 in the euphotic layer of the oceans.
LocationReferenceNDepth rangeSlope (mg C m-2 m)InterceptCommentsNorth Atlanticsubpolar gyreCetinić et al. (2012)3210–60035 422 ± 1754-14.4 ± 5.8Downcast3210–60043 317 ± 2092-18.4 ± 5.8UpcastBol et al., 2018NA0–1041 550Forced through 0Subset of Cetinić et al. (2012) at the surfaceMediterraneanLoisel et al. (2001)NANA41 3051.43Original measurements at 555 nmSubtropical andtropical Pacificand Atlantic(upwellingsexcluded)Stramski et al. (2008)544–858 9682.75Original measurements at 555 nmSubantarcticJohnson et al. (2017)670–10031 200 ± 24703.0 ± 6.8
NA: not available.
For measurements taken at 555 nm, which was the wavelength used in two
studies (Table A1), this resulted in 10 % lower bbp700 compared to
bbp555 and thus higher POC /bbp700 ratios.
The dataset of Cetinić et al. (2012) was the only one showing a
significant negative intercept of the POC vs. bbp700 linear regression.
Unlike the other datasets, where the POC vs. bbp700 relationship
reflected mostly sea-surface variability in a given biome, this was the only
dataset that included data collected between the sea surface and 600 m. Bol
et al. (2018) reprocessed this dataset by computing the linear regression
between POC and bbp700 in different depth bins, forcing the intercept
through zero. The slope of the POC vs. bbp700 regressions, and
therefore the POC /bbp700 ratio, decreased more than 2-fold between
the surface and the 600 m bins. These results suggest that the POC vs.
bbp700 relationship may be better modeled with nonlinear equations, as
done for surface data in some previous studies
(Balch et
al., 2010; Johnson et al., 2017; Stramski et al., 1999). However, one must
keep in mind that the ecosystem processes that define POC /bbp700 ratios
in the surface layer may be different from those occurring in the
mesopelagic (see Sect. 4.2).
The relationship between POC and particulate beam attenuation at 660 nm,
cp, has been analyzed on more occasions than the POC /bbp700
relationship. The backscattering ratio, bbp/bp, relates
particulate backscattering to the total particulate scattering and is
directly related to the refractive index of the particle assemblage
(Babin
et al., 2003; Dall'Olmo et al., 2009; Loisel et al., 2011; Organelli et al.,
2018; Stramski and Kiefer, 1991; Stramski et al., 1999; Twardowski et al.,
2001; Ulloa et al., 1994). Light absorption by particles is negligible in
the 650–700 nm spectral region, such that total beam attenuation cp is
a good approximation of the scattering coefficient bp. Thus, once the
known absorption and scattering coefficients of seawater are removed,
bbp/cp is a good approximation of the backscattering ratio and can
be used to compare the relationships between POC vs. cp and POC vs.
bbp700.
The observed POC /bbp700 variability in the surface layer across biomes,
reflected in our POC estimation algorithm, seems analogous to that found for
the relationship between POC and cp by Cetinić et al. (2012) (Fig. A1). The POC content per unit bbp700 decreases with the maximum
bbp700 of each dataset, which may be related to the structure and
species composition of upper-ocean ecosystems (see Sect. 4.3). It is
also plausible that the fraction of POC (in terms of particle size or type)
that contributes to backscattering varies across sites, further enhancing
POC /bbp700 variability.
The number of studies that tackled the interconversion between POC and
bio-optical proxies in the mesopelagic layer is much smaller than those that
focused on the epipelagic layer. Besides Cetinić et al. (2012) in the
subpolar North Atlantic, we are only aware of the studies of Bishop (1999)
and Bishop et al. (1999). The latter two studies found a POC vs. cp
slope of around 16 mmol C m-2 (= mmol C m-3 m). Given
that modern transmissometers accept more forward scattered light that those
used by Bishop (1999), the corresponding slope would be approximately 27 mmol C m-2 (Jim K. B. Bishop, personal communication, 2021). Bishop (1999) found this slope to be nearly
constant in contrasting areas over the 0–1000 m depth range, as depicted in
Fig. A1 with the datasets labeled “Bishop1999eqpac” (Equatorial Pacific)
and “Bishop1999all” (Equatorial Pacific, NE Pacific, and North Atlantic
together).
The linear relationship between POC and cp is compatible with the
nonlinear relationship between POC and bbp700 along the vertical
profile if the backscattering ratio also increases with depth, as found by
Cetinić et al. (2012). The latter study found an increase in the
backscattering ratio (bbp700/cp653) from around 1.2 % at the
surface to around 1.5 % in the upper mesopelagic. The
bbp700/cp653 ratio was more variable in deeper layers and values
> 2 % were not rare (see
also
Nencioli et al., 2010; Organelli et al., 2018; Twardowski et al., 2001). The
POC /bbp700 ratio of 1000 mmol C m-2 used here for deep waters (Eq. 1), based on the analysis of Bol et al. (2018), would correspond to a POC
vs. cp slope of 27 mmol C m-2 if the backscattering ratio was
2.7 % in the lower mesopelagic (600–1000 m, Fig. 2). Analogously, the
range of POC /bbp700 used in our sensitivity tests, 500 to 2000 mmol m-2, would correspond to a backscattering ratio between 5.4 % (likely
too high) and 1.35 % (closer to available estimates). More collocated
measurements of POC, bbp700, and cp in the mesopelagic and below
are needed to reduce these uncertainties.
Beyond the natural variability, the interconversion between POC and
bio-optical proxies is also confounded by methodological issues (Cetinić
et al., 2012; Strubinger-Sandoval et al., 2021), most of which were not
fully addressed in the studies compiled here. In particular, filtration of
large sample volumes in the cubic meter range with in situ pumps yielded much
stronger POC–cp relationships than small-volume sampling (Bishop,
1999).
Code availability
The model code used in this paper is available under 10.5281/zenodo.5243343 (Galí et al., 2021a). The authors can provide the code used to
process the datasets on reasonable request.
Data availability
The simulated and observed datasets analyzed in this article are available
at 10.5281/zenodo.5139602 (Galí et al., 2021b). The code and
documentation of NEMO and PISCES are available at https://www.nemo-ocean.eu/ (Madec and NEMO System Team, 2019; NEMO TOP Working Group, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-1245-2022-supplement.
Author contributions
MG and RB designed the study. MF produced and/or reprocessed global
climatological datasets. RB produced the NEMO dynamical fields used to
force PISCES 1D offline simulations. OA provided the global PISCES
simulation. MG processed BGC-Argo coherent annual time series, ran PISCES
1D simulations, analyzed data and produced the figures, and wrote the
paper with contributions from all coauthors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors acknowledge Antoine Poteau for guidance with BGC-Argo data;
Margarida Samsó and Pierre-Antoine Bretonnière for downloading and
storing the Argo data; and Thomas Arsouze, Vladimir Lapin, and Joan Llort for
advice on the PISCES 1D configuration. Argo data were collected and made
freely available by the International Argo Program, which is part of the
Global Ocean Observing System, and the national programs that contribute to
it. The authors thank Jim K. B. Bishop and an anonymous reviewer for their
constructive criticisms that improved the paper.
Financial support
Martí Galí has received financial support through the Postdoctoral Junior Leader
Fellowship Programme from “La Caixa” Banking Foundation (ORCAS project;
LCF/BQ/PI18/11630009) and through the OPERA project funded by the Ministerio
de Ciencia, Innovación y Universidades (PID2019-107952GA-I00). Raffaele Bernardello
received support from the Ministerio de Ciencia, Innovación y
Universidades as part of the DeCUSO project (CGL2017-84493-R).
Review statement
This paper was edited by Carolin Löscher and reviewed by Jim K. B. Bishop and one anonymous referee.
ReferencesAlonso-González, I. J., Arístegui, J., Vilas, J. C., and
Hernández-Guerra, A.: Lateral POC transport and consumption in surface
and deep waters of the Canary Current region: A box model study, Global
Biogeochem. Cy., 23, 1–12, 10.1029/2008GB003185, 2009.Alonso-Gonzalez, I. J., Aristegui, J., Lee, C., Sanchez-Vidal, A., Calafat,
A., Fabres, J., Sangra, P., Masque, P., Hernandez-Guerra, A., and
Benitez-Barrios, V.: Role of slowly settling particles in the ocean carbon
cycle, Geophys. Res. Lett., 37, 1–5, 10.1029/2010GL043827, 2010.Argo: Argo float data and metadata from Global Data Assembly Centre (Argo
GDAC), SEANOE [Data set], 10.17882/42182, 2000.Arístegui, J., Gasol, J. M., Duarte, C. M., and Herndl, G. J.: Microbial
oceanography of the dark ocean's pelagic realm, Limnol. Oceanogr., 54,
1501–1529, 10.4319/lo.2009.54.5.1501, 2009.Arnosti, C., Fuchs, B. M., Amann, R., and Passow, U.: Contrasting
extracellular enzyme activities of particle-associated bacteria from
distinct provinces of the north Atlantic Ocean, Front. Microbiol., 3,
1–9, 10.3389/fmicb.2012.00425, 2012.Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2:
An ocean biogeochemical model for carbon and ecosystem studies, Geosci.
Model Dev., 8, 2465–2513, 10.5194/gmd-8-2465-2015, 2015.Aumont, O., van Hulten, M., Roy-Barman, M., Dutay, J.-C., Éthé, C., and Gehlen, M.: Variable reactivity of particulate organic matter in a global ocean biogeochemical model, Biogeosciences, 14, 2321–2341, 10.5194/bg-14-2321-2017, 2017.
Babin, M., Morel, A., Fournier-sicre, V., Fell, F., Stramski, D., Mar, N.,
Villefranche, D., Cedex, V., and Morel, A.: Light Scattering Properties of
Marine Particles in Coastal and Open Ocean Waters as Related to the Particle
Mass Concentration Light scattering properties of marine particles in
coastal and open ocean waters as related to the particle mass concentration,
Limnology, 48, 843–859, 2003.Baker, C. A., Henson, S. A., Cavan, E. L., Giering, S. L. C., and Sanders,
R.: Slow-sinking particulate organic carbon in the Atlantic Ocean:
Magnitude, flux, and potential controls, Global Biogeochem. Cy., 31,
1051–1065, 10.1002/2017GB005638, 2017.Balch, W. M., Bowler, B. C., Drapeau, D. T., Poulton, A. J., and Holligan, P.
M.: Biominerals and the vertical flux of particulate organic carbon from the
surface ocean, Geophys. Res. Lett., 37, 1–6, 10.1029/2010GL044640, 2010.Baltar, F., Arístegui, J., Gasol, J. M., Sintes, E., Van Aken, H. M.,
and Herndl, G. J.: High dissolved extracellular enzymatic activity in the
deep central Atlantic ocean, Aquat. Microb. Ecol., 58, 287–302,
10.3354/ame01377, 2010a.Baltar, F., Arístegui, J., Sintes, E., Gasol, J. M., Reinthaler, T., and
Herndl, G. J.: Significance of non-sinking particulate organic carbon and
dark CO2 fixation to heterotrophic carbon demand in the mesopelagic
northeast Atlantic, Geophys. Res. Lett., 37, 1–6,
10.1029/2010GL043105, 2010b.Baumas, C. M., Le Moigne, F. A., Garel, M., Bhairy, N., Guasco, S., Riou,
V., Armougom, F., Grossart, H. P., and Tamburini, C.: Mesopelagic microbial
carbon production correlates with diversity across different marine particle
fractions, ISME J., 15, 1695–1708, 10.1038/s41396-020-00880-z, 2021.Belcher, A., Iversen, M., Giering, S., Riou, V., Henson, S. A., Berline, L.,
Guilloux, L., and Sanders, R.: Depth-resolved particle-associated microbial
respiration in the northeast Atlantic, Biogeosciences, 13, 4927–4963,
10.5194/bg-13-4927-2016, 2016.Bellacicco, M., Cornec, M., Organelli, E., Brewin, R. J. W., Neukermans, G.,
Volpe, G., Barbieux, M., Poteau, A., Schmechtig, C., D'Ortenzio, F.,
Marullo, S., Claustre, H., and Pitarch, J.: Global Variability of Optical
Backscattering by Non-algal particles From a Biogeochemical-Argo Data Set,
Geophys. Res. Lett., 46, 9767–9776, 10.1029/2019GL084078, 2019.Bianchi, D., Weber, T. S., Kiko, R., and Deutsch, C.: Global niche of marine
anaerobic metabolisms expanded by particle microenvironments, Nat.
Geosci., 11, 263–268, 10.1038/s41561-018-0081-0,
2018.Bishop, J. K. B.: Transmissometer measurement of POC, Deep-Sea Res. Pt.
I, 46, 353–369, 10.1016/S0967-0637(98)00069-7, 1999.Bishop, J. K. and Wood, T. J.: Particulate matter chemistry and dynamics in the twilight zone at VERTIGO ALOHA and K2 sites, Deep-Sea Res. Pt. I, 55, 1684–1706, 10.1016/j.dsr.2008.07.012, 2008.Bishop, J. K. B. and Wood, T. J.: Year-round observations of carbon biomass
and flux variability in the Southern Ocean, Global Biogeochem. Cy.,
23, GB2019, 10.1029/2008GB003206, 2009.Bishop, J. K. B., Collier, R. W., Kettens, D. R., and Edmond, J. M.: The
chemistry, biology, and vertical flux of particulate matter from the upper
1500 m of the Panama Basin, Deep-Sea Res. Pt. A, 27, 615–640, 10.1016/0198-0149(80)90077-1,
1980.Bishop, J. K. B., Conte, M. H., Wiebe, P. H., Roman, M. R., and Langdon, C.:
Particulate matter production and consumption in deep mixed layers:
observations in a warm-core ring, Deep-Sea Res. Pt. A,
33, 1813–1841, 10.1016/0198-0149(86)90081-6, 1986.Bishop, J. K. B., Calvert, S. E., and Soon, M. Y. S.: Spatial and temporal
variability of POC in the northeast subarctic Pacific, Deep-Res. Pt. II, 46, 2699–2733,
10.1016/S0967-0645(99)00081-8, 1999.Bishop, J. K. B., Wood, T. J., Davis, R. E., and Sherman, J. T.: Robotic
Observations of Enhanced Carbon Biomass and Export at 55∘ S during
SOFeX, Science, 304, 417–420, 10.1126/science.1087717, 2004.Bisson, K., Siegel, D. A., DeVries, T., Cael, B. B., and Buesseler, K. O.:
How data set characteristics influence ocean carbon export models, Global
Biogeochem. Cy., 32, 1312–1328, 10.1029/2018GB005934, 2019.Bode, A., Olivar, M. P., and Hernández-León, S.: Trophic indices for
micronektonic fishes reveal their dependence on the microbial system in the
North Atlantic, Sci. Rep., 11, 8488, 10.1038/s41598-021-87767-x, 2021.Bol, R., Henson, S. A., Rumyantseva, A., and Briggs, N.: High-Frequency
Variability of Small-Particle Carbon Export Flux in the Northeast Atlantic,
Global Biogeochem. Cy., 32, 1803–1814, 10.1029/2018GB005963,
2018.Boss, E., Guidi, L., Richardson, M. J., Stemmann, L., Gardner, W., Bishop, J. K. B., Anderson, R. F., and Sherrell, R. M.: Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES, Prog. Oceanogr., 133, 43–54, 10.1016/j.pocean.2014.09.007, 2015.Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A., and Weber, T.:
Multi-faceted particle pumps drive carbon sequestration in the ocean,
Nature, 568, 327–335, 10.1038/s41586-019-1098-2, 2019.Briggs, N., Perry, M. J., Cetinić, I., Lee, C., D'Asaro, E., Gray, A. M.,
and Rehm, E.: High-resolution observations of aggregate flux during a
sub-polar North Atlantic spring bloom, Deep-Res. Pt. I, 58, 1031–1039, 10.1016/j.dsr.2011.07.007, 2011.Briggs, N., Olmo, G. D., and Claustre, H.: Major role of particle
fragmentation in regulating the biological sequestration of CO2 by the
oceans, Science, 793, 791–793, 2020.Bourne, H. L., Bishop, J. K., Connors, E. J., and Wood, T. J.: Carbon export
and fate beneath a dynamic upwelled filament off the California coast,
Biogeosciences, 18, 3053–3086, 10.5194/bg-18-3053-2021, 2021.Bricaud, A., Claustre, H., Ras, J., and Oubelkheir, K.: Natural variability
of phytoplanktonic absorption in oceanic waters: Influence of the size
structure of algal populations, J. Geophys. Res.-Ocean.,
109, C11010, 10.1029/2004JC002419, 2004.Buesseler, K. O. and Boyd, P. W.: Shedding light on processes that control
particle export and flux attenuation in the twilight zone of the open ocean,
Limnol. Oceanogr., 54, 1210–1232, 10.4319/lo.2009.54.4.1210, 2009.Buesseler, K. O., Boyd, P. W., Black, E. E., and Siegel, D. A.: Metrics that
matter for assessing the ocean biological carbon pump, P. Natl. Acad.
Sci. USA, 117, 9679–9687, 10.1073/pnas.1918114117, 2020.Cael, B. B., Cavan, E. L., and Britten, G. L.: Reconciling the
size-dependence of marine particle sinking speed, Geophys. Res. Lett., 48, e2020GL091771, 10.1029/2020GL091771,
2021.Calbet, A.: The trophic roles of microzooplankton in marine systems, ICES
J. Mar. Sci., 65, 325–331, 10.1093/icesjms/fsn013, 2008.Cetinić, I., Perry, M. J., Briggs, N. T., Kallin, E., D'Asaro, E. A., and
Lee, C. M.: Particulate organic carbon and inherent optical properties
during 2008 North Atlantic bloom experiment, J. Geophys. Res.-Ocean.,
117, C06028,
10.1029/2011JC007771, 2012.Ciotti, A. M., Lewis, M. R., and Cullen, J. J.: Assessment of the
relationships between dominant cell size in natural phytoplankton
communities and the spectral shape of the absorption coefficient, Limnol.
Oceanogr., 47, 404–417, 10.4319/lo.2002.47.2.0404, 2002.Claustre, H.: Is desert dust making oligotrophic waters greener?, Geophys.
Res. Lett., 29, 10–13, 10.1029/2001GL014056, 2002.Claustre, H., Morel, A., Babin, M., Cailliau, C., Marie, D., Marty, J. C.,
Tailliez, D., and Vaulot, D.: Variability in particle attenuation and
chlorophyll fluorescence in the tropical Pacific: Scales, patterns, and
biogeochemical implications, J. Geophys. Res.-Ocean., 104, 3401–3422,
10.1029/98jc01334, 1999.Claustre, H., Johnson, K. S., and Takeshita, Y.: Observing the Global Ocean
with Biogeochemical-Argo, Ann. Rev. Mar. Sci., 12, 23–48, 10.1146/annurev-marine-010419-010956,
2020.Cornec, M., Claustre, H., Mignot, A., Guidi, L., Lacour, L., Poteau, A.,
D'Ortenzio, F., Gentili, B., and Schmechtig, C.: Deep Chlorophyll Maxima in
the Global Ocean: Occurrences, Drivers and Characteristics, Global
Biogeochem. Cy., 35, 1–30, 10.1029/2020gb006759, 2021.Dall'Olmo, G. and Mork, K. A.: Carbon export by small particles in the
Norwegian Sea, Geophys. Res. Lett., 41, 2921–2927,
10.1002/2014GL059244, 2014.Dall'Olmo, G., Westberry, T. K., Behrenfeld, M. J., Boss, E., and Slade, W.
H.: Significant contribution of large particles to optical backscattering in
the open ocean, Biogeosciences, 6, 947–967, 10.5194/bg-6-947-2009,
2009.Doney, S. C., Lindsay, K., Caldeira, K., Campin, J. M., Drange, H., Dutay,
J. C., Follows, M., Gao, Y., Gnanadesikan, A., Gruber, N., Ishida, A., Joos,
F., Madec, G., Maier-Reimer, E., Marshall, J. C., Matear, R. J., Monfray,
P., Mouchet, A., Najjar, R., Orr, J. C., Plattner, G. K., Sarmiento, J.,
Schlitzer, R., Slater, R., Totterdell, I. J., Weirig, M. F., Yamanaka, Y.,
and Yool, A.: Evaluating global ocean carbon models: The importance of
realistic physics, Global Biogeochem. Cy., 18, GB3017,
10.1029/2003GB002150, 2004.Druffel, E. R., Williams, P. M., Bauer, J. E., and Ertel, J. R.: Cycling of dissolved and particulate organic matter in the open ocean, J. Geophys. Res.-Ocean., 97, 15639–15659, 10.1029/92JC01511, 1992.Duret, M. T., Lampitt, R. S., and Lam, P.: Prokaryotic niche partitioning
between suspended and sinking marine particles, Env. Microbiol.
Rep., 11, 386–400, 10.1111/1758-2229.12692, 2019.Duteil, O., Koeve, W., Oschlies, A., Aumont, O., Bianchi, D., Bopp, L.,
Galbraith, E., Matear, R., Moore, J. K., Sarmiento, J. L., and Segschneider,
J.: Preformed and regenerated phosphate in ocean general circulation models:
Can right total concentrations be wrong?, Biogeosciences, 9, 1797–1807,
10.5194/bg-9-1797-2012, 2012.Dutkiewicz, S., Hickman, A. E., Jahn, O., Henson, S., Beaulieu, C., and
Monier, E.: Ocean colour signature of climate change, Nat. Commun., 10, 578,
10.1038/s41467-019-08457-x, 2019.Evers-King, H., Martinez-Vicente, V., Brewin, R. J. W., Dall'Olmo, G.,
Hickman, A. E., Jackson, T., Kostadinov, T. S., Krasemann, H., Loisel, H.,
Röttgers, R., Roy, S., Stramski, D., Thomalla, S., Platt, T., and
Sathyendranath, S.: Validation and intercomparison of ocean color algorithms
for estimating particulate organic carbon in the oceans, Front. Mar. Sci.,
4, 1–20, 10.3389/fmars.2017.00251, 2017.Falls, M., Bernardello, R., Castrillo, M., Acosta, M., Llort, J., and Galí, M.: Use of Genetic Algorithms for Ocean Model Parameter Optimisation, Geosci. Model Dev. Discuss. [preprint], 10.5194/gmd-2021-222, in review, 2021.Fay, A. R. and McKinley, G. A.: Global open-ocean biomes: Mean and temporal
variability, Earth Syst. Sci. Data, 6, 273–284,
10.5194/essd-6-273-2014, 2014.Flament, P.: A state variable for characterizing water masses and their
diffusive stability: Spiciness, Prog. Oceanogr., 54, 493–501,
10.1016/S0079-6611(02)00065-4, 2002.François, R., Honjo, S., Krishfield, R., and Manganini, S.: Factors
controlling the flux of organic carbon to the bathypelagic zone of the
ocean, Global Biogeochem. Cy., 16, 1087,
10.1029/2001gb001722, 2002.Galí, M., Benardello, R., Falls, M., Claustre, H., and Aumont, O.:
PISCES-v2 1D configuration used to study POC dynamics as observed by BGC-Argo floats, Zenodo [code], 10.5281/zenodo.5243343, 2021a.Galí, M., Benardello, R., Falls, M., Claustre, H., and Aumont, O.:
Datasets for the comparison between POC estimated from BGC-Argo floats and PISCES model simulations, Zenodo [data set], 10.5281/zenodo.5139602, 2021b.García‐Martín, E. E., Davidson, K., Davis, C. E., Mahaffey, C., Mcneill, S., Purdie, D. A., and Robinson, C.: Low contribution of the fast‐sinking particle fraction to total plankton metabolism in a temperate shelf sea, Global Biogeochem. Cy., 35, e2021GB007015, 10.1029/2021GB007015, 2021.Gardner, W. D., Richardson, M. J., and Smith Jr., W. O.: Seasonal patterns of
water column particulate organic carbon and fluxes in the Ross Sea,
Antarctica, Deep-Sea Res. Pt. II,
47, 3423–3449, 10.1016/S0967-0645(00)00074-6, 2000.Gardner, W. D., Mishonov, A. V., and Richardson, M. J.: Global POC
concentrations from in-situ and satellite data, Deep-Res. Pt. II, 53, 718–740, 10.1016/j.dsr2.2006.01.029, 2006.
Gasol, J. M., del Giorgio, P. A., and Duarte, C. M.: Biomass distribution in
marine planktonic communities, Limnol. Oceanogr., 42, 1353–1363, 1997.Giering, S. L. C., Sanders, R., Lampitt, R. S., Anderson, T. R., Tamburini,
C., Boutrif, M., Zubkov, M. V., Marsay, C. M., Henson, S. A., Saw, K., Cook,
K., and Mayor, D. J.: Reconciliation of the carbon budget in the ocean's
twilight zone, Nature, 507, 480–483, 10.1038/nature13123, 2014.Goldthwait, S., Yen, J., Brown, J., and Alldredge, A.: Quantification of
marine snow fragmentation by swimming euphausiids, Limnol. Oceanogr., 49, 940–952, 10.4319/lo.2004.49.4.0940, 2004.Graff, J. R., Westberry, T. K., Milligan, A. J., Brown, M. B., Dall'Olmo,
G., van Dongen-Vogels, V., Reifel, K. M., and Behrenfeld, M. J.: Analytical
phytoplankton carbon measurements spanning diverse ecosystems, Deep-Sea Res.
Pt. I, 102, 16–25, 10.1016/j.dsr.2015.04.006,
2015.Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, 10.5194/gmd-9-3231-2016, 2016.Guidi, L., Legendre, L., Reygondeau, G., Uitz, J., Stemmann, L., and Henson, S. A.: A new look at ocean carbon remineralization for estimating deepwater sequestration, Global Biogeochem. Cy., 29, 1044–1059, 10.1002/2014GB005063, 2015.Haëntjens, N., Della Penna, A., Briggs, N., Karp-Boss, L., Gaube, P.,
Claustre, H., and Boss, E.: Detecting Mesopelagic Organisms Using
Biogeochemical-Argo Floats, Geophys. Res. Lett., 47, e2019GL08608,
10.1029/2019GL086088, 2020.Hayes, C. T., Anderson, R. F., Fleisher, M. Q., Huang, K. F., Robinson, L.
F., Lu, Y., Cheng, H., Edwards, R. L., and Moran, S. B.: 230Th and 231Pa on
GEOTRACES GA03, the U.S. GEOTRACES North Atlantic transect, and implications
for modern and paleoceanographic chemical fluxes, Deep-Res. Pt. II, 116, 29–41, 10.1016/j.dsr2.2014.07.007, 2015.Henson, S. A., Yool, A., and Sanders, R.: Variability in efficiency of
particulate organic carbon export: A model study, Global Biogeochem. Cy., 29,
33–45, 10.1002/2014GB004965, 2015.Hernández-León, S., Koppelmann, R., Fraile-Nuez, E., Bode, A.,
Mompeán, C., Irigoien, X., Olivar, M. P., Echevarría, F.,
Fernández de Puelles, M. L., González-Gordillo, J. I., Cózar,
A., Acuña, J. L., Agustí, S., and Duarte, C. M.: Large deep-sea
zooplankton biomass mirrors primary production in the global ocean, Nat.
Commun., 11, 6048, 10.1038/s41467-020-19875-7, 2020.Herndl, G. J. and Reinthaler, T.: Microbial control of the dark end of the biological pump, Nat. Geosci., 6, 718–724, 10.1038/ngeo1921, 2013.Holte, J. and Talley, L.: A new algorithm for finding mixed layer depths with applications to Argo data and Subantarctic Mode Water formation, J. Atmos. Ocean. Technol., 26, 1920–1939, 10.1175/2009JTECHO543.1, 2009.Honjo, S., Manganini, S. J., Krishfield, R. A., and Francois, R.: Particulate
organic carbon fluxes to the ocean interior and factors controlling the
biological pump: A synthesis of global sediment trap programs since 1983,
Prog. Oceanogr., 76, 217–285, 10.1016/j.pocean.2007.11.003, 2008.Ikenoue, T., Kimoto, K., Okazaki, Y., Sato, M., Honda, M. C., Takahashi, K.,
Harada, N., and Fujiki, T.: Phaeodaria: An Important Carrier of Particulate
Organic Carbon in the Mesopelagic Twilight Zone of the North Pacific Ocean,
Global Biogeochem. Cy., 33, 1146–1160, 10.1029/2019GB006258,
2019.Jiao, N., Herndl, G. J., Hansell, D. a, Benner, R., Kattner, G., Wilhelm, S.
W., Kirchman, D. L., Weinbauer, M. G., Luo, T., Chen, F., and Azam, F.:
Microbial production of recalcitrant dissolved organic matter: long-term
carbon storage in the global ocean, Nat. Rev. Microbiol., 8, 593–599,
10.1038/nrmicro2386, 2010.Johnson, K. S., Plant, J. N., Coletti, L. J., Jannasch, H. W., Sakamoto, C.
M., Riser, S. C., Swift, D. D., Williams, N. L., Boss, E., Haëntjens,
N., Talley, L. D., and Sarmiento, J. L.: Biogeochemical sensor performance in
the SOCCOM profiling float array, J. Geophys. Res.-Ocean., 122, 6416–6436,
10.1002/2017JC012838, 2017.Karthäuser, C., Ahmerkamp, S., Marchant, H. K., Bristow, L. A., Hauss,
H., Iversen, M. H., Kiko, R., Maerz, J., Lavik, G., and Kuypers, M. M. M.:
Small sinking particles control anammox rates in the Peruvian oxygen minimum
zone, Nat. Commun., 12, 3235, 10.1038/s41467-021-23340-4, 2021.Kelley, D.: Package “oce”: Analysis of Oceanographic data, R Package,
available at: https://dankelley.github.io/oce/ (last access: 27 November 2018), 2011.Kharbush, J. J., Close, H. G., Van Mooy, B. A. S., Arnosti, C., Smittenberg,
R. H., Le Moigne, F. A. C., Mollenhauer, G., Scholz-Böttcher, B.,
Obreht, I., Koch, B. P., Becker, K. W., Iversen, M. H., and Mohr, W.:
Particulate Organic Carbon Deconstructed: Molecular and Chemical Composition
of Particulate Organic Carbon in the Ocean, Front. Mar. Sci., 7, 518,
10.3389/fmars.2020.00518, 2020.Kiørboe, T.: How zooplankton feed: Mechanisms, traits and trade-offs,
Biol. Rev., 86, 311–339, 10.1111/j.1469-185X.2010.00148.x, 2011.Klaas, C. and Archer, D. E.: Association of sinking organic matter with
various types of mineral ballast in the deep sea: Implications for the rain
ratio, Global Biogeochem. Cy., 16, 1116,
10.1029/2001gb001765, 2002.Kriest, I., Kähler, P., Koeve, W., Kvale, K., Sauerland, V., and
Oschlies, A.: One size fits all? Calibrating an ocean biogeochemistry model
for different circulations, Biogeosciences, 17, 3057–3082,
10.5194/bg-17-3057-2020, 2020.Kwon, E. Y., Primeau, F., and Sarmiento, J. L.: The impact of
remineralization depth on the air-sea carbon balance, Nat. Geosci., 2,
630–635, 10.1038/ngeo612, 2009.Lacour, L., Briggs, N., Claustre, H., Ardyna, M., and Dall'Olmo, G.: The
Intraseasonal Dynamics of the Mixed Layer Pump in the Subpolar North
Atlantic Ocean: A Biogeochemical-Argo Float Approach, Global Biogeochem.
Cy., 33, 266–281, 10.1029/2018GB005997, 2019.Lam, P. J., Doney, S. C., and Bishop, J. K. B.: The dynamic ocean biological
pump: Insights from a global compilation of particulate organic carbon, CaCO3, and opal concentration profiles from the mesopelagic, Global Biogeochem.
Cy., 25, 1–14, 10.1029/2010GB003868, 2011.Lam, P. J., Ohnemus, D. C., and Auro, M. E.: Size-fractionated major particle
composition and concentrations from the US GEOTRACES North Atlantic Zonal
Transect, Deep-Res. Pt. II, 116, 303–320,
10.1016/j.dsr2.2014.11.020, 2015.Lampitt, R. S., Wishner, K. F., Turley, C. M., and Angel, M. V.: Marine snow
studies in the Northeast Atlantic Ocean: distribution, composition and role
as a food source for migrating plankton, Mar. Biol. Int. J. Life Ocean.
Coast. Waters, 116, 689–702, 10.1007/BF00355486, 1993.Laufkötter, C., Vogt, M., Gruber, N., Aumont, O., Bopp, L., Doney, S.
C., Dunne, J. P., Hauck, J., John, J. G., Lima, I. D., Seferian, R., and
Völker, C.: Projected decreases in future marine export production: The
role of the carbon flux through the upper ocean ecosystem, Biogeosciences,
13, 4023–4047, 10.5194/bg-13-4023-2016, 2016.Laurenceau-Cornec, E. C., Le Moigne, F. A., Gallinari, M., Moriceau, B.,
Toullec, J., Iversen, M. H., Engel, A., and De La Rocha, C. L.: New
guidelines for the application of Stokes' models to the sinking velocity of
marine aggregates, Limnol. Oceanogr., 65, 1264–1285, 10.1002/lno.11388, 2020.Lebeaupin Brossier, C., Béranger, K., Deltel, C., and Drobinski, P.: The
Mediterranean response to different space–time resolution atmospheric
forcings using perpetual mode sensitivity simulations, Ocean Model.,
36, 1–25, 10.1016/j.ocemod.2010.10.008, 2011.Lee, S., Kang, Y. C., and Fuhrman, J. A.: Imperfect retention of natural
bacterioplankton cells by glass fiber filters, Mar. Ecol. Prog. Ser.,
119, 285–290, 10.3354/meps119285, 1995.Legendre, L., Rivkin, R. B., Weinbauer, M. G., Guidi, L., and Uitz, J.: The
microbial carbon pump concept: Potential biogeochemical significance in the
globally changing ocean, Prog. Oceanogr., 134, 432–450,
10.1016/j.pocean.2015.01.008, 2015.Llort, J., Lévy, M., Sallée, J.-B., and Tagliabue, A.: Onset,
intensification, and decline of phytoplankton blooms in the Southern Ocean,
ICES J. Mar. Sci., 72, 1971–1984, 10.1093/icesjms/fst176, 2015.Llort, J., Langlais, C., Matear, R., Moreau, S., Lenton, A., and Strutton, P.
G.: Evaluating Southern Ocean Carbon Eddy-Pump From Biogeochemical Argo
Floats, J. Geophys. Res.-Ocean., 123, 971–984, 10.1002/2017JC012861, 2018.Loisel, H. and Morel, A.: Light Scattering and Chlorophyll Concentration in
Case 1 Waters: A Reexamination, Limnol. Oceanogr., 43, 847–858,
10.4319/lo.1998.43.5.0847, 1998.Loisel, H., Vantrepotte, V., Norkvist, K., Mriaux, X., Kheireddine, M., Ras,
J., Pujo-Pay, M., Combet, Y., Leblanc, K., Dall'Olmo, G., Mauriac, R.,
Dessailly, D., and Moutin, T.: Characterization of the bio-optical anomaly
and diurnal variability of particulate matter, as seen from scattering and
backscattering coefficients, in ultra-oligotrophic eddies of the
Mediterranean Sea, Biogeosciences, 8, 3295–3317,
10.5194/bg-8-3295-2011, 2011.Löptien, U. and Dietze, H.: Reciprocal bias compensation and ensuing
uncertainties in model-based climate projections: Pelagic biogeochemistry
versus ocean mixing, Biogeosciences, 16, 1865–1881,
10.5194/bg-16-1865-2019, 2019.Madec, G. and NEMO System Team: NEMO ocean engine,
Scientific Notes of Climate Modelling Center (27), ISSN 1288-1619,
Institut Pierre-Simon Laplace (IPSL), 10.5281/zenodo.1464816, 2019.Marsay, C. M., Sanders, R. J., Henson, S. A., Pabortsava, K., Achterberg, E.
P., and Lampitt, R. S.: Attenuation of sinking particulate organic carbon
flux through the mesopelagic ocean, P. Natl. Acad. Sci. USA, 112,
1089–1094, 10.1073/pnas.1415311112, 2015.
Martin, J. H., Knauer, G. A., Karl, D. M., and Broenkow, W. W.: VERTEX:
carbon cycling in the northeast Pacific, Deep. Res., 34, 267–285, 1987.Mayor, D. J., Gentleman, W. C., and Anderson, T. R.: Ocean carbon
sequestration: Particle fragmentation by copepods as a significant
unrecognised factor?, BioEssays, 42, 2000149,
10.1002/bies.202000149, 2020.Menden-Deuer, S. and Lessard, E. J.: Carbon to volume relationships for
dinoflagellates, diatoms, and other protist plankton, Limnol. Oceanogr.,
45, 569–579, 10.4319/lo.2000.45.3.0569, 2000.McDonnell, A. M. and Buesseler, K. O.: Variability in the average sinking
velocity of marine particles, Limnol. Oceanogr., 55, 2085–2096,
10.4319/lo.2010.55.5.2085, 2010.Mestre, M., Ruiz-González, C., Logares, R., Duarte, C. M., Gasol, J. M.,
and Sala, M. M.: Sinking particles promote vertical connectivity in the
ocean microbiome, P. Natl. Acad. Sci. USA, 115,
E6799–E6807, 10.1073/pnas.1802470115, 2018.Moore, T. S., Dowell, M. D., and Franz, B. A.: Detection of coccolithophore
blooms in ocean color satellite imagery: A generalized approach for use with
multiple sensors, Remote Sens. Environ., 117, 249–263,
10.1016/j.rse.2011.10.001, 2012.
Morán, X. A. G., Gasol, J. M., Arin, L., and Estrada, M.: A comparison
between glass fiber and membrane filters for the estimation of phytoplankton
POC and DOC production, Mar. Ecol. Prog. Ser., 187, 31–41, 1999.Morel, A. and Ahn, Y. H.: Optical efficiency factors of free-living marine bacteria: Influence of bacterioplankton upon the optical properties and particulate organic carbon in oceanic waters, J. Mar. Res., 48, 145–175, 10.1357/002224090784984632, 1990.Mouw, C. B., Barnett, A., McKinley, G. A., Gloege, L., and Pilcher, D.:
Global ocean particulate organic carbon flux merged with satellite
parameters, Earth Syst. Sci. Data, 8, 531–541, 10.5194/essd-8-531-2016, 2016.Mullin, M. M.: Size fractionation of particulate organic carbon in the
surface waters of the western Indian Ocean, Limnol. Oceanogr., 10,
459–462, 10.4319/lo.1965.10.3.0459, 1965.NEMO TOP Working Group: Tracer in Ocean Paradigm (TOP) – The NEMO passive tracer engine,
Scientific Notes of Climate Modelling Center (28) [data set], ISSN 1288-1619,
Institut Pierre-Simon Laplace (IPSL), 10.5281/zenodo.1471700, 2019.Nencioli, F., Chang, G., Twardowski, M., and Dickey, T. D.: Optical
characterization of an eddy-induced diatom bloom west of the island of
Hawaii, Biogeosciences, 7, 151–162, 10.5194/bg-7-151-2010, 2010.Omand, M. M., D'Asaro, E. A., Lee, C. M., Perry, M. J., Briggs, N., Cetini,
I., and Mahadevan, A.: Eddy-driven subduction exports particulate organic
carbon from the spring bloom, Science, 348, 222–225,
10.1126/science.1260062, 2015.Organelli, E., Dall'Olmo, G., Brewin, R. J. W., Tarran, G. A., Boss, E., and
Bricaud, A.: The open-ocean missing backscattering is in the structural
complexity of particles, Nat. Commun., 9, 5439, 10.1038/s41467-018-07814-6,
2018.Organelli, E., Dall'Olmo, G., Brewin, R. J. W., Nencioli, F., and Tarran, G.
A.: Drivers of spectral optical scattering by particles in the upper 500 m
of the Atlantic Ocean, Opt. Exp., 28, 34147, 10.1364/oe.408439,
2020.Oschlies, A., Brandt, P., Stramma, L., and Schmidtko, S.: Drivers and
mechanisms of ocean deoxygenation, Nat. Geosci., 11, 467–473,
10.1038/s41561-018-0152-2, 2018.Oubelkheir, K., Claustre, H., Sciandra, A., and Babin, M.: Bio-optical and
biogeochemical properties of different trophic regimes in oceanic waters,
Limnol. Oceanogr., 50, 1795–1809, 10.4319/lo.2005.50.6.1795, 2005.Pachiadaki, M. G., Sintes, E., Bergauer, K., Brown, J. M., Record, N. R.,
Swan, B. K., Mathyer, M. E., Hallam, S. J., Lopez-Garcia, P., Takaki, Y.,
Nunoura, T., Woyke, T., Herndl, G. J., and Stepanauskas, R.: Major role of
nitrite-oxidizing bacteria in dark ocean carbon fixation, Science,
358, 1046–1051, 10.1126/science.aan8260, 2017.Palevsky, H. I. and Doney, S. C.: How Choice of Depth Horizon Influences the
Estimated Spatial Patterns and Global Magnitude of Ocean Carbon Export Flux,
Geophys. Res. Lett., 45, 4171–4179, 10.1029/2017GL076498, 2018.Passow, U.: Switching perspectives: Do mineral fluxes determine particulate
organic carbon fluxes or vice versa?, Geochem. Geophy. Geosy.,
5, Q04002, 10.1029/2003GC000670, 2004.Passow, U. and Carlson, C. A.: The biological pump in a high CO2 world,
Mar. Ecol. Prog. Ser., 470, 249–271, 10.3354/meps09985, 2012.Poteau, A., Boss, E., and Claustre, H.: Particulate concentration and
seasonal dynamics in the mesopelagic ocean based on the backscattering
coefficient measured with Biogeochemical-Argo floats, Geophys. Res. Lett.,
44, 6933–6939, 10.1002/2017GL073949, 2017.Resplandy, L., Lévy, M., and McGillicuddy, D. J.: Effects of Eddy-Driven
Subduction on Ocean Biological Carbon Pump, Global Biogeochem. Cy.,
33, 1071–1084, 10.1029/2018GB006125, 2019.Roemmich, D., Alford, M. H., Claustre, H., Johnson, K. S., King, B., Moum,
J., Oke, P. R., Owens, W. B., Pouliquen, S., Purkey, S., Scanderbeg, M.,
Suga, T., Wijffels, S. E., Zilberman, N., Bakker, D., Baringer, M. O.,
Belbeoch, M., Bittig, H. C., Boss, E., Calil, P., Carse, F., Carval, T.,
Chai, F., Conchubhair, D. O., D'Ortenzio, F., Dall'Olmo, G.,
Desbruyères, D., Fennel, K., Fer, I., Ferrari, R., Forget, G., Freeland,
H., Fujiki, T., Gehlen, M., Greenan, B., Hallberg, R., Hibiya, T., Hosoda,
S., Jayne, S., Jochum, M., Johnson, G. C., Kang, K. R., Kolodziejczyk, N.,
Koertzinger, A., Le Traon, P. Y., Lenn, Y. D., Maze, G., Mork, K. A.,
Morris, T., Nagai, T., Nash, J., Garabato, A. N., Olsen, A., Pattabhi, R.
R., Prakash, S., Riser, S., Schmechtig, C., Shroyer, E., Sterl, A., Sutton,
P., Talley, L., Tanhua, T., Thierry, V., Thomalla, S., Toole, J., Troisi,
A., Trull, T., Turton, J. D., Velez-Belchi, P. J., Walczowski, W., Wang, H.,
Wanninkhof, R., Waterhouse, A., Watson, A., Wilson, C., Wong, A. P., Xu, J.,
and Yasuda, I.: On the future of Argo: A global, full-depth,
multi-disciplinary array, Front. Mar. Sci., 6, 439,
10.3389/fmars.2019.00439, 2019.Sallée, J. B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Naveira Garabato, A., Sutherland, P., and Kuusela, M.: Summertime increases in upper-ocean stratification and mixed-layer depth, Nature, 591, 592–598, 10.1038/s41586-021-03303-x, 2021.Sarmiento, J. and Gruber, N.: Organic Matter Export and Remineralization, in:
Ocean Biogeochemical Dynamics, Princeton University Press,
Princeton, New Jersey, 173–226, 10.2307/j.ctt3fgxqx.8, 2006.Sauzède, R., Johnson, J. E., Claustre, H., Camps-Valls, G., and Ruescas,
A. B.: Estimation of Oceanic Particulate Organic Carbon with Machine
Learning, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 5,
949–956, 10.5194/isprs-annals-V-2-2020-949-2020, 2020.Sauzède, R., Johnson, J., Claustre, H., Camps-Valls, G., and Ruescas, A.:
MULTIOBS_GLO_BIO_BGC_3D_REP_015_010, Copernicus Monitoring Environment Marine Service (CMEMS) [Data set], https://marine.copernicus.eu/node/18802 (last access: 1 July 2021), 2021.Schmechtig, C., Thierry, V., and Bio Argo Team: Argo Quality Control Manual for Biogeochemical Data, Version 1, 1st March 2016, Villefranche-sur-Mer, France, CNRS, UMR 7093, LOV, Observatoire Océanologique, Bio-Argo Group, 36 pp., 10.13155/40879, 2016.Schmechtig, C., Poteau, A., Claustre, H., D'Ortenzio, F., Dall@Olmo, G., and Boss, E.: Processing BGC–Argo particle backscattering at the DAC level, IFREMER for Argo Data Management, 15 pp., 10.13155/39459, 2018.Schmidtko, S., Johnson, G. C., and Lyman, J. M.: MIMOC: A global monthly isopycnal upper‐ocean climatology with mixed layers, J. Geophys. Res.-Ocean., 118, 1658–1672, 10.1002/jgrc.20122, 2013.Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L.,
Tagliabue, A., Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J.,
Gehlen, M., Ilyina, T., John, J. G., Li, H., Long, M. C., Luo, J. Y.,
Nakano, H., Romanou, A., Schwinger, J., Stock, C., Santana-Falcón, Y.,
Takano, Y., Tjiputra, J., Tsujino, H., Watanabe, M., Wu, T., Wu, F., and
Yamamoto, A.: Tracking Improvement in Simulated Marine Biogeochemistry
Between CMIP5 and CMIP6, Curr. Clim. Chang. Rep., 6, 95–119,
10.1007/s40641-020-00160-0, 2020.Siegel, D. A. and Deuser, W. G.: Trajectories of sinking particles in the
Sargasso Sea: Modeling of statistical funnels above deep-ocean sediment
traps, Deep-Res. Pt. I, 44, 1519–1541,
10.1016/S0967-0637(97)00028-9, 1997.Siegel, D. A., Buesseler, K. O., Doney, S. C., Sailley, S. F., Behrenfeld,
M. J., and Boyd, P. W.: Global assessment of ocean carbon export by combining
satellite observations and food-web model, Global Biogeochem. Cy., 28,
181–196, 10.1002/2013GB004743.Received, 2014.Snoejis, P., Busse, S., and Potapova, M.: The importance of diatom cell size
in community analysis, J. Phycol., 38, 265–281, 10.1046/j.1529-8817.2002.01105.x, 2002.Stemmann, L. and Boss, E.: Plankton and Particle Size and Packaging: From
Determining Optical Properties to Driving the Biological Pump, Ann. Rev.
Mar. Sci., 4, 263–290, 10.1146/annurev-marine-120710-100853, 2012.Stemmann, L., Jackson, G. A., and Ianson, D.: A vertical model of particle
size distributions and fluxes in the midwater column that includes
biological and physical processes – Part I: Model formulation, Deep-Res.
Pt. I., 51, 865–884, 10.1016/j.dsr.2004.03.001,
2004a.Stemmann, L., Jackson, G. A., and Gorsky, G.: A vertical model of particle
size distributions and fluxes in the midwater column that includes
biological and physical processes – Part II: Application to a three year
survey in the NW Mediterranean Sea, Deep-Res. Pt. I,
51, 885–908, 10.1016/j.dsr.2004.03.002, 2004b.Stemmann, L., Prieur, L., Legendre, L., Taupier-Letage, I., Picheral, M.,
Guidi, L., and Gorsky, G.: Effects of frontal processes on marine aggregate
dynamics and fluxes: An interannual study in a permanent geostrophic front
(NW Mediterranean), J. Mar. Syst., 70, 1–20,
10.1016/j.jmarsys.2007.02.014, 2008.Stramska, M.: Particulate organic carbon in the global ocean derived from SeaWiFS ocean color, Deep-Sea Res. Pt. I, 56, 1459–1470, 10.1016/j.dsr.2009.04.009, 2009.Stramski, D. and Kiefer, D.: Light scattering by microorganisms in the open
ocean, Prog. Oceanogr., 28, 343–383, 10.1016/0079-6611(91)90032-H,
1991.Stramski, D., Reynolds, R. A., Kahru, M., and Mitchell, B. G.: Estimation of
particulate organic carbon in the ocean from satellite remote sensing,
Science, 285, 239–242, 10.1126/science.285.5425.239, 1999.Stramski, D., Reynolds, R. A., Babin, M., Kaczmarek, S., Lewis, M. R.,
Röttgers, R., Sciandra, A., Stramska, M., Twardowski, M. S., and
Claustre, H.: Relationships between the surface concentration of particulate
organic carbon and optical properties in the eastern South Pacific and
eastern Atlantic Oceans, Biogeosciences, 5, 171–201,
10.5194/bg-5-171-2008, 2008.Strubinger Sandoval, P., Dall'Olmo, G., Rasse, R., Ross, J., and Haines, K.:
Uncertainties of particulate organic carbon concentrations in the
mesopelagic zone of the Atlantic ocean, Open Res. Eur., 1, 43, 10.12688/openreseurope.13395.2, 2021.Stukel, M. R., Ohman, M. D., Kelly, T. B., and Biard, T.: The roles of
suspension-feeding and flux-feeding zooplankton as gatekeepers of particle
flux into the mesopelagic ocean in the Northeast Pacific, Front. Mar. Sci.,
6, 1–16, 10.3389/fmars.2019.00397, 2019.Takeuchi, M., Doubell, M. J., Jackson, G. A., Yukawa, M., Sagara, Y., and
Yamazaki, H.: Turbulence mediates marine aggregate formation and destruction
in the upper ocean, Sci. Rep., 9, 1–8, 10.1038/s41598-019-52470-5, 2019.Terzić, E., Lazzari, P., Organelli, E., Solidoro, C., Salon, S., D'Ortenzio, F., and Conan, P.: Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry, Biogeosciences, 16, 2527–2542, 10.5194/bg-16-2527-2019, 2019.Tonani, M., Pinardi, N., Dobricic, S., Pujol, I., and Fratianni, C.: A high-resolution free-surface model of the Mediterranean Sea, Ocean Sci., 4, 1–14, 10.5194/os-4-1-2008, 2008.Trudnowska, E., Lacour, L., Ardyna, M., Rogge, A., Irisson, J. O., Waite, A.
M., Babin, M., and Stemmann, L.: Marine snow morphology illuminates the
evolution of phytoplankton blooms and determines their subsequent vertical
export, Nat. Commun., 12, 2816, 10.1038/s41467-021-22994-4, 2021.Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 3643–3708, 10.5194/gmd-13-3643-2020, 2020.Twardowski, M. S., Boss, E., Macdonald, J. B., Pegau, W. S., Barnard, A. H.,
and Zaneveld, J. R. V: A model for estimating bulk refractive index from the
optical backscattering ratio and the implications for understanding particle
composition in case I and case II waters, J. Geophys. Res., 106,
14129–14142, 10.1029/2000JC000404, 2001.Uitz, J., Claustre, H., Morel, A., and Hooker, S. B.: Vertical distribution
of phytoplankton communities in open ocean: An assessment based on surface
chlorophyll, J. Geophys. Res., 111, C08005, 10.1029/2005JC003207, 2006.
Ulloa, O., Sathyendranath, S., and Platt, T.: Effect of the particle-size
distribution on the backscattering ratio in seawater, Appl. Opt., 33,
7070, 10.1364/ao.33.007070, 1994.van Sebille, E., Griffies, S. M., Abernathey, R., Adams, T. P., Berloff, P.,
Biastoch, A., Blanke, B., Chassignet, E. P., Cheng, Y., Cotter, C. J.,
Deleersnijder, E., Döös, K., Drake, H. F., Drijfhout, S., Gary, S.
F., Heemink, A. W., Kjellsson, J., Koszalka, I. M., Lange, M., Lique, C.,
MacGilchrist, G. A., Marsh, R., Mayorga Adame, C. G., McAdam, R., Nencioli,
F., Paris, C. B., Piggott, M. D., Polton, J. A., Rühs, S., Shah, S. H.
A. M., Thomas, M. D., Wang, J., Wolfram, P. J., Zanna, L., and Zika, J. D.:
Lagrangian ocean analysis: Fundamentals and practices, Ocean Model., 121,
49–75, 10.1016/j.ocemod.2017.11.008, 2018.Vaulot, D., Eikrem, W., Viprey, M., and Moreau, H.: The diversity of small
eukaryotic phytoplankton (≤ 3 µm) in marine ecosystems, FEMS
Microbiol. Rev., 32, 795–820, 10.1111/j.1574-6976.2008.00121.x,
2008.Volk, T. and Hoffert, M. I.: Ocean carbon pumps: Analysis of relative strengths and efficiencies in ocean‐driven atmospheric CO2 changes, The carbon cycle and atmospheric CO2: natural variations Archean to present, Geophysical Monograph Series, edited by: Sundquist, E. T. and Broecker, W. S., 32, 99–110, 10.1029/GM032p0099, 1985.Weber, T., Cram, J. A., Leung, S. W., DeVries, T., and Deutsch, C.: Deep
ocean nutrients imply large latitudinal variation in particle transfer
efficiency, P. Natl. Acad. Sci. USA, 113, 8606–8611, 10.1073/pnas.1604414113,
2016.Wong, A., Keeley, R., Carval, T., and Argo Data Management Team: Argo Quality
Control Manual for CTD and Trajectory Data, 10.13155/33951, 2021.Woodstock, M. S., Sutton, T. T., Frank, T., and Zhang, Y.: An early warning
sign: trophic structure changes in the oceanic Gulf of Mexico from
2011–2018, Ecol. Model., 445, 109509, 10.1016/j.ecolmodel.2021.109509, 2021.