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Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the ĝ€unit of accountingĝ€ in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2ĝ€-μm in diameter), nanophytoplankton (2-20ĝ€-μm) and microphytoplankton (20-50ĝ€-μm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global <span styleCombining double low line"" classCombining double low line"text">SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield ĝ-1/4 ĝ€-0.25ĝ€-Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter N o which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the N o parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.
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Ocean Sci., 12, 561–575, 2016
© Author(s) 2016. CC Attribution 3.0 License.
Carbon-based phytoplankton size classes retrieved via ocean color
estimates of the particle size distribution
Tihomir S. Kostadinov1, Svetlana Milutinovi´
c2, Irina Marinov2, and Anna Cabré2
1Department of Geography and the Environment, 28 Westhampton Way, University of Richmond, Richmond, VA 23173, USA
2Department of Earth & Environmental Science, Hayden Hall, University of Pennsylvania,
240 South 33rd St., Philadelphia, PA 19104, USA
Correspondence to: Tihomir S. Kostadinov (
Received: 12 March 2015 – Published in Ocean Sci. Discuss.: 6 May 2015
Revised: 16 March 2016 – Accepted: 30 March 2016 – Published: 18 April 2016
Abstract. Owing to their important roles in biogeochemi-
cal cycles, phytoplankton functional types (PFTs) have been
the aim of an increasing number of ocean color algorithms.
Yet, none of the existing methods are based on phytoplankton
carbon (C) biomass, which is a fundamental biogeochemical
and ecological variable and the “unit of accounting” in Earth
system models. We present a novel bio-optical algorithm to
retrieve size-partitioned phytoplankton carbon from ocean
color satellite data. The algorithm is based on existing meth-
ods to estimate particle volume from a power-law particle
size distribution (PSD). Volume is converted to carbon con-
centrations using a compilation of allometric relationships.
We quantify absolute and fractional biomass in three PFTs
based on size – picophytoplankton (0.5–2µm in diameter),
nanophytoplankton (2–20µm) and microphytoplankton (20–
50µm). The mean spatial distributions of total phytoplank-
ton C biomass and individual PFTs, derived from global
SeaWiFS monthly ocean color data, are consistent with cur-
rent understanding of oceanic ecosystems, i.e., oligotrophic
regions are characterized by low biomass and dominance of
picoplankton, whereas eutrophic regions have high biomass
to which nanoplankton and microplankton contribute rela-
tively larger fractions. Global climatological, spatially in-
tegrated phytoplankton carbon biomass standing stock esti-
mates using our PSD-based approach yield 0.25Gt of C,
consistent with analogous estimates from two other ocean
color algorithms and several state-of-the-art Earth system
models. Satisfactory in situ closure observed between PSD
and POC measurements lends support to the theoretical ba-
sis of the PSD-based algorithm. Uncertainty budget analy-
ses indicate that absolute carbon concentration uncertainties
are driven by the PSD parameter Nowhich determines parti-
cle number concentration to first order, while uncertainties in
PFTs’ fractional contributions to total C biomass are mostly
due to the allometric coefficients. The C algorithm presented
here, which is not empirically constrained a priori, partitions
biomass in size classes and introduces improvement over the
assumptions of the other approaches. However, the range
of phytoplankton C biomass spatial variability globally is
larger than estimated by any other models considered here,
which suggests an empirical correction to the Noparameter
is needed, based on PSD validation statistics. These corrected
absolute carbon biomass concentrations validate well against
in situ POC observations.
1 Introduction
Oxygenic photosynthesis by marine phytoplankton is re-
sponsible for fixing 50Gt Cyr1(Field et al., 1998; Carr
et al., 2006) and powers the biological pump, which is
an important part of the carbon cycle (e.g., Siegel et al.,
2014). Phytoplankton are grouped into phytoplankton func-
tional types (PFTs) according to their differing biogeochem-
ical roles (IOCCG, 2014). Since size is a master trait (e.g.,
Marañon, 2015), phytoplankton size classes (PSCs) are of-
ten used to define the PFTs (e.g., Le Quéré et al., 2005).
Commonly, three PSCs are defined (Sieburth et al., 1978)
– picophytoplankton (<2µm in diameter), nanophytoplank-
ton (2–20µm) and microphytoplankton (>20µm), referred
to as pico-, nano- and microplankton henceforth for brevity.
The global spatiotemporal distribution of the PFTs both in-
Published by Copernicus Publications on behalf of the European Geosciences Union.
562 T. S. Kostadinov et al.: Carbon-based phytoplankton size classes
fluences (e.g., Falkowski and Oliver, 2007) and can be in-
fluenced by (e.g., Marinov et al., 2013; Cabré et al., 2014)
climate (and shorter-term processes such as seasonality; e.g.,
Kostadinov et al., 2016a). Therefore, detailed characteriza-
tion of the structure and function of oceanic ecosystems (i.e.,
descriptive and predictive understanding of the PFTs) is re-
quired as a crucial component of Earth system and climate
Operational quantification of the PFTs on the required spa-
tiotemporal scales can only be achieved via remote sens-
ing. Remote-sensing reflectance as a function of wavelength,
Rrs(λ), quantifies ocean color; the canonical derived variable
has been chlorophyll concentration (Chl) in surface waters
(e.g., O’Reilly et al., 1998; Maritorena et al., 2002), inter-
preted as a proxy for phytoplankton biomass. However, to-
tal Chl does not provide a full description of the state of
the ecosystem, since physiological acclimation to differing
light levels can cause the ratio of intracellular Chl to car-
bon (C) concentrations to change, confounding interpretation
of changes in Chl (Geider et al., 1987, 1998; Behrenfeld et
al., 2005). It is carbon biomass in the living phytoplankton
that is the variable of more direct relevance to the carbon
cycle, other biogeochemical cycles and climate. It is also
the tracer variable most commonly used in biogeochemical
routines of climate models (e.g., Gregg, 2008; Dunne et al.,
2013). In addition, a more complete characterization of an
oceanic ecosystem also necessitates partitioning of the car-
bon biomass into the different PFTs comprising the ecosys-
tem. The Chl:C ratio itself can be used as a proxy for phys-
iological status and independent assessments of Chl and C
allow for the building of carbon-based productivity models
(Behrenfeld et al., 2005; Westberry et al., 2008).
In light of the above, recent ocean color algorithm devel-
opments have provided products beyond Chl. First, multi-
ple algorithms for the estimation of various PFTs have been
developed (IOCCG, 2014). Some algorithms retrieve multi-
ple PFT groups using differential absorption (Bracher et al.,
2009) or second-order anomalies of the reflectance spectra
(Alvain et al., 2008). Others (e.g., Brewin et al., 2010; Hi-
rata et al., 2011; Uitz et al., 2006) are based on total (Chl)
abundance and the ecological premise that smaller cells are
associated with oligotrophic conditions whereas larger cells
are associated with eutrophic conditions (Chisholm, 1992).
Yet another class of algorithms relies on various spectral fea-
tures, either absorption (Ciotti and Bricaud, 2006; Mouw and
Yoder, 2010; Roy et al., 2013), or backscattering (Kostadinov
et al., 2009, 2010) or both (Fujiwara et al., 2011). A summary
of the available algorithms and their technical basis can be
found in IOCCG (2014) and Hirata (2015). Of particular im-
portance is that none of the existing algorithms retrieve C or
base their PFT/PSC retrievals on total or fractional C content
per PFT. Second, algorithms have been developed to retrieve
particulate organic carbon (POC, e.g., Stramski et al., 2008
– henceforth, S08). However, these are empirical band–ratio
algorithms the output of which is expected to be tightly cor-
related to Chl, which is derived in a similar way. The retrieval
of just the living phytoplankton carbon concentration repre-
sents significant progress (Behrenfeld et al., 2005 – hence-
forth, B05). However, the B05 method is based on a constant
empirical scaling with particulate backscattering at 440nm
(bbp(440)) which does not take into account the effects of
variable particle size distributions (PSDs). Changes in the
PSD will change the backscattering per unit C biomass due
to different scattering efficiencies (e.g., Stramski and Kiefer,
1991; Kostadinov et al., 2009).
Recent advances allow for the quantification of the PSD
from ocean color satellite data and thus the estimation of
particulate volume in any size class (Kostadinov et al., 2009
– henceforth, KSM09; Kostadinov et al., 2010). Hence-
forth, this PSD algorithm is referred to as the KSM09 algo-
rithm. Here, we leverage the KSM09 algorithm and an exist-
ing compilation of allometric relationships that link cellular
C content to cellular volume (Menden-Deuer and Lessard,
2000 – henceforth, MDL2000), in order to (1) estimate to-
tal phytoplankton C biomass using the power-law PSD pa-
rameters as input and (2) recast the volume-based PSCs of
the KSM09 algorithm in terms of C biomass instead of bio-
volume. The effects of variable PSD have been taken into
account for the first time, relaxing the assumption of a con-
stant backscattering to carbon relationship. Importantly, to
our knowledge this is the first attempt to provide size class
partitioning of phytoplankton C biomass from space. We
first present the methodology and apply the algorithm to
SeaWiFS global monthly reflectance data, focusing on clima-
tological patterns and comparison with existing phytoplank-
ton carbon estimates and Earth system model results. We
then assess global mixed-layer phytoplankton biomass stock
and compare to existing estimates. We quantify partial un-
certainties on a per-pixel basis by propagating existing input
parameter uncertainties to the C-based products. In addition,
we present an in situ POC–PSD closure analysis as verifi-
cation of the method, propose an empirical correction to the
algorithm to improve absolute carbon estimates and validate
our results using in situ POC measurements.
2 Data and methods
2.1 Estimation of carbon biomass using PSD retrievals
2.1.1 Step 1: retrieval of suspended particulate volume
from ocean color remote sensing data
We first quantify the volume concentration of suspended par-
ticulate matter from ocean color data by applying the KSM09
algorithm to estimate the parameters of an assumed power-
law particle size distribution. These parameters are retrieved
using lookup tables (LUTs) constructed using Mie theory of
scattering (Mie, 1908). The LUTs relate the spectral shape
and magnitude of the particulate backscattering coefficient
at blue–green wavelengths (bbp(λ) (m1)) to the power-law
Ocean Sci., 12, 561–575, 2016
T. S. Kostadinov et al.: Carbon-based phytoplankton size classes 563
slope ξ(unitless) of the PSD and the differential number
concentration of suspended particles at a reference diameter
(here, Do=2 µm), No(m4) (Junge, 1963; Boss et al., 2001;
N(D) =NoD
In Eq. (1), D(m) is the equivalent spherical diameter (ESD)
(Jennings and Parslow, 1988) and N(D) (m4) is the dif-
ferential number concentration of particles of diameter D.
Volume concentration (m3of particles/m3seawater) can be
computed from the PSD as (Kostadinov et al., 2010):
Dmin πD3
DoξdD. (2)
Note that Eq. (2) is an estimate of the volume of all backscat-
tering in-water constituents in a given size range because the
KSM09 algorithm uses total backscattering for the retrieval.
Even though the power-law PSD is considered a simple two-
parameter model, in reality it has four parameters, because
in practical applications the upper and lower limits of inte-
grals such as Eq. (2) need to be known (Boss et al., 2001).
Assuming biogenic origin of scattering particles, Kostadinov
et al. (2010) developed a novel method of estimating three
PSCs, defining each class as its fractional contribution to to-
tal biovolume.
2.1.2 Step 2: retrieval of size-partitioned absolute and
fractional phytoplankton carbon biomass
Estimation of carbon concentration follows the methodology
first outlined in Kostadinov (2009). The volume-to-carbon
allometric relationships compiled by MDL2000 are used to
quantify POC by converting the volume estimates of Eq. (2)
to C concentration. The relationships in MDL2000 have the
general form:
Ccell =aV b
where Ccell is cellular carbon content (pgCcell1), aand b
are group-specific constants and Vcell is cell volume (µm3).
Incorporating the allometric relationship of Eq. (3) into
Eq. (2) yields an estimate of particulate carbon mass concen-
tration (i.e., POC) in a given size range, Dmin to Dmax. The
carbon biomass of living phytoplankton only (C, (mgm3))
can then be estimated by multiplication by 1/3:
109a 1018π D 3
DoξdD. (4)
The factor of 1/3 is used because it is approximately in
the middle of the published range for the phytoplankton
C:POC ratio in ocean regions of variable trophic status
(0.14–0.49) (B05; DuRand et al., 2001; Eppley et al., 1992;
Gundersen et al., 2001; Oubelkheir et al., 2005). The fac-
tors 109and 1018 are applied in Eq. (4) for conversion
from picogram (Eq. 3) to milligram of C and from m3to
µm3, respectively. The formulation of Eq. (4) allows phyto-
plankton carbon biomass to be estimated for any size range.
Here, we partition the biomass in three classical phytoplank-
ton size classes (PSCs, Sieburth et al., 1978): picoplank-
ton (0.5 µm D2µm), nanoplankton (2 µm D20µm)
and microplankton (20µmD50µm). The three PSCs
are expressed as relative fractions of total phytoplankton C
biomass, by dividing the PSC’s biomass by total biomass
in the 0.5–50µm range. This expression of the PSCs is
a recast of the volume-fraction-based PSCs of KSM09 in
terms of carbon biomass. Further details of application of the
MDL2000 allometric relationships are given in Sect. S1.1 in
the Supplement.
2.2 Input ocean color satellite data
Global mapped monthly composites of remote sensing
reflectance Rrs(λ) (sr1) nominally at 412, 443, 490,
510 and 555nm, measured by the Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) (reprocessing R2010.0)
were downloaded from the NASA Ocean Biology Pro-
cessing Group (OBPG) Ocean Biology Distributed Ac-
tive Archive Center (OB.DAAC) at http://oceandata.sci. (NASA
Goddard Spaceflight Center, 2010). The data have a nominal
resolution of 9km and are mapped to an equidistant cylin-
drical projection. Measurements were available for the pe-
riod September 1997–December 2010, with the exception of
a few months after 2007, when technical problems occurred.
The monthly Rrs(λ) maps were used to retrieve the spec-
tral particulate backscattering coefficient (bbp(λ), (m1), λ
same as for the input reflectances), using the algorithm of
Loisel and Stramski (2000) and Loisel et al. (2006) (hence-
forth, the LAS2006 algorithm), with a solar zenith angle
(SZA) of 0because the input Rrs(λ) are fully normalized.
The spectral slope of bbp(λ), η, was calculated using a lin-
ear regression on the log-transformed data at the 490, 510
and 555nm bands. The KSM09 algorithm (Sect. 2.1.1) was
then applied to ηand bbp at 443nm in order to obtain the
PSD parameters ξand No, which were subsequently used in
Eq. (4) (specifically as shown in Eq. (S1) in the Supplement)
to obtain monthly 9km maps of total and PSC-partitioned
absolute and fractional C biomass.
2.3 Additional methods information
Additional details of the methodology are provided in the
Supplement Sect. S1. Specifically, Sect. S1.1 presents details
of the application of the MDL2000 allometric relationships.
Total phytoplankton carbon was also derived from the output Ocean Sci., 12, 561–575, 2016
564 T. S. Kostadinov et al.: Carbon-based phytoplankton size classes
of a group of Earth system simulations from the recent Cou-
pled Model Intercomparison Project CMIP5 (Taylor et al.,
2012). Details of the methods are provided in Sect. S1.2. Sec-
tion S1.3 presents the methods for an entirely in situ POC–
PSD closure analysis. Section S1.4 details the propagation
of uncertainty to the carbon-based products and the compos-
ite (averaged) images calculated from monthly input data.
Section S1.5 provides details of algorithm output analyses
and additional ancillary data sets used. Importantly, details
are presented on the computation of global carbon biomass
stock within the mixed layer, using the PSD/allometric phy-
toplankton carbon retrievals presented here. Section S1.6 de-
scribes the methodology used for validation of total phyto-
plankton carbon using matchups between empirically cor-
rected (see Sect. 3.7) SeaWiFS retrievals and in situ POC
measurements provided by the SEABASS database. Results
of this validation are discussed in Sect. 3.7.
3 Results and discussion
3.1 Global phytoplankton carbon biomass from
SeaWiFS observations and CMIP5 models
The mission climatology of total phytoplankton carbon (C)
(Fig. 1a) indicates that biomass is lowest in the oligotrophic
subtropical gyres, while higher values occur in more eu-
trophic regions, such as the equatorial and eastern-boundary
currents, other upwelling regions and high-latitude oceans.
This general pattern corresponds to first order to the climato-
logical Chl spatial patterns (Fig. S2 in the Supplement) and
is consistent with current oceanic ecosystem understanding
(e.g., Longhurst, 2007). Comparisons with two existing satel-
lite methods (the B05 values, Fig. 1b; the S08 POC retrievals
divided by 3, Fig. 1c) reveal that the PSD-based approach for
quantifying C biomass results in a significantly wider range
of spatial variability, as illustrated also by the histograms in
Fig. S3. The PSD-based biomass estimates are the lowest in
the subtropical oligotrophic gyres (by about an order of mag-
nitude) and generally highest (generally by less than an order
of magnitude) in more productive areas. The three methods
are in relatively good agreement in the Pacific equatorial up-
welling region. A considerable difference also exists between
the B05 and the S08-based values – the former vary the least
spatially, mostly due to relatively high biomass estimates in
the subtropical oligotrophic gyres.
While it is likely that the PSD-based values in the olig-
otrophic gyres are underestimated and values in some eu-
trophic areas are overestimated, a global validation with con-
current field measurements of phytoplankton C biomass (to-
tal or partitioned) is not feasible at present since in situ ana-
lytical measurements of phytoplankton carbon are difficult
and made possible only recently by emerging techniques
(Graff et al., 2012, 2015). The S08 method is developed with
in situ POC and reflectance data, and the constant conversion
factor in B05 is picked empirically, so these algorithms are
Figure 1. SeaWiFS mission composite mean (September 1997–
December 2010) of total phytoplankton carbon biomass (mg Cm3
in log10 space), derived from monthly data using (a) the allomet-
ric PSD method presented here, (b) the method of Behrenfeld et
al. (2005) and (c) the Stramski et al. (2008) POC retrieval, mul-
tiplied by 1/3. (d) Ensemble mean of the CMIP5 models’ (Ta-
ble S3) climatologies (1990–2010) of the surface phytoplankton
carbon biomass (mg m3). The white contours are the 0.08 mgm3
isoline of Chl. Both model and satellite composite means are com-
puted from monthly data in linear space.
Ocean Sci., 12, 561–575, 2016
T. S. Kostadinov et al.: Carbon-based phytoplankton size classes 565
designed a priori to match in situ measurements. The method
presented here is derived mostly from theory (apart from the
allometric relationships) and is not subject to such constraints
(Sect. 3.6). Importantly, even if the absolute carbon concen-
tration values are inaccurate, the PSCs expressed as percent
contribution to C biomass should still be reliable and subject
to much less uncertainty (Sects. 3.3 and 3.6). The PSC frac-
tions can also be used with other absolute carbon estimates.
An empirical correction to address the spatial exaggeration
of absolute carbon concentrations is presented in Sect. 3.7
together with a validation for corrected total phytoplankton
carbon estimates using in situ POC measurements.
Some degree of exaggeration of the global range of values
of the PSD-based mean algal biomass field (Fig. 1a) as com-
pared to the approach of B05 (Fig. 1b) is expected because
the former relaxes the assumption of a constant conversion
factor in B05 by taking into account the varying backscat-
tering per unit cell volume and carbon. According to Mie
theory calculations, bbp(λ) normalized to volume of parti-
cles in the 0.5–50µm range is 3 orders of magnitude higher
when the PSD slope ξ=6, as compared to when ξ=3 (not
shown). Thus, the same backscattering coefficient will be at-
tributed to less particle total volume if the particles are rela-
tively smaller in size (higher ξ). Since PSD slopes are highest
in the oligotrophic gyres (KSM09), the PSD-based approach
is expected to exhibit smaller total volume of particles and
thus smaller carbon concentrations as compared to the direct
scaling with bbp(443) in B05.
The CMIP5 models’ ensemble mean of phytoplankton C
biomass (Fig. 1d) is independent of the satellite data sets
(refs. in Table S3) and resembles the S08 POC-based esti-
mate the most in spatial patterns and values, with somewhat
lower values in the subtropical gyres, but not quite as low as
the PSD-based method (Fig. 1a). Notably, the models yield
higher values in the Pacific equatorial upwelling zone than
any of the satellite data sets.
3.2 Global phytoplankton biomass stock
Estimates of total global phytoplankton biomass stock
(Sect. S1.5) from the three satellite methods and the CMIP5
models (using the SeaWiFS mission climatological fields)
are remarkably consistent (Fig. 2), yielding 0.2–0.3GtC
standing biomass stock (1 gigaton (Gt)=1012 kg). Biomass
in open ocean areas (with the continental shelves excluded)
accounts for most global biomass according to all estimates.
However, the models attribute very little biomass to the
shelves as compared to the satellite methods, which is proba-
bly due to the lower underlying spatial resolution of the mod-
els. Since satellite algorithms are generally subject to higher
uncertainties in coastal zones, it is best to develop technology
to measure C biomass in situ (Graff et al., 2012, 2015) and
inform both satellite algorithms and biogeochemical models.
The satellite estimates in Fig. 2 are based on mission com-
posites and are globally representative since 99–100% of the
Allometric Behrenfeld Stramski CMIP5 models
Phytoplankton carbon, Gt C
Total area: 99.71 %
Area (excluding shelves): 98.74 %
Global phytoplankton carbon biomass stock
Figure 2. Global spatially integrated mixed-layer phytoplankton
carbon biomass stock (Gt C), as estimated with three different satel-
lite algorithms (as in Fig. 1a–c) from the SeaWiFS mission com-
posite and from the CMIP5 model ensemble mean (Fig. 1d), using
the same climatological MLD estimate for all estimates. Horizon-
tal black lines within each bar on all panels represent the estimate
when continental shelves (<200 m depth) are excluded. The sum of
the areas of valid pixels used in the estimates is given as a percent-
age of total ocean area (3.608×108km2)and area excluding the
shelves (3.4×108km2), respectively.
respective ocean areas participate in the estimate (Fig. 2).
However, some bias remains because high latitudes are ob-
servable only in summer months (Fig. S4). Monthly esti-
mates of the global phytoplankton carbon stock are discussed
in Supplement Sect. S2. It is notable that the novel PSD-
based method is not empirically restricted or tuned a priori
and yields reasonable estimates. Admittedly, this global spa-
tially integrated result may be fortuitous due to cancellation
of uncertainties with opposite signs in the oligotrophic vs.
eutrophic areas, so it is not claimed that this result necessar-
ily constitutes algorithm verification (also see Sect. 3.7 and
Fig. S7). Previous estimates of global phytoplankton C stock
use different methodologies and range from 0.30 to 0.86 GtC
(Antoine et al., 1996; Behrenfeld and Falkowski, 1997b; Le
Quéré et al., 2005). Further discussion of these estimates and
the effects of MLD assumptions is provided in Sect. S2.
3.3 Size-partitioned biomass
Maps of absolute C biomass partitioned among picoplankton
(Fig. 3a), nanoplankton (Fig. 3b) and microplankton (Fig. 3c)
reveal a general global spatial pattern for all three size classes
similar to the global total distribution (Fig. 1a), namely the
lowest biomass values are encountered in the oligotrophic
gyres, whereas higher latitudes, coastal and upwelling areas
exhibit higher biomass. According to contemporary under-
standing of oceanic ecosystems (e.g., Uitz et al., 2010) we Ocean Sci., 12, 561–575, 2016
566 T. S. Kostadinov et al.: Carbon-based phytoplankton size classes
Figure 3. SeaWiFS mission composite (September 1997–
December 2010) of size-partitioned phytoplankton carbon biomass,
C (mgC m3in log10 space) estimated with the PSD/allometric
method for (a) picoplankton, (b) nanoplankton and (c) microplank-
ton. The white contours are the 0.08mgm3isoline of Chl. Note
that the color scale is different from that of Fig. 1.
expect large cells (such as diatoms) to be opportunistic, re-
sponding via strong localized blooms to changes in nutrient
inputs or grazing. This opportunistic response, which con-
trasts the smaller picoplankton adaptation to constant envi-
ronmental conditions, explains the widely different spatial
and temporal variability of these groups. Accordingly, we
find that the range of spatial variability of carbon for pi-
coplankton (<3 orders of magnitude) is a lot smaller than
the range of variability for nanoplankton (4) and espe-
cially microplankton (5 orders of magnitude) (Fig. S5).
Negligible biomass is found in microplankton for most of the
ocean area, except for eutrophic areas characterized by sea-
sonal blooms and/or higher overall productivity such as the
Equatorial Upwelling, whereas picoplankton are more glob-
ally ubiquitous.
The fractional contribution of each PSC to total C biomass
reveals the climatological dominance of each group in the
Figure 4. SeaWiFS mission composite (September 1997–
December 2010) of percentage contributions of three PSCs to total
phytoplankton carbon biomass, estimated with the PSD/allometric
method: (a) picoplankton, (b) nanoplankton and (c) microplank-
ton. This mission composite is computed by averaging the fractional
contributions to C biomass for each available month (Fig. S4). The
white contours are the 0.08 mgm3isoline of Chl.
various oceanic regions (Fig. 4). Picoplankton emerge as the
dominant size group in oligotrophic areas (Fig. 4a), because
their large cellular surface-area-to-volume ratio enables them
to acquire scarce nutrients very efficiently (Agawin et al.,
2000; Falkowski and Oliver, 2007). By contrast, larger phy-
toplankton contribute relatively more biomass in the regions
where nutrients are generally more abundant, because they
can take up nutrients at a faster rate and store them in-
side vacuoles as a reserve for less favorable spells (e.g.,
Eppley and Peterson, 1979; Chisholm, 1992; Falkowski et
al., 1998; Falkowski and Oliver, 2007). Together, nano- and
microplankton achieve dominance (between 50 and 90%)
along the Antarctic coastline, in much of the zone between
40S and 50S (in the South Atlantic, the southwest-
ern Indian Ocean, southeast of Australia and east of New
Zealand), along the eastern boundaries of the Pacific and At-
Ocean Sci., 12, 561–575, 2016
T. S. Kostadinov et al.: Carbon-based phytoplankton size classes 567
lantic oceans, in the northwestern Arabian Sea and almost
everywhere north of 40N.
The total biomass patterns in the Southern Ocean (Fig. 1a)
are characterized by more or less continuous bands of high
biomass (a) along the frontal structures around 40–45S, a
transitional region from the iron-limited upwelling regime
in the south to the nitrate-limited downwelling subtropical
gyres in the north and (b) in the marginal sea ice regions
next to the Antarctic continent, where continental iron (Fe)
inputs likely result in biomass and production spikes during
the spring and summer. Both these large-biomass bands tend
to be dominated by the larger opportunistic groups of nano-
and microplankton (Fig. 4b–c). In between these two bands
of high production we find a relatively lower biomass band
from roughly 50–60S, where picoplankton thrive (Fig. 4a).
The lower total biomass here is probably due to a combina-
tion of iron limitation and deep summertime mixed layers,
resulting in strong light limitation during the growing sea-
son. Large areas in the Southern Hemisphere are character-
ized by lower total (Fig. 1a) and group-specific C biomass
(Fig. 3a–c), as compared to the Northern Hemisphere. This
interhemispheric disproportionality is dominated by high-
latitude summer values (not shown) and is in agreement with
findings that the Southern Ocean sustains relatively low phy-
toplankton biomass, in spite of high ambient macronutrient
concentrations (e.g., Dugdale and Wilkerson, 1991).
We emphasize that our methodology is unique in its ability
to partition phytoplankton carbon biomass in any desired size
classes. It essentially represents a recast of the biovolume-
based PSC/PFT definition of Kostadinov et al. (2010) that is
also based on the KSM09 PSD retrieval. The effect of re-
casting to carbon using the allometric relationships is illus-
trated in Fig. 5, and further discussion is provided in Sect. S3.
Comparison with other PFT algorithms is outside the scope
of this work, but summaries of the available algorithms can
be found in IOCCG, 2014 and Hirata, 2015. Kostadinov et
al. (2016a) compare phenological parameters among 10 PFT
algorithms and 7 CMIP5 models as part of the PFT Intercom-
parison Project (Hirata et al., 2012; Hirata, 2015).
3.4 In situ POC–PSD closure
As a verification of the phytoplankton C retrieval methodol-
ogy presented here, we test the closure between in situ deter-
minations of POC and the PSD; specifically, we compare two
different ways to compute phytoplankton carbon: (1) using a
chemical POC determination, divided by 1/3, and (2) using
Coulter counter PSD measurements in the same way as satel-
lite PSDs (Sect. 2.1). Two different sets of integration limits
(Eq. 4) for the power-law PSD are tested: 0.5–50µm (Fig. 6a)
and 0.7–200µm (Fig. 6b). The first set of limits matches the
operational satellite carbon algorithm (Table S1), and the sec-
ond – the operational POC measurement. Both closure re-
gressions are highly significant (p< 0.01), indicating that the
PSD method can reasonably predict carbon content of parti-
PSD slope ξ
2.5 3 3.5 4 4.5 5 5.5 6
Percent volume or C
pico (% C)
nano (% C)
micro (% C)
pico (% V)
nano (% V)
micro (% V)
Figure 5. Fractional contribution of the three PSCs, picoplankton
(red), nanoplankton (green) and microplankton (blue), to total phy-
toplankton carbon biomass (solid lines) and to total biovolume con-
centration (dashed lines), as functions of the PSD slope ξ. Limits of
integration are the operational limits as indicated in Figs. 3 and 4,
and Sect. 2.1.2 (also see Sect. S1.1). Also shown is the histogram of
PSD slopes ξfrom the mapped image of SeaWiFS mission climatol-
ogy (September 1997–December 2010), normalized to the highest
count bin.
cles in natural seawater samples. However, the smaller size
limits (Fig. 6a) exhibit a better R2value (in log10 space),
while the slope, bias and rms are better for the larger limits
(Fig. 6b). Clearly, the PSD method is sensitive to the chosen
limits of integration, and the satellite operational limits un-
derestimate the POC values. Better agreement is found when
the 0.7–200µm limits are used, (matching the nominal pore
size of the filters used for the POC measurements).
Kostadinov et al. (2012) similarly found a relatively good
agreement between in situ POC and PSD measurements for
a semi-arid coastal site – the Santa Barbara Channel (SBC)
off the coast of California. Both sets of results suggest that
reasonable internal agreement exists between these two very
different methods of in situ assessment of living carbon, even
in optically complex coastal sites such as the SBC, where
terrigenous material can contribute to the PSD and affect op-
tical properties (Toole and Siegel, 2001; Otero and Siegel,
2004; Kostadinov et al., 2007). This PSD-POC closure anal-
ysis uses no satellite data or bio-optical algorithms and is
thus is not subject to the associated uncertainties, e.g., mis-
match of the scales of measurement. However, the estimation
of phytoplankton carbon from the total PSD or from POC
in situ does share some uncertainties and limitations as the
satellite algorithm, e.g., the PSD does not always have to con-
form closely to a power law (Reynolds et al., 2010), although
this is assumed here. Section 3.6 discusses such assumptions
and uncertainties in detail. Ocean Sci., 12, 561–575, 2016
568 T. S. Kostadinov et al.: Carbon-based phytoplankton size classes
Figure 6. Matchups between phytoplankton carbon estimated by
applying allometric relationships to in situ measurements of the
PSD (xaxis) and by multiplying in situ chemical POC determi-
nations by 1/3 (yaxis). Measurements are coincident in time and
space and were conducted on AMT cruises 2, 3 and 4. Two differ-
ent limits of integration are used for the allometric estimate: (a) 0.5–
50µm, as in the operational satellite algorithm presented here, and
(b) 0.7–200µm, matching the GF /F filter pore size used in POC
3.5 Relationship between phytoplankton carbon
biomass and chlorophyll concentration
The spatial distributions of Chl (Fig. S2) and total C biomass
(Fig. 1a) and nano- and microplankton fractions (Fig. 4b–c)
suggest strong positive correlations between these variables,
whereas the picoplankton fraction (Fig. 4a) is negatively cor-
related with Chl. The bivariate histogram of Chl vs. total C
biomass (Fig. 7a) confirms this strong correlation. However,
for a given Chl value, total biomass can vary considerably
Figure 7. Smoothed bivariate histograms of chlorophyll concen-
tration and (a) total phytoplankton C biomass, (b) picoplankton,
(c) nanoplankton and (d) microplankton fractional contributions
to the total algal C biomass. The histograms were computed from
the global mission composite of standard mapped SeaWiFS obser-
vations (September 1997–December 2010). The colors indicate the
number of pixels that fall into each bivariate bin. The counts are
shown in linear space, whereas the bins themselves are in logarith-
mic space. Data from continental shelf regions (<200 m depth) are
(rarely, over an order of magnitude). For example, for the
common Chl value of 0.25mg m3, biomass frequently
varies between 10 and 30mg m3and less frequently be-
tween 1 and 100mg m3. Although some of this spread may
stem from underlying uncertainties in C biomass (Sect. 3.6)
and Chl (Gregg et al., 2009; Sathyendranath, 2000), some of
it is likely attributable to ecological variability that is cap-
tured by estimating C biomass and taking into account the
PSD, indicating that the biomass retrieval contains new in-
formation and is not merely a deterministic function of Chl.
Indeed to first order Chl can serve as an indicator of phyto-
plankton C biomass (e.g., Behrenfeld and Falkowski, 1997a),
but their relationship can also be affected by physiologi-
cal changes in Chl without accompanying biomass changes
(Behrenfeld et al., 2005, 2006) in response to variability in
the ambient levels of light (i.e., photoacclimation), nutrients
and temperature (e.g., Geider et al., 1998). Notably, the his-
togram of Fig. 7a exhibits a pronounced sigmoidal shape in
logarithmic space. At low and medium Chl values, increases
in Chl do not lead to large biomass increases, which is consis-
tent with the idea that Chl variability in oligotrophic areas is
due mostly to physiological adaptation, rather than biomass
growth and loss. Conversely at higher Chl values in more
eutrophic areas, Chl variability is accompanied by biomass
changes (B05; Behrenfeld et al. (2006); Siegel et al., 2013).
B05 also observe that for low Chl, “background” low values
of bbp(440) do not covary strongly with Chl; then for higher
Chl values there is a positive linear correlation which tends
Ocean Sci., 12, 561–575, 2016
T. S. Kostadinov et al.: Carbon-based phytoplankton size classes 569
to level off a bit for high Chl values (see their Fig. 1), broadly
consistent with the sigmoid curve of Fig. 7a. This confirms
their (and our) choice to use backscattering as a first order
proxy of biomass.
Bivariate histograms between Chl and the fractional
PSCs (Fig. 7b–d) indicate that the picoplankton fraction
(Fig. 7b) decreases with increasing Chl, whereas nanoplank-
ton (Fig. 7c) and microplankton (Fig. 7d) fractions increase.
The pico- and nanoplankton relationships also exhibit the
sigmoidal shape. The considerable noise in these relation-
ships is likely due to natural ecosystem variability that oc-
curs for a given Chl value, illustrating that PFT algorithms
based on Chl abundance (IOCCG, 2014, e.g., Brewin et al.,
2010; Hirata et al., 2011; Uitz et al., 2006) may miss this
variability. In spite of that, to first order the relationships of
Fig. 7b–d are broadly consistent with the observations of Hi-
rata et al. (2011) who use global in situ HPLC measurements
to also derive Chl–PSC relationships. Further details of com-
parison with Hirata et al. (2011) are provided in Sect. S4.
3.6 Algorithm assumptions and uncertainty budget
There are multiple steps involved in the retrieval of the
carbon-based biomass products presented here. Namely,
Rrs(λ) is obtained from top of the atmosphere radiance af-
ter atmospheric correction, then spectral bbp(λ) is retrieved
and used to estimate the power-law PSD parameters; the
PSD is then used to estimate particle volume, which is fi-
nally converted to phytoplankton carbon. Each of the above
steps is associated with a set of assumptions and uncertain-
ties which combine and propagate to the final products. Only
some of these uncertainties are quantifiable at present. Be-
low, we (1) make a quantitative assessment of propagated
partial uncertainties of the retrieved carbon-based products,
and (2) offer a general discussion of algorithm assumptions
and other unquantified uncertainties. In addition, in Sect. S.5,
we assess the sensitivity of the carbon products to the input
PSD parameters (including the limits of integration of Eq. 4).
Quantified uncertainties propagated (Sect. S1.4, Eqs. S2
and S3) to the final C products include: (1) partial uncer-
tainties of the PSD algorithm products (ξand No)that are
due to natural variability of the complex index of refrac-
tion and the maximum diameter of the particles consid-
ered (KSM09), and (2) uncertainties in the allometric coef-
ficients of MDL2000. The resulting partial uncertainty esti-
mate for the total phytoplankton C biomass mission compos-
ite (Fig. 8a) is generally less than 1mgCm3in the olig-
otrophic subtropics, higher in more productive regions, and
exceeds 10mg Cm3only in some limited high-latitude
and coastal areas. Examination of relative uncertainty for the
global composite image indicates that it rarely exceeds 20 %,
except for the very high latitudes (prominently south of 60S
and in the Arctic Ocean), and in the oligotrophic gyres, where
some pixels exceed 50% relative uncertainty (not shown).
The gyres are characterized by noisy uncertainty patterns
Figure 8. (a) Propagated uncertainty in the mission mean of to-
tal phytoplankton carbon concentration (1 standard deviation in
mgC m3, shown in log10 space). This is a partial uncertainty
estimate due to the quantifiable PSD parameter uncertainties and
the uncertainties of the allometric coefficients. Uncertainties are
propagated to the individual monthly images using Eq. (S2) and
then composite imagery uncertainty is estimated using Eq. (S3)
(Sect. S1.4). Panel (b) is the same as (a) but shows uncertainty for
the mission mean of percent picoplankton contribution to carbon
biomass (1 standard deviation in percent).
(large variability on the pixel scale, not shown). The relative
uncertainty of a typical individual monthly image is between
85% and 115% globally, illustrating the significant uncer-
tainty reduction for the mission composite product (Eq. S3).
The uncertainty of the mission composite fractional pi-
coplankton contribution to carbon biomass is very low
(Fig. 8b), less than 1% over most of the ocean, and
not exceeding 7% anywhere. The uncertainties for the
other PSCs are similar (somewhat higher for microplank-
ton, but only at the very high latitudes, not shown). Indi-
vidual imagery uncertainty for the fractional picoplankton
vary between 3% to 8 % (1–7% for nanoplankton frac-
tions, and 0–2% for microplankton, higher in eutrophic
areas), illustrating that even for individual images fractional
PSC uncertainties are quite low. This result is expected be-
cause the Noparameter, which is a large source of error
(Sects. 3.6 and S6), cancels in the computation of fractional
PSCs (Eq. S1) and thus does not contribute to error in the
PSCs. Thus, the carbon-based PSCs are likely to be a reli-
able product even if absolute carbon concentrations are not
accurate. In fact, these PSCs can readily be used to partition
other, independent estimates of phytoplankton carbon, such Ocean Sci., 12, 561–575, 2016
570 T. S. Kostadinov et al.: Carbon-based phytoplankton size classes
Figure 9. SeaWiFS mission composite mean (September 1997–
December 2010) of total phytoplankton carbon biomass (mg Cm3
in log10 space) calculated with the PSD method described here, as
in Fig. 1a, but with an empirical correction applied to the Nopa-
rameter first (see Sect. 3.7).
as those from the algorithms of B05 and S08, or even climate
model data.
Analytical error propagation (Eq. S2) permits tracing the
relative contribution of the various input variables to the un-
certainty (variance) of the dependent variable. Calculations
for the example month of May 2004 indicate that almost the
entire variance (>95 % nearly everywhere) in total carbon
is driven by uncertainties in No(Fig. S6a). The remainder is
mostly due to the allometric coefficients in oligotrophic areas
(Fig. S6b), and only in some eutrophic areas the PSD slope ξ
has a non-negligible contribution to total C variance. For the
oligotrophic gyres and some transitional areas around them,
most of the uncertainty in picoplankton fractional contribu-
tion to carbon biomass is due to the allometric coefficients
(Fig. S6c), whereas for the higher latitudes and more pro-
ductive areas 80% of the variance is due to the PSD slope.
For the nanoplankton fraction, almost everywhere the uncer-
tainty is due primarily to the allometric coefficients. For the
microplankton fraction in oligotrophic areas, the error is due
almost exclusively to the allometric coefficients, but in eu-
trophic areas it is usually about equally due to the allometric
coefficients and the PSD slope.
The propagated quantified uncertainties presented above
are only partial estimates. There are other (not necessarily
quantifiable) factors that contribute to the total uncertainty
budget. For example, uncertainties in the spectral bbp(λ) re-
trieval are not taken into account. The assumptions of a
single-slope power-law PSD (that applies across a wide range
of particle sizes) and the sphericity and homogeneity as-
sumptions of the KSM09 algorithm contribute to uncertainty
as well and are discussed elsewhere (KSM09). For absolute
C retrievals, we assume that all particles belong to the POC
pool (i.e., that they are biogenic in origin), that the propor-
tion of phytoplankton in POC is constant (i.e., equal to 1/3
of POC by mass), and that the allometric coefficients apply
to the heterotrophic and nonliving (detrital) pools as well.
The assumption of biogenic nature of the particle assem-
blage is most likely to be violated in shallow coastal waters
where processes such as river discharge, wind-driven dust de-
position and tidal mixing can introduce large and variable
amounts of inorganic particles into the water column (e.g.,
Otero and Siegel, 2004). Additional uncertainties also exist
that are external to the MDL2000 data set and therefore not
included in their variance estimates. Finally, the assumption
of equal contributions of diatoms and non-diatoms to the to-
tal carbon pool for cells larger than 3000µm3is not expected
to hold globally everywhere, and should be relaxed in the
future by combining with other PFT methods capable of de-
tecting diatoms (e.g., Hirata et al., 2011) and/or integrated
ecosystem approaches based on regional knowledge (Raitsos
et al., 2008; Fay and McKinley, 2014). A more detailed dis-
cussion of algorithm assumptions and additional uncertainty
sources is provided in Sect. S6.
3.7 Empirical correction of absolute carbon
concentrations. Validation with in situ total POC
As discussed in Sects. 3.1 and S6, underestimates of abso-
lute carbon concentrations in oligotrophic gyres and overes-
timates in eutrophic areas (Figs. 1 and S3) seem likely and
are probably due to the treatment of the index of refraction
in the KSM09 model, which likely leads to underestimates
of Noin the oligotrophic gyres. The validation regression for
Noin KSM09 indeed has a slope significantly larger than
unity, suggesting that satellite retrievals are underestimates
at low Novalues (typical in oligotrophic waters) and are
overestimates at high Novalues, typical for eutrophic wa-
ters. This suggests a lack of full optical closure between the
in situ Coulter counter determinations of the Noparameter
and the satellite retrievals. Comparison with more empirical
determinations of POC and phytoplankton carbon (Fig. 1; see
also Graff et al., 2015) and the validation regression statistics
of the KSM09 model suggest a simple empirical correction
needs to be applied to satellite Nodata in order to achieve
more realistic values of absolute carbon concentrations on a
per pixel basis. Using the slope and intercept of the KSM09
Novalidation (KSM09, their Fig. 14b), satellite Novalues
can be corrected as follows:
2.0475 +16.7353
2.0475 .(5)
Application of Eq. (5) to Nobefore absolute carbon calcula-
tions leads to global climatological values from the SeaWiFS
mission that are indeed much more realistic and resemble the
S08-based estimates (Figs. 9 and S3). Using the empirically
corrected values to estimate total global phytoplankton car-
bon stock yields a value of 0.17Gt of C from the SeaWiFS
mission climatology. This value is lower than the S08, B05
and CMIP5 model estimates, and it is also lower than the
value using the uncorrected No(Fig. S7). This is an indica-
tion that the lowered values of the more eutrophic regions
dominate the global biomass result. Indeed the contribution
Ocean Sci., 12, 561–575, 2016
T. S. Kostadinov et al.: Carbon-based phytoplankton size classes 571
log10 (in situ POC/3, mg m )
0 0.5 1 1.5 2 2.5 3
log10(SeaWiFS Phytoplankton C or POC/3, mg m-3 )
Allometric C, corrected
N =260
Slope(RMA) =1.16 (0.0450)
y-int.(RMA) =-0.276 (0.0807)
R2 =0.65
p =0.00
rms =0.18
bias =0.00
Stramski POC/3
N =294
Slope(RMA) =0.922 (0.0307)
y-int.(RMA) =0.192 (0.0554)
R2 =0.70
p =0.00
rms =0.16
bias =0.05
Behrenfeld C
N =286
Slope(RMA) =0.981 (0.0466)
y-int.(RMA) =-0.0197 (0.0845)
R2 =0.46
p =0.00
rms =0.22
bias =-0.05
Allometric C, corrected
S08 POC/3
B05 phyto C
Figure 10. Validation of the total phytoplankton carbon satellite estimates discussed here, using SeaWiFS matchups of in situ POC measure-
ments from the SeaBASS database. The three methods compared are: the allometric PSD method with the Noempirical correction applied
(Sect. 3.7) (green circles and line), the S08 POC retrieval multiplied by 1/3 (red crosses and line) and the B05 retrieval (blue triangles and
line). All available matchups are used, including those from shallow waters (<200 m depth).
by the shallow shelf regions is considerably reduced com-
pared to the uncorrected estimate.
The empirically corrected allometric/PSD-based determi-
nations of phytoplankton carbon validate well against in situ
POC measurements from the SeaBASS data set (Werdell et
al., 2003) that are multiplied by 1/3 (Fig. 10). The valida-
tion statistics are highly significant in log10 space (p<0.01).
Compared to the validation results for the S08 and B05 meth-
ods, the PSD methods exhibits similar results. Namely, the
PSD method slope is somewhat worse than the others, the
R2value is about as good as that of S08 and better than
the one for B05, and the same holds for the rms values. The
PSD method exhibits no overall bias, but a few of the lowest
POC values still exhibit underestimation. In addition, about
10% fewer retrievals are available from the PSD/allometric
method. The same validation performed on PSD/allometric
retrievals using uncorrected Novalues yields a slope of 2.20,
an rms of 0.48 and a bias of 0.25 (not shown), indicating
that the proposed empirical correction greatly improves al-
gorithm performance for total absolute phytoplankton C con-
centrations. Because these empirically corrected values are
more realistic and validate much better at the POC level, they
are used in the published data set (see ”Data availability and
archival” below). Importantly, this is a validation of only total
phytoplankton carbon, and uses in situ POC measurements as
a proxy for it. The fractional contributions to the total phyto-
plankton carbon by the PSCs do not depend on the value of
Noand are thus not affected by the empirical Nocorrection.
Finally, Eq. (5) is based on PSD validation in KSM09 which
has few matchups (N=22) from a single type of PSD mea-
surement. Many more measurements of the PSD are needed
to make this empirical correction robust and possibly region-
alize it.
4 Summary and Conclusions
We presented a novel method to retrieve phytoplankton car-
bon biomass from ocean color satellite data, based on com-
bining volume determinations using backscattering-based
PSD retrievals of Kostadinov et al. (2009) with carbon-to-
volume allometric relationships compiled by Menden-Deuer
and Lessard (2000). We use monthly SeaWiFS data to es-
timate total and size-partitioned absolute and fractional C
biomass in three PSCs: pico-, nano- and microplankton.
These PSCs can be treated as PFTs to first order. The clima-
tological spatial patterns of the C-based PSCs broadly agree
with current knowledge of phytoplankton biogeography and
While there are other remote sensing methods capable of
producing algal biomass or PFT estimates, our methodology
is unique and novel in the following key ways: (1) ability to
partition algal community biomass into any number of de-
sired size classes in terms of absolute or fractional carbon
concentration, which is the most relevant variable of interest
in terms of biogeochemistry and is the unit of quantification
of phytoplankton in Earth system models; (2) it is overall less
empirical in nature and is based more on first principles of
bio-optics, i.e., it builds on the concept of constant backscat- Ocean Sci., 12, 561–575, 2016
572 T. S. Kostadinov et al.: Carbon-based phytoplankton size classes
tering to carbon relationship of Behrenfeld et al. (2005) by
explicitly taking into account the underlying PSD that pro-
duced the backscattering and thus relaxing the assumed con-
stant relationship. Satisfactory in situ closure is observed be-
tween a limited number of observations of PSD and POC on
AMT cruises, which supports the PSD/allometric approach
we take here.
Detailed uncertainty analysis indicates that total carbon
concentration retrievals are sensitive to assumptions about
the underlying bulk particle index of refraction, which may
lead to exaggeration of the spatial range of concentration,
calling for caution when interpreting absolute concentra-
tions. This exaggeration is improved with an empirical cor-
rection which leads to satisfactory validation of total phy-
toplankton carbon determinations against in situ POC mea-
surements. Fractional PSCs, which are more reliable than the
absolute carbon values, are subject to much smaller uncer-
tainties due mostly to uncertainties in the allometric coef-
ficients. The bio-optical algorithm presented here is a first-
order, global, proof-of-concept approach that can be further
improved in multiple ways by addressing its assumptions and
sources of uncertainty and incorporating new advancements
in laboratory and satellite techniques (e.g., in situ phyto-
plankton carbon measurements and space-borne hyperspec-
tral ocean color sensors).
Data availability and archival
The SeaWiFS data set produced for and used in this pub-
lication has been archived in the PANGAEA data repository
(Kostadinov et al. (2016b), doi:10.1594/PANGAEA.859005)
and is publicly available at
PANGAEA.859005. The following variables are provided:
slope of the power-law PSD (unitless), the Noparameter
(Eq. (1), units of m4, decimal logarithm of the data be-
fore the empirical correction of Eq. (5) is applied), total car-
bon biomass (mgCm3)and carbon biomass in the three
PSCs (mgCm3)with the empirical Nocorrection applied
(Sect. 3.7), and the fractional contribution of the three PSCs
(picoplankton (0.5–2 µm ESD), nanoplankton (2–20µm) and
microplankton (20–50µm)) to the total biomass (unitless).
Partial propagated uncertainties quantified here are also pro-
vided for all variables (1 standard deviation in the units of
the respective variable). The effects of the empirical correc-
tion Eq. (5) on propagated uncertainty have been ignored,
i.e., we assume that the corrected Noparameter has the same
uncertainty as the uncorrected one. The monthly and over-
all composite imagery (i.e., climatologies) are also provided,
with the respective propagated uncertainties for the compos-
ite imagery (Sect. S1.4). Important: the provided data set uses
the empirically corrected Noparameter (See Sect. 3.7 and
Figs. 9 and 10) in order to provide more realistic absolute
phytoplankton concentration values. Note that analyses in
this paper use mostly the uncorrected Novalues, unless oth-
erwise indicated. Use of this data set is subject to the appro-
priate license as indicated in PANGAEA, and the SeaWiFS
input data set (
and this paper must be properly cited and acknowledged.
The Supplement related to this article is available online
at doi:10.5194/os-12-561-2016-supplement.
Acknowledgements. This work is supported by NASA Ocean
Biology and Biogeochemistry Grant no. NNX13AC92G
to Irina Marinov and Tihomir S. Kostadinov. NASA grants
NNG06GE77G, NNX08AG82G and NNX08AF99A also funded
Tihomir S. Kostadinov for parts of this work. Tihomir S. Kostadi-
nov is indebted to David Siegel and Stéphane Maritorena for
multiple discussions and input that inspired and improved this
work. We specifically thank Stéphane Maritorena for help with the
formulating of uncertainty propagation. We thank David Shields,
Danica Fine, Brian Hahn and Dave Menzies for help with data
processing, and Peter Perkins for his bivariate histogram script.
The WolframAlpha®online service is acknowledged for providing
derivative calculation verification. All other data processing,
analysis and plotting was done in MATLAB®. We acknowledge the
NASA Ocean Biology Processing Group (OBPG) for maintaining
and providing the SeaWiFS data set, the SeaBASS in situ data
set and validation search tool (at the Ocean Biology Distributed
Active Archive Center, OB.DAAC) and generally for their support
for ocean color research. DigitalGlobe and predecessor companies
GeoEye, Inc. and ORBIMAGE are also acknowledged for their
role in SeaWiFS data acquisition. We further acknowledge the
SEABASS data providers for providing the in situ POC data sets.
We acknowledge the British Oceanographic Data Centre (BODC)
for providing the AMT Cruise PSD and POC data. We acknowl-
edge the World Climate Research Programme’s Working Group
on Coupled Modelling, which is responsible for CMIP, and we
thank the climate modeling groups (listed in Table S3 of this
paper) for producing and making available their model output. For
CMIP the US Department of Energy’s Program for Climate Model
Diagnosis and Intercomparison provides coordinating support
and led development of software infrastructure in partnership
with the Global Organization for Earth System Science Portals.
Additional ancillary data providers are indicated in the text. We
also acknowledge two anonymous reviewers and the topical editor
for their comments, which significantly improved the manuscript.
Edited by: P. Chapman
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... Necessarily, some key simplifying assumptions are made here in order to construct an algorithm with operational application to modern multi-spectral ocean color sensors. The two key assumptions are: 1) Phytoplankton and NAP have a power-law PSD (Eq. 1) with the same slope ξ, and 2) The scaling parameter N 0 for NAP is twice that of N 0 for phytoplankton (the forward model uses default values as in Tables 1 and 2. The latter assumption is chosen so that it results in a phyto C:POC ratio of 1:3 (see Kostadinov et al. (2016a) and Behrenfeld et al. (2005) (2018)) that is scaled to the value at n ′ (675) using Eq. 2. ...
... For operational application to OC-CCI v5.0 (Sathyendranath et al., 2021) data (which does not have the 550 nm band), band-shifting was applied to the input R rs (560) to estimate the corresponding R rs (550), which is used in the Loisel and Stramski (2000) IOP inversion. The band-shifting was constructed using the band ratios between the respective original and target bands from a hyperspectral run of the Morel and Once the PSD parameters are known, they can be used to compute derived products (Kostadinov et al., 2010(Kostadinov et al., , 2016aRoy et al., 2017)). Phytoplankton carbon in any size class spanning from cell diameter D min to cell diameter D max (in m) can be estimated as: ...
... the other PSD parameters are as in Eq. 1. Equation 5 was used to compute size-partitioned phyto C in three size classespicophytoplankton (0.2 to 2 µm in diameter), nanophytoplankton (2 to 20 µm in diameter) and microphytoplankton (20 to 50 µm in diameter), as well as total phyto C as the sum of the three classes. Carbon-based PSCs are defined as the fractional contribution of each of the three size classes to total phyto C (Kostadinov et al., 2016a). Given the first-order correspondence between PSCs and PFTs (e.g. ...
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The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g. in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs) and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations (particle assemblage categories) — phytoplankton and non-algal particles (NAP). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAP are modeled as homogeneous spheres. The forward model uses Mie and Aden-Kerker scattering computations, for homogeneous and coated spheres (for phytoplankton and NAP, respectively) to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via Spectral Angle Mapping (SAM) which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e. carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. The EAP-based formulation allows for the estimation of chlorophyll-a concentration via the retrieved PSD, as well as the estimation of the percent of backscattering due to NAP vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented, using PSD, POC, and pico-phytoplankton carbon in-situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of phytoplankton carbon and related variables to facilitate future algorithm improvements.
... Typical remote-sensed descriptors of the marine ecosystem include total chlorophyll (mg m − 3 ), as well as absorption and backscattering by Inherent Optical Properties (IOPs) of the water. Several studies have attempted to derive metrics of the plankton size structure from these quantities, e.g., from total chlorophyll (Brewin et al., 2010(Brewin et al., , 2014, from absorption (Roy et al., 2010), or backscattering (Kostadinov et al., 2009(Kostadinov et al., , 2010(Kostadinov et al., , 2016. Many of these studies employ complex models to convert remote sensed quantities into size structure. ...
... As these models are built on several generalizing assumptions, their results are subject to considerable uncertainty. This can be seen, for instance, in their very different predictions of phytoplankton carbon ( Fig. 1 in Kostadinov et al., 2016). ...
Full-text available
Squid species show pronounced interannual variability in population size. While this may partially reflect changes in fisheries pressure, it is thought to be primarily the result of environmental variability. Most squid have an annual life cycle with only a short period dedicated to reproduction. With little overlap between generations, the environment can exert a major influence on stock size. In this study we explore, through a combination of process-based modelling and statistical analysis, whether environmental variability explains variability in catch of the chokka squid, Loligo reynaudii, over the Agulhas Bank off South Africa. We focus on growth and survival during the first two months spent as “paralarva” in the pelagic. This period has been suggested to be a key bottleneck and a potential predictor of catch. To describe prey availability and predation pressure, we develop a dynamic model of the size spectrum (1 mg–1000 kg) of the ecosystem over the Agulhas Bank, with trophic interactions governed by size. In tandem, we develop a model for the growth of individual L. reynaudii, which specifies where in the size spectrum individual squid can be found at each stage of their development. We find a correlation of 0.74 between modelled biomass representative for L. reynaudii at the end of its paralarval stage and catch per unit effort (CPUE) in the subsequent season in the period 1995–2015. This suggests that the paralarval stage is indeed a bottleneck: modelled food availability and predation pressure experienced by paralarvae explains 55% of the variability in CPUE, which is a proxy for spawning stock biomass. As the paralarval stage ends approximately nine months before the time of spawning and maximum catch, this work could be used to develop catch predictor with a nine-month lag.
... Monitoring the rates of change in POC in the upper ocean using satellite observations provides a means for advancing a methodology to diagnose the POC fluxes, such as primary production, export to the deep ocean and transformations to DOC and DIC pools, and constrain the uncertainties of carbon budget (Allison et al., 2010a). The total POC pool in the upper ocean also provides essential information and constraints for the estimation of phytoplankton carbon biomass and carbon-based primary production from satellite observations (e.g., Behrenfeld et al., 2005;Behrenfeld et al., 2013;Evers-King et al., 2017;Kostadinov et al., 2016). The assimilation of satellite-derived POC products into global coupled models of physical, biogeochemical and radiative processes provides an added value in the quest for better understanding and quantifying the effects and fate of carbon entering the oceans from the atmosphere, and potential responses and feedbacks of ocean ecosystems to climate change. ...
... This latter category has limitations related to mismatch between the temporal and/or spatial scales in the determinations of variables involved in the algorithm formulation. More recently, a few alternative approaches with potential for global applications were proposed, specifically the estimation of POC from satellitederived information on particle size distribution and relationship that converts particle size to carbon content (Kostadinov et al., 2016) and the estimation of POC from satellite-derived color index parameter (Le et al., 2018). In recent years, increased efforts have been also made with a focus on POC algorithms for coastal environments (Hu et al., 2016;Le et al., 2017;Liu et al., 2015;Tran et al., 2019;Woźniak et al., 2016). ...
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As the concentration of particulate organic carbon (POC) in the surface ocean plays a key role in marine biogeochemical cycles and ecosystems, its assessment from satellite observations of the global ocean is of significant interest. To achieve a global multi-decadal data record of POC by merging observations from multiple satellite ocean color missions, we formulated a new suite of empirical POC algorithms for several satellite sensors. For the algorithm development we assembled a field dataset of concurrent POC and remote-sensing reflectance, R rs (λ), measurements collected in all major ocean basins encompassing tropical, subtropical, and temperate latitudes as well as the northern and southern polar latitudes. This dataset is characterized by a globally-representative probability distribution of POC with a broad range of values between about 10 and 1000 mg m − 3. This development dataset was created with the use of additional inclusion and exclusion criteria based on well-assured and documented consistency of measurement protocols as well as specific bio-optical and particle characteristics of seawater which are consistent with vast areas of open-ocean pelagic environments. To formulate the algorithms the development dataset was subject to parametric regression analysis. Overall we evaluated over seventy formulas for estimating POC from R rs (λ) using seven distinctly different algorithmic categories, each with a fundamentally different definition of independent variable involving R rs (λ). Through the goodness-of-fit analysis, we selected the best candidate POC algorithms, referred to as the hybrid algorithms, which are tuned specifically for the spectral bands of SeaWiFS, MODIS, VIIRS, MERIS, and OLCI satellite sensors. These hybrid algorithms consist of two components, the MBR (Maximum Band Ratio)-OC4 cubic polynomial function and BRDI (Band Ratio Difference Index) quintic polynomial function. The MBR-OC4 uses four spectral bands and the BRDI three spectral bands from the blue-green spectral region. The MBR-OC4 algorithm is used for POC > 25 mg m − 3 and the BRDI for POC < 15 mg m − 3. In the transition region the weighting approach is applied to POC derived from the two algorithmic formulas. While the main role of the BRDI is to improve POC estimates in ultraoligotrophic waters where POC is very low, the MBR-OC4 provides improvements, compared with the predecessor algorithms, over a broader range of POC but especially for relatively high POC values. A preliminary analysis of field-satellite matchup datasets based on SeaWiFS and MODIS-Aqua observations shows improved performance of hybrid algorithms compared with current standard algorithms for both SeaWiFS and MODIS. In addition, a reasonable consistency is demonstrated between POC derived from hybrid algorithms applied to example satellite observations with SeaWiFS, MODIS-Aqua, and VIIRS-SNPP. The suite of newly developed algorithms provides the potential next generation version of global algorithms that better represents the spatial and temporal variability within a broader range of POC than the predecessor global algorithms, while also offering a capability to generate a long-term sensor-to-sensor consistent data record of POC that begins with the launch of SeaWiFS mission in 1997.
... Satellite remote sensing is a powerful tool providing a global view of biogeochemical and optical components in the surface ocean. While surface POC has been successfully estimated over the global ocean (Gardner et al., 2006;Kostadinov et al., 2016;Loisel et al., 2002;Stramski et al., 1999;Stramski et al., 2008), the global DOC estimation is still challenging, despite some few attempts using sea surface temperature, SST, or OCR data (Aurin et al., 2018). The possibility to use the absorption coefficient of the colored dissolved organic, a cdom (λ) (with λ the wavelength), that can potentially be estimated from OCR, to estimate DOC concentration over natural waters has been explored. ...
The Dissolved Organic Carbon (DOC) represents the largest organic carbon reservoir in the ocean. Therefore, describing its spatio-temporal distribution is crucial for better understanding the global carbon cycle. Recent studies have demonstrated the possibility to determine DOC in coastal waters from ocean color radiometry (OCR) based on its strong correlation with the absorption coefficient of Colored Dissolved Organic Matter (acdom(λ)). However, in the open ocean, the CDOM to DOC relationship is highly variable as they present different sources, sinks, and kinetics. Here we present a new approach to estimating DOC over the open ocean based on an Artificial Neural Network (ANN) algorithm. This model accounts for i) Optical Water Classes (OWC) ii) sea surface temperature (SST), mixed layer depth (MLD), acdom(443), and chlorophyll-a (Chl-a) concentration, and iii) different time lags depending on the input parameter. The satellite DOC estimated with this model is in good agreement with in situ measurements (MAPD = 7.04%), while the spatial patterns follow former observations and model outputs. A sensitivity analysis has shown that the main descriptors to assess satellite DOC at a given time for oligotrophic and mesotrophic open ocean waters are SST one week before, and acdom(443) two weeks before; with Chl-a one week before as an additional input parameter for more productive waters. This study allows for the first time the assessment of the contribution of the particulate organic carbon (POC) to the total organic carbon (TOC) over the global ocean. The POC/TOC ratio value varies between 1.31% and 9.07%, with a mean value of about 4.57 ± 1.87%.
... However, phytoplankton often consist of hundreds of species, and different groups have different roles in biogeochemical processes (such as silicon absorption and carbon and nitrogen fixation). Thus, it is not sufficient to quantify the composition information of the phytoplankton community structure by total chlorophyll a concentration [6]. ...
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Marine phytoplankton are the basis of the whole marine ecosystem, and different groups of phytoplankton play different roles in the biogeochemical cycle. Satellite remote sensing is widely used in the retrieval of marine phytoplankton over a wide range and long time series, but not yet for taxonomical composition. In this study, we used coincident in situ measurement data from high-performance liquid chromatography (HPLC) and remote sensing reflectance (Rrs) to investigate the empirical relationships between phytoplankton groups and satellite measurements. A nonparametric model, generalized additive model (GAM), is introduced to establish inversion models of various marine phytoplankton groups. Seven inversion models (two sizes classes among the microphytoplankton and nanophytoplankton and four groups among the diatoms, dinoflagellates, chrysophytes, and cryptophytes) are applied to the South China Sea (SCS) for 2020, and satellite images of phytoplankton sizes and groups are presented. Microphytoplankton prevails in the coastal and continental shelf, and nanophytoplankton prevails in oligotrophic oceans. Among them, the dominant contribution of microphytoplankton comes from diatoms, and nanophytoplankton comes from chrysophytes. Diatoms (nearshore) and chrysophytes (outside the continental shelf) are the dominant groups in the SCS throughout the year. Dinoflagellates only become dominant in some coastal areas, while cryptophytes rarely become dominant.
... Third, the partitioning of POC export into differently sized particles based on the relative abundance of opal and aragonite export has the advantage of being able to respond to changing forcing, specifically any forcing that affects the prognostic calculation of export production, which is not the case for, e.g., a power law-based particle size distribution. Fourth, the resultant spatial pattern of σ L ( Figure A1) is consistent with global distributions of size-partitioned carbon biomass derived from the satellite-based particulate backscattering spectrum (Kostadinov et al., 2016), showing that large particles make a larger contribution in the opal-dominated high latitudes than in the CaCO 3 -dominated low latitudes. Future studies may benefit from improvements in the choice of κ L . ...
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A key challenge for current‐generation Earth system models (ESMs) is the simulation of the penetration of sinking particulate organic carbon (POC) into the ocean interior, which has implications for projections of future oceanic carbon sequestration in a warming climate. This paper presents a new, cost‐efficient, mechanistic 1D model that prognostically calculates POC fluxes by carrying four component particles in two different size classes. Gravitational settling and removal/transformation processes are represented explicitly through parameterizations that incorporate the effects of particle size and density, dissolved oxygen, calcite and aragonite saturation states, and seawater temperature, density, and viscosity. The model reproduces the observed POC flux attenuation at 22 locations in the North Atlantic and North Pacific. The model is applied over a global ocean domain with seawater properties prescribed from observation‐based climatologies in order to address an important scientific question: What controls the spatial pattern of mesopelagic POC transfer efficiency? The simulated vertical POC transfer is more efficient at high latitudes than at low latitudes with the exception of oxygen minimum zones, which is consistent with recent inverse modeling and neutrally buoyant sediment trap studies. Here, model experiments show that the relative abundance of large‐sized, rapidly sinking particles and the slower rate of remineralization at high latitudes compensate for the region's lack of calcium carbonate ballast and the cold‐water viscous resistance, leading to higher transfer efficiencies compared to low‐latitude regions. The model could be deployed in ESMs in order to diagnose the impacts of climate change on oceanic carbon sequestration and vice versa.
... We note that this interannual variability is different than the internal variability (ensemble spread) that we discuss at 130 length in this study, but is nevertheless a target for model validation. Although phytoplankton carbon concentrations cannot be measured directly by satellites, they can be reconstructed using algorithms that incorporate remotely sensed chlorophyll concentrations, detrital backscattering coefficients, and phytoplankton absorption (Kostadinov et al., 2016;Martinez-Vicente et al., 2017;Roy et al., 2017;Sathyendranath et al., 2020;Brewin et al., 2021). We use the observational phytoplankton carbon dataset of Bellacicco et al. (2020), annually averaged and interpolated onto a 1°grid, to evaluate temporal variability in phytoplankton biomass in a single model ensemble member. ...
Multiple studies conducted with Earth System Models suggest that anthropogenic climate change will influence marine phytoplankton over the coming century. Light limited regions are projected to become more productive and nutrient limited regions less productive. Anthropogenic climate change can influence not only the mean state, but also the variance around the mean state, yet little is known about how variance in marine phytoplankton will change with time. Here, we quantify the influence of anthropogenic climate change on internal variability in marine phytoplankton biomass from 1920 to 2100 using the Community Earth System Model 1 Large Ensemble (CESM1-LE). We find a significant decrease in the internal variance of global phytoplankton carbon biomass under a high emission (RCP8.5) scenario, with heterogeneous regional trends. Decreasing variance in biomass is most apparent in the subpolar North Atlantic and North Pacific. In these high-latitude regions, zooplankton grazing acts as a top-down control in reducing internal variance in phytoplankton biomass, with bottom-up controls (e.g., light, nutrients) having only a small effect on biomass variance. Grazing-driven declines in phytoplankton variance are also apparent in the biogeochemically critical regions of the Southern Ocean and the Equatorial Pacific. Our results suggest that climate mitigation and adaptation efforts that account for marine phytoplankton changes (e.g., fisheries) should also consider changes in phytoplankton and zooplankton variance driven by anthropogenic warming, particularly on regional scales.
... This is not to say that other processes do not also impact the POC flux. Dynamics not explicitly represented in the model such as particle aggregation and disaggregation 32,35,39 , zooplankton grazing on particles 30,36,37,40,82 , phytoplankton dynamics [83][84][85] , and the formation of new particles within the water column 83-85 also play an important role. An exciting avenue of future work is to investigate the extent to which complex ecological interactions between the microbial communities and zooplankton dynamics impact the POC flux, the relative importance of these different processes for determining the rate of organic carbon export, and how these dynamics may vary geographically. ...
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Sinking particulate organic carbon out of the surface ocean sequesters carbon on decadal to millennial timescales. Predicting the particulate carbon flux is therefore critical for understanding both global carbon cycling and the future climate. Microbes play a crucial role in particulate organic carbon degradation, but the impact of depth-dependent microbial dynamics on ocean-scale particulate carbon fluxes is poorly understood. Here we scale-up essential features of particle-associated microbial dynamics to understand the large-scale vertical carbon flux in the ocean. Our model provides mechanistic insight into the microbial contribution to the particulate organic carbon flux profile. We show that the enhanced transfer of carbon to depth can result from populations struggling to establish colonies on sinking particles due to diffusive nutrient loss, cell detachment, and mortality. These dynamics are controlled by the interaction between multiple biotic and abiotic factors. Accurately capturing particle-microbe interactions is essential for predicting variability in large-scale carbon cycling. Micro-scale microbial community dynamics can substantially alter the fate of sinking particulates in the ocean thus playing a key role in setting the vertical flux of particulate carbon in the ocean.
... We chose to use two target observational datasets. The first dataset was from Kostadinov et al. 12,13 , which contains estimates for phytoplankton size classes as carbon derived from remote sensing measurements. Briefly, the spectral shape and magnitude of particulate backscattering at blue-green wavelengths is used to relate them to the particle size distribution and concentration of suspended particles of a reference diameter, with the assumption that the particles are spherical. ...
... Assessing carbon phytoplankton dynamics by EO is challenging, and in a review paper by Brewin et al. (2021), there is an increasing interest in satellite phytoplankton carbon products. This work notes that only a few approaches for detecting phytoplankton carbon products from space are available (Kostadinov et al., 2016;Jackson et al., 2017;Roy et al., 2017). In addition to PSCs, interest has also focused on retrieving phytoplankton functional group (PFT) abundance (see Nair et al., 2008;Hirata et al., 2011;Losa et al., 2017;amongst others) and is presently considered a priority in ocean colour remote sensing (see IOCCG, 2014;Bracher et al., 2017 for a review). ...
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Phytoplankton biomass, through its proxy, Chlorophyll a , has been assessed at synoptic temporal and spatial scales with satellite remote sensing (RS) for over two decades. Also, RS algorithms to monitor relative size classes abundance are widely used; however, differentiating functional types from RS, as well as the assessment of phytoplankton structure, in terms of carbon remains a challenge. Hence, the main motivation of this work it to discuss the links between size classes and phytoplankton groups, in order to foster the capability of assessing phytoplankton community structure and phytoplankton size fractionated carbon budgets. To accomplish our goal, we used data (on nutrients, photosynthetic pigments concentration and cell numbers per taxa) collected in surface samples along a transect on the Atlantic Ocean, during the 25th Atlantic Meridional Transect cruise (AMT25) between 50° N and 50° S, from nutrient-rich high latitudes to the oligotrophic gyres. We compared phytoplankton size classes from two methodological approaches: (i) using the concentration of diagnostic photosynthetic pigments, and assessing the abundance of the three size classes, micro-, nano-, and picoplankton, and (ii) identifying and enumerating phytoplankton taxa by microscopy or by flow cytometry, converting into carbon, and dividing the community into five size classes, according to their cell carbon content. The distribution of phytoplankton community in the different oceanographic regions is presented in terms of size classes, taxonomic groups and functional types, and discussed in relation to the environmental oceanographic conditions. The distribution of seven functional types along the transect showed the dominance of picoautotrophs in the Atlantic gyres and high biomass of diatoms and autotrophic dinoflagellates (ADinos) in higher northern and southern latitudes, where larger cells constituted the major component of the biomass. Total carbon ranged from 65 to 4 mg carbon m –3 , at latitudes 45° S and 27° N, respectively. The pigment and cell carbon approaches gave good consistency for picoplankton and microplankton size classes, but nanoplankton size class was overestimated by the pigment-based approach. The limitation of enumerating methods to accurately resolve cells between 5 and 10 μm might be cause of this mismatch, and is highlighted as a knowledge gap. Finally, the three-component model of Brewin et al. was fitted to the Chlorophyll a (Chl a ) data and, for the first time, to the carbon data, to extract the biomass of three size classes of phytoplankton. The general pattern of the model fitted to the carbon data was in accordance with the fits to Chl a data. The ratio of the parameter representing the asymptotic maximum biomass gave reasonable values for Carbon:Chl a ratios, with an overall median of 112, but with higher values for picoplankton (170) than for combined pico-nanoplankton (36). The approach may be useful for inferring size-fractionated carbon from Earth Observation.
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Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2 µm in diameter), nanophytoplankton (2-20 µm) and microphytoplankton (20-50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield - 0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.
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Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes classified from four observational data sets: sea surface temperature (SST), spring/summer chlorophyll a concentrations (Chl a), ice fraction, and maximum mixed layer depth (maxMLD) on a 1° × 1° grid (available at doi:10.1594/PANGAEA.828650). By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the grid cells that do not change biome assignment across the 13 years of the time-varying biomes. These biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.
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A new method of retrieving the parameters of a power-law particle size distribution (PSD) from ocean color remote sensing data was used to assess the global distribution and dynamics of phytoplankton functional types (PFT's). The method retrieves the power-law slope, ξ, and the abundance at a reference diameter, N <sub>0</sub>, based upon the shape and magnitude of the particulate backscattering coefficient spectrum. Relating the PSD to PFT's on global scales assumes that the open ocean particulate assemblage is biogenic. The retrieved PSD's can be integrated to define three size-based PFT's by the percent volume concentration contribution of three phytoplankton size classes – picoplankton (0.5–2 μm in equivalent spherical diameter), nanoplankton (2–20 μm) and microplankton (20–50 μm). Validation with in-situ HPLC diagnostic pigments results in satisfactory match-ups for the pico- and micro-phytoplankton size classes. Global climatologies derived from SeaWiFS monthly data reveal PFT and particle abundance spatial patterns that are consistent with current understanding. Oligotrophic gyres are characterized by lower particle abundance and higher contribution by picoplankton-sized particles than transitional or eutrophic regions. Seasonal succession patterns for size-based PFT's reveal good correspondence between increasing chl and percent contribution by microplankton, as well as increasing particle abundance. Long-term trends in particle abundances are generally inconclusive yet are well correlated with the MEI index indicating increased oligotrophy (i.e. lower particle abundance and increased contribution of picoplankton-sized particles) during the warm phase of an El Niño event. This work demonstrates the utility and future potential of assessing phytoplankton functional types using remote characterization of the particle size distribution.
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Recent ocean warming and subsequent sea ice decline resulting from climate change could affect the northward shift of the ecosystem structure in the Chukchi Sea and Bering Sea shelf region (Grebmeier et al., 2006b). The size structure of phytoplankton communities provides an index of trophic levels that is crucial to understanding the mechanisms underlying such ecosystem changes and their implications for the future. This study proposes a new ocean color algorithm for deriving this characteristic by using the region's optical properties. The size derivation model (SDM) estimates the phytoplankton size index F <sub>L</sub> on the basis of size-fractionated chlorophyll- a (chl- a ) using the light absorption coefficient of phytoplankton, a <sub>ph</sub>(λ), and the backscattering coefficient of suspended particles including algae, b <sub>bp</sub>(λ). F <sub>L</sub> was defined as the ratio of algal biomass attributed to cells larger than 5 μm to the total. It was expressed by a multiple regression model using the a <sub>ph</sub>(λ) ratio, a <sub>ph</sub>(488)/ a <sub>ph</sub>(555), which varies with phytoplankton pigment composition, and the spectral slope of b <sub>bp</sub>(λ), γ, which is an index of the mean suspended particle size. A validation study demonstrated that 69% of unknown data are correctly derived within F <sub>L</sub> range of ±20%. The spatial distributions of F <sub>L</sub> for the cold August of 2006 and the warm August of 2007 were compared to examine application of the SDM to satellite remote sensing. The results suggested that phytoplankton size was responsive to changes in sea surface temperature. Further analysis of satellite-derived F <sub>L</sub> values and other environmental factors can advance our understanding of ecosystem structure changes in the shelf region of the Chukchi and Bering Seas.
This book presents an in-depth discussion of the biological and ecological geography of the oceans. It synthesizes locally restricted studies of the ocean to generate a global geography of the vast marine world. Based on patterns of algal ecology, the book divides the ocean into four primary compartments, which are then subdivided into secondary compartments. *Includes color insert of the latest in satellite imagery showing the world's oceans, their similarities and differences *Revised and updated to reflect the latest in oceanographic research *Ideal for anyone interested in understanding ocean ecology -- accessible and informative.
The observation that the relative importance of picophytoplankton is greatest in warm and nutrient-poor waters was tested here based on a comprehensive review of the data available in the literature from oceanic and coastal estuarine areas. Results show that picophytoplankton dominate (50%) the biomass and production in oligotrophic (chlorophyll a [Chl a] 0.3 mg m 3), nutrient poor (NO 3 NO 2 1 M), and warm (26C) waters, but represent 10% of autotrophic biomass and production in rich (Chl a 5 mg m 3) and cold (3C) waters. There is, however, a strong covariation between temperature and nutrient concentration (r 0.95, P 0.001), but the number of observations where both temperature and nutrient concentrations are available is too small to allow attempts to statistically separate their effects. The results of mesocosm nutrient addition experiments during summer in the Mediterranean Sea allowed the dissociation of the effects of temperature from those of nutrients on pico-phytoplankton production and biomass and validated the magnitude at which picoplankton dominates (50%) autotrophic biomass and production obtained in the comparative analysis. The fraction contributed by picoplankton significantly declined (r 2 0.76 and 0.90, respectively, P 0.001) as total autotrophic production and biomass increased. These results support the increasing importance of picophytoplankton in warm, oligotrophic waters. The reduced contribution of picophytoplankton in warm productive waters is hypothesized here to be due to increased loss rates, whereas the dominance of picophytoplankton in warm, oligotrophic waters is attributable to the differential capacity to use nutrients as a function of differences in size and capacity of intrinsic growth of picophyto-plankton and larger phytoplankton cells.