ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-861-2017Two databases derived from BGC-Argo float measurements for marine
biogeochemical and bio-optical applicationsOrganelliEmanueleemo@pml.ac.ukhttps://orcid.org/0000-0001-8191-8179BarbieuxMarieClaustreHervéhttps://orcid.org/0000-0001-6243-0258SchmechtigCatherinePoteauAntoinehttps://orcid.org/0000-0002-0519-5180BricaudAnnickBossEmmanuelhttps://orcid.org/0000-0002-8334-9595BriggsNathanhttps://orcid.org/0000-0003-1549-1386Dall'OlmoGiorgioD'OrtenzioFabrizioLeymarieEdouardhttps://orcid.org/0000-0001-9705-407XManginAntoineObolenskyGrigorPenkerc'hChristophePrieurLouisRoeslerCollinSerraRomainUitzJuliaXingXiaogangSorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 7093,
Laboratoire d'Océanographie de Villefranche (LOV), 181 Chemin du
Lazaret, 06230 Villefranche-sur-mer, FrancePlymouth Marine Laboratory, PL1 3DH Plymouth, UKSorbonne Universités, UPMC Université Paris 06, CNRS, UMS
3455, OSU Ecce-Terra, Paris, FranceSchool of Marine Sciences, University of Maine, Orono, Maine, USANational Centre for Earth Observation, Plymouth Marine Laboratory,
PL1 3DH Plymouth, UKACRI-ST, 260 route du Pin Montard, 06904 Sophia-Antipolis, FranceERIC Euro-Argo, 29280 Plouzané, FranceDepartment of Earth and Oceanographic Science, Bowdoin College,
Brunswick, Maine, USAState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration,
Hangzhou, 310012, ChinaEmanuele Organelli (emo@pml.ac.uk)22November20179286188018June201713July20175October201710October2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/9/861/2017/essd-9-861-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/861/2017/essd-9-861-2017.pdf
Since 2012, an array of 105 Biogeochemical-Argo (BGC-Argo) floats
has been deployed across the world's oceans to assist in filling
observational gaps that are required for characterizing open-ocean
environments. Profiles of biogeochemical (chlorophyll and dissolved organic
matter) and optical (single-wavelength particulate optical backscattering,
downward irradiance at three wavelengths, and photosynthetically available
radiation) variables are collected in the upper 1000 m every 1 to 10 days.
The database of 9837 vertical profiles collected up to January 2016 is
presented and its spatial and temporal coverage is discussed. Each variable
is quality controlled with specifically developed procedures and its time
series is quality-assessed to identify issues related to biofouling and/or
instrument drift. A second database of 5748 profile-derived products within
the first optical depth (i.e., the layer of interest for satellite remote
sensing) is also presented and its spatiotemporal distribution discussed.
This database, devoted to field and remote ocean color applications, includes
diffuse attenuation coefficients for downward irradiance at three narrow
wavebands and one broad waveband (photosynthetically available radiation),
calibrated chlorophyll and fluorescent dissolved organic matter
concentrations, and single-wavelength
particulate optical backscattering. To demonstrate the applicability of these
databases, data within the first optical depth are compared with previously
established bio-optical models and used to validate remotely derived
bio-optical products. The quality-controlled databases are publicly available
from the SEANOE (SEA scieNtific Open data Edition) publisher at
10.17882/49388 and 10.17882/47142 for vertical profiles and
products within the first optical depth, respectively.
Introduction
In the early 2000s, the international oceanographic community raised
concerns about the large uncertainties still limiting the estimation of key
biogeochemical processes in the ocean that contribute to controlling the
Earth's climate (e.g., primary production and carbon export). The spatial and
temporal under-sampling of most of the world's oceans was considered the
main cause of this limitation (Munk, 2000; Hall et al., 2010). The same
community thus proposed the implementation of autonomous platforms, such as
the Biogeochemical-Argo profiling floats (hereafter BGC-Argo floats), as one
solution to fill this observational gap (Johnson et al., 2009; Claustre et
al., 2010a). Unlike sampling from vessels, BGC-Argo floats operate with high
temporal and spatial coverage, including remote areas and periods when
ship-based sampling is difficult. BGC-Argo can therefore help the scientific
community to accumulate observations on biogeochemical properties from the
surface to the interior of the ocean in a new and systematic way (Claustre
et al., 2010a; Biogeochemical-Argo Planning Group, 2016; Johnson and
Claustre, 2016). This, together with several other recent efforts to compile
global biologically or biogeochemically relevant datasets (Peloquin et al.,
2013; Sauzède et al., 2015; Bakker et al., 2016; Mouw et al., 2016;
Valente et al., 2016), may provide new insights on marine ecological and
biogeochemical processes and help better understand if oceans and their
properties have changed and/or are changing over the decades (Organelli et
al., 2017).
In 2012, an array of BGC-Argo floats started to be deployed in several
oceanic areas encompassing a wide range of biogeochemical and trophic
conditions, from subpolar to tropical and from eutrophic systems to
oligotrophic mid-ocean gyres (Organelli et al., 2016a, 2017). This array of
floats was devoted to the acquisition of profiles of key biogeochemical
quantities via their optical properties (i.e., chlorophyll a and
colored dissolved organic matter, CDOM) and of hydrological variables (i.e., temperature and
salinity). In addition, the array provided measurements of the underwater
light field (i.e., irradiance) and of the inherent optical properties (i.e.,
particulate optical beam attenuation and backscattering coefficients) of the
oceans. All these measurements, and derived quantities, are useful for both
biogeochemical and bio-optical studies, to address the variability in
biological processes (e.g., phytoplankton phenology and primary production;
Lacour et al., 2015) and linkages with physical drivers (Boss et al., 2008;
Boss and Behrenfeld, 2010; Lacour et al., 2017; Mignot et al., 2017; Stanev
et al., 2017), to estimate particulate organic carbon concentrations and
export (e.g., Bishop et al., 2002; Dall'Olmo and Mork, 2014; Poteau et al.,
2017), and to support satellite missions through validation of bio-optical
products retrieved from ocean color remote sensing (e.g., chlorophyll
concentration; Claustre et al., 2010b; IOCCG, 2011, 2015; Gerbi et al., 2016;
Haëntjens et al., 2017) or by identification of those regions with
bio-optical behaviors departing from mean-statistical trends (i.e.,
bio-optical anomalies; Organelli et al., 2017).
The study reported here presents a quality-controlled database of
biogeochemical and bio-optical vertical profiles acquired by more than 100
BGC-Argo floats equipped with a homogeneous and interoperable instrument
configuration. The “Biogeochemical and OPtical Argo Database – profiles”
(BOPAD-prof; Barbieux et al., 2017a) includes 0–1000 m measurements of
calibrated fluorometric chlorophyll a (Chl, mg m-3) and fluorescent dissolved organic matter
(FDOM, ppb of quinine sulfate) concentrations, the particulate
optical backscattering coefficient at 700 nm (bbp(700), m-1),
downward irradiance Ed(λ) at three wavelengths (380, 412, and
490 nm, µW cm-2 nm-1), and the spectrally integrated
photosynthetically available radiation (PAR,
µmol quanta m-2 s-1). Temperature (T, ∘C) and
salinity (S, PSU) provide the hydrographic context for the optical
observations. The geographic and temporal distribution of each parameter is
described and discussed. A second database is specifically devoted to field
and remote ocean color applications (Organelli et al., 2016b). It is focused
on observations and derived products within the first optical depth
Zpd (also known as the penetration depth, i.e., the layer of
interest for satellite remote sensing; Gordon and McCluney, 1975; units of
m) and includes the “Biogeochemical and OPtical Argo Database – surface”
(BOPAD-surf) Chl, FDOM, and bbp(700) quantities derived from the
quality-controlled vertical profiles in addition to the diffuse attenuation
coefficients for downward irradiance (Kd(λ), m-1) and
PAR (Kd(PAR), m-1). Data presented in BOPAD-surf are compared
with existing bio-optical models and used in conjunction with products
derived from satellite platforms in order to show applicability for
validating ocean color bio-optical products. Finally, sources of
uncertainties are presented, and errors discussed, for variables in
BOPAD-prof and profile-derived products within BOPAD-surf.
Material and methodsBiogeochemical-Argo floats: instruments, sampling strategy, and
data
The PROVOR CTS4 profiling float used in this study is one of the latest
models of autonomous platforms developed by NKE Marine Electronics Inc.
(France). Designed in the context of the Remotely-Sensed Biogeochemical
Cycles in the Ocean (remOcean) and Novel Argo Ocean Observing System (NAOS)
projects, this profiling float has also been adopted by several international
collaborators and research programs. A full technical description of the
platform and instrument arrangements can be found in Leymarie et al. (2013)
and Organelli et al. (2016a).
All PROVOR CTS4 profiling floats were programmed to acquire 0–1000 m
vertical profiles every 1 to 10 days while operating (every 4 ± 2 days
on average; see Appendix A), depending on the mission and scientific
objectives. Upward profiles commenced from the 1000 m parking depth in time
for surfacing at local noon. Data acquisition was nominally at 0.20 m
resolution between surface and 10 m, 1 m resolution between 10 and 250 m,
and generally 10 m resolution between 250 and 1000 m (except on some
occasions when it was 1 m).
Red diamonds indicate the 9837 stations collected by 105
Biogeochemical-Argo floats in the period of October 2012–January 2016 that
compose the database of vertical profiles (BOPAD-prof). Blue dots indicate
the 5748 stations used to assemble the database within the first optical
depth and devoted to bio-optical applications (BOPAD-surf). Abbreviations for
the 25 geographic regions used to group the stations are also displayed (see Appendix A for full
description). The map was drawn using the Ocean Data View software
(R. Schlitzer, Ocean Data View, http://odv.awi.de).
An array of 105 BGC-Argo floats acquired more than 10 000 vertical profiles
of bio-optical and biogeochemical variables over a broad range of oceanic
environments and trophic conditions between October 2012 and January 2016. A
WET Labs ECO (Environmental Characterization Optics) sensor installed on each
BGC-Argo float provided 0–1000 m vertical profiles of chlorophyll
(fluorometer with excitation and emission of 470 and 695 nm) and dissolved organic
matter (fluorometer with excitation and emission of 370 and 460 nm) fluorescence and
of the volume scattering coefficient (β(θ, λ)) measured
at an angle of 124∘ and a wavelength λ of 700 nm (Sullivan
et al., 2013; Schmechtig et al., 2016). The multispectral ocean color
radiometer OCR-504 (Satlantic Inc.) provided vertical profiles of
PAR and downward irradiance
Ed(λ) at three wavelengths (380, 412, and 490 nm) in the
upper 250 m. Electronic counts of each measured variable were converted into
geophysical quantities using the calibration factors and standard practices
provided by the manufacturers (Satlantic, 2013; WET Labs, 2016). According to
the standard procedures for Argo data management (Wong et al., 2015), each
profile was quality-controlled by applying methods specifically developed for
each parameter (see Sect. 2.2). Because sensor performance might degrade over
the float lifetime, each float was evaluated for possible corruption by
biofouling or instrument drift (see Sect. 2.3). A total of 9837 BGC-Argo
stations, each one corresponding to an upward profile, composed the database
BOPAD-prof presented in this study (Fig. 1). To discuss the geographic and
temporal representativeness of the database, the 9837 quality-controlled
stations were grouped into 25 geographic areas. BGC-Argo float names,
number of profiles, lifetime, and additional details are presented in
Appendix A.
Quality control of vertical profiles
Vertical profiles of Chl concentration were quality-controlled following
procedures and recommendations in Schmechtig et al. (2014). Profiles were
(1) adjusted for nonzero deep values and (2) corrected by removing negative
spikes lower than twice the 10th
quantiles of the residual signal calculated as the difference between the
profile values and a median filter (five-point window). No interpolation of
missing data was performed. Positive spikes were retained; (3) measured
values outside the specific range reported in the manufacturer's technical
specifications were removed (WET Labs, 2016). No interpolation of missing
data was performed and (4) profiles were corrected for non-photochemical
quenching (NPQ; Kiefer, 1973) by extrapolation of the fluorescence
at the bottom of the mixed layer to the
surface following Xing et al. (2012) and Schmechtig et al. (2014).
Profile-by-profile analysis–visualization, number of invalid
points, and related origin are available on
http://seasiderendezvous.eu. Profiles collected in areas such as the
Black Sea and subtropical regions were further corrected for the contribution
of fluorescence originating from non-algal matter following procedures
described in Xing et al. (2017). The correction was applied when Chl and FDOM
concentrations were positively correlated below the depth at which Chl was
supposed to be zero (for equations and quantitative metrics see Xing et al.,
2017). The magnitude of this correction within the mixed layer
varied between 3 and 50 %
(for details see Table 2 in Xing et al., 2017, for the same database).
Finally, according to the recommendations by Roesler et al. (2017) on the
overestimation by standard WET Labs fluorometers, remaining Chl values were
divided by a factor of 2 to correct for the global bias in factory
calibration.
The FDOM vertical profiles were quality-controlled according to the following
procedures: (1) measured values outside the specific range
reported in the manufacturer's technical specifications were removed (WET Labs, 2016). No
interpolation of missing data was performed; (2) negative and
positive spikes outside the 25th and 75th quantiles of the raw profile were removed, and
subsequently any measurement with an absolute residual value > 4
calculated as the difference between the profile and a mean filter was removed. Finally,
according to the assumption that deep CDOM concentrations are conservative in a
given water body (Nelson et al., 2010), and assuming that the BGC-Argo floats
included in this database spent their lifetime mainly within the same deep
water mass, an offset was applied to each FDOM profile to align the median
value between 950 and 1000 m with the first profile and correct for possible
sensor drift.
Following procedures described in Schmechtig et al. (2016), vertical profiles
of the angular scattering coefficient β(124∘, 700) were
(1) converted into the particulate angular scattering coefficient by removing
the contribution of pure seawater, which depends on water temperature and
salinity (Zhang et al., 2009); (2) converted to the particulate optical
backscattering coefficient at 700 nm (bbp(700)) using a χ
factor equal to 1.076 (Sullivan et al., 2013); and (3) verified for measured
values outside the specific range reported in the manufacturer's technical
specifications (WET Labs, 2016). No interpolation of removed data was
performed; (4) the profiles were corrected by removing negative spikes lower than twice the
10th quantiles of the residual signal calculated as the difference between the
profile and a median filter (Briggs et al., 2011). No interpolation of
missing data was performed. Positive spikes were retained.
Vertical profiles of PAR and Ed(λ) were quality controlled
following the procedures detailed in Organelli et al. (2016a). This protocol
accepts measurements acquired both under clear and cloudy sky
conditions as good as soon as these remain stable during the cast (Organelli et al.,
2016a). A first step of the quality control consisted of identifying and
discarding each profile acquired under very unstable sky and sea conditions
(see quantitative metrics in Organelli et al., 2016a). The remaining profiles
were quality controlled to identify and remove (1) nonzero dark measurements
at depth, (2) sporadic atmospheric clouds, and (3) wave focusing (Zaneveld et
al., 2001) in the upper part of the profile. Because Ed(λ)
and PAR measurements are collected up to a few centimeters from the sea
surface, quality-controlled vertical profiles were completed by values just
below it (Ed(0-)). The Ed(0-) values were
calculated by extrapolation within the first optical depth (Zpd)
using a second-degree polynomial fit (Organelli et al., 2016a), with
Zpd calculated as Zeu/4.6 (Morel, 1988). The euphotic
depth, Zeu, is the depth at which PAR is reduced to 1 % of its
value just below the surface and was calculated from measured PAR profiles.
To achieve Ed(0-) calculations, initial values of
Zeu and Zpd were first estimated from the shallowest
PAR measurement and subsequently from that corresponding to 0-. For
radiometric data prior to the application of the above-mentioned
quality-control procedures, the reader is referred to the archive at
https://doi.org/10.17882/42182 (Argo, 2000).
Salinity (PSU) vs. FDOM
(ppb of quinine sulfate) data collected during drift at the 1000 m parking
depth for two Biogeochemical-Argo floats: (a) float WMO 6901768
(eastern Mediterranean Sea) showing no FDOM changes for a similar salinity
(7 months of sampling) and (b) float WMO 6901474 (North Atlantic
subtropical gyre) showing a decrease in FDOM for a similar salinity (more
than 24 months of sampling). In plot (b), FDOM values around
2.5 ppb of quinine sulfate represent measurements collected during the first
2 years of the float lifetime and which have not been discarded. Colors
indicate density of measurements for a given salinity vs. the FDOM value
(red > blue).
Testing for biofouling and instrument drift
A set of four tests was specifically developed to identify potential
biofouling and instrument drift. To achieve a reliable evaluation for each of
the 105 BGC-Argo floats, each variable was examined both individually and in
conjunction with the others, which is greatly aided by redundancy among
derived quantities. A combination of raw profiles and quality-controlled
products was needed for the analysis. Ancillary data such as measurements
acquired in drift mode at 1000 m (i.e., between two following ascent
profiles) were also included in the analysis and they can be publicly
accessed at http://www.oao.obs-vlfr.fr/maps/en/. Test 1 was conducted
on raw time series of salinity, Chl, FDOM, bbp(700), and
Ed(λ), i.e, before the application of the quality-control
procedures described in Sect. 2.2. Test 1 aimed to identify sharp gradients in
measured variables over the entire profile (i.e., sudden decrease or increase of
Chl and FDOM concentrations or increase in bbp(700) values) not
attributable to any biological or hydrological cause (e.g., particle
aggregates or nepheloid layer of particles). Tests 2 and 3 were conducted on
raw measurements collected by each profiler when in drift mode. Test 2
analyzed time series of the sensors' dark measurements for Chl and Ed(λ) at the 1000 m parking depth. Test 3 consisted of the analysis of the
relationship between raw FDOM and salinity at the 1000 m parking depth over
time. Assuming that deep CDOM concentrations are conservative in the same
water body (Nelson et al., 2010), variations in deep FDOM values for a given
salinity are likely due to changes in sensor performances (Fig. 2). Test 4 was
based on the comparison between irradiance values just above the sea surface
(Ed(0+)) and those modeled by Gregg and Carder (1990) for
clear cloudless sky, as described by Organelli et al. (2016a). The
performance of this test, which assesses the accuracy of measured irradiance
values, strongly depends on the value extrapolated to the sea surface (i.e.,
Ed(0-)). Ed(0+) values at 380, 412, and 490 nm
were obtained by dividing Ed(0-) derived from quality-controlled profiles as described in Sect. 2.2 by the transmission across the
sea–air interface factor (Austin, 1974). When the results of the tests above
indicated possible measurement issues (i.e., 1710 profiles spanned across 70
floats), each preprocessing variable time series was interrupted and only
previously collected profiles were retained (i.e., 9837 stations in
BOPAD-prof).
Bio-optical products within the first optical depth
BOPAD-surf was compiled using 5748 stations with quality-controlled
Zeu and Zpd values (Fig. 1). The procedure of Organelli
et al. (2016a) reduced the number of PAR profiles that can be exploited for
deriving optical quantities within the first optical depth (see Sect. 2.2 for
computation) by about 40 % (e.g., because of atmospheric clouds) with
respect to BOPAD-prof. To compute vertical diffuse attenuation coefficients
for downward irradiance (Kd(λ)) and PAR (Kd(PAR))
within Zpd, each radiometric profile was binned in 1 m intervals.
Kd(λ) and Kd(PAR) values were then derived from a
linear fit, after removal of outliers, between the natural logarithm of
the radiometric quantity and depth (in units of pressure) following Mueller
et al. (2003). Kd(λ) and Kd(PAR) values obtained
from linear fits based on less than three points or with a determination
coefficient (r2) lower than 0.90 were discarded (Organelli et al.,
2017). Values of Chl, FDOM, and bbp(700) were also derived, within
the first optical depth, from quality-controlled vertical profiles. Before
computation, FDOM quality-controlled profiles were smoothed by applying first
a median filter (five-point window) and then an average filter (seven-point window).
A median filter (five-point window) was applied to quality-controlled
bbp(700) profiles to identify and subsequently remove positive
spikes. Finally, Chl, FDOM, and bbp(700) profiles were binned in 1 m
intervals, and the average within Zpd was computed.
Comparison with satellite data
To demonstrate the applicability of these BGC-Argo databases,
satellite-derived diffuse attenuation coefficients of downward irradiance at
490 nm (Kd(490)sat) obtained by the GlobColour project
(ACRI-ST, 2017) were downloaded from the web portal
http://seasiderendezvous.fr/matchup.php and compared to the in situ
BGC-Argo counterparts. Kd(490)sat values were obtained, for the
period October 2012 to January 2016, from daily Level 3 Chl merged products
using the empirical algorithm by Morel et al. (2007a). Chl products were
merged using MODIS-Aqua and VIIRS Level 3 products (NASA reprocessing
R2014.0); see fully detailed merging procedures in ACRI-ST (2017). As
statistics of the match-up analysis, the RMSE (m-1) and the median percentage difference (MPD) were calculated
according to Organelli et al. (2016c).
Quality-controlled vertical profiles
In this section, specific examples of quality control are presented for each
examined variable to provide context for the database. In the case of Chl
profiles, three examples extracted from floats operating in different trophic
and optical environments are presented (North Atlantic subpolar gyre, Black
Sea, and South Atlantic subtropical gyre; Fig. 3). It is recalled here that
all quality-controlled Chl values are divided by 2 as recommended by Roesler
et al. (2017). The raw North Atlantic profile (Fig. 3a) exhibits strong
non-photochemical quenching at the surface and positive spikes at depth.
After the quality control, NPQ is corrected and the positive spikes that are
likely related to biological information are retained (Fig. 3a). The Black
Sea vertical chlorophyll profile (Fig. 3b) is mainly characterized by a
monotonic Chl increase to depth, where the concentration is expected to be
null. As Proctor and Roesler (2010) and Xing et al. (2017) stated, the
observed Chl increase at depth is due to very high CDOM (which is a
consequence of the anoxic conditions prevailing at depth in the Black Sea)
and non-algal matter concentrations that can affect the chlorophyll
fluorescence signal. After correcting the profile according to Xing et
al. (2017), Chl concentrations below 100 m are zero. The profile from the
South Atlantic subtropical gyre mostly exhibits a nonzero dark offset, which
is removed in quality control (Fig. 3c).
Raw and quality-controlled vertical profiles of chlorophyll a concentration
(Chl) for the following areas: (a) the North Atlantic subpolar gyre (float
WMO 6901516), (b) the Black Sea (float WMO 7900591), and (c) the South Atlantic
subtropical gyre (float WMO 6901439).
Raw FDOM vertical profiles are generally noisy and spiky, especially in the
upper water column (Fig. 4). After quality control, large spikes are
identified and removed, and the profile is aligned to match the 950–1000 m
median value of the first profile acquired by the float (Fig. 4). Depending
on the application, further processing of FDOM profiles such as smoothing and
filtering is recommended before use (see, for example, Sect. 2.4).
In the case of bbp(700) vertical profiles, the examples in Fig. 5
represent quality-controlled profiles with and without positive spikes (see
Sect. 2.4). Although positive spikes likely indicate the occurrence of large
aggregates and are essential to monitoring carbon fluxes towards the deep ocean
(Briggs et al., 2011), they can introduce some noise when export of
particulate organic carbon due to small particles (Dall'Olmo and Mork, 2014),
the physiological status of the algal community (Barbieux et al., 2017b), or
the bio-optical behavior of world's oceans (Organelli et al., 2017) is
analyzed. Both versions of bbp(700) profiles are archived in
BOPAD-prof.
(a, b) Raw and quality-controlled vertical profiles of fluorescent
dissolved organic matter (FDOM, ppb of quinine sulfate) collected by the
profiling float WMO 6901440 in the South Atlantic subtropical gyre. Open cyan
circles indicate positive spikes for the raw profile. Both profiles
were acquired within 1 week from the deployment.
All the quality-controlled profiles of Ed(λ) and PAR
included in the database presented correspond to Type 1 (i.e., best quality)
in Organelli et al. (2016a). The examples in Fig. 6 represent
Ed(412) profiles collected in eastern Mediterranean Sea waters
under different sky conditions. The profile in Fig. 6a is acquired under
nearly clear sky conditions. In this case, the quality-control procedure only
identifies and removes dark values at depth (not shown) and those
corresponding to wave focusing (Zaneveld et al., 2001) at the surface. The
profile in Fig. 6b is instead characterized by nonzero dark values in deep
waters (not shown) and sporadic atmospheric clouds, with the major cloud
perturbing data acquisition for at least 2 min. The ensemble of tests of the
applied quality-control procedure (Organelli et al., 2016a) detects the
various perturbations (Fig. 6b). Additional examples showing performances of
applied procedure for Ed(380), Ed(412),
Ed(490),
and PAR can be found in Organelli et al. (2016a).
BOPAD-prof: spatiotemporal distribution of the biogeochemical and
optical Argo database of vertical profiles
Deployment of BGC-Argo floats has been mainly focused, within limitations of
project-driven resources, on some of the important carbon-export regions of
the Atlantic Ocean (Alkire et al., 2012), on areas with dynamic trophic
regimes (e.g., Mediterranean Sea; D'Ortenzio and Ribera d'Alcalà, 2009),
and on oligotrophic mid-ocean gyres, in all cases in regions with depths
greater than 1000 m (except on a very few occasions). As a result, the 9837
BGC-Argo stations of vertical profiles within BOPAD-prof cover a wide range
of trophic conditions and represent the first step to set up a publicly
available and interoperable database for biogeochemical and bio-optical
studies. Hereafter, we present the spatial and temporal coverage of
quality-controlled vertical profiles for each biogeochemical and bio-optical
variable between the world's hemispheres and among regions. The
spatiotemporal distribution of temperature profiles, which are
representative of the entire raw database, is also shown.
(a, b) Raw (i.e., before positive spike removal) and
quality-controlled (i.e., after application of a median filter) vertical
profiles of particulate optical backscattering at 700 nm (bbp(700))
collected in the South Atlantic subtropical (float WMO 6901439) and North
Atlantic subpolar (float WMO 6901516) gyres, respectively. Open cyan circles
indicate positive spikes (Briggs et al., 2011).
The latitudinal and monthly distributions of the quality-controlled profiles
show similar patterns among the eight variables (Fig. 7), which indicates that
the quality-control procedures do not bias the sampling spatially or
temporally. However, the total number of
profiles for a given latitude and month of the year is different among
variables. It is generally the highest for Chl (Fig. 7c, d) and
bbp(700) (Fig. 7g, h). Because of the strict quality control by
Organelli et al. (2016a) that removes radiometric profiles acquired under
very unstable meteorological conditions, the total number of
Ed(λ) and PAR profiles is generally the lowest (Fig. 7i–p).
In the Northern Hemisphere, the database covers a broader latitudinal range
than in the Southern Hemisphere. Data range from the Equator to the Arctic
Ocean, and late spring to midsummer are the most represented periods. The
number of profiles is substantially lower between January and April. This
occurs especially for radiometric quantities (Fig. 7) as a consequence of the
decreasing stability of the water column associated with deteriorated sky and
sea conditions (D'Ortenzio et al., 2005; Lacour et al., 2015). This high
contribution of the Northern Hemisphere to the database is due to the first
projects piloting the deployment of BGC-Argo floats that were mainly focused
on the North Atlantic subpolar gyre (i.e., 48–65∘ N; remOcean
project) and the Mediterranean Sea (i.e., 31–44∘ N; NAOS project).
Latitudes higher than 67∘ N are included thanks to a 3-year
operating float collecting all variables except FDOM (Fig. 7e). Latitudes
between 0 and 30∘ N (i.e., subtropical gyres and surrounding zones)
are also represented owing to measurements acquired by 10 BGC-Argo floats
(Fig. 7). Note, however, that the number of FDOM profiles at these latitudes
is lower than for the other variables as a consequence of sensor failure on
some floats and absence in those floats deployed in the framework of the UK
Bio-Argo and E-AIMS projects (in which the FDOM sensor was replaced by a sensor
measuring particulate backscattering coefficient at 532 nm,
bbp(532)). The Northern Hemisphere is also represented by data
collected in two marginal seas (Fig. 1): the Black and Red seas. Similar to
subtropical gyres and surrounding areas, the number of FDOM profiles in the
Black Sea is lower than for other variables because half of the floats
deployed in this area measured bbp(532) instead of FDOM (see
Sect. 7 for bbp(532) data availability).
Raw and quality-controlled vertical profiles of downward
irradiance at 412 nm (Ed(412)) collected by the same profiling float
(WMO 6901528) in the eastern Mediterranean Sea under (a) nearly clear and
(b) cloudy skies.
Latitudinal and monthly distributions of the 9837 vertical
profiles, presented as stacked histograms, for (a–b) temperature
(T),
(c–d) chlorophyll a concentration (Chl), (e–f) fluorescent dissolved organic matter
(FDOM), (g–h) particulate optical backscattering coefficient at 700 nm
(bbp(700)), (i–j) downward irradiance at 380 nm
(Ed(380)),
(k–l) downward irradiance at 412 nm (Ed(412)), (m–n) downward irradiance
at 490 nm (Ed(490)), and (o–p) photosynthetically available radiation (PAR).
Vertical profile distributions are displayed for both Northern and Southern
hemispheres.
The Southern Hemisphere is primarily represented by data collected at
latitudes between 38 and 56∘ S (Fig. 7) in the Atlantic and Indian
sectors of the Southern Ocean (Fig. 1). In contrast to the Northern
Hemisphere, no floats have been deployed or reached latitudes higher than
60∘ S (Fig. 7). Measurements of each variable are also acquired by
seven
floats in southern subtropical gyres (around 16–25∘ S) both in the
Atlantic and Pacific oceans and by two floats in the region close to New
Caledonia in the South Pacific (Fig. 1). The temporal coverage of data
collected in the Southern Hemisphere remains uniform from January to
September for each variable, but then increases from October to December
(Fig. 7). This reflects a switch to adaptive sampling to better resolve the
phytoplankton bloom. Similar to the Northern Hemisphere, the number of
radiometric profiles tends, however, to slightly decrease during the autumn and
the austral summer (from June to August) as a consequence of the worsening
meteorological conditions and deepening mixed layer depths (Dong et al.,
2008).
The 25 selected regions (grouped into nine major areas) contribute, in terms of
number of profiles, in different proportions to the database (Fig. 8). This
is a consequence of the different number of floats deployed in each area
together with a modulated profiling frequency (from 1 to every 10 days). The
North Atlantic Ocean dominates BOPAD-prof, as a consequence of the intensive
sampling characterizing the subpolar gyre area in multiple programs. Vertical
profiles acquired in the Southern Ocean and the western Mediterranean Sea
each represent on average 18 % of the database. The eastern Mediterranean
Sea is about 14 %, while the South Atlantic subtropical gyre and
surrounding areas contribute 6.3 % on average. The South Pacific Ocean
represents only 3 to 5 % of the vertical profiles within BOPAD-prof,
while polar and marginal seas individually represent a proportion
< 3 % of each collected variable. Large areas such as the North
Pacific and Indian oceans equal 0 % as no deployments occurred in those
regions.
Number of profiles and minimum, maximum, and average (± standard
deviation) values for each variable included in the Biogeochemical-Argo
database within the first optical depth (BOPAD-surf).
Relative contributions (%) of the 9837 vertical profiles among
nine
regions and subregions sampled by Biogeochemical-Argo floats: (a) temperature
(T),
(b) chlorophyll a concentration (Chl), (c) fluorescent
dissolved organic matter (FDOM), (d) particulate backscattering coefficient at
700 nm (bbp(700)), (e) downward irradiance at 380 nm
(Ed(380)), (f) downward irradiance at 412 nm
(Ed(412)),
(g) downward irradiance at 490 nm (Ed(490)), and (h) photosynthetically
available radiation (PAR). See Appendix A for basins included within each
region and/or subregion.
BOPAD-surf: properties of the bio-optical database within the first
optical depth and joint use with remote sensing of ocean color
Because of the unique in situ spatial and temporal coverage, the
international community of optical oceanographers (Claustre et al., 2010b;
IOCCG, 2011, 2015; Biogeochemical-Argo Planning Group, 2016) has recently
recognized measurements collected by BGC-Argo floats as a fruitful resource
of data for bio-optical applications, such as the identification of regions
with optical properties departing from mean statistical relationships
(Organelli et al., 2017) as well as the validation of ocean color
reflectances
(Gerbi et al., 2016) and bio-optical products (IOCCG, 2015; Haëntjens et
al., 2017). In this context, BOPAD-surf has been compiled with 5748 stations
of biogeochemical (i.e., Chl and FDOM) and bio-optical (i.e.,
Kd(λ), Kd(PAR), and bbp(700)) variables
within the first optical depth (i.e., the layer of interest for ocean color)
as derived from previously quality-controlled vertical profiles. The
characteristics of this database are described hereafter.
Relative contribution (%) of biogeochemical and bio-optical
variables for the 25 geographic regions included in the Biogeochemical-Argo
database within the first optical depth (BOPAD-surf).
Distribution of (a) euphotic depth (Zeu), (b) first
optical depth (Zpd), (c) average value of the diffuse attenuation
coefficient for downward irradiance at 412 nm within the first optical depth
(Kd(412)), and (d) average value of the diffuse attenuation coefficient
for the photosynthetically available radiation within the first optical depth
(Kd(PAR)).
All the 5748 BGC-Argo stations correspond to quality-controlled measurements
of euphotic and first optical depths and represent about 60 % of the
database of quality-controlled vertical profiles. Ranges and averages (and
associated standard deviations) of Zeu and Zpd and of the
other variables are reported in Table 1. In agreement with previous
observations (Morel and Maritorena, 2001; Lee et al., 2007; Morel et al.,
2007a, b; Soppa et al., 2013; Organelli et al., 2014), values of
Zeu and Zpd vary mostly in the ranges of 10.5–180.2 and
2.3–39.2 m, respectively, with the deepest values characterizing the Atlantic
and South Pacific ocean gyres (Fig. 9a, b). The shallowest Zeu and
Zpd layers are instead characteristic of the North Atlantic
subpolar gyre in spring, the western Mediterranean Sea, and the Black Sea
(Fig. 9a, b). The observed ranges of Chl, FDOM, bbp(700),
Kd(λ), and Kd(PAR) values derived from BGC-Argo
measurements (Table 1) are also in good agreement with previous observations
(Morel and Maritorena, 2001; Morel et al., 2007a, b; Cetinić et al.,
2012; Dall'Olmo et al., 2012; Peloquin et al., 2013; Sauzède et al.,
2015; Valente et al., 2016). As examples of their spatial distribution across
the explored regions, Kd(412) and Kd(PAR) are shown in
Fig. 9c and d, respectively. The reader is referred to the work by Organelli
et al. (2017) for regional variability in Kd(380) and
Kd(490) coefficients.
As a consequence of the variable-specific quality-control procedures, each
variable within BOPAD-surf is represented with different proportions in the
25 regions (Table 2). Of the 5748 stations with quality-controlled
Zeu, 83–90 % contain Chl, FDOM, and bbp(700)
measurements; 62–72 % contain Kd(λ) values within
Zpd; and > 90 % contain Kd(PAR). The Labrador
Sea region contains the highest fraction of profiles of each variable
(13.81–17.08 %), while the Iceland Basin and the Irminger Sea contribute
on average 7.6–7.8 % of the profiles in the database (Table 2). In the
Mediterranean Sea, the northwestern, southwestern, and Ionian basins each
contribute between 5.5 and 9.7 % of the profiles, while the Levantine and
Tyrrhenian seas each contribute about 4 % on average (Table 2). In the
Southern Hemisphere, the eastern Atlantic and the Indian sectors of the
Southern Ocean each contribute about 6–10 % of the entire database,
while the relative contribution of the western part of the Atlantic sector is
< 4.45 % (Table 2). Subtropical gyres of both hemispheres contribute
from 1.47 to 4.43 % according to the variable (Table 2). Marginal seas
(i.e., Black and Red seas) and transition zones among various trophic regimes
represent less than 3 % of the whole database within the first optical
depth (Table 2). The North Pacific and Indian oceans equal 0 %.
The goal of BOPAD-surf supporting in situ and remote bio-optical applications
is demonstrated by two examples of possible use. As a first exercise,
previously established bio-optical relationships (Morel et al., 2007a) are
evaluated against the BGC-Argo database. It is important to identify the
regions with bio-optical behaviors deviating from the average trend because
a bio-optical anomaly could likely lead to uncertainties in retrieving
bio-optical and biogeochemical quantities from satellite ocean color
observations (Organelli et al., 2017). The relationship of Kd(PAR)
as a function of Kd(490) for the BGC-Argo database is in good
agreement with those by Morel et al. (2007a). Slight deviations appear,
however, at the lowest Kd(PAR) and Kd(490) values and mainly
correspond to samples collected in the subtropical gyres and eastern
Mediterranean Sea (Fig. 10). In a second exercise, Kd(490) values
obtained from the merged GlobColour satellite products (ACRI-ST, 2017) are
compared to Kd(490) coefficients obtained from BGC-Argo floats
(Fig. 11). While the two products agree approximately at moderate values
(Kd(490) ∼ 0.1 m-1), estimates from BGC-Argo floats
are considerably lower on average, especially at high and very low water
clarity. This result strongly warrants further investigation. Thanks to the
unprecedented spatial and temporal distribution provided by these autonomous
platforms, as well as to the understanding of the associated uncertainty, ocean color
algorithm and product validation can routinely be performed in several
regions so that errors and possible causes of failure (e.g., influence of
Raman scattering; Westberry et al., 2013) could be assessed and/or solved and
algorithms for improving the quality of retrievals may be refined.
Log–log plot of the diffuse attenuation coefficient for PAR
(Kd(PAR)) as a function of the diffuse attenuation coefficient for
downward irradiance at 490 nm averaged within the first optical depth
(Kd(490)). The dashed line is the fit to all data and corresponds
to Kd(PAR) = 0.062 (±0.002) + 0.869 (±0.011)
Kd(490) - 0.001 (±4 × 10-5)Kd(490)-1,
with n=3402. Dotted and solid lines represent relationships established by
Morel et al. (2007a; Eqs. 9 and 9′, respectively) limited to the range of
Kd(490) found in that study. Biogeochemical-Argo data are grouped
into seven major areas: Norwegian Sea, North Atlantic subpolar gyre and surrounding
areas (NASPG); western Mediterranean Sea (WMED); eastern Mediterranean Sea
(EMED); Black Sea (BLACK); Red Sea (RED); subtropical gyres and surrounding
areas (STG); and Southern Ocean (SO).
Comparison (n=658) between the diffuse attenuation coefficient for
downward irradiance at 490 nm as derived from satellite measurements
(Kd(490)sat) as a function of Kd(490) derived from
Biogeochemical-Argo float measurements within the first optical depth
(Kd(490)insitu). The solid line represents the
1 : 1 line. Biogeochemical-Argo data are grouped in seven major areas:
Norwegian Sea, North Atlantic subpolar gyre and surrounding areas (NASPG);
western Mediterranean Sea (WMED); eastern Mediterranean Sea (EMED); Black Sea
(BLACK); Red Sea (RED); subtropical gyres and surrounding areas (STG);
and Southern Ocean (SO). The RMSE and the median
percentage difference (MPD) for all data are shown.
Ratios between sensor sensitivity thresholds and
quality-controlled values of (a) chlorophyll a concentration (Chl),
(b) fluorescent dissolved organic matter (FDOM), and
(c) particulate backscattering coefficients at 700 nm
(bbp(700)). Sensor sensitivity limits are 0.007 mg m-3,
0.28 ppb of quinine sulfate, and 2.2 × 10-6 m-1 for Chl,
FDOM, and bbp(700), respectively. Measurements used as examples
correspond to quality-controlled profiles shown in Figs. 3a, 4b, and 5b.
BOPAD-surf does not, however, represent the only effort in compiling an
extensive database tailored for in situ and remote bio-optical applications.
BOPAD-surf and the compilation published by Valente et al. (2016; hereafter
VL2016) may be good partners, and thus be beneficial to the largest community
of oceanographers as soon as complementarities and differences are
highlighted. Though BOPAD-surf's temporal coverage is shorter than for
VL2016, it extends bio-optical measurements through 2013–2015. It includes
regions such as the North Atlantic subpolar gyre, the Southern Ocean, and the
Red Sea that are not archived in VL2016. Conversely, VL2016 offers data from
the North Pacific and Indian oceans where no PROVOR CTS4 profiling floats
have been deployed (Fig. 1). BOPAD-surf complements VL2016 by also providing
a balanced acquisition of variables during wintertime and harsh periods.
Considering variables and differences in acquisition and processing, only the
diffuse attenuation coefficients for downward irradiance at 412 and 490 nm
(i.e., Kd(412) and Kd(490)) are directly comparable
between the two databases. VL2016 offers a 25-band resolution of these
coefficients in the visible range, while BOPAD-surf extends such a
measurement to a single wavelength in the UV region (i.e., Kd(380))
and includes attenuation coefficients for one broad waveband (i.e.,
Kd(PAR)). Similarly, BOPAD-surf provides measurements of the
particulate optical backscattering at 700 nm, a band not included in VL2016
(27 bands between 405 and 683 nm). The main differences between the two
databases appear for the variables relating to Chl and colored dissolved or
detrital material. Because of calibration challenges for deriving
accurate Chl concentrations from in vivo fluorescence measurements (see
Sect. 6), VL2016 is only compiled with Chl concentrations obtained from
high-performance liquid chromatography (HPLC) and/or
spectrophotometric or fluorometric measurements on algal pigment extracts. FDOM is a different
parameter from adg(λ) in Valente et al. (2016). While
adg(λ) relies on the light absorption properties of the
whole pool of colored dissolved and/or particulate organic material, FDOM
only measures the fluorescence emitted by a fraction of this matter.
Depending on the excitation and emission wavelengths of the
sensor, FDOM can be a proxy of concentrations of freshly produced material or
more aged humic substances (Nelson and Gauglitz, 2016). However, in some
regions, FDOM can be significantly correlated to adg(λ)
and thus be retrievable from ocean color remote sensing (e.g., Matsuoka et
al., 2017). FDOM data included in BOPAD-surf also represent a useful resource
to improve the understanding of the optical behavior of the oceans (Organelli
et al., 2017). Finally, no measurements of remote sensing reflectance are
archived within BOPAD-surf, but successors of the PROVOR CTS4 profiling
floats used in BOPAD-surf are planned to be deployed in order to collect
multispectral downward irradiance and upwelling radiance measurements.
Data uncertainty
Through this section, characterization of the uncertainty associated with
each quality-controlled variable within BOPAD-prof, and for derived products
contained in BOPAD-surf, is provided. No error propagation and budgets are
presented
here.
When using fluorescence measurements as a proxy of Chl concentration, the
uncertainty may propagate from conversion of electronic counts in geophysical
quantities, through the application of quality-control procedures for the
influence of the NPQ and/or other environmental
variables (e.g., non-algal matter), to calibration corrections. The sensor
sensitivity of 0.007 mg m-3 (i.e., one digital count) is critical at the
surface of most oligotrophic environments or for deep low Chl values, where
it may be twice as high as the signal (Fig. 12a). Correction for the
NPQ may also introduce uncertainties depending on the
procedure and assumptions on which the method relies. However, a
comparison between the method by Xing et al. (2012) used here and based on
the calculation of the mixed layer depth, and an alternative correction
developed by Sackmann et al. (2008) based on the use of particulate optical
backscattering, showed similar performances for BGC-Argo Chl measurements
(X. Xing, unpublished data). As discussed by Xing et al. (2017), the
correction of Chl profiles for non-algal matter disturbance by using
alternative procedures with respect to the one applied here may also introduce errors,
which vary regionally and are the highest in the Black Sea area
(∼ 0.1 mg m-3), while the lowest are observed in the subpolar
North Atlantic Ocean and Mediterranean Sea (∼ 0.007 and 0.004 mg m-3,
respectively). A main challenge in quality-assessing fluorescence Chl
measurements relies on the assumption of what is measured and what is
actually phytoplankton biomass (Roesler et al., 2017). The
fluorescence-to-chlorophyll ratio depends on changes in nutrient
availability, growth phase, photophysiology, and taxonomic composition of
algal communities (Cullen, 1982). This implies that calibration factors may
change regionally and seasonally. Indeed, standard fluorometer corrections
rely on the comparison with contemporaneous HPLC-determined chlorophyll
concentrations, which are the most accurate estimates for phytoplankton
pigments. However, given the BGC-Argo particularity of sampling autonomously,
over long periods and across different regions, any HPLC-based calibration
performed at the time of the deployment may become invalid during the float's
voyage. Haëntjens et al. (2017) recommend the use of radiometric data
available on floats together with models (Xing et al., 2011) to
systematically verify the calibration of the Chl fluorometer, and applied
corrections, over time. In this study, no spatiotemporal variability in the
fluorescence-to-chlorophyll ratio has been taken into account to correct
BGC-Argo Chl measurements, and no radiometry-based corrections have
been used to avoid redundancy among variables and derived quantities (i.e.,
Kd(490)). Only the correction for the instrument-induced bias
recommended by Roesler et al. (2017) has been applied, though it might be
insufficient and thus under-correct Chl values measured at high latitudes and
especially in the Southern Ocean (Roesler et al., 2017). Chl profiles prior
to the application of any quality-control procedures used here, including
NPQ and the recommended calibration factor by Roesler
et al. (2017), are also archived in BOPAD-prof so that alternative chains of
protocols can be applied at the user's discretion.
FDOM measurements within BOPAD-prof appeared very noisy even after
quality control and spike detection (see Sect. 3). However, using the profile
in Fig. 4b as a specific example, the impact of the sensor sensitivity
(0.28 ppb of quinine sulfate, ∼ one digital count) on the measured values may be critical
for surface measurements (Fig. 12b). Low FDOM values at the surface may be a
result of the attenuation by other optically significant substances of the
light fluoresced by the dissolved material (Downing et al., 2012) and/or be
quenched as an effect of increasing temperature (Baker et al., 2005). No
specific methods for BGC-Argo floats measurements are currently available to
correct for the thermal fluorescence quenching properties, and it has been
preferred to avoid implementation of published procedures (Wratas et al.,
2011; Downing et al., 2012; Ryder et al., 2012) as they can be applied at the
user's discretion.
Uncertainties related to the particulate optical backscattering, as acquired
by WET Labs ECO sensors or instruments with similar or same technical and
geometrical characteristics, have been discussed by already published studies
(Dall'Olmo et al., 2009; Briggs et al., 2011; Sullivan et al., 2013; Poteau
et al., 2017). Experimental errors may arise from multiple sources such as
conversion and calibration coefficients (e.g., scaling factor and dark
counts), instrument age, and sensor responsiveness to environmental factors
such as temperature and light (Sullivan et al., 2013). The impact of the
sensor sensitivity (2.2 × 10-6 m-1) on the measured
values is low (Fig. 12c). The combined uncertainty is generally less than
10 % (Sullivan et al., 2013), but it may increase up to about 30 % in
most oligotrophic environments (Dall'Olmo et al., 2009). In particular, the
recent analysis by Poteau et al. (2017), which includes the same BGC-Argo
floats used in this study, suggests that more consistent bbp(700)
measurements would be achieved by taking into account a bias equal to
3.5 × 10-5 m-1 due to changes in dark counts from the
time of the sensor's purchase to that of deployment. Disagreement between
different sensor models measuring bbp(700) in the same areas may
yield a bias of up to 30 % (Poteau et al., 2017).
Experimental uncertainties in radiometric profiles may arise from instrument
tilt with respect to the vertical (maximum of ±10 %; E. Leymarie,
unpublished data) and sensor calibration (2–4 %; Hooker et al., 2002).
The shading of the float's antenna and conductivity–temperature–depth (CTD) sensor head is negligible for the
Ed(λ) sensor, except over a few degrees of the sun's azimuth
(direct shading; E. Leymarie, unpublished data). The study by Briggs et
al. (2017) on radiometers implemented on the PROVOR CTS4 BGC-Argo floats also
evidences the dependency of sensor dark counts on ambient temperature. The
uncertainty in factory dark measurements is the lowest near 20 ∘C
(< 0.01 µW cm-2 nm-1 for Ed(λ);
< 1.4 µmol quanta m-2 s-1 for PAR), for both
Ed(λ) and PAR. The highest errors occur when the radiometer
operates near 0 ∘C, as the uncertainty grows up to about
0.06 µW cm-2 nm-1 for Ed(490) and
2.6 µmol quanta m-2 s-1 for PAR. Similarly, higher
uncertainties are also observed when radiometric measurements are acquired
around 30 ∘C (∼ 0.03 µW cm-2 nm-1 for
Ed(412) and Ed(490); Briggs et al., 2017) rather than
near 20 ∘C. It is important to note, however, that dark offsets
generally affect profiles at depth as the irradiance drops to 0, whilst their
impact is less than 1 % for the highest values at the top of the ocean
(Organelli et al., 2016a).
In BOPAD-surf, the standard error is associated with each value of diffuse
attenuation coefficient for downward irradiance and PAR (Kd(λ) and Kd(PAR)) as derived from the linear fit on log quantities
within the first optical depth Zpd (see Sect. 2.4). Errors can
have an impact of up to 33 % on the measured coefficients, although the
median value for the entire database is less than 5 % regardless of the
waveband, with the minimum found for Kd(380) (i.e., 3.4 %).
Because Chl, FDOM, and bbp(700) represent the mean value of the
profile within Zpd, the standard deviations are archived in
BOPAD-surf. The median value of the coefficient of variation (CV%;
calculated as 100 (SD-to-mean ratio)), for the entire database, is
low for all three variables and around 5 % in the case of FDOM and
bbp(700). The variability in Chl concentration is close to
0 % as a consequence of the application of the method by Xing et
al. (2012), which corrects the NPQ by extrapolating the
Chl value at the bottom of the mixed layer to the surface. More importantly,
such a low variability in the observed variables suggests that they were
homogenously distributed within the first optical depth as derived from PAR
measurements and that Zpd was similar or shallower than the
mixed layer depth.
BOPAD-prof (Barbieux et al., 2017a) and BOPAD-surf
(Organelli et al., 2016b) are publicly available from the SEANOE (SEA scieNtific
Open data Edition) publisher at 10.17882/49388 and 10.17882/47142, respectively. BOPAD-surf version 2 is the one used and
described in this study. Float name, number of cycles, and profile, date,
latitude, and longitude are reported in both databases. In BOPAD-prof,
vertical profiles of Chl before quality control and bbp(700)
with removal of positive spikes (see Sect. 2.4) are also included. BOPAD-surf
includes standard errors of Kd(λ) and Kd(PAR) as
derived from a linear fit (see Sect. 2.4) and standard deviations of
averaged Chl, FDOM, and bbp(700) values within the first optical
depth. BGC-Argo raw data used in this study are publicly available online (Argo, 2000) and distributed as
netCDF files. Vertical profiles of bbp(532) collected in the
frame of the UK Bio-Argo (nine floats) and E-AIMS (five floats) projects can
be downloaded at 10.17882/42182 (Argo, 2000). Files
included in BOPAD-prof and BOPAD-surf can be read in table format by using
standard functions of most common programming languages.
Conclusions and recommendations for use
The first measurements of biogeochemical and bio-optical variables collected
by the PROVOR CTS4 generation of autonomous BGC-Argo floats have been
quality-controlled and synthesized into a single database of nearly 10 000
vertical profiles (BOPAD-prof), collected in just 3 years despite
meteorological conditions in several oceanic areas with depths greater than
1000 m. Profile-derived bio-optical variables within the first optical depth
have also been condensed into a database dedicated to support field and remote
bio-optical applications (BOPAD-surf). Spatial and temporal coverages have
been presented. Possible uncertainties for each variable have been provided.
The two databases presented here can be directly exploited for several
applications, from biogeochemistry and primary production estimation and
modeling, to the analysis of the physical forcing on biology together with
the assessment of any seasonal and sub-seasonal dependence, and to the
evaluation of the ocean's bio-optical variability. For specific examples based on
same PROVOR CTS4 profiling floats included in this study, the reader is
referred to the works by Dall'Olmo and Mork (2014) and Poteau et al. (2017)
for estimation and analysis of particulate organic carbon concentrations and
fluxes; Lacour et al. (2017), Mignot et al. (2017), and Stanev et al. (2017)
for observing the impact of physical drivers on biology; and Organelli et al. (2017) and
Barbieux et al. (2017b) for analysis of the variability in diffuse
attenuation coefficients for downward irradiance and particulate optical
backscattering-to-chlorophyll ratios across different oceanic areas,
respectively. It is worth noting that the latter two studies have been
pursued by exclusively exploiting BOPAD-surf and BOPAD-prof. The new and
systematic way BGC-Argo floats collect data, and their potential in
dramatically increasing oceanic observations in a restricted time, also
supplement and complement published carbon cycle and optically relevant
pan-oceanic data compilations (Peloquin et al., 2013; Sauzède et al.,
2015; Bakker et al., 2016; Mouw et al., 2016; Valente et al., 2016).
BOPAD-surf has also proved to directly support ocean color algorithm and
product validation. Online platforms (i.e., http://seasiderendezvous.eu)
are already available to support nearly real-time ocean color applications and
interactive management of BGC-Argo profiles. We remind readers that, according to the
specific use intended for these data, further processing may be needed.
Additional corrections, e.g., dark counts and temperature dependence for
radiometric or FDOM measurements, might be required at the user's discretion.
Additional or regional adjustments on the calibration factor for chlorophyll
fluorescence might also be needed (Roesler et al., 2017). The quality-control
procedures applied here remove only major, known sensor issues.
Finally, these two databases are a first step to provide users with the
unprecedented quantity of autonomous in situ measurements processed with
common internationally accepted procedures. However, due to the
characteristics of the Biogeochemical-Argo network (Johnson and Claustre,
2016; Biogeochemical Argo Group, 2016) and its youthfulness, both databases
are likely to evolve as new regions are explored, improved vertical and
temporal frequency is achieved, and more advanced quality-control procedures
are developed. Therefore, it is expected that BOPAD-prof and BOPAD-surf
could be amended and/or enriched in the future with new quality-controlled profiles
and products, or they might be merged with other already-operating configurations
of autonomous profiling floats and sensors (e.g., Johnson et al., 2017). The
way the two databases have been built makes them potentially fully
interoperable with future compilations.
Region, basin, abbreviation, and a list of the
BGC-Argo floats for the 25 geographic regions included in the Biogeochemical-Argo
database. Lifetime and average profile interval for each float is shown.
Research project and principal investigator are also reported for each float.
Note that the total number of floats is > 105 because some floats moved
across two or more basins during their lifetime. An average profile interval
of > 10 days indicates a temporary loss of communication between the server
and the profiling float, which resulted in periods of inactivity. Dates in the table are given as dd/mm/yy.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study received funds and support from the following research projects:
remOcean (funded by the European Research Council, grant agreement no.
246777), NAOS (funded by the Agence Nationale de la Recherche in the frame of
the French “Equipement d'avenir” program, grant agreement no. ANR
J11R107-F), AtlantOS (funded by the European Union's Horizon 2020 research
and innovation program, grant agreement no. 2014-633211), E-AIMS (funded by
the European Commission's FP7 project, grant agreement no. 312642), UK
Bio-Argo (funded by the Natural Environment Research Council, grant agreement
no. NE/L012855/1), REOPTIMIZE (funded by the European Union's Horizon 2020
research and innovation program, Marie Skłodowska-Curie grant agreement
no. 706781), Argo-Italy (funded by the Italian Ministry of Education,
University and Research, MIUR), and the French Bio-Argo program (Bio-Argo
France; funded by CNES-TOSCA, LEFE Cyber, and GMMC). We thank the
principal investigators
of several BGC-Argo float missions and projects: Sorin Balan (GeoEcoMar,
Romania), Pascal Conan (Observatoire Océanologique de Banyuls sur mer,
France; Bio-Argo France), Laurent Coppola (Laboratoire d'Océanographie de
Villefranche, France; Bio-Argo France), Claire Lo Monaco (Laboratoire
d'Océanographie et du Climat, France; OISO program), Kjell Arne Mork
(Institute of Marine Research, Norway; E-AIMS), Anne Petrenko (Mediterranean
Institute of Oceanography, France; Bio-Argo France), Pierre-Marie Poulain
(National Institute of Oceanography and Experimental Geophysics, Italy;
Argo-Italy), Jean-Baptiste Sallée (Laboratoire d'Océanographie et du
Climat, France; Bio-Argo France), Violeta Slabakova (Bulgarian Academy of
Sciences, Bulgaria; E-AIMS), Sabrina Speich (Laboratoire de
Météorologie Dynamique, France; Bio-Argo France), Emil Stanev
(University of Oldenburg, Germany; E-AIMS), and Virginie Thierry (Ifremer,
France; Bio-Argo France). We also thank the Coriolis program for providing
standard Argo floats to be equipped with additional bio-optical and
biogeochemical sensors. Sandy Thomalla (CSIR, South Africa) and the anonymous
reviewer are acknowledged for constructive comments and suggestions on a
previous version of the paper. Edited by:
David Carlson Reviewed by: Sandy Thomalla and one anonymous
referee
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