The Global Ocean Data Analysis Project (GLODAP) is a
synthesis effort providing regular compilations of surface-to-bottom ocean
biogeochemical bottle data, with an emphasis on seawater inorganic carbon
chemistry and related variables determined through chemical analysis of
seawater samples. GLODAPv2.2022 is an update of the previous version,
GLODAPv2.2021 (Lauvset et al., 2021). The major changes are as follows: data
from 96 new cruises were added, data coverage was extended until 2021, and
for the first time we performed secondary quality control on all sulfur
hexafluoride (SF6) data. In addition, a number of changes were made to
data included in GLODAPv2.2021. These changes affect specifically the
SF6 data, which are now subjected to secondary quality control, and
carbon data measured on board the RV Knorr in the Indian Ocean in 1994–1995 which
are now adjusted using certified reference material (CRM) measurements made at the time. GLODAPv2.2022
includes measurements from almost 1.4 million water samples from the global
oceans collected on 1085 cruises. The data for the now 13 GLODAP core
variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved
inorganic carbon, total alkalinity, pH, chlorofluorocarbon-11 (CFC-11), CFC-12, CFC-113, CCl4,
and SF6) have undergone extensive quality control with a focus on
systematic evaluation of bias. The data are available in two formats: (i) as
submitted by the data originator but converted to World Ocean Circulation
Experiment (WOCE) exchange format and (ii) as a merged data product with
adjustments applied to minimize bias. For the present annual update,
adjustments for the 96 new cruises were derived by comparing those data with
the data from the 989 quality-controlled cruises in the GLODAPv2.2021 data
product using crossover analysis. SF6 data from all cruises were
evaluated by comparison with CFC-12 data measured on the same cruises. For
nutrients and ocean carbon dioxide (CO2) chemistry comparisons to
estimates based on empirical algorithms provided additional context for
adjustment decisions. The adjustments that we applied are intended to remove
potential biases from errors related to measurement, calibration, and data
handling practices without removing known or likely time trends or
variations in the variables evaluated. The compiled and adjusted data
product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate,
4 µmol kg-1 in dissolved inorganic carbon, 4 µmol kg-1
in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in
the halogenated transient tracers. The other variables included in the
compilation, such as isotopic tracers and discrete CO2 fugacity
(fCO2), were not subjected to bias comparison or adjustments.
The original data, their documentation, and DOI codes are available at the
Ocean Carbon and Acidification Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/, last access: 15 August 2022). This site also provides access to the
merged data product, which is provided as a single global file and as four
regional ones – the Arctic, Atlantic, Indian, and Pacific oceans –
under 10.25921/1f4w-0t92 (Lauvset et al.,
2022). These bias-adjusted product files also include significant ancillary
and approximated data, which were obtained by interpolation of, or
calculation from, measured data. This living data update documents the
GLODAPv2.2022 methods and provides a broad overview of the secondary quality
control procedures and results.
Introduction
The oceans mitigate climate change by absorbing both atmospheric CO2
corresponding to a significant fraction of anthropogenic CO2 emissions
(Friedlingstein et al., 2019; Gruber et al., 2019) and most of the excess
heat in the Earth system caused by the enhanced greenhouse effect (Cheng et
al., 2017, 2020). The objective of GLODAP (Global Ocean Data
Analysis Project; http://www.glodap.info, last access: 27 June 2022) is to provide
high-quality and bias-corrected water column bottle data from the ocean
surface to the sea floor. These data should be used to document the state
and the evolving changes in physical and chemical ocean properties, e.g.,
the inventory of anthropogenic CO2 in the ocean, natural oceanic
carbon, ocean acidification, ventilation rates, oxygen levels, and vertical
nutrient transports (Tanhua et al., 2021). The core quality-controlled and
bias-adjusted variables of GLODAP are salinity, dissolved oxygen, inorganic
macronutrients (nitrate, silicate, and phosphate), seawater CO2
chemistry variables (dissolved inorganic carbon – TCO2, total
alkalinity – TAlk, and pH on the total hydrogen ion, or H+, scale),
the halogenated transient tracers chlorofluorocarbon-11 (CFC-11), CFC-12,
CFC-113, carbon tetrachloride (CCl4), and sulfur hexafluoride
(SF6).
Other chemical tracers are measured on many cruises included in GLODAP, such
as dissolved organic carbon and nitrogen, as well as stable and radioactive isotope
ratios. In many cases, a subset of these data is distributed as part of the
GLODAP data product; however, such data have not been extensively quality
controlled or checked for measurement biases in this effort. For some of
these variables better sources of data exist, for example the product by
Jenkins et al. (2019) for helium isotope and tritium data. GLODAP also
includes some common derived variables to facilitate interpretation, such as
potential density anomalies and apparent oxygen utilization (AOU). A full
list of variables included in the data product is provided in Table 1.
Variables in the GLODAPv2.2022 comma separated (csv) product files,
their units, short and flag names, and corresponding names in the individual
cruise exchange files. In the MATLAB product files that are also supplied a
“G2” has been added to every variable name (e.g., G2cruise).
VariableUnitsProduct fileWOCE flagSecond QC flagWHP-exchangenamenameanamebnameEXPOCODEexpocodeDigital object identifierdoiAssigned sequential cruise numbercruiseBasin identifiercregionStationstationSTNNBRCastcastCASTNOYearyearDATEMonthmonthDATEDaydayDATEHourhourTIMEMinuteminuteTIMELatitudelatitudeLATITUDELongitudelongitudeLONGITUDEBottom depthmbottomdepthPressure of the deepest sampledbarmaxsampdepthDEPTHNiskin bottle numberbottleBTLNBRSampling pressuredbarpressureCTDPRSSampling depthmdepthTemperature∘CtemperatureCTDTMPpotential temperature∘CthetaSalinitysalinitysalinityfsalinityqcCTDSAL/SALNTYPotential density anomalykg m-3sigma0(salinityf)Potential density anomaly, ref 1000 dbarkg m-3sigma1(salinityf)Potential density anomaly, ref 2000 dbarkg m-3sigma2(salinityf)Potential density anomaly, ref 3000 dbarkg m-3sigma3(salinityf)Potential density anomaly, ref 4000 dbarkg m-3sigma4(salinityf)Neutral density anomalykg m-3gamma(salinityf)Oxygenµmol kg-1oxygenoxygenfoxygenqcCTDOXY/OXYGENApparent oxygen utilizationµmol kg-1aouaoufNitrateµmol kg-1nitratenitratefnitrateqcNITRATNitriteµmol kg-1nitritenitritefNITRITSilicateµmol kg-1silicatesilicatefsilicateqcSILCATPhosphateµmol kg-1phosphatephosphatefphosphateqcPHSPHTTCO2µmol kg-1tco2tco2ftco2qcTCARBONTAlkµmol kg-1talktalkftalkqcALKALIpH on total scale, 25 ∘C, and 0 dbar of pressurephts25p0phts25p0fphtsqcPH_TOTpH on total scale, in situ temperature, and pressurephtsinsitutpphtsinsitutpfphtsqcfCO2 at 20 ∘C and 0 dbar of pressureµatmfco2fco2fFCO2/PCO2fCO2 temperatured∘Cfco2temp(fco2f)FCO2_TMP/PCO2_TMPCFC-11pmol kg-1cfc11cfc11fcfc11qcCFC-11pCFC-11pptpcfc11(cfc11f)CFC-12pmol kg-1cfc12cfc12fcfc12qcCFC-12pCFC-12pptpcfc12(cfc12f)CFC-113pmol kg-1cfc113cfc113fcfc113qcCFC-113pCFC-113pptpcfc113(cfc113f)CCl4pmol kg-1ccl4ccl4fccl4qcCCL4pCCl4pptpccl4(ccl4f)SF6fmol kg-1sf6sf6fsf6qcSF6pSF6pptpsf6(sf6f)δ13C‰c13c13fc13qcDELC13Δ14C‰c14c14fDELC14Δ14C counting error‰c14errC14ERR3HTUh3h3fTRITIUM3H counting errorTUh3errTRITERδ3He%he3he3fDELHE3
a The only derived variable assigned a separate WOCE flag is AOU as it
depends strongly on both temperature and oxygen (and less strongly on
salinity). For the other derived variables, the applicable WOCE flag is
given in parentheses. b Secondary QC flags indicate whether data have
been subjected to full secondary QC (1) or not (0), as described in Sect. 3.
c 1 is the Atlantic Ocean, 4 is the Arctic Mediterranean Sea (i.e.,
the Arctic Ocean plus the Nordic Seas), 8 is the Pacific Ocean, and 16 is
the Indian Ocean. d Included for clarity and is 20 ∘C for all
occurrences. e Units have not been checked; some values in micromoles per
kilogram (for TOC, DOC, DON, TDN) or microgram per liter (for Chl a) are
probable.
The oceanographic community largely adheres to principles and practices for
ensuring open access to research data, such as the FAIR (Findable,
Accessible, Interoperable, Reusable) initiative (Wilkinson et al., 2016),
but the plethora of file formats and different levels of documentation,
combined with the need to retrieve data on a per cruise basis from different
access points, limit the realization of their full scientific potential. In
addition, the manual data retrieval is time consuming and prone to data
handling errors (Tanhua et al., 2021). For biogeochemical data there is the
added complexity of different levels of standardization and calibration and
even different units and scales used for the same variable such that the
comparability between datasets is often poor. Standard operating procedures
have been developed for some variables (Dickson et al., 2007; Hood et al.,
2010; Becker et al., 2020), and certified reference materials (CRMs) exist
for seawater TCO2 and TAlk measurements (Dickson et al., 2003) and
reference materials for nutrients in seawater (RMNS, certified based on
International Organization for Standardization Guide 34; Aoyama et al.,
2012; Ota et al., 2010). Despite all this, biases in data still exist. These
can arise from poor sampling and preservation practices, calibration
procedures, instrument design and calibration, and inaccurate calculations.
The use of CRMs does not by itself ensure accurate measurements of seawater
CO2 chemistry (Bockmon and Dickson, 2015), and the RMNS have only
become available recently and are not universally used. For salinity and
oxygen, the lack of calibration of the data from
conductivity–temperature–depth (CTD) profiler mounted sensors is an
additional and widespread problem, particularly for oxygen (Olsen et al.,
2016). For halogenated transient tracers, uncertainties in standard gas
composition, extracted water volume, and purge efficiency typically provide
the largest sources of uncertainty. In addition to bias, occasional outliers
occur. In rare cases poor precision – many multiples worse than that
expected with current measurement techniques – can render a set of data of
limited use. GLODAP deals with these issues by presenting the data in a
uniform format, including any metadata either publicly available or
submitted by the data originator, and by subjecting the data to rigorous
primary and secondary quality control assessments, focusing on precision and
consistency, respectively. The secondary quality control focuses on deep
data, in which natural variability is minimal. Adjustments are applied to
the data to minimize cases of bias that could be confidently established
relative to the measurement precision for the variables and cruises
considered. Key metadata are provided in the header of each data file, and
original unadjusted data along with full cruise reports submitted by the
data providers (where available) are accessible through the GLODAPv2 cruise
summary table hosted by the Ocean Carbon and Acidification Data System
(OCADS) at the National Oceanographic and Atmospheric Administration (NOAA)
National Centers for Environmental Information (NCEI) (https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/cruise_table_v2022.html, last access: 15
August 2022).
This most recent GLODAPv2.2022 data product builds on earlier synthesis
efforts for biogeochemical data obtained from research cruises, namely,
GLODAPv1.1 (Key et al., 2004; Sabine et al., 2005), Carbon dioxide in the
Atlantic Ocean (CARINA) (Key et al., 2010), Pacific Ocean Interior Carbon
(PACIFICA) (Suzuki et al., 2013), and notably GLODAPv2 (Olsen et al., 2016).
GLODAPv1.1 combined data from 115 cruises with biogeochemical measurements
from the global ocean. The vast majority of these were the sections covered
during the World Ocean Circulation Experiment and the Joint Global Ocean
Flux Study (WOCE/JGOFS) in the 1990s, but data from important “historical”
cruises were also included, such as from the Geochemical Ocean Sections
Study (GEOSECS), Transient Traces in the Ocean (TTO), and South Atlantic
Ventilation Experiment (SAVE). GLODAPv2, which forms the basis for the
update presented here, was released in 2016 with data from 724 scientific
cruises, including those from GLODAPv1.1, CARINA, and PACIFICA, as well as
data from 168 additional cruises. GLODAPv2 not only combined all previous
efforts, but it also created ocean-wide consistency across all cruise data
through an inversion analysis. A particularly important source of additional
data was the cruises executed within the framework of the “repeat
hydrography” program (Talley et al., 2016), instigated in the early 2000s
as part of the Climate and Ocean – Variability, Predictability and Change
(CLIVAR) program and since 2007 organized as the Global Ocean Ship-based
Hydrographic Investigations Program (GO-SHIP) (Sloyan et al., 2019).
GLODAPv2 is updated regularly using the “living data process” of Earth System Science Data to
document significant additions and modifications to the data product.
There are two types of GLODAP updates: full and intermediate. Full updates
involve a reanalysis, notably crossover and inversion, of the entire dataset
(both historical and new cruises) in which all data points are subject to
potential adjustment. This was carried out for the creation of GLODAPv2. For
intermediate updates, recently available data are added following quality
control procedures to ensure their consistency with the cruises included in
the latest GLODAP release. Except for obvious outliers and similar types of
errors (Sect. 3.3.1), the data from previous releases are not changed or
adjusted during intermediate updates. Note that the GLODAP mapped
climatologies (Lauvset et al., 2016) are not updated for these intermediate
products. A naming convention has been introduced to distinguish
intermediate from full product updates. For the latter the version number
will change, while for the former the year of release is appended. The exact
version number and release year (if appended) of the product used should
always be reported in studies rather than making a generic reference to
GLODAP.
Creating and interpreting inversions, as well as other checks of the entire
dataset needed for full updates, are too demanding in terms of time and
resources to be performed every year or every 2 years. The aim is to conduct
a full analysis (i.e., including an inversion) again after the third GO-SHIP
survey has been completed. This completion is currently scheduled for 2024,
and we anticipate that GLODAPv3 will become available a few years thereafter
(pending funding). In the interim, the fourth intermediate update is
presented here, which adds data from 96 cruises to the last update,
GLODAPv2.2021 (Lauvset et al., 2021).
Key features of the update
GLODAPv2.2022 contains data from 1085 cruises covering the global ocean from
1972 to 2021, compared to 989 for the period 1972–2020 for the previous
GLODAPv2.2021 (Lauvset et al., 2021). Information about the 96 cruises added
to this version is provided in Table A1 in the Appendix. Cruise sampling
locations are shown alongside those of GLODAPv2.2021 in Fig. 1, while the
coverage in time is shown in Fig. 2. Not all cruises have data for all the
above-mentioned 13 core variables. For example, cruises with only seawater
CO2 chemistry or transient tracer data are still included even without
accompanying nutrient data due to their value towards the computation of carbon
inventories. In a few cases, cruises without any of these properties are
included because they do contain data for other carbon-related tracers such
as carbon isotopes, with the intention of ensuring their wider availability.
The added cruises are from 2003 to 2021, with the majority being more recent
than 2018. The largest data contribution comes from the Coastal Ocean Data
Analysis Product in North America (CODAP-NA; Jiang et al., 2021), which is a
comprehensive compilation of carefully quality-assessed coastal carbon data
covering all continental shelves of North America, from Alaska to Mexico in
the west and from Canada to the Caribbean in the east. Another large
addition are the 29 new cruises from the RV Keifu Maru II and RV Ryofu Maru III in the western North
Pacific (Oka et al., 2018, 2017). In the Arctic Ocean we update
the time series from Weather Station M in the Norwegian Sea with an
additional 10 years of data and add five new Arctic cruises from RV
Healy. In the Indian Ocean the 2019 repeat of GO-SHIP line I08N by the RV Mirai is
included. In addition, we are for the first time including the cruises in
the GEOTRACES intermediate data product where seawater CO2 chemistry
data are available (https://www.geotraces.org/geotraces-intermediate-data-product-2021/, last
access: 23 June 2022). The GEOTRACES mission is “to identify processes and
quantify fluxes that control the distributions of key trace elements and
isotopes in the ocean, and to establish the sensitivity of these
distributions to changing environmental conditions”, but several cruises
that measure trace elements and isotopes also measure CO2 chemistry, and
these have now been included in GLODAPv2. All new data in GLODAPv2.2022
include seawater CO2 chemistry, and additionally, 10 new cruises
include halogenated transient tracers.
Location of stations in (a) GLODAPv2.2021 and for (b) the new data
added in this update.
All new cruises were subjected to primary (Sect. 3.1) and secondary (Sect. 3.2) quality control (QC). These procedures are very similar to those used
for GLODAPv2.2021 and previous versions, aiming to ensure the consistency of
the data from the 96 new cruises with the previous release of the GLODAP
data product (in this case, the GLODAPv2.2021 adjusted data product). For
the first time we also apply secondary QC routines to SF6 data, thus
increasing the number of core variables from 12 to 13.
For GLODAPv2.2021 we added a basin identifier to the product files, where 1
is the Atlantic Ocean, 4 the Arctic Mediterranean Sea (i.e., the Arctic
Ocean plus the Nordic Seas), 8 the Pacific Ocean, and 16 the Indian Ocean.
These regions are abbreviated AO, AMS, PO, and IO, respectively, in the
adjustment table. Data in the Mediterranean Sea, Caribbean Sea, and Gulf of
Mexico are classified as belonging to the Atlantic Ocean (1). The basin
identifiers are unchanged in GLODAPv2.2022 and added to the product files to
make it easier for users to identify which ocean basin an individual cruise
belongs to without having to use one of the four regional files. Note that
there is no overlap between the regional files or for our basin
identifiers, and cruises in the Southern Ocean are placed in the basin where
most of the data were collected. As in GLODAPv2.2021 we include the DOI for
each cruise in all product files with the aim of easing access to the
original data and metadata, as well as improving the visibility of data
providers.
MethodsData assembly and primary quality control
Data from the 96 new cruises were submitted directly to us or retrieved from
data centers – typically OCADS
(https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system,
last access: 9 August 2022), the CLIVAR and Carbon Hydrographic Data Office
(https://cchdo.ucsd.edu, last access: 27 June 2022), and
PANGAEA (https://pangaea.de, last access: 27 June 2022). Each
cruise is identified by an expedition code (EXPOCODE). The EXPOCODE is
guaranteed to be unique and constructed by combining the country code and
platform code with the date of departure in the format YYYYMMDD. The country
and platform codes were taken from the ICES (International Council for the
Exploration of the Sea) library (https://vocab.ices.dk/, last access: 27 June
2022).
The individual cruise data files were converted to the WHP-exchange format:
a comma-delimited ascii format for data from hydrographic cruises, with
different and specific versions for CTD and bottle data. GLODAP only
includes WHP-exchange in bottle format, with data and CTD data at bottle
trip depths. An overview of the significant points is given below, with full
details provided at https://exchange-format.readthedocs.io/
(v1.2.0 as of 22 March 2022, last access: 16 June 2022), derived from Swift and
Diggs (2008). The first line of each exchange file specifies the data type
– in the case of GLODAP this is “BOTTLE” – followed by a creation
date time stamp in ISO8601 (YYYYMMDD) format, as well as the identification of the
group and person who prepared the file. The latter follows a convention of
including the division/group, the institution, and the initials of the
person. The omnipresent “PRINUNIVRMK” thus acknowledges the enormous
effort by Robert M. Key at Princeton University. Next follows the README
section, which provides brief cruise-specific information, such as dates,
ship, region, method plus quality notes for each variable measured, citation
information, and references to any papers that used or presented the data.
The README information is typically assembled from the information contained
in the metadata submitted by the data originator. In some cases, issues
noted during the primary QC and other information such as file update notes
are included. The only rule for the README section is that it must be
concise and informative, and each line must start with the comment character (#). The README is followed by variable names and units on separate lines
and then the data. The names and units are standardized and provided in
Table 1 for the variables included in GLODAP, with full specifications
provided at https://exchange-format.readthedocs.io/en/latest/parameters.html (v1.2.0
as of 22 March 2022, last access: 16 June 2022). For consistency with previous
updates and to ease the use of existing methods and code, GLODAP still uses
the WHP-exchange format instead of adopting the new naming structure as
outlined in Jiang et al. (2022).
Exchange file preparation required unit conversion in some cases, most
frequently from concentrations expressed as milliliters per liter (mL L-1; oxygen) or micromoles per liter (µmol L-1; nutrients) to
substance contents expressed as micromoles per kilogram of seawater (µmol kg-1). Procedures as described in Jiang et al. (2022) were used
for these conversions. The default conversion procedure for nutrients was to
use seawater density at reported salinity, an assumed
measurement temperature of 22 ∘C, and pressure of 1 atm.
For oxygen, the factor 44.66 was used for the “milliliters of oxygen” to
“micromoles of oxygen” conversion, while the density required for the
“per liter” to “per kilogram” conversion was calculated from the
reported salinity and draw temperatures whenever possible. However,
potential density was used instead when draw temperature was not reported.
The potential errors introduced by any of these procedures are
insignificant. Missing numbers are indicated by -999.
WOCE flags in GLODAPv2.2022 exchange-format original data files
(briefly; for full details see Swift, 2010) and the simplified scheme used
in the merged product files.
WOCE flag valueInterpretation Original data exchange filesMerged product files0Flag not usedInterpolated or calculated value1Data not receivedFlag not useda2AcceptableAcceptable3QuestionableFlag not usedb4BadFlag not usedb5Value not reportedFlag not usedb6Average of replicateFlag not usedc7Manual chromatographic peak measurementFlag not usedc8Irregular digital peak measurementFlag not usedb9Sample not drawnNo data
a Flag set to 9 in product files.
b Data are not included in the GLODAPv2.2022 product files and their
flags set to 9.
c Data are included, but flag is set to 2.
Each data column (except temperature and pressure, which are assumed
“good” if they exist) has an associated column of data flags (Joyce and
Corry, 1994). For the original data exchange files, these flags conform to
the WOCE definitions for water samples and are listed in Table 2. For the
merged and adjusted product files these flags are simplified: questionable
(WOCE flag 3) and bad (WOCE flag 4) data are removed, and their flags are set
to 9. The same procedure is applied to data flagged 8 (very few such data
exist); 1 (data not received) and 5 (data not reported) are also set to 9,
while flags of 6 (mean of replicate measurements) and 7 (manual
chromatographic peak measurement) are set to 2 if the data appear good.
Also, in the merged product files a flag of 0 is used to indicate a value
that could be measured but is approximated: for salinity, oxygen, phosphate,
nitrate, and silicate, the approximation is conducted using vertical
interpolation; for seawater CO2 chemistry variables (TCO2, TAlk,
pH, and fCO2), the approximation is conducted using the calculation from
two measured CO2 chemistry variables (Sect. 3.2.2). Importantly, the
interpolation of CO2 chemistry variables is never performed, and thus a
flag value of 0 has a unique interpretation.
If no WOCE flags were submitted with the data, then they were assigned by
us. Regardless, all incoming files were subjected to primary QC to detect
questionable or bad data – this was carried out following Sabine et al. (2005) and Tanhua et al. (2010), primarily by inspecting property–property
plots. For this task, the GLODAP primary quality control software (Velo et
al., 2021) was used, as it presents a custom pre-defined schema of
property–property plots designed by the consortium to ease the detection of
outliers. Outliers showing up in two or more different such plots were
generally defined as questionable and flagged. In some cases, outliers were
detected during the secondary QC; the consequent flag changes have then also
been applied in the GLODAP versions of the original cruise data files in
agreement with the data submitter.
Secondary quality control
The aim of the secondary QC was to identify and correct any significant
biases in the data from the 96 new cruises relative to GLODAPv2.2021 while
retaining any signal due to temporal changes. To this end, secondary QC in
the form of consistency analyses was conducted to identify offsets in the
data. All identified offsets were scrutinized by the GLODAP reference group
through a series of teleconferences during May 2022 to decide the
adjustments to be applied to reduce the apparent offset (if any). To guide
this process, a set of initial minimum adjustment limits was used (Table 3).
These represent the minimum bias that can be confidently established
relative to the measurement precision for the variables and cruises
considered and are the same as those used for GLODAPv2.2021. In addition to
the average magnitude of the offsets, factors such as the precision of the
offsets, persistence towards the various cruises used in the comparison,
regional dynamics, and the occurrence of time trends or other variations
were considered. Thus, not all offsets larger than the initial minimum
limits have been adjusted. A guiding principle for these considerations was
to not apply an adjustment whenever in doubt. Conversely, in some cases when
data and offsets were very precise and the cruise had been conducted in a
region where variability is expected to be small, adjustments lower than the
minimum limits were applied. Any adjustment was applied uniformly to all
values for a variable and cruise; i.e., an underlying assumption is that
cruises suffer from either no or a single and constant measurement bias.
Adjustments for salinity, TCO2, TAlk, and pH are always additive, while
adjustments for oxygen, nutrients, and the halogenated transient tracers are
always multiplicative. Except where explicitly noted (Sect. 3.3.1 and Table A2 in the Appendix) adjustments were not changed for data previously
included in GLODAPv2.2021.
Initial minimum adjustment limits. These limits represent the
minimum bias that can be confidently established relative to the measurement
precision for the variables and cruises considered. Note that these limits
are not uncertainties but rather a priori estimates of global inter-cruise
consistency in the data product.
Crossover comparisons were the primary source of information used to
identify offsets for salinity, oxygen, nutrients, TCO2, TAlk, and pH
(Sect. 3.2.2). As in GLODAPv2.2021 and GLODAPv2.2020 but in contrast to
GLODAPv2 and GLODAPv2.2019, the evaluation of the internal consistency of
the seawater CO2 chemistry variables was not used for the evaluation of
pH (Sect. 3.2.3). As in the two previous updates (2020 and 2021) we made
extensive use of two predictions from two empirical algorithms – CArbonate
system And Nutrients concentration from hYdrological properties and Oxygen
using a Neural-network version B (CANYON-B) and CONsisTency EstimatioN and
amounT (CONTENT) (Bittig et al., 2018) – for the evaluation of offsets in
nutrients and seawater CO2 chemistry data (Sect. 3.2.4). For previous
versions we have also used multiple linear regression analyses and deep
water averages, broadly following Jutterström et al. (2010), for
additional information for the secondary QC of salinity, oxygen, nutrients,
TCO2, and TAlk data. In GLODAPv2.2022 we did not have to rely on the
results of the multiple linear regression (MLR) analyses to make decisions about adjustments, and, in
general, we are increasingly moving towards only using CANYON-B and CONTENT
estimates (Sect. 3.2.4) as additional information when the crossover
analysis is insufficient.
For the halogenated transient tracers, comparisons of surface saturation
levels and the relationships among the tracers were used to assess the data
consistency (Sect. 3.2.5). For salinity and oxygen, CTD and bottle values
were merged into a “hybrid” variable prior to the consistency analyses
(Sect. 3.2.1).
Merging of sensor and bottle data
Salinity and oxygen data can be obtained by analysis of water samples
(bottle data) and/or directly from the CTD sensor pack. These two
measurement types are merged and presented as a single variable in the
product. The merging was conducted prior to the consistency checks, ensuring
their internal calibration in the product. The merging procedures were only
applied to the bottle data files, which commonly include values recorded by
the CTD at the pressures where the water samples are collected. Whenever
both CTD and bottle data were present in a data file, the merging step
considered the deviation between the two and calibrated the CTD values if
required and possible. Altogether seven scenarios (Table 4) are possible for
each of the CTD conductivity and oxygen (O2) sensor properties
individually, in which the fourth never occurred during our analyses but is
included to maintain consistency with GLODAPv2. For 39 % of the 96 new
cruises both CTD and bottle data were included in the original cruise files
for salinity and oxygen, and for all these cruises the two data types were
found to be consistent. These new data have a lower proportion of cruises
with both bottle and CTD measurements than GLODAPv2.2021 (75 % and 63 %, respectively, for salinity and oxygen). For salinity the remaining 61 % have only CTD data, while for oxygen 30 % have only CTD data and 21 % have only bottle data. Having both CTD and bottle values in the data
files is highly preferred as the information is valuable for quality control
(bottle mistrips, leaking Niskin bottles, and oxygen sensor drift are among
the issues that can be revealed). The extent to which the bottle data (i.e.,
OXYGEN in the individual cruise exchange files) is mislabeled CTD data
(i.e., should be CTDOXY) is uncertain. Regardless, all CTD and bottle data
for salinity were consistent and did not need any further calibration, and
only 3 out of the 96 cruises required calibration of the oxygen data.
Summary of salinity and oxygen calibration needs and actions;
number of cruises with each of the scenarios identified.
CaseDescriptionSalinityOxygen1No data are available: no action needed.072No bottle values are available: use CTD values.58303No CTD values are available: use bottle values.0194Too few data of both types are available for comparison, and > 80 % of the records have bottle values: use bottle values.005The CTD values do not deviate significantly from bottle values: replace missing bottle values with CTD values.38376The CTD values deviate significantly from bottle values: calibrate CTD values using linear fit and replace missing bottle values with calibrated CTD values.017The CTD values deviate significantly from bottle values, and no good linear fit can be obtained for the cruise: use bottle values and discard CTD values.02Crossover analyses
The crossover analyses were conducted with the MATLAB toolbox prepared by
Lauvset and Tanhua (2015) and with GLODAPv2.2021 as the reference data
product. The toolbox implements the “running-cluster” crossover analysis
first described by Tanhua et al. (2010). This analysis compares data from
two cruises on a station-by-station basis and calculates a weighted mean
offset between the two and its weighted standard deviation. The weighting is
based on the scatter in the data such that data that have less scatter have
a larger influence on the comparison than data with more scatter. Whether
the scatter reflects actual variability or data precision is irrelevant in
this context as increased scatter nevertheless decreases the confidence in
the comparison. Stations are compared when they are within 2 arcdeg distance
(∼ 200 km) of each other. To minimize the effects of natural
variability only deep data are used. Either the 1500 or 2000 dbar pressure
surface was used as the upper bound, depending on the amount of available data,
their variation at different depths, and the region in question. Which one
to use was determined on a case-by-case basis by comparing crossovers with
the two depth limits and using the one that provided the clearest and most
robust information. In regions where deep mixing or convection occurs, such
as the Nordic, Irminger, and Labrador seas, the upper bound was always placed
at 2000 dbar; while winter mixing in the first two regions is normally not
deeper than this (Brakstad et al., 2019; Fröb et al., 2016), convection
beyond this limit has occasionally been observed in the Labrador Sea
(Yashayaev and Loder, 2017). However, using an upper depth limit deeper than
2000 dbar will quickly give too few data for robust analysis. In addition,
even below the deepest winter mixed layers, properties do change over the
time periods considered (e.g., Falck and Olsen, 2010), so this limit does
not guarantee steady conditions. In the Southern Ocean deep convection
beyond 2000 dbar seldom occurs, an exception being the processes
accompanying the formation of the Weddell Polynya in the 1970s (Gordon,
1978). Deep and bottom water formation usually occurs along the Antarctic
coasts, where relatively thin nascent dense water plumes flow down the
continental slope. We avoid such cases, which are easily recognizable. To
avoid removing persistent temporal trends, all crossover results are also
evaluated as a function of time (see below).
As an example of crossover analysis, the crossover for silicate measured on
the two cruises 49UF20190207, which is new to this version, and
49RY20110515, which was included in GLODAPv2, is shown in Fig. 3. For
silicate the offset is determined as the ratio, in accordance with the
procedures followed for GLODAPv2. The silicate values from 49UF20190207 are
slightly higher, with a weighed mean offset of 1.02 ± 0.01 compared to
those measured on 49RY20110515.
Number of cruises per year in GLODAPv2, GLODAPv2.2021, and
GLODAPv2.2022.
Example crossover figure for silicate for cruises 49UF20190207
(blue) and 49RY20110515 (red), as was generated during the crossover
analysis. Panel (a) shows all station positions for the two cruises, and (b)
shows the specific stations used for the crossover analysis. Panel (d) shows
the data of silicate (µmol kg-1) below the upper depth limit (in
this case 2000 dbar) versus potential density anomaly referenced to 4000 dbar as points and the interpolated profiles as lines. Non-interpolated
data either did not meet minimum depth separation requirements (Table 4 in
Key et al., 2010) or are the deepest sampling depth. The interpolation does
not extrapolate. Panel (e) shows the mean silicate difference profile
(black, dots) with its standard deviation, as well as also the weighted mean
offset (straight red lines) and weighted standard deviation. Summary
statistics are provided in (c).
For each of the 96 new cruises, such a crossover comparison was conducted
against all possible cruises in GLODAPv2.2021, i.e., all cruises that had
stations closer than 2 arcdeg distance to any station for the cruise in
question. The summary figure for silicate on 49UF20190207 is shown in Fig. 4. The silicate data measured on this cruise are 1.01 ± 0.00 higher
when compared to the data measured on nearby cruises included in
GLODAPv2.2021. This is smaller than the initial minimum adjustment limit for
silicate of 2 % (Table 3) and as such does not automatically lead to an
adjustment of the data in the merged data product. However, in this case the
offset, while small, is very consistent and present in silicate data from
many different cruises. Since we have also been able to identify a cause of
the offset (see Sect. 4), an adjustment of 1 % has been applied. All other
variables show very high consistency; thus, no adjustment is given to any
other variable on cruise 49UF20190207 in GLODAPv2.2021. This is supported by
the CANYON-B and CONTENT results (Sect. 3.2.4). Note that adjustments, when
applied, are typically round numbers (e.g., -3 not -3.4 for TCO2 and
0.005 not 0.0047 for pH) to avoid communicating that the ideal adjustments
are accurately known.
Example summary figure for silicate crossovers for 49UF20190207
versus the cruises in GLODAPv2.2021 (with cruise EXPOCODE listed on the x axis sorted according to the year the cruise was conducted). The black dots and
vertical error bars show the weighted mean offset and standard deviation for
each crossover (as a ratio). The weighted mean and standard deviation of all
these offsets are shown in the red lines and are 1.01 ± 0.00. The
dashed black lines are the reference line for a ± 2 % offset.
pH scale conversion and quality control
Altogether 60 of the 96 new cruises included measured, spectrophotometric
pH data, and only one required an adjustment (Sect. 4). We also excluded
(flag -777) pH on one cruise as a result of the QC work. All except one
cruise reported pH data on the total scale and at 25 ∘C. For the
one cruise reporting pH on the seawater scale the data were converted
following established routines (Olsen et al., 2020). For details on scale
and temperature conversions in previous versions of GLODAPv2, we refer to
Olsen et al. (2020). In contrast to quality control of pH data in GLODAPv2
(Olsen et al., 2016), the evaluation of the internal consistency of CO2
system variables has not been used for the secondary quality control of the
pH data in the GLODAPv2 updates of 2020 and onwards. For the 60 new cruises
with pH in GLODAPv2.2022 only crossover analysis was used, supplemented by
CONTENT and CANYON-B comparisons (Sect. 3.2.4). Recent literature has
demonstrated that internal consistency evaluation procedures are subject to
errors owing to an incomplete understanding of the thermodynamic constants,
major ion contents, measurement biases, and potential contribution of
organic compounds or other unknown protolytes to alkalinity. These
complications lead to pH-dependent offsets in calculated pH compared with
cruise spectrophotometric pH measurements (Álvarez et al., 2020; Carter
et al., 2018; Fong and Dickson, 2019; Takeshita et al., 2020). The
pH-dependent offsets may be interpreted as biases and generate false
corrections (Álvarez et al., 2020; García-Ibáñez et al.,
2022). The offsets are particularly strong at pH levels below 7.7, where
calculated and measured pH values are different by on average between 0.01 and
0.02. For the North Pacific this is a problem as pH values below 7.7 can
occur at the depths used during the QC (> 1500 dbar for this
region; Olsen et al., 2016). Since any correction, which may be an artifact,
would be applied to the full profiles, we use a minimum adjustment of 0.02
for the North Pacific pH data in the merged product files. Elsewhere, the
inconsistencies that may have arisen are smaller, since deep pH is typically
higher than 7.7 (Lauvset et al., 2020), and at such levels the difference
between calculated and measured pH is less than 0.01 on average (Álvarez
et al., 2020; Carter et al., 2018). Outside the North Pacific, we believe
that the pH data are consistent to within 0.01. Avoiding CO2 chemistry
internal consistency considerations for these intermediate products helps to
reduce the problem, but since the reference dataset (as also used for the
generation of the CANYON-B and CONTENT algorithms) may have these issues, a
future full re-evaluation, envisioned for GLODAPv3, is needed to address the
problem completely.
CANYON-B and CONTENT analyses
CANYON-B and CONTENT (Bittig et al., 2018) were used to support decisions
regarding the application of adjustments (or not). CANYON-B is a neural network
for estimating nutrients and seawater CO2 chemistry variables from
temperature, salinity, and oxygen content. CONTENT additionally considers
the consistency among the estimated CO2 chemistry variables to further
refine them. These approaches were developed using the data included in the
GLODAPv2 data product (i.e., the 2016 version without any more recent
updates). Their advantage compared to crossover analyses for evaluating
consistency among cruise data is that effects of water mass changes on ocean
properties are represented in the nonlinear relationships in the underlying
neural network. For example, if elevated nutrient values measured on a
cruise are not due to a measurement bias but actual aging of the water
masses that have been sampled and as such accompanied by a decrease in
oxygen content, the measured values and the CANYON-B estimates are likely to
be similar. Vice versa, if the nutrient values are biased, the measured
values and CANYON-B predictions will be dissimilar.
Used in the correct way and with caution this tool is a powerful supplement
to the traditional crossover analyses which form the basis of our analyses.
Specifically, we gave no weight to comparisons in which the crossover
analyses had suggested that the salinity and/or O2 data were biased, as
this would lead to error in the predicted values. We also considered the
uncertainties of the CANYON-B and CONTENT estimates. These uncertainties are
determined for each predicted value, and for each comparison the ratio of
the difference (between measured and predicted values) to the local
uncertainty was used to gauge the comparability. As an example, the CANYON-B
and CONTENT analyses of the data obtained for 49UF20190207 are presented in
Fig. 5. The CANYON-B and CONTENT results confirmed the crossover comparisons
for silicate discussed in Sect. 3.2.2 showing an inconsistency of 1.01. For
the other variables, the inconsistencies are low and agree with the
crossover results (not shown here but results can be accessed through the
adjustment table).
Example summary figure for CANYON-B and CONTENT analyses
for 49UF20190207. Any data from regions where CONTENT and CANYON-B were not
trained are excluded. The top row shows the nutrients and the bottom row the
seawater CO2 chemistry variables. All are shown versus sampling
pressure (dbar), and the unit is micromoles per kilogram (µmol kg-1) for all except pH, which is on the total scale at in situ temperature
and pressure. Black dots (which to a large extent are hidden by the
predicted estimates) are the measured data, blue dots are CANYON-B estimates,
and red dots are the CONTENT estimates. Each variable has two figure panels.
The left shows the depth profile, while the right shows the absolute
difference between measured and estimated values divided by the CANYON-B and
CONTENT uncertainty estimate, which is determined for each estimated value.
These values are used to gauge the comparability; a value below 1 indicates
a good match, as it means that the difference between measured and estimated
values is less than the uncertainty of the latter. The statistics in each
panel are for all data deeper than 500 dbar, and N is the number of samples
considered. A multiplicative adjustment and its interquartile range are
given for the nutrients. For the seawater CO2 chemistry variables the
numbers in each panel are the median difference between measured and
predicted values for CANYON-B (upper) and CONTENT (lower). Both are given
with their interquartile range.
Another advantage of the CANYON-B and CONTENT comparisons is that these
procedures provide estimates at the level of individual data points; e.g.,
pH values are determined for every sampling location and depth where
temperature, salinity, and O2 data are available. Cases of strong
differences between measured and estimated values are always examined. This
has helped us to identify primary QC issues for some cruises and variables,
for example a case of an inverted pH profile on cruise 32PO20130829, which
was identified and amended in GLODAPv2.2020.
Halogenated transient tracers and SF6
For the halogenated transient tracers (CFC-11, CFC-12, CFC-113, and
CCl4; CFCs for short), an inspection of surface saturation levels and an
evaluation of relationships between the tracers for each cruise were used to
identify biases rather than crossover analyses. Crossover analysis is of
limited value for these variables given their transient nature and low
contents at depth. As for GLODAPv2, the procedures were the same as those
applied for CARINA (Jeansson et al., 2010; Steinfeldt et al., 2010).
Beginning with GLODAPv2.2022, we have performed secondary quality control
for SF6 data, as this tracer is increasingly being measured and has
proven a valuable addition to CFCs. The procedure is mainly based on
comparisons with the quality-controlled CFC-12 data, which are available for
all cruises with SF6 measurements. We compare the surface saturation
of SF6 with that of CFC-12 and also consider the correlation between
SF6 and CFC-12 in the ocean interior. Typically, this relation shows
some scatter and does not follow a distinct curve (Fig. 6). However, for a
given CFC-12 value the SF6 content should fall into a certain range,
and this range can be estimated by the transit time distribution (TTD; Hall
et al., 2002) method. Note that we are not trying to adjust SF6 to
perfectly correlate with CFC-12 as that would severely decrease the value of
SF6 as an independent constraint on ocean circulation. We merely
confirm that the SF6 content is within an allowable range and only
apply adjustments if all lines of evidence suggest it is warranted. In
GLODAPv2.2022 no adjustment smaller than 10 % has been applied.
Example of plots used as basis for the SF6 QC
procedure. Shown are results for cruises 096U20160426 (left) and
320620170703 (right). (a, e) CFC-12 versus pressure for the specific cruise
(red), together with all data from the corresponding GLODAP region (Pacific
in this case, grey). (b, f) Same as upper row but for SF6. (c, g) CFC-12
versus SF6 (red dots), here the measured contents have been converted
into atmospheric mixing ratios. Solid black line: atmospheric time history
of CFC-12 versus that of SF6. Dotted lines: CFC-12 versus SF6 derived
from the TTD method for two different sets of TTD parameters. (d, h) CFC-12
versus SF6 saturation for the surface layer (P<20 dbar), where
the numbers give the mean saturation.
As TTD, we use an inverse Gaussian function, which can be described by two
parameters: the mean age (Γ) and the width (Δ) (Hall et al.,
2002). Typically, the ratios of Δ/Γ are chosen as a fixed
parameter, and Γ is varied. Here, we use a range of Γ between
0 and 2000 years and two values for Δ/Γ: 0.5 and 2. This
range of TTD parameters reproduces simultaneous observation of different
tracers, like CFC-12 and SF6, when calculating the tracer contents from
the TTD and the atmospheric mixing ratio (Steinfeldt et al., 2009).
Typically, for the same CFC-12 value derived from the TTD, the corresponding
SF6 value increases with the Δ/Γ ratio of the TTD, and
it also increases with decreasing saturation (α). As range for the
expected SF6 to CFC-12 relation we use the TTD with Δ/Γ= 0.5 and α=1 as the lower boundary and the TTD with Δ/Γ=0.5 and 80 % saturation as the upper boundary. In some cases,
like deep water formation or an ice-covered region, the tracer saturation
might be lower, as the minimum of 65 % from Steinfeldt et al. (2009)
indicates, but the majority of the data is actually located between our
assumed lower and upper boundaries (see results for cruise 096U20160426 in
Fig. 6). A few exceptions are found for cruises in the Southern Ocean, as
has already been shown in Stöven et al. (2015). Note that in 1996, a
SF6 release experiment was performed in the Greenland Sea (Watson et
al., 1999). This leads to a large excess of SF6 compared to CFC-12 in
the Nordic Seas, which is clearly visible in our analyses and hampers the
quality control of the SF6 data in this region.
Distribution of applied adjustments for each core variable that
received secondary QC, in micromoles per kilogram (µmol kg-1) for TCO2 and TAlk and unitless for salinity and pH (but multiplied by
1000 in both cases so a common x axis can be used), while for the other
properties adjustments are given in percent ((adjustment ratio -1)×100).
Grey areas depict the initial minimum adjustment limits. The figure includes
numbers for data subjected to secondary quality control only. Note also that
the y-axis scale is set to render the number of adjustments visible, so the
bar showing zero offset (the 0 bar) for each variable is cut off (see Table 6 for these numbers).
Possible outcomes of the secondary QC and their codes in the online
adjustment table.
Secondary QC resultCodeThe data are of good quality, are consistent with the rest of the dataset, and should not be adjusted0/1*The data are of good quality but are biased: adjust by adding (for salinity, TCO2, TAlk, pH) or by multiplying (for oxygen, nutrients, CFCs) the adjustment valueAdjustment valueThe data have not been quality controlled, are of uncertain quality, and are suspended until full secondary QC has been carried out-666The data are of poor quality and excluded from the data product-777The data appear of good quality, but their nature, being from shallow depths and coastal regions without crossovers or similar, prohibits full secondary QC-888No data exist for this variable for the cruise in question-999
* The value of 0 is used for variables with additive adjustments
(salinity, TCO2, TAlk, pH) and 1 for variables with multiplicative
adjustments (for oxygen, nutrients, CFCs). This is mathematically equivalent
to “no adjustment” in both cases.
Summary of secondary QC results for the 96 new cruises, in number
of cruises per result and per variable.
a The data are included in the data product file as is, with a secondary QC flag of 1.
b The adjusted data are included in the data product file with a
secondary QC flag of 1.
c Data appear of good quality but have not been subjected to full
secondary QC. They are included in data product with a secondary QC flag of
0.
d Data are of uncertain quality and suspended until full secondary QC
has been carried out; they are excluded from the data product.
e Data are of poor quality and excluded from the data product.
Merged product generation
The merged product file for GLODAPv2.2022 was created by updating cruises
and correcting known issues in the GLODAPv2.2021 merged file and then
appending a merged and bias-corrected file containing the 96 new
cruises – sorted according to EXPOCODE, station, and pressure – to this
updated GLODAPv2.2021 file. GLODAP cruise numbers were assigned
consecutively, starting from 4001, so they can be distinguished from the
GLODAPv2.2021 cruises, which ended at 3043. The merging was otherwise
performed following the procedures used for previous GLODAP versions (Olsen
et al., 2019, 2020; Lauvset et al., 2021).
Updates and corrections for GLODAPv2.2021
For GLODAPv2.2022 we made several updates to cruises included in
GLODAPv2.2021 (and earlier versions). The major updates were (i) to perform
secondary quality control on all SF6 data (see Sect. 3.2.5) and (ii) to apply small adjustments to TCO2 and TAlk data measured on board the
RV Knorr in 1994–1995 (EXPOCODES 316N199*; Table A2). These adjustments are
derived from offsets in the CRM measurements which were previously reported
but never applied to the seawater measurements (Christopher Sabine and Douglas Wallace, personal communication, 2022; Johnson et al., 2002). These offsets are lower than the minimum
adjustment limits defined for GLODAP. Applying these adjustments achieves
procedural consistency with other CO2 chemistry data that are usually
corrected for CRM offsets before being subjected to secondary QC.
For TAlk the original CRM offsets were derived from Table 2 in Millero et
al. (1998), who reported repeated CRM measurements on different titration
cells for each cruise. The mean measured CRM value across all cells was
calculated and compared to the published reference value for the same batch,
and, if necessary, the offsets obtained from multiple CRM batches measured
on one cruise were averaged. For TCO2 the original CRM offsets were
calculated from Table 3 in Johnson et al. (1998), who reported offsets for
two measurement systems, which were here averaged. Johnson et al. (2002)
report that their TCO2 measurements were affected by changes in pipette
volumes, which they were able to correct for in the CRM measurements.
However, these volume corrections were most likely not applied to the
seawater measurements (Douglas Wallace, personal communication, 2022; Johnson et al., 2002), and we
therefore use the CRM offsets reported before correcting for the changes in
pipette volume. For both TAlk and TCO2 we calculate and use the mean
CRM offset across all Indian Ocean cruises on the RV Knorr from 1994–1995 (-3.5 µmol kg-1 for TAlk and 1.7 µmol kg-1 for TCO2) as a
bulk adjustment value for the seawater measurements on these cruises. The
GLODAP policy for avoiding small adjustments does not apply in this instance
because there is a documented reason for the adjustment beyond improving
internal consistency of the GLODAPv2 data product. Encouragingly, we also
note that applying these adjustments improves the consistency with more
recent (post-2000) Indian ocean data in GLODAPv2: for TAlk the mean absolute
offset decreased from 2.8 µmol kg-1 for the unadjusted data to
-0.7 µmol kg-1 for the adjusted data, while for TCO2 the mean
absolute offset decreased from -2.3 µmol kg-1 for the unadjusted
data to -0.6 µmol kg-1 for the adjusted data, respectively.
Table A2 in the Appendix shows a list of the cruises that have been updated,
as well as what the update consists of. In addition, several minor omissions
and errors have been identified and corrected.
An error was corrected in the QC flagging of calculated CO2 chemistry
variables when fCO2 was used as one of the inputs (changed from 1 to 0).
CFC-12 data were added to cruise 06M320150501.
Missing bottle number were added to cruises 29AH20160617 and 29HE20190406.
For cruise 316N19831007 the WOCE flag on TAlk was changed from 2 to 0.
Oxygen concentrations of 49UP19970912 have been adjusted 1.5 % upward.
pH values of 49HG19960807 have been adjusted downward by 0.05.
The time series from Weather Station M in the Norwegian Sea was updated with
data from 2008–2021.
In addition to DOIs for all original data files, DOIs for the included data
products (CODAP-NA and GEOTRACES) have been added to the product files.
An extra column “G2expocode” has been added, listing the EXPOCODE for each
entry.
Secondary quality control results and adjustments
The secondary QC has five possible outcomes which are summarized in Table 5,
along with the corresponding codes that appear in the online adjustment
table and that are also occasionally used as shorthand for decisions in the
text below. Some cruises were not applicable for full secondary QC.
Specifically, in some cases data were too shallow or geographically too
isolated for full and conclusive consistency analyses. In other cases, the
results of these analyses were inconclusive, but we have no reason to
believe that the data in question are of poor quality. A secondary QC flag
has been included in the merged product files to enable their
identification, with “0” used for variables and cruises not subjected to
full secondary QC (corresponding to code -888 in Table 5) and “1” for
variables and cruises that were subjected to full secondary QC. The
secondary QC flags are assigned per cruise and variable, not for individual
data points, and are independent of – and included in addition to – the
primary (WOCE) QC flag on individual measurements. For example, interpolated
(salinity, oxygen, nutrients) or calculated (TCO2, TAlk, pH) values,
which have a primary QC flag of 0, may have a secondary QC flag of 1 if the
measured data these values are based on have been subjected to full
secondary QC. Conversely, individual data points may have a secondary QC
flag of 0 even if their primary QC flag is 2 (good data). Prominent examples
for this version are the CODAP-NA data (Jiang et al., 2021), which as a
primarily coastal dataset typically has quite shallow sampling depths that
rendered conclusive secondary QC impossible. As a consequence, most, but not
all, of these data are included with a secondary QC flag of 0.
The secondary QC actions for the 13 core variables and the distribution of
adjustments applied on the 96 new cruises are summarized in Table 6 and Fig. 7, respectively. For most variables only a small fraction of the data were
adjusted: no salinity, TCO2, or nitrate data, 1.1 % TAlk data and
phosphate data, 2.2 % of oxygen data, and 31 % of silicate data. The
large percentage of silicate data requiring adjustment in this version is
due to a consistent 1 % offset in the silicate data from the Japan
Meteorological Agency (JMA) after 2018 (compared to older data from JMA).
This offset has been traced to a change in the batch of Merck silicate
standard solution used. In GLODAPv2.2022 this offset has been corrected by
adjusting the new data (after 2018) to be consistent with the older data.
For the CFCs, CFC-11 required adjustment for one out of the five new cruises and
CFC-12 required adjustment on one out of six new cruises. For the total of 82
cruises with SF6 data in GLODAPv2.2022, two cruises (06MT20060712 and
325020080826) could not be subjected to secondary quality control (-888), and
five cruises received an upward adjustment (see example for cruise 320620170703
in Fig. 6). The magnitude of the adjustment was calculated using the
saturation of CFC-12 as a benchmark. Additionally, for two cruises
(49K619990523 and 58GS20090528), the SF6 values are out of the TTD-derived range, as are the surface saturations. In these cases, the SF6 data are discarded (QC flag -777). Of the 96 new cruises in GLODAPv2.2022
only two include SF6, and neither required an adjustment. Overall, the
magnitudes of the various adjustments applied are small, and the tendency
observed during the production of the three previous updates remains, namely
that the large majority of recent cruises are consistent with earlier
releases of the GLODAP data product. A total of 60 out of the 96 new cruises included
measured pH data, but only one received an adjustment (and one was flagged
-777). However, the new crossover and inversion analysis of all pH data in
the northwestern Pacific that was planned following the release of
GLODAPv2.2020 has not yet been performed. Such an analysis is planned for
the next full update of GLODAP, i.e., GLODAPv3. Therefore, the conclusion
from GLODAPv2.2020 remains that some caution should be exercised if looking
at trends in ocean pH in the northwestern Pacific using GLODAPv2.2022 or
earlier versions.
For the nutrients, adjustments were applied to maintain consistency with
data included in GLODAPv2.2021 and earlier versions. An alternative goal for
the adjustments would be maintaining consistency with data from cruises that
employed reference materials (RMNS) to ensure accuracy of nutrient analyses.
Such a strategy was adopted by Aoyama (2020) for preparation of the Global
Nutrients Dataset 2013 (GND13) and is being considered for GLODAP as well.
However, as this would require a re-evaluation of the entire dataset, this
will not occur until the next full update of GLODAP. For now, we note the
overall agreement between the adjustments applied in these two efforts
(Aoyama, 2020) and that most disagreements appear to be related to cases
where no adjustments were applied in GLODAP.
The improvement in data consistency resulting from the secondary QC process
is evaluated by comparing the weighted mean of the absolute offsets for all
crossovers before and after the adjustments have been applied. This
“consistency improvement” for core variables is presented in Table 7. The
data for CFCs were omitted from these analyses for previously discussed
reasons (Sect. 3.2.5). Globally, the improvement is modest. Considering the
initial data quality, this result was expected. However, this does not imply
that the data initially were consistent everywhere. Rather, for some regions
and variables there are substantial improvements when the adjustments are
applied. For example, oxygen, silicate, and phosphate in the Atlantic Ocean
all show a considerable improvement.
Improvements resulting from quality control of the 96 new cruises
per basin and for the global dataset. The values in the table are the
weighted mean of the absolute offset of unadjusted and adjusted data versus
GLODAPv2.2021. The total number of valid crossovers in the global ocean for
the variable in question is n. The values in this table represent the
inter-cruise consistency in the GLODAPv2.2022 product.
Arctic Atlantic Indian Pacific Global Unadj.Adj.Unadj.Adj.Unadj.Adj.Unadj.Adj.Unadj.Adj.n (global)Sal (×1000)NA⇒NA4.6⇒4.60.7⇒0.71.2⇒1.21.3⇒1.31105Oxy (%)NA⇒NA1.5⇒0.80.5⇒0.50.4⇒0.40.5⇒0.41064NO3 (%)NA⇒NA1.7⇒1.70.7⇒0.70.4⇒0.40.4⇒0.4940Si (%)NA⇒NA3.0⇒2.60.9⇒0.91.4⇒0.61.4⇒0.6916PO4 (%)NA⇒NA2.0⇒1.10.7⇒0.70.7⇒0.70.7⇒0.7936TCO2 (µmol kg-1)NA⇒NA7.3⇒7.32.0⇒2.01.8⇒1.82.4⇒2.4544TAlk (µmol kg-1))NA⇒NA4.5⇒3.15.2⇒5.21.8⇒1.81.9⇒1.8515pH (×1000)NA⇒NA11.6⇒11.6NA⇒NA5.5⇒5.35.5⇒5.4462
NA – not available
Magnitude of applied adjustments relative to minimum adjustment
limits (Table 3) per decade for the 1085 cruises included in GLODAPv2.2022.
Locations of stations included in the (a) Arctic, (b) Atlantic,
(c) Indian, and (d) Pacific ocean product files for the complete
GLODAPv2.2022 dataset.
The various iterations of GLODAP provide insight into initial data quality
covering more than 4 decades. Figure 8 summarizes the applied absolute
adjustment magnitude per decade. These distributions are broadly unchanged
compared to GLODAPv2.2021 (Fig. 7 in Lauvset et al., 2021). Most TCO2
and TAlk data from the 1970s needed an adjustment, but this fraction
steadily declines until only a small percentage is adjusted in recent years.
This is encouraging and demonstrates the value of standardizing sampling and
measurement practices (Dickson et al., 2007), the widespread use of CRMs
(Dickson et al., 2003), and instrument automation. The pH adjustment
frequency also has a downward trend; however, there remain issues with the
pH adjustments, and this is a topic for future development in GLODAP, with
the support from the Ocean Carbon & Biogeochemistry (OCB) Ocean Carbonate System Intercomparison Forum (OCSIF,
https://www.us-ocb.org/ocean-carbonate-system-intercomparison-forum/, last
access: 27 June 2022) working group (Álvarez et al., 2020). For the
nutrients and oxygen, only the phosphate adjustment frequency decreases from
decade to decade. However, we do note that the more recent data from the
2010s receive the fewest adjustments. This may reflect recent increased
attention that seawater nutrient measurements have received through an
operation manual (Becker et al., 2020; Hydes et al., 2010), availability of
RMNS (Aoyama et al., 2012; Ota et al., 2010), and the Scientific Committee
on Oceanic Research (SCOR) working group no. 147 towards comparability of
global oceanic nutrient data (COMPONUT). For silicate, the fraction of
cruises receiving adjustments peaks in the 1990s and 2000s. This is related
to the 2 % offset between US and Japanese cruises in the Pacific Ocean
that was revealed during production of GLODAPv2 and discussed in Olsen et
al. (2016). For salinity and the halogenated transient tracers, the number
of adjusted cruises is small in every decade.
Table listing the number of data points in GLODAPv2.2022,
as well as the number of data with various combinations of variables.
VariablesNumber of recordsAll core (salinity, oxygen, nitrate, silicate, phosphate, TCO2, TAlk, pH, CFC-11, CFC-12, CFC-113, CCl4, and SF6)174All core except SF62029Salinity, oxygen, nitrate, silicate, phosphate, CFC-11, CFC-12, CFC-113, CCl4, and SF6 plus two of TCO2, TAlk, and pH636Salinity, oxygen, nitrate, silicate, phosphate, TCO2, TAlk, and pH168 330CFC-11, CFC-12, CFC-113, CCl4, and SF6926At least one transient tracer species or SF6427 913SF698 951Two out of the three CO2 chemistry core variables (TCO2, TAlk, pH)448 024Measured fCO233 844Salinity, oxygen, nitrate, silicate, and phosphate861 650Salinity and oxygen1 165 389No salinity27 906Total in GLODAPv2.20221 381 248Data availability
The GLODAPv2.2022 merged and adjusted data product is archived at the OCADS
of NOAA NCEI (10.25921/1f4w-0t92, Lauvset et
al., 2022). These data and ancillary information are also available via our
web pages and https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/ (last access: 15 August 2022). The data are available as
comma-separated ascii files (*.csv) and as binary MATLAB files (*.mat) that
use the open-source Hierarchical Data Format version 5 (HDF5). The data
product is also made available as an Ocean Data View (ODV) file which can be
easily explored using the “webODV Explore” online data service (https://explore.webodv.awi.de/, webODV Explore, 2022). Regional
subsets are available for the Arctic, Atlantic, Pacific, and Indian oceans.
There are no data overlaps between regional subsets, and each cruise exists
in only one basin file even if data from that cruise cross basin
boundaries. The station locations in each basin file are shown in Fig. 9.
The product file variables are listed in Table 1. As well as being included
in the .csv and .mat files, lookup tables for matching the EXPOCODE and DOI
of a cruise with GLODAP cruise number are provided with the data files. A
“known issues document” accompanies the data files and provides an
overview of known errors and omissions in the data product files. It is
regularly updated, and users are encouraged to inform us whenever any new
issues are identified. It is critical that users consult this document
whenever the data products are used.
All material produced during the secondary QC is available via the online
GLODAP adjustment table hosted by GEOMAR, Kiel, Germany, at
https://glodapv2-2022.geomar.de/ (GLODAP, 2022a) and can also
be accessed through http://www.glodap.info (GLODAP, 2022b). This is similar in form and function to the GLODAPv2 adjustment table
(Olsen et al., 2016) and includes a brief written justification for any
adjustments applied.
The original cruise files, with updated flags determined during additional
primary GLODAP QC, are available through the GLODAPv2.2022 cruise summary
table (CST) hosted by OCADS: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/cruise_table_v2022.html (GLODAP, 2022c). Each of these files has been assigned a DOI, which is included
in the data product files but not listed here. The CST also provides brief
information on each cruise and access to metadata, cruise reports, and its
adjustment table entry.
While GLODAPv2.2022 is made available without any restrictions, users of the
data should adhere to the fair data use principles: for investigations that
rely on a particular (set of) cruise(s), recognize the contribution of
GLODAP data contributors by at least citing both the cruise DOI and any
articles where the data are described, as well as, preferably, contacting
principal investigators to explore opportunities for collaboration and
co-authorship. To this end, DOIs are provided in the product files, as well as
relevant articles and principal investigator names in the cruise summary
table. Contacting principal investigators comes with the additional benefit
that the principal investigators often possess expert insight into the data
and/or specific region under investigation. This can improve scientific
quality and promote data sharing.
This paper should be cited in any scientific publications that result from
usage of the product. Citations provide the most efficient means to track
use, which is important for attracting funding to enable the preparation of
future updates.
Summary
GLODAPv2.2022 is an update of GLODAPv2.2021. Data from 96 new cruises have
been added to supplement the earlier release and extend temporal coverage by
1 year. GLODAP now includes 48 years, 1972–2021, of global interior ocean
biogeochemical data from 1085 cruises. The total number of data records is
1 381 248 (Table 8). Records with measurements for all 13 core variables
(salinity, oxygen, nitrate, silicate, phosphate, TCO2, TAlk, pH,
CFC-11, CFC-12, CFC-113, CCl4, and SF6) are very rare (174), and
requiring only two out of the three core seawater CO2 chemistry
variables, in addition to all the other core variables, is still very rare
with only 636 records (Table 8). A major limiting factor to having all core
variables is the simultaneous availability of data for all four transient
tracer species and SF6. In GLODAPv2.2022 there are 98 951 records with
SF6 data and 427 913 records with at least one transient tracer or
SF6. A total of 2 % (27 906) of all data records do not have
salinity. There are several reasons for this, the main one being the
inability to vertically interpolate due to a separation that is too large
between measured samples. Other reasons for missing salinity include
salinity not being reported and missing depth or pressure.
Distribution of data in GLODAPv2.2022 in (a) December–February,
(b) March–May, (c) June–August, and (d) September–November, as well as
(e) number of observations for each month in four latitude bands.
Number (a) and density (b) of observations in 100 m depth layers.
The latter was calculated by dividing the number of observations in each
layer by its global volume calculated from ETOPO2 (National Geophysical Data
Center, 2006). For example, in the layer between 0 and 100 m there are on
average 0.0075 observations per cubic kilometer. One observation is one
water sampling point and has data for several variables.
As for previous versions there is a bias toward summertime in the data in
both hemispheres; most data are collected during April through November in
the Northern Hemisphere, while most data are collected during November
through April in the Southern Hemisphere (Fig. 10). These tendencies are
strongest for the poleward regions and reflect the harsh conditions during
winter months which make fieldwork difficult. The upper 100 m is the
best-sampled part of the global ocean, both in terms of number (Fig. 11a)
and density (Fig. 11b) of observations. The number of observations steadily
declines with depth. In part, this is caused by the reduction in ocean
volume towards greater depths. Below 1000 m the density of observations
stabilizes and even increases between 5000 and 6000 m; the latter is a zone
where the volume of each depth surface decreases sharply (Weatherall et al.,
2015). In the deep trenches, i.e., areas deeper than ∼ 6000 m,
both the number and density of observations are low.
Except for salinity and oxygen, the core data were collected exclusively
through chemical analyses of collected water samples. The data of the 13
core variables were subjected to primary quality control to identify
questionable or bad data points (outliers) and secondary quality control to
identify systematic measurement biases. The data are provided in two ways:
as a set of individual exchange-formatted original cruise data files with
assigned WOCE flags and as globally and regionally merged data product
files with adjustments applied to the data according to the outcome of the
consistency analyses. Importantly, no adjustments were applied to data in
the individual cruise files, while primary QC changes were applied.
The consistency analyses were conducted by comparing the data from the 96
new cruises to the previous data product GLODAPv2.2021. Adjustments were
only applied when the offsets were believed to reflect biases relative to
the earlier data product release related to measurement calibration and/or
data handling practices and not to natural variability or anthropogenic
trends. For GLODAPv2.2022 a special case is the RV Knorr cruises in 1994–1995
in which the adjustment reflects offsets in CRM measurements that have not
previously been corrected for. The adjustment table at
https://glodapv2-2022.geomar.de/ (last access: 15 August 2022) lists all
applied adjustments and provides a brief justification for each. The
consistency analyses rely on deep ocean data (> 1500 or 2000 dbar
depending on region), but supplementary CANYON-B and CONTENT analyses
consider data below 500 dbar. Data consistency for cruises with exclusively
shallow sampling was not examined. All new pH data for this version were
comprehensively reviewed using crossover analysis, and only one required
adjustment, while another had to be flagged bad (-777) and removed from the
product. Regardless, full reanalysis of all available pH data, particularly
in the North Pacific, will be conducted for GLODAPv3.
Secondary QC flags are included for the 13 core variables in the product
files. These flags indicate whether (1) or not (0) the data successfully
received secondary QC. A secondary QC flag of 0 does not by itself imply
that the data are of lower quality than those with a flag of 1. It means
these data have not been as thoroughly checked. For δ13C, the
QC results by Becker et al. (2016) for the North Atlantic were applied, and
a secondary QC flag was therefore added to this variable.
The primary WOCE QC flags in the product files are simplified (e.g., all
questionable and bad data were removed). For salinity, oxygen, and the
nutrients, any data flagged 0 are interpolated rather than measured. For
TCO2, TAlk, pH, and fCO2 any data flags of 0 indicate that the
values were calculated from two other measured seawater CO2 variables.
Finally, while questionable (WOCE flag = 3) and bad (WOCE flag = 4) data
have been excluded from the product files, some may have gone unnoticed
through our analyses. Users are encouraged to report on any data that appear
suspicious.
Based on the initial minimum adjustment limits and the improvement in the
consistency resulting from the adjustments (Table 7), the data subjected to
consistency analyses are believed to be consistent to better than 0.005 in
salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in
phosphate, 4 µmol kg-1 in TCO2, 4 µmol kg-1 in
TAlk, and 5 % for the halogenated transient tracers and SF6. For pH,
the consistency among all data is estimated as 0.01–0.02, depending on the
region. As mentioned above, the included fCO2 data have not been
subjected to quality control; therefore no consistency estimate is given for
this variable. This should be conducted in future efforts.
Supplementary tables
Cruises included in GLODAPv2.2022 that did not appear in
GLODAPv2.2021. Complete information on each cruise, such as variables
included, and chief scientist and principal investigator names is provided
in the cruise summary table at https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/cruise_table_v2022.html (last access: 15
August 2022).
No.EXPOCODERegionAliasStartEndShip400118DD20100720Salish Sea2.010.0362010072020100817John P. Tully400218DD20110621Salish Sea2.011.0092011062120110625John P. Tully400318DL20150710ArcticArcticNet15022015071020150820CCGS Amundsen400418DL20150905ArcticArcticNet15032015090520151001CCGS Amundsen400518DL20200722AtlanticAZOMP, AR07W2020072220200811Amundsen400618VT20030902Salish Sea2.003.0292003090220030906Vector400718VT20031201Salish Sea2.003.0412003120120031206Vector400818VT20100403Salish Sea2.010.0162010040320100406Vector400918VT20100805Salish Sea2.010.0572011080520110808Vector401018VT20101029Salish Sea2.010.0732010102920101102Vector401118VT20110404Salish Sea2.011.0282011040420110411Vector401218VT20110805Salish Sea2.011.0062011080520110808Vector401318VT20110909Salish Sea2011.012011090920110914Vector401418VT20111124Salish Sea2.011.0762011112420111128Vector401518VT20120401Salish Sea2.012.0192012040120120405Vector401618VT20120405Salish Sea2.012.0042012040520120410Vector401718VT20120613Salish Sea2.012.0052012061320120619Vector401818VT20120714Salish Sea2.012.0572012071420120717Vector401918VT20120919Salish Sea2.012.0062012091920120925Vector4020316G20120202AtlanticDE12022012020220120219Delaware4021316N20090614PacificKN1952009061420090730Knorr402231FN20090924PacificMF09042009092420091013Miller Freeman4023332220120904PacificWCOA20122012090420120917Bell M. Shimada4024332220170918PacificSH17092017091820170928Bell M. Shimada4025334A20140510AtlanticEX14032014051020140517Okeanos Explorer4026334B20121026AtlanticPC12072012102620121114Pisces4027334B20141103AtlanticPC14052014110320141121Pisces4028334B20160807AtlanticPC16042016080720160819Pisces4029334B20161018AtlanticPC16092016101820161019Pisces403033FA20180624PacificFK1806242018062420180713Falkor403133GG20130609AtlanticGU13022013060920130623Gordon Gunter403233GG20131113AtlanticGU13052013111320131125Gordon Gunter403333GG20140301AtlanticGU1401 Leg22014030120140308Gordon Gunter403433GG20150619AtlanticGU15-04, ECOA12015061920150723Gordon Gunter403533GG20151012AtlanticGU1506 Leg22015101320151024Gordon Gunter403633GG20160521AtlanticGU1608 Leg12016052120160602Gordon Gunter403733GG20160607AtlanticGU1608 Leg22016060720160612Gordon Gunter403833GG20170516AtlanticGU1701 Leg12017051720170525Gordon Gunter403933GG20170530AtlanticGU1701 Leg22017053020170605Gordon Gunter404033GG20170610AtlanticGU17022017061020170621Gordon Gunter404133GG20171031AtlanticGU17062017103120171111Gordon Gunter404233GG20180822AtlanticGU18042018082220180831Gordon Gunter404333H520181102AtlanticS118022018110220181112Hugh R. Sharp404433HH20120531AtlanticHB12022012060220120613Henry B. Bigelow404533HH20150519AtlanticHB15022015052020150602Henry B. Bigelow404633HH20170211AtlanticHB17012017021120170223Henry B. Bigelow404733HH20180523AtlanticHB18032018052320180604Henry B. Bigelow404833HH20180625AtlanticHB-18-04, ECOA22018062520180729Henry Bigelow404933HQ20080329PacificBEST '08 Spring; HLY08022008032920080506Healy405033HQ20080703PacificBEST '08 Summer; HLY08032008070320080731Healy
List of cruises included in GLODAPv2.2021 which have been updated
as part of GLODAPv2.2022. Complete information on each cruise, such as
variables included, and chief scientist and principal investigator names is
provided in the cruise summary table at https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/cruise_table_v2022.html (last access: 15
August 2022).
No.EXPOCODERegionAliasUpdateAdjustment2606M220090714AtlanticCLIVAR AR07W_2009, MSM12_3Performed second QC on SF61.05506MT20030626Atlantic06MT591Performed second QC on SF61.05706MT20030831Atlantic06MT593Performed second QC on SF61.05806MT20040311Atlantic06MT605Performed second QC on SF61.06206MT20060712AtlanticMT68_3_2006Performed second QC on SF6-8886306MT20091026AtlanticMT80/1_2009Performed second QC on SF61.06406MT20110405AtlanticMT84_3Performed second QC on SF61.0263316N20020530ArcticNS02, KN166_11Performed second QC on SF61.0273318M20091121PacificCLIVAR P06_2009Performed second QC on SF61.0295320620110219PacificCLIVAR S04P_2011Performed second QC on SF61.0307325020080826PacificCLIVAR_TN224_2008Performed second QC on SF6-88832432OC20080510Atlantic32OC446Performed second QC on SF61.032933AT20120324AtlanticCLIVAR_A22_2012Performed second QC on SF61.033033AT20120419AtlanticCLIVAR_A20_2012Performed second QC on SF61.034533RO20071215PacificCLIVAR P18_2007Performed second QC on SF61.034633RO20100308AtlanticCLIVAR A13.5_2010, RB_07-05Performed second QC on SF61.034733RO20110926AtlanticCLIVAR A10_2011, RB-11-02Performed second QC on SF61.035533RR20090320IndianCLIVAR I05_2009Performed second QC on SF61.043449HG19971110PacificNH97Performed second QC on SF61.243549HG19980812PacificNH98Performed second QC on SF61.246149K619990523Pacific49EWMI9905_1Performed second QC on SF6-77763158AA20010527Arctic58AA0113, TRACTOR 13Performed second QC on SF61.063558GS20090528ArcticSARS09, CLIVAR 75N_2009Performed second QC on SF6-777674740H20081226AtlanticJC30Performed second QC on SF61.070274JC19960720Arctic74JC9608Performed second QC on SF61.070374JC20100319AtlanticJR239, ANDREX-2Performed second QC on SF61.070677DN20020420Arctic77DN0204Performed second QC on SF61.070877DN20050819ArcticODEN05, AOS-2005Performed second QC on SF61.0724ZZIC2005SWYDArcticSWITCHYARDPerformed second QC on SF61.0100206AQ20120107AtlanticANT-XXVIII/3Performed second QC on SF61.0100306AQ20120614ArcticARK XXVII/1Performed second QC on SF61.0100506AQ20150817ArcticPS-94, ARK-XXIX/3Performed second QC on SF61.0100706M220080723AtlanticMSM09-1Performed second QC on SF61.0100806M220170104AtlanticMSM60-1 SAMOCPerformed second QC on SF61.0101106M320150501AtlanticM116/1Performed second QC on SF61.0101206M220081031AtlanticMSM10/1Performed second QC on SF61.0101306MT20091126AtlanticMT80/2Performed second QC on SF61.1101406MT20101014AtlanticM83/1Performed second QC on SF61.0101606MT20140317AtlanticM105Performed second QC on SF61.01020096U20160426PacificIN2016_V03, P15SPerformed second QC on SF61.0102518HU20130507AtlanticAR07W_2013Performed second QC on SF61.0102618HU20140502AtlanticAR07W_2014Performed second QC on SF61.0102718HU20150504AtlanticAR07W_2015Performed second QC on SF61.0102918MF20120601AtlanticAR07W_2012Performed second QC on SF61.01033316N20111106AtlanticGT11, NAT-11Performed second QC on SF61.01035318M20130321PacificPerformed second QC on SF61.01036320620140320PacificGO-SHIP P16S_2014Performed second QC on SF61.01038325020131025PacificTGT303, P21_2013Performed second QC on SF61.0104033HQ20150809ArcticHLY1502Performed second QC on SF61.0104133RO20130803AtlanticA16N_2013Performed second QC on SF61.0104233RO20131223AtlanticRB1307, A16S_2013Performed second QC on SF61.0104333RO20150410PacificGO-SHIP P16N_2015 Leg 1Performed second QC on SF61.0104433RO20150525PacificGO-SHIP P16N_2015 Leg 2Performed second QC on SF61.0
Continued.
No.EXPOCODERegionAliasUpdateAdjustment104533RO20161119PacificRB1606, GO-SHIP P18_2016Performed second QC on SF61.0104633RR20160208IndianI08S_2016Performed second QC on SF61.0105049NZ20121128IndianP14S_S04_2012; MR12-05 Leg 2Performed second QC on SF61.0105149NZ20130106IndianS04I_2013Performed second QC on SF61.0105349NZ20140717PacificMR14-04, GO-SHIP P01_2014Performed second QC on SF61.0105449NZ20151223IndianMR15-05, I10_2015Performed second QC on SF61.0105549NZ20170208PacificMR16-09, P17EPerformed second QC on SF61.0110358GS20150410AtlanticAR07E_2015Performed second QC on SF61.0110458GS20160802Arctic75N_2016Performed second QC on SF61.0200306M220130509AtlanticMSM28Performed second QC on SF61.0200506M220150502AtlanticMSM42Performed second QC on SF61.0200606M220150525AtlanticMSM43Performed second QC on SF61.02008096U20180111IndianSR03.2018Performed second QC on SF61.0201129AH20160617AtlanticOVIDE-16Performed second QC on SF61.02020316N20101015AtlanticKN199-04Performed second QC on SF61.02023316N20150906AtlanticDavis Strait 2015Performed second QC on SF61.0202635TH20080825AtlanticSUBPOLAR08Performed second QC on SF61.0202745CE20170427AtlanticCE17007Performed second QC on SF61.0300206M220160331AtlanticMSM53Performed second QC on SF61.0300306MT20160828AtlanticM130Performed second QC on SF61.0300406MT20170302PacificM135Performed second QC on SF61.0300506MT20180213AtlanticM145Performed second QC on SF61.03029320620170703PacificPerformed second QC on SF61.23030320620170820PacificPerformed second QC on SF61.13031320620180309PacificNBP18_02Performed second QC on SF61.03033325020190403IndianTN366Performed second QC on SF61.0303433RO20180423IndianPerformed second QC on SF61.0304149NZ20191229IndianMR19-04 (Leg 3)Performed second QC on SF61.0304258JH20190515ArcticJH2019205Performed second QC on SF61.0249316N19941201Indian316N145_5Performed second QC on TCO21.7249316N19941201Indian316N145_5Performed second QC on TAlk-3.5250316N19950124Indian316N145_6Performed second QC on TCO21.7250316N19950124Indian316N145_6Performed second QC on TAlk-3.5251316N19950310Indian316N145_7Performed second QC on TCO21.7251316N19950310Indian316N145_7Performed second QC on TAlk-3.5252316N19950423Indian316N145_8Performed second QC on TCO21.7252316N19950423Indian316N145_8Performed second QC on TAlk-3.5253316N19950611Indian316N145_9Performed second QC on TCO21.7253316N19950611Indian316N145_9Performed second QC on TAlk-3.5254316N19950715Indian316N145_10Performed second QC on TCO21.7254316N19950715Indian316N145_10Performed second QC on TAlk-3.5255316N19950829Indian316N145_11, 316N145_12Performed second QC on TCO21.7255316N19950829Indian316N145_11, 316N145_12Performed second QC on TAlk-3.5256316N19951111Indian316N145_13Performed second QC on TCO21.7256316N19951111Indian316N145_13Performed second QC on TAlk-3.5257316N19951202Indian316N145_14, 316N145_15Performed second QC on TCO21.7257316N19951202Indian316N145_14, 316N145_15Performed second QC on TAlk-3.543349HG19960807PacificNH96-2Performed second QC on pH-0.0557449UP19970912PacificRF97-09Performed second QC on oxygen1.015101106M320150501AtlanticM116/1Added CFC-12 data65658P320011031ArcticStation MAdded new data from 2008 until 2021201129AH20160617AtlanticOVIDE-16Added bottle numbers201329HE20190406AtlanticFICARAM_XIXAdded bottle numbers239316N19831007AtlanticAJAXChanged TAlk WOCE flag from 2 to 0Note on former version
Former versions of this article were published on 15 August 2016, 25 September 2019, 23 December 2020, and 3 December 2021 and are available at 10.5194/essd-8-297-2016, 10.5194/essd-11-1437-2019, 10.5194/essd-12-3653-2020, and 10.5194/essd-13-5565-2021.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-14-5543-2022-supplement.
Author contributions
SKL and TT led the team that produced this update. RMK, AK, BP, and SDJ
compiled the original data files. NL conducted the primary and secondary QC
analyses. HCB conducted the CANYON-B and CONTENT analyses. CS manages the
adjustment table e-infrastructure. AK maintains the GLODAPv2 web pages at
NCEI/OCADS. JDM was responsible for identifying the small offsets in the
historical Indian Ocean data. LQJ, RAF, BRC, SRA, and LB conducted CODAP-NA
QC efforts prior to ingestion into GLODAP. TT, RS, and EJ performed the
secondary QC on all transient tracers. All authors contributed to the
interpretation of the secondary QC results and made decisions on whether to
apply adjustments. Many conducted ancillary QC analyses. SKL updated the
living data manuscript with contributions from all authors.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Earth System Science Data. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
GLODAPv2.2022 would not have been possible without the effort of the many
scientists who secured funding, dedicated time to collect data, and shared the
data that are included. Chief scientists at the various cruises and
principal investigators for specific variables are listed in the online
cruise summary table. The author team also want to thank the large GLODAP
user community for useful input and notification about potential issues in
the data products. Such input is invaluable and helps ensure that GLODAP
maintains its high quality and consistency over time. This is CICOES and
PMEL contribution numbers 2022-1223 and 5414, respectively. This activity is
supported by the International Ocean Carbon Coordination Project (IOCCP).
The authors thank Christopher Sabine, Douglas Wallace, Ernie Lewis, and
Kenneth M. Johnson for advising the author team with respect to additional
corrections for the 1994–1995 Indian Ocean data from the RV Knorr. The authors
thank the CODAP-NA team, including Dana Greeley, Denis Pierrot, Charles
Featherstone, James Hooper, Chris Melrose, Natalie Monacci, Jonathan Sharp,
Shawn Shellito, Yuan-Yuan Xu, Alex Kozyr, Robert H. Byrne, Wei-Jun Cai,
Jessica Cross, Gregory C. Johnson, Burke Hales, Chris Langdon, Jeremy
Mathis, Joe Salisbury, and David W. Townsend for contributing cruise data
and participating in the quality control efforts of CODAP-NA and for
providing advice on how to perform secondary QC on these data. The authors
thank the GEOTRACES data management team for help in identifying and
retrieving the data files relevant for GLODAP.
Financial support
Nico Lange was funded by EU Horizon 2020 through the EuroSea action (grant agreement
862626). Siv K. Lauvset acknowledges internal strategic funding from NORCE Climate. Leticia Cotrim da Cunha
was supported by Prociencia/UERJ 2022-2024 and CNPq/PQ2 309708/2021-4
grants. Marta Álvarez was supported by IEO RADPROF project. Peter J. Brown was partly funded by the
UK Climate Linked Atlantic Sector Science (CLASS) NERC National Capability
Long-term Single Centre Science Programme (grant NE/R015953/1). Anton Velo and Fiz F. Pérez
were supported by BOCATS2 (PID2019-104279GB-C21) project funded by
MCIN/AEI/10.13039/501100011033 and contributing to WATER:iOS CSIC PTI.
Funding for Li-Qing Jiang and the CODAP-NA development team (Simone R. Alin, Leticia Barbero, Richard A. Feely, Brendan R. Carter) comes
from the NOAA Ocean Acidification Program (OAP, project number: OAP 1903-1903)
and NOAA National Centers for Environmental Information (NCEI). Brendan R. Carter thanks
the Global Ocean Monitoring and Observing (GOMO) program of the National
Oceanic and Atmospheric Administration (NOAA) for funding their
contributions (project no. 100007298) through the Cooperative Institute for
Climate, Ocean, & Ecosystem Studies (CIOCES) under NOAA Cooperative
Agreement NA20OAR4320271, contribution no. 2022-2012. Richard A. Feely and Simone R. Alin
acknowledge the NOAA GOMO (project no. 100007298) and the NOAA Pacific
Marine Environmental Laboratory. Henry C. Bittig gratefully acknowledges financial
support by the BONUS INTEGRAL project (grant no. 03F0773A). Bronte Tilbrook was supported
through the Australian Antarctic Program Partnership and the Integrated
Marine Observing System. Matthew P. Humphreys acknowledges EU Horizon 2020 action SO-CHIC
(grant no. 821001). Adam Ulfsbo was supported by the Swedish Research
Council FORMAS (grant no. 2018-01398). Jens Daniel Müller acknowledges support from the
European Union's Horizon 2020 research and innovation program under grant
agreement no. 821003 (project 4C). Alex Kozyr and Li-Qing Jiang were supported by NOAA grant
NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies
– CISESS) at the University of Maryland/ESSIC. GLODAP also acknowledge
funding from the Initiative and Networking Fund of the Helmholtz Association
through the project “Digital Earth” (ZT-0025) and from the United States
National Science Foundation grant OCE-2140395 to the Scientific Committee on
Oceanic Research (SCOR, United States) for International Ocean Carbon
Coordination Project. The contribution of Leticia Barbero was carried out under the auspices
of CIMAS and NOAA, cooperative agreement no. NA20OAR4320472.
Review statement
This paper was edited by Giuseppe M. R. Manzella and reviewed by two anonymous referees.
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