The Global Ocean Data Analysis Project (GLODAP) is a
synthesis effort providing regular compilations of surface to bottom ocean
biogeochemical data, with an emphasis on seawater inorganic carbon chemistry
and related variables determined through chemical analysis of water samples.
This update of GLODAPv2, v2.2019, adds data from 116 cruises to the previous
version, extending its coverage in time from 2013 to 2017, while also adding
some data from prior years. GLODAPv2.2019 includes measurements from more
than 1.1 million water samples from the global oceans collected on 840
cruises. The data for the 12 GLODAP core variables (salinity, oxygen,
nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity,
pH, CFC-11, CFC-12, CFC-113, and
The original data, their documentation and DOI codes are available in the
Ocean Carbon Data System of NOAA NCEI
(
The oceans mitigate climate change by absorbing
Other chemical tracers have been measured on the cruises included in GLODAP.
A subset of these data is also distributed as part of the product but has
not been extensively quality controlled or checked for measurement biases in
this effort. Examples include stable isotopes of carbon and oxygen (
Variables in the GLODAPv2.2019 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.
The first version of GLODAP, GLODAPv1.1, was released in 2005 (Key et al., 2004; Sabine et al., 2005). It contains data from 115 cruises with biogeochemical measurements from the global ocean. The vast majority of these are 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). The second version of GLODAP, GLODAPv2, was released in 2016 (Key et al., 2015; Lauvset et al., 2016; Olsen et al., 2016) with data from 724 scientific cruises: those included in GLODAPv1.1, those amassed for the Carbon in the Atlantic Ocean (CARINA) data synthesis (Key et al., 2010); those amassed for the Pacific Ocean Interior Carbon (PACIFICA) synthesis (Suzuki et al., 2013), and data from 168 additional cruises. The additional cruises include many collected within the framework of the “repeat hydrography” program (Talley et al., 2016), instigated in the early 2000s as part of CLIVAR and since 2007 organized as the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP). Both GLODAPv1.1 and GLODAPv2 data were released in three formats: (i) as submitted by the data originator but reformatted to WOCE exchange format (Swift and Diggs, 2008) and subjected to primary quality control to flag outliers, (ii) as a merged data product with bias minimization adjustments applied, and (iii) as globally mapped climatological distributions. We refer to the first as the original data, to the second as the data product, and to the third as the mapped product.
The GLODAP products have been widely used. The first version formed the
basis for the first data-based estimate of the global ocean inventory of
anthropogenic carbon (Sabine et al., 2004), and the descriptive paper on
GLODAPv1.1 (Key et al., 2004) has been cited more than 800 times
according to Web of Science (Clarivate Analytics). For GLODAPv2, we have
registered more than 120 applications. Examples include model evaluation
(Beadling et al., 2018; Goris et al., 2018; Tjiputra et al., 2018; Ward
et al., 2018), model initialization (Orr et al., 2017), water mass
analyses (Jeansson et al., 2017; Peters et al., 2018; Rae and Broecker,
2018), ocean acidification (Fassbender et al., 2017; García-Ibáñez et
al., 2016; Perez et al., 2018) calibration of Argo biogeochemical sensor
measurements (Bushinsky et al., 2017; Johnson et al., 2017), calibration
of multiple linear regression (MLR) and neural-network-based methods for
biogeochemical data estimation (Bittig et al., 2018; Carter et al., 2018;
Fry et al., 2016; Sauzède et al., 2017), contextualization of
paleo-oceanographic data (Glock et al., 2018; Sessford et al., 2018), and
calculation of inventory, transport, and variability of ocean carbon
(DeVries et al., 2017; Fröb et al., 2016, 2018;
Gruber et al., 2019; Panassa et al., 2018; Pardo et al., 2017; Quay et al.,
2017). A full list of GLODAPv2 citations is provided at
Principles and practices for ensuring open access to research data have been
established, in particular the Findable, Accessible, Interoperable, Reusable
(FAIR) principles (Wilkinson et al., 2016), and are largely adhered to
by the oceanographic community. Data are routinely made available on a per
cruise basis through national and international data centers. However, 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
limits the realization of the full scientific potential of the data. For
biogeochemical data there is the added complexity of different levels of
standardization and calibration, and even variable units, such that the
comparability between many data sets is poor. Standard operating procedures
have been developed for some variables (Dickson et al., 2007; Hood et
al., 2010; Hydes et al., 2012) and certified reference materials (CRMs) exist
for seawater
A total of 12 years separated the release of the two versions of GLODAP. The
urgency and complexity of modern climate change issues necessitate more
frequent updates. Ocean carbon uptake responds quickly to annual-to-decadal
changes in ocean circulation (Fröb et al., 2016; Landschützer et
al., 2015), ocean acidification is progressing at unprecedented rates and
already causing carbonate mineral undersaturation in some regions (Feely
et al., 2008; Qi et al., 2017), oxygen minimum zones are rapidly expanding
(Breitburg et al., 2018), and declining nutrient
supply to the euphotic zone is potentially changing phytoplankton
composition in certain large ocean regions (Rousseaux and Gregg, 2015).
In addition, improvements in data management practices and increased
computational resources are transforming approaches to, and expectations
for, integrated data products. The Surface Ocean
This contribution documents the first such regular update of GLODAP, which
adds data from 116 new cruises to the 724 included in GLODAPv2 and corrects
errors and omissions in GLODAPv2. It also forms the basis for the
documentation of future updates, adopting the
GLODAPv2.2019 (Olsen et al., 2019) contains data from 840 cruises, covering the global ocean from 1972 to 2017. The sampling locations of the 116 cruises added in this update are shown alongside those of GLODAPv2 in Fig. 1, while the coverage in time is shown in Fig. 2. Compared to GLODAPv2, the added data are mostly repeat observations and extend the coverage in time. Information on cruises added to this version is provided in Table A1 in the Appendix.
Location of stations in
Number of cruises per year in GLODAPv2 and GLODAPv2.2019.
All new cruises were subjected to primary (Sect. 3.1) and secondary (Sect. 3.2) quality control (QC). These procedures remain essentially the same as those for GLODAPv2. However, the secondary QC aimed only to ensure the consistency of the data from the 116 new cruises to GLODAPv2. A consistency analysis of the full GLODAPv2.2019 product (as done with the original GLODAPv2 product) has not been carried out, as it would be too demanding in terms of time and resources to allow for frequent updates, particularly in terms of application of inversion results. The QC of GLODAPv2 produced a sufficiently accurate data set that can serve as a reliable reference (this is in fact already done by some investigators to test their newly collected data; e.g., Panassa et al., 2018). The aim is to conduct a full analysis (i.e., including an inversion) again after the completion of the third GO-SHIP survey, currently scheduled to be completed by 2023. Until that time, intermediate products like this will be released regularly (every 1 or 2 years). 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 added.
The data for the 116 new cruises were retrieved from data centers (typically
CCHDO, NCEI, PANGAEA) or submitted directly to us. Each cruise is identified
by an 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
library (
The individual cruise data files were converted to WOCE exchange format: a
comma-delimited ASCII format for CTD and bottle data from hydrographic
cruises. GLODAP deals only with bottle data, and their exchange format is
briefly reviewed here with full details provided in 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 date and time stamp and
identification of the person/group who prepared the file, e.g.,
“PRINUNIVRMK” is Princeton University, Robert M. Key. Next follows the
README section. This provides brief cruise-specific information, such as
dates, ship, region, method and quality notes for each variable measured,
citation information, and references to any papers that used or presented
the data. The README information was 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 be concise, informative, and as correct as possible. The README section is
followed by data column headers, their units, and then the actual data. The
headers and units are standardized and provided in Table 1 for the variables
included in GLODAPv2.2019. Exchange file preparation entailed units
conversion in some cases, most frequently from milliliters per liter (mL L
Each data column (except temperature and pressure, which are assumed “good” if they exist) has an associated column of data flags. For the exchange files, these flags conform to the WOCE definitions for water sample bottles and are listed in Table 2. If no such WOCE flags were submitted with the data, they were assigned by us. In any case, 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. Outliers showing up in two or more different such plots were generally defined as questionable and flagged as such. In some cases, outliers were only detected during the secondary QC; the consequential flag changes have then also been applied in the original cruise data files.
WOCE flags in GLODAPv2.2019 exchange format original data files and product files.
The aim for the secondary QC was to identify and correct any significant
biases in the data from the 116 new cruises relative to GLODAPv2, while
retaining any signal due to time 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 at a
meeting in Seattle in September 2018 in order to decide the adjustments to
be applied to correct for the offset (if any). To guide this process, a set
of initial minimum adjustment limits was used (Table 3). These are set
according to the expected measurement precision for each variable, and are
the same as those used for GLODAPv2, apart from TAlk and pH. For TAlk the
limit was lowered from 6 to 4
Initial minimum adjustment limits.
Crossover comparisons, MLRs, and comparison of deep-water averages were used
to identify offsets for salinity, oxygen, nutrients,
Salinity and oxygen data can be obtained either by analysis of water samples
(bottle data) and/or directly from the CTD sensor pack. These two 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. Note that we did not add data from the
high-resolution CTD files (as obtained on the downcast) to the bottle data
files. The merging procedures were only applied to the bottle data files,
which commonly include values recorded by the CTD at the pressures of the
upcast when 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 are possible, where the fourth never occurred
during our analyses, but is included to maintain consistency with GLODAPv2.
The number of cases encountered for each scenario is summarized in Sect. 4.1.
No data are available: no action needed. No bottle values: use CTD values. No CTD values: use bottle values. Too few data of both types for comparison and more than 80 % of the
records have bottle values: use bottle values. The CTD values do not deviate significantly from bottle values: replace
missing bottle values with CTD values. The CTD values deviate significantly from bottle values: calibrate CTD
values using linear fit with respect to bottle data and replace missing
bottle values with the so-calibrated CTD values. The 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.
The crossover analyses were conducted with the MATLAB toolbox prepared by
Lauvset and Tanhua (2015) and with the GLODAPv2 data product as
reference. In areas where a strong trend in salinity was present, the TAlk
and
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 regardless decreases the
confidence in the comparison. Stations that are compared must be within
2
Example crossover figure, for phosphate for cruises 58GS20150410
(blue) and 64PE20070830 (red), as it was generated during the crossover
analysis. Panels
Example summary figure, for phosphate crossovers for 58GS20150410
versus the cruises in GLODAPv2 (with cruise EXPOCODE listed on the
For each of the 116 new cruises, such a crossover comparison was conducted
against all cruises possible in GLODAPv2, i.e., all cruises that had
stations closer than 2
A few new cruises had no or very few valid crossovers with GLODAPv2 data. In
that situation two other consistency analyses were carried out for salinity,
oxygen, nutrients,
A total of 77 of the 116 new cruises included pH data. For about 30 % of these, the
pH data were not supplied on the total scale, and at 25
The secondary quality control of the pH data also followed previous
procedures, using a combination of crossovers and internal consistency
calculations. The latter were conducted when a cruise had data for
For the halogenated transient tracers (CFC-11, CFC-12, CFC-113, and
The merged product file for GLODAPv2.2019 was created by correcting known issues in the GLODAPv2 merged file, and then appending a merged and bias-corrected file containing the 116 new cruises to this error-corrected GLODAPv2 file.
Several minor omissions and errors have been identified in the GLODAPv2 data product since its release in early 2016. Most of these have been corrected in this release. In addition, some recently available data have been added for a few cruises. The changes are as follows.
For 29 cruises spectrophotometric pH data were available but not included in
the data product despite having passed secondary quality control. The data
from 24 of these cruises are now included, while for the other five cruises the data
have been discarded following more in-depth quality control. Whenever
possible (Sect. 3.3.2), TAlk or The extension “.1” has been removed from the three EXPOCODES
316N19720718.1, 316N19871123.1, and 316N19871123.1. For 33LG20090901 salinity has been included. For 35TH20040604 nutrient data have been replaced with updated data from the
PI. For 09AR20071216 TAlk and For 33AT20120324 and 33AT20120419 DOC, TAlk, and For 35UCKERFIXTS TAlk and Secondary QC flags for calculated carbon variables are corrected. For 99 records in GLODAPv2 unrealistic differences between sampling pressure
and depth were noted. This has been corrected by using the original reported
pressure and recalculating depth. Impossible dates (e.g., 31 November) and time stamps (e.g., minute
Recently available/updated data for radioisotope and stable isotopes as well as noble gases were added to eight cruises. For 06AQ19960317 the For 21 cruises the For 64PE20070830 and 06M220090714 halogenated transient tracer data have
been updated. Some outliers detected since the release have been removed (from the merged
GLODAPv2.2019 product) and flagged as bad/questionable (in the original
cruise data files). Neutral density,
The new data were merged into a bias-minimized product file following the procedures used for GLODAPv1.1 (Key et al., 2004; Sabine et al., 2005), CARINA (Key et al., 2010), PACIFICA (Suzuki et al., 2013), and GLODAPv2 (Olsen et al., 2016), but with minor changes.
Data from the 116 new cruises were merged and sorted according to EXPOCODE, station, and pressure. Cruise numbers were assigned consecutively, starting from 1001, so they can be distinguished from the GLODAPv2 cruises that ended at 724.
Whenever nitrate plus nitrite were reported instead of nitrate and explicit
nitrite concentrations were also given, these were subtracted to get the
nitrate values; otherwise,
When bottom depths were not given, they were approximated as the deepest
sample pressure
Whenever temperature was missing, all data for that record were removed and their flags set to 9. The same was done when both pressure and depth were missing. For all surface samples collected using buckets or similar, the bottle number was set to zero.
All data with WOCE quality flags 3, 4, 5, or 8 were excluded from the
product files (value set to
Whenever either sampling pressure or depth was missing this was calculated following UNESCO (1981).
For both oxygen and salinity, any reported CTD and bottle values were merged following procedures summarized in Sect. 3.2.1.
Missing salinity, oxygen, nitrate, silicate, and phosphate values were vertically interpolated whenever practical, using a quasi-Hermitian piecewise polynomial. “Whenever practical” means that interpolation was limited to the vertical data separation distances given in Table 4 in Key et al. (2010). Interpolated values have been assigned a WOCE quality flag 0.
Summary of salinity and oxygen calibration needs and actions; number of occurrences for each of the scenarios identified.
The data for the 12 core variables were corrected for bias using the
adjustments determined during the secondary QC. For each of these variables
the data product also has separate columns of secondary QC flags, indicating
by cruise and variable whether (“1”) or not (“0”) data successfully
received secondary QC. A 0 flag here means that data were too shallow or
geographically too isolated for consistency analyses. For one of the new
cruises, an adjustment that had been recommended for the
Values for potential temperature and potential density anomalies (referenced to 0, 1000, 2000, 3000, and 4000 dbar) were calculated following Fofonoff (1977) and Bryden (1973). Neutral density was calculated using Sérazin (2011). Apparent oxygen utilization was determined using the combined fit in Garcia and Gordon (1992).
Partial pressures for CFC-11, CFC-12, CFC-113, CCl4, and SF6 were calculated using the solubilities by Warner and Weiss (1985), Bu and Warner (1995), Bullister and Wisegarver (1998), and Bullister et al. (2002).
Whenever only two seawater
The resulting merged file for the 116 new cruises was appended to the merged product file for GLODAPv2.
All material produced during the secondary QC is available at the online
GLODAP Adjustment Table hosted by GEOMAR, Kiel, Germany, at
Table 4 summarizes the actions taken for the merging of the CTD and bottle
data for salinity and oxygen. For most cruises (88 %) both CTD and bottle
data were included for salinity in the original cruise data files and for
all these cruises the two data types were found to be consistent. For
comparison, only 52 % of the GLODAPv2 entries included both, and for a
large fraction of these (35 %) the CTD values were uncalibrated (Olsen
et al., 2016). For oxygen, 50 % of the cruises included both CTD
The secondary QC actions for the 12 core variables are summarized in Table 5. Compared to GLODAPv2, the fraction of data that is adjusted is smaller. A
percentage of 0 %–10 % of the 116 new cruises are adjusted for each core
variable, whereas for the 724 cruises in GLODAPv2, 5 %–30 % were adjusted
for each core variable. The number of adjusted cruises is particularly low
for salinity (only one of the new cruises was adjusted, i.e., 1 % compared
to 5 % for the 724 GLODAPv2 cruises), for the halogenated transient
tracers (0 %–3 % adjusted, depending on variable, compared to 6 %–10 %
for GLODAPv2), and for
Summary of secondary QC actions per variable for the 116 new cruises.
The distributions of the magnitude of adjustments applied are presented in
Fig. 5 and Table 6. For salinity, oxygen, and silicate, adjustments between
1 and 2 times the initial minimum adjustment limit are most prevalent. For
nitrate, phosphate, CFC-11, and CFC-12, adjustments equal to or larger than
2 times the limit are most prevalent. For the salinity and oxygen this
reflects that any biases in the data tend to be between 1 and 2 times the
limit, while for CFC-11 and CFC-12 it also likely reflects limitations in
our ability to confidently identify small biases. These limitations are
related to the strongly transient nature of the CFCs. For
Distribution of applied adjustments for each core variable that
received secondary QC. Grey areas depict the initial minimum adjustment
limits. The figure includes numbers for data subjected to secondary quality control only. Note also that the
Summary of the distribution of applied adjustments per variable, in number of adjustments applied for each variable.
For TAlk, seven out of eight adjustments are positive (i.e., the data are biased low), for pH nine out of 10 adjustments are positive, and for oxygen six out of seven are positive. The adjustments for the other variables were more distributed around zero. For TAlk, prevalence of a negative bias was also observed in the interlaboratory comparison reported by Bockmon and Dickson (2015), who suggested the cause being the use of end point titrations rather than the (preferred) equivalence point titrations. However, six out of seven of the negative bias cruises were Japanese. A tendency for bias in Japanese cruises to be negative was also identified in GLODAPv2 and may be due to the use of internal reference material. We note that the TAlk data from 23 out of 29 Japanese cruises with viable deep crossover checks had no apparent deep offset, so the majority of new TAlk data from Japan were consistent with GLODAPv2 even with the lowered threshold.
The prevalence of positive pH adjustments may relate to the fact that at low
pH (as is common in the deeper waters where crossover analyses are done),
measurements made with purified dyes tend to be lower than pH determined
using electrodes, using impure spectrophotometric dyes with older dye
coefficients (Clayton and Byrne, 1993), or calculated from
Crossover comparison is conducted on deep-water samples so atmospheric exchange during sample collection on the new cruises is not a viable explanation for the trend of positive oxygen adjustments. Atmospheric contamination would usually increase deep-water oxygen concentrations since deep oxygen levels are usually low. The data are not collected in any particular region, or associated with any specific laboratory, country, or method. Consequently, no particular explanation can be offered for the prevalence of positive adjustments.
Improvements resulting from quality control of the 116 new cruises,
per basin and for the global data set. The numbers in the table are the weighted mean of the absolute offset of unadjusted and adjusted data versus GLODAPv2.
The improvement in data consistency 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. CFCs were omitted for previously
discussed reasons (Sect. 3.2.5). Globally, the improvement is modest, except
for TAlk, where the consistency was improved from 3.3 to 2.7
Distribution of applied adjustments per decade for the 840 cruises included in GLODAPv2.2019. Dark blue: not adjusted; light blue: absolute adjustment is smaller than initial minimum adjustment limit (Table 3); orange: absolute adjustment is between limit and 2 times the limit, red: absolute adjustment is larger than 2 times the limit.
For the Arctic and Atlantic oceans there are substantial offsets for many
variables with respect to GLODAPv2 even after the adjustments have been
applied. This relates to actual variability in deep waters of the northern
North Atlantic and Arctic regions. For example, the weighted mean of the
absolute offset for Arctic Ocean silicate for the adjusted data is 11.1 %
and that for salinity is 10 ppm (i.e., a salinity of 0.01). This can be
ascribed to two cruises, 58GS20130717 and 58GS20160802, conducted in the
Greenland Sea where an increasing presence of Arctic sourced deep waters
generates changes in these properties (Blindheim and Rey, 2004; Lauvset
et al., 2018; Olafsson and Olsen, 2010; Olsen et al., 2009) that have not
been corrected for. The impact of northern variability on the final
consistency estimate can be determined for the Atlantic Ocean by excluding
all data north of 50
Improvements resulting from the quality control of Atlantic cruises
south of 50
The various iterations of GLODAP now provide insight into initial data quality
covering more than 4 decades. Figure 6 summarizes the applied absolute
adjustment magnitude per decade. For several variables improvement is
evident over time. Most
Distribution of data in GLODAPv2.2019 in
The GLODAPv2.2019 merged and adjusted data product is archived at NOAA NCEI
under
The original cruise files are available through the GLODAPv2.2019 cruise
summary table (CST) hosted by NOAA NCEI:
While GLODAPv2.2019 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 the articles where the data are described and, preferably, contacting principal investigators for exploring opportunities for collaboration and co-authorship. To this end, relevant articles and principle investigator names are provided in the CST. This comes with the additional benefit that the principal investigators often possess expert insight into the data and/or particular region under investigation. This can improve scientific quality and promote data sharing.
Cite this paper in any scientific publications that result from usage of the product. Citations provide us with the most efficient means to track the use of this product, which is important for attracting funding to enable the preparation of future updates.
Number
Locations of stations included in the
GLODAPv2.2019 is an update of GLODAPv2. Data from 116 new cruises have been
added to supplement the earlier release and extend temporal coverage by 4
years. GLODAP now includes 45 years, 1972–2017, of global interior ocean
biogeochemical data from 840 cruises. Figure 7 illustrates the seasonal
distribution of the data. There is a bias around 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. These tendencies are strongest for
the poleward regions and reflect the harsh conditions during winter months,
which make fieldwork difficult. Figure 8 illustrates the distribution of data
with depth. The upper 100 m is the best sampled part of the global ocean,
in terms of both number (Fig. 8a) and density (Fig. 8b) of observations. The
number of observations steadily declines with depth. In part, this is caused
by the reduction of 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
Except for salinity and oxygen, the core data were collected exclusively
through chemical analyses of individually collected water samples. The data
of 12 core variables: salinity, oxygen, nitrate, silicate, phosphate,
The consistency analyses were conducted by comparing the data from the 116
new cruises to GLODAPv2. Adjustments were only applied when the offsets were
believed to reflect biases related to measurement, calibration, and/or data
handling practices. The Adjustment Table at https://glodapv2-2019.geomar.de
lists all applied adjustments and provides a brief justification for each.
The consistency analyses rely on deep ocean data (
The primary, WOCE, QC flags in the product files are also important,
although 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
Based on the initial minimum adjustment limits and the improvement of the
consistency from the adjustments (Tables 7 and 8), 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
Cruises included in GLODAPv2.2019 that did not appear in GLODAPv2.
Complete information on each cruise, such as variables included and chief
scientist and principal investigator names, is provided in the cruise summary
table at
Continued.
Continued.
AO and TT led the team that produced this update. RMK, AK, MKK, and BP
compiled the original data files. NL conducted the secondary QC analyses. CS
manages the Adjustment Table e-infrastructure. AK maintains the GLODAPv2
web pages at NCEI/OCADS while SDJ maintains
The authors declare that they have no conflict of interest.
GLODAPv2.2019 would not have been possible without the effort of the many scientists who secured funding, dedicated time to collect, and willingly 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.
Meeting and travel support was provided by the IOCCP (via the US National Science Foundation grant OCE-1840868 to the Scientific Committee on Oceanic Research), NOAA PMEL, the AtlantOS project (EU H2020 grant agreement 633211), and the Bjerknes Centre for Climate Research. Henry C. Bittig, Nico Lange, Fiz F. Pérez, Anton Velo, and Siv K. Lauvset were funded by the AtlantOS project. Robert M. Key received partial support from NOAA CICS grant NA14OAR4320106 during the last year of this effort. Contributions from Rik Wanninkhof, Brendan R. Carter, and Richard R. Feely are supported by the Ocean Observing and Monitoring Division, Office of Oceanic and Atmospheric Research of NOAA (data management and synthesis grant N8R3CEA-PDM).
This research has been supported by the Horizon 2020 (AtlantOS (grant no. 633211)), the National Science Foundation (grant no. OCE-1840868), and the National Oceanic and Atmospheric Administration (grant nos. NA14OAR4320106 and N8R3CEAPDM).
This paper was edited by Giuseppe M. R. Manzella and reviewed by Nicolas Gruber and one anonymous referee.