Water transparency can be measured with optical instruments and estimated with satellite sensors, but such measurements have been widely available for only a few decades. Estimates of water ...transparency using a white disk called a Secchi disk have been made for over a century and can be used to estimate long-term trends. However, historic
in situ
measurements of the Secchi depth
(Z
Sd
) were irregular in space and time and are difficult to interpret in regular time series due to biases introduced by changing locations and the timing of measurements. Satellite data time series, on the other hand, have consistent resolution in both space and time but cover too short a time to resolve climate-scale trends. We normalized historic
Z
Sd
measurements in the Baltic Sea with a satellite-derived mean climatology at 5 d temporal and 4 km spatial resolutions and created a merged time series of
Z
Sd
for the last century. The mean
Z
Sd
in the Baltic Sea from 1927-2020 decreased by 4.2 ± 0.6 m at a rate of 0.045 ± 0.06 m yr
-1
. Most of the change happened before 1987, and a further decrease was evident primarily in the satellite data during the 1998-2008 period. After 2008, no significant trend in
Z
Sd
and or the coefficient of diffuse light attenuation was detected in the Baltic Sea. However, in some sub-basins of the Baltic Sea, the decrease in
Z
Sd
continued even after that. The decrease in spectral water transparency in recent decades was highest in the 412 nm band, indicating an increase in the concentration of chromophoric dissolved organic matter.
Since a pH sensor has become available that is principally suitable for use on demanding autonomous measurement platforms, the marine CO.sub.2 system can be observed independently and continuously by ...Biogeochemical Argo floats. This opens the potential to detect variability and long-term changes in interior ocean inorganic carbon storage and quantify the ocean sink for atmospheric CO.sub.2 . In combination with a second parameter of the marine CO.sub.2 system, pH can be a useful tool to derive the surface ocean CO.sub.2 partial pressure (pCO.sub.2). The large spatiotemporal variability in the marine CO.sub.2 system requires sustained observations to decipher trends and study the impacts of short-term events (e.g., eddies, storms, phytoplankton blooms) but also puts a high emphasis on the quality control of float-based pH measurements. In consequence, a consistent and rigorous quality control procedure is being established to correct sensor offsets or drifts as the interpretation of changes depends on accurate data. By applying current standardized routines of the Argo data management to pH measurements from a pH / O.sub.2 float pilot array in the subpolar North Atlantic Ocean, we assess the uncertainties and lack of objective criteria associated with the standardized routines, notably the choice of the reference method for the pH correction (CANYON-B, LIR-pH, ESPER-NN, and ESPER-LIR) and the reference depth for this adjustment. For the studied float array, significant differences ranging between ca. 0.003 pH units and ca. 0.04 pH units are observed between the four reference methods which have been proposed to correct float pH data. Through comparison against discrete and underway pH data from other platforms, an assessment of the adjusted float pH data quality is presented. The results point out noticeable discrepancies near the surface of 0.004 pH units. In the context of converting surface ocean pH measurements into pCO.sub.2 data for the purpose of deriving air-sea CO.sub.2 fluxes, we conclude that an accuracy requirement of 0.01 pH units (equivalent to a pCO.sub.2 accuracy of 10 µatm as a minimum requirement for potential future inclusion in the Surface Ocean CO.sub.2 Atlas, SOCAT, database) is not systematically achieved in the upper ocean.
GLODAPv2.2019 – an update of GLODAPv2 Olsen, Are; Lange, Nico; Key, Robert M ...
Earth system science data,
09/2019, Volume:
11, Issue:
3
Journal Article
Peer reviewed
Open access
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 CCl.sub.4) have undergone extensive quality control, especially systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. These adjustments were derived by comparing the data from the 116 new cruises with the data from the 724 quality-controlled cruises of the GLODAPv2 data product. They correct for errors related to measurement, calibration, and data handling practices, taking into account any known or likely time trends or variations. 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.sup.-1 in dissolved inorganic carbon, 4 µmol kg.sup.-1 in total alkalinity, 0.01-0.02 in pH, and 5 % in the halogenated transient tracers. The compilation also includes data for several other variables, such as isotopic tracers. These were not subjected to bias comparison or adjustments.
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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. GLODAPv2.2021 is an update of the previous version, GLODAPv2.2020 (Olsen et al., 2020). The major changes are as follows: data from 43 new cruises were added, data coverage was extended until 2020, all data with missing temperatures were removed, and a digital object identifier (DOI) was included for each cruise in the product files. In addition, a number of minor corrections to GLODAPv2.2020 data were performed. GLODAPv2.2021 includes measurements from more than 1.3 million water samples from the global oceans collected on 989 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 CCl.sub.4) 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 updated to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality controlled cruises in the GLODAPv2.2020 data product using crossover analysis. Comparisons to estimates of nutrients and ocean CO.sub.2 chemistry based on empirical algorithms provided additional context for adjustment decisions in this version. The adjustments 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 with to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg.sup.-1 in dissolved inorganic carbon, 4 µmol kg.sup.-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 CO.sub.2 fugacity (fCO.sub.2 ), were not subjected to bias comparison or adjustments.
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Ocean surface pCO2 estimates are of great interest for the calculation of air–sea CO2 fluxes, oceanic uptake of anthropogenic CO2, and eventually the Global Carbon Budget. They are accessible from ...direct observations, which are discrete in space and time and thus always sparse, or from biogeochemical models, which only approximate reality. Here, a combined method for the extrapolation of pCO2 observations is presented that uses (1) model-based patterns of variability from an empirical orthogonal function (EOF) analysis of variability with (2) observational data to constrain EOF pattern amplitudes in (3) an ensemble approach, which locally adjusts the spatial scale of the mapping to the density of the observations. Thus, data-constrained, gap- and discontinuity-free mapped fields including local error estimates are obtained without the need for or dependence on ancillary data (e.g. satellite sea surface temperature maps). This extrapolation approach is generic in that it can be applied to any oceanic or coastal region covered by a suitable model and observations. It is used here to establish a regional pCO2 climatology of the Baltic Sea (: 10.1594/PANGAEA.961119), largely based on ICOS-DE ship of opportunity (SOOP) Finnmaid surface pCO2 observations between Lübeck-Travemünde (Germany) and Helsinki (Finland). The climatology can serve as improved input for atmosphere–ocean CO2 flux estimation in this coastal environment.
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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 seawater samples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019. The major changes are data from 106 new cruises added, extension of time coverage to 2019, and the inclusion of available (also for historical cruises) discrete fugacity of CO.sub.2 (fCO.sub.2) values in the merged product files. GLODAPv2.2020 now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 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 CCl.sub.4) 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 updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. These adjustments were derived by comparing the data from the 106 new cruises with the data from the 840 quality-controlled cruises of the GLODAPv2.2019 data product using crossover analysis. Comparisons to empirical algorithm estimates provided additional context for adjustment decisions; this is new to this version. The adjustments 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.sup.-1 in dissolved inorganic carbon, 4 µmol kg.sup.-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 fCO.sub.2, were not subjected to bias comparison or adjustments.
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Oxygen optode measurements on floats and gliders suffer from a slow time response and various sources of drift in the calibration coefficients. Based on two dual-O2 Argo floats, we show how to ...post-correct for the effect of the optode's time response and give an update on optode in situ drift stability and in-air calibration. Both floats are equipped with an unpumped Aanderaa 4330 optode and a pumped Sea-Bird SBE63 optode. Response times for the pumped SBE63 were derived following Bittig et al. (2014) and the same methods were used to correct the time response bias. Using both optodes on each float, the time response regime of the unpumped Aanderaa optode was characterized more accurately than previously possible. Response times for the pumped SBE63 on profiling floats are in the range of 25–40 s, while they are between 60 and 95 s for the unpumped 4330 optode. Our parameterization can be employed to post-correct the slow optode time response on floats and gliders. After correction, both sensors agree to within 2–3 µmol kg−1 (median difference) in the strongest gradients (120 µmol kg−1 change over 8 min or 20 dbar) and better elsewhere. However, time response correction is only possible if measurement times are known, i.e., provided by the platform as well as transmitted and stored with the data. The O2 in-air measurements show a significant in situ optode drift of −0.40 and −0.27 % yr−1 over the available 2 and 3 years of deployment, respectively. Optode in-air measurements are systematically biased high during midday surfacings compared to dusk, dawn, and nighttime. While preference can be given to nighttime surfacings to avoid this in-air calibration bias, we suggest a parameterization of the daytime effect as a function of the Sun's elevation to be able to use all data and to better constrain the result. Taking all effects into account, calibration factors have an uncertainty of 0.1 %. In addition, in-air calibration factors vary by 0.1–0.2 % when using different reanalysis models as a reference. The overall accuracy that can be achieved following the proposed correction routines is better than 1 µmol kg−1.
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 (SF.sub.6) data. In addition, a number of changes were made to data included in GLODAPv2.2021. These changes affect specifically the SF.sub.6 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, CCl.sub.4, and SF.sub.6) 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. SF.sub.6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO.sub.2) 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.sup.-1 in dissolved inorganic carbon, 4 µmol kg.sup.-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 CO.sub.2 fugacity (fCO.sub.2 ), were not subjected to bias comparison or adjustments.
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Autonomous measurements aboard ships of opportunity (SOOP) provide in situ data sets with high spatial and temporal coverage. In this study, we use 8 years of carbon dioxide (CO2) and methane (CH4) ...observations from SOOP Finnmaid to study the influence of upwelling on trace gas dynamics in the Baltic Sea. Between spring and autumn, coastal upwelling transports water masses enriched with CO2 and CH4 to the surface of the Baltic Sea. We study the seasonality, regional distribution, relaxation, and interannual variability in this process. We use reanalysed wind and modelled sea surface temperature (SST) data in a newly established statistical upwelling detection method to identify major upwelling areas and time periods. Large upwelling-induced SST decrease and trace gas concentration increase are most frequently detected around August after a long period of thermal stratification, i.e. limited exchange between surface and underlying waters. We found that these upwelling events with large SST excursions shape local trace gas dynamics and often lead to near-linear relationships between increasing trace gas levels and decreasing temperature. Upwelling relaxation is mainly driven by mixing, modulated by air–sea gas exchange, and possibly primary production. Subsequent warming through air–sea heat exchange has the potential to enhance trace gas saturation. In 2015, quasi-continuous upwelling over several months led to weak summer stratification, which directly impacted the observed trace gas and SST dynamics in several upwelling-prone areas. Trend analysis is still prevented by the observed high variability, uncertainties from data coverage, and long water residence times of 10–30 years. We introduce an extrapolation method based on trace gas–SST relationships that allows us to estimate upwelling-induced trace gas fluxes in upwelling-affected regions. In general, the surface water reverses from CO2 sink to source, and CH4 outgassing is intensified as a consequence of upwelling. We conclude that SOOP data, especially when combined with other data sets, enable flux quantification and process studies addressing the process of upwelling on large spatial and temporal scales.
We report on data from an oceanographic cruise, covering western, central and eastern parts of the Mediterranean Sea, on the French research vessel Tethys 2 in May 2015. This cruise was fully ...dedicated to the maintenance and the metrological verification of a biogeochemical observing system based on a fleet of BGC-Argo floats. During the cruise, a comprehensive data set of parameters sensed by the autonomous network was collected. The measurements include ocean currents, seawater salinity and temperature, and concentrations of inorganic nutrients, dissolved oxygen and chlorophyll pigments. The analytical protocols and data processing methods are detailed, together with a first assessment of the calibration state for all the sensors deployed during the cruise. Data collected at stations are available at https://doi.org/10.17882/51678 and data collected along the ship track are available at https://doi.org/10.17882/51691.
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