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25 pages, 10 figures, 7 tables.-- This work is distributed under the Creative Commons Attribution 4.0 License, 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 CCl4) 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 CO2 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 μmolkg-1 in dissolved inorganic carbon, 4 μmolkg-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 Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/GLODAPv2_2021/, last access: 7 July 2021). 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 https://doi.org/10.25921/ttgq-n825 (Lauvset et al., 2021). These bias-adjusted product files also include significant ancillary and approximated data and can be accessed via https://www.glodap.info (last access: 29 June 2021). These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2021 methods and provides a broad overview of the secondary quality control procedures and results, This research has been supported by EU Horizon 2020 through the EuroSea action (grant no. 862626), internal strategic funding from NORCE Climate, Prociencia/UERJ (grant 2019–2021), IEO RADIALES and RADPROF projects, the UK Climate Linked Atlantic Sector Science (CLASS) NERC National Capability Long-term Single Centre Science Programme(grant no. NE/R015953/1), the BOCATS2 (PID2019-104279GBC21) project funded by MCIN/AEI/10.13039/501100011033 and contributing toWATER:iOS CSIC PTI, the NOAA Global Observations and Monitoring Division (fund reference 100007298), the Office of Oceanic and Atmospheric Research of NOAA Global Observations and Monitoring Division (fund reference 100007298), the Office of Oceanic and Atmospheric Research of NOAA, the BONUS INTEGRAL project (grant no. 03F0773A), the Australian Antarctic Program Partnership and the Integrated Marine Observing System, EU Horizon 2020 action SO-CHIC (grant no. 821001), and the Initiative and Networking Fund of the Helmholtz Association through the project “Digital Earth” (grant no. ZT-0025)

We introduce three new Empirical Seawater Property Estimation Routines (ESPERs) capable of predicting seawater phosphate, nitrate, silicate, oxygen, total titration seawater alkalinity, total hydrogen scale pH (pHT), and total dissolved inorganic carbon (DIC) from up to 16 combinations of seawater property measurements. The routines generate estimates from neural networks (ESPER_NN), locally interpolated regressions (ESPER_LIR), or both (ESPER_Mixed). They require a salinity value and coordinate information, and benefit from additional seawater measurements if available. These routines are intended for seawater property measurement quality control and quality assessment, generating estimates for calculations that require approximate values, original science, and producing biogeochemical property context from a data set. Relative to earlier LIR routines, the updates expand their functionality, including new estimated properties and combinations of predictors, a larger training data product including new cruises from the 2020 Global Data Analysis Project data product release, and the implementation of a first-principles approach for quantifying the impacts of anthropogenic carbon on DIC and pHT. We show that the new routines perform at least as well as existing routines, and, in some cases, outperform existing approaches, even when limited to the same training data. Given that additional training data has been incorporated into these updated routines, these updates should be considered an improvement over earlier versions. The routines are intended for all ocean depths for the interval from 1980 to ~2030 c.e., and we caution against using the routines to directly quantify surface ocean seasonality or make more distant predictions of DIC or pHT., 1,823

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesise data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data-products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr-1 (9.9 ± 0.5 GtC yr-1 when the cement carbonation sink is included), ELUC was 1.1 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission of 11.1 ± 0.8 GtC yr-1 (40.8 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr-1 (2.5 ± 0.1 ppm yr-1), SOCEAN was 2.9 ± 0.4 GtC yr-1 and SLAND was 3.5 ± 0.9 GtC yr-1, with a BIM of -0.6 GtC yr-1 (i.e. total estimated sources too low or sinks too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022, suggest an increase in EFOS relative to 2021 of +1.1 % (0 % to 1.7 %) globally, and atmospheric CO2 concentration reaching 417.3 ppm, more than 50 % above pre-industrial level. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2022a; Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

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 (f CO2), 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 https://doi.org/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., Earth System Science Data, 14 (12), ISSN:1866-3516, ISSN:1866-3508

4 pages, 1 figure.-- This article is licensed under a Creative Commons Attribution 4.0 International License, The ocean is mitigating global warming by absorbing large amounts of excess carbon dioxide from human activities. To quantify and monitor the ocean carbon sink, we need a state-of-the-art data resource that makes data submission and retrieval machine-compatible and efficient, This effort has been supported by EU Horizon 2020 through the EuroSea action (grant no. 862626), the Helmholtz Association through “Digital Earth” (grant no. ZT-0025), L.C.C. acknowledges the UERJ/Prociencia grant (2018-2021), M.H. acknowledge EU Horizon 2020 action SO-CHIC (grant N°821001). B.C., R.A.F., E.B., K.O’.B., and R.W. were supported by NOAA/OAR and NOAA/GOMO (fund ref. 100007298), B.T. was partly supported by Australia’s Integrated Marine Observing System (IMOS), S.K.L. acknowledges internal strategic funding from NORCE Climate, M.T. acknowledges support from the US NSF grant OCE-1840868 to SCOR. The PMEL and CICOES contributions are 5253 and 2020-1141, respectively

26 pages, 11 figures, 7 tables.-- This work is distributed under the Creative Commons Attribution 4.0 License, 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 CO2 (fCO2) 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 CCl4) 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 μmolkg-1 in dissolved inorganic carbon, 4 μmolkg-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 fCO2, were not subjected to bias comparison or adjustments. The original data and their documentation and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, last access: 20 June 2020). 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 https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). These bias-adjusted product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2020 methods and provides a broad overview of the secondary quality control procedures and results, Nico Lange was funded by EU Horizon 2020 through the EuroSea action (grant no. 862626). Leticia Cotrim da Cunha was supported by Prociencia/UERJ (grant no. 2019-2021). Marta Álvarez was supported by the IEO RADIALES and RADPROF projects. Peter J. Brown was partially funded by the UK Climate Linked Atlantic Sector Science (CLASS) NERC National Capability Long-term Single Centre Science Programme (grant no. NE/R015953/1). Anton Velo and Fiz F. Pérez were supported by the BOCATS2 Project (grant no. PID2019-104279GBC21) co-funded by the Spanish Government and the Fondo Europeo de Desarrollo Regional (FEDER). RikWanninkhof and Brendan R. Carter were supported by the NOAA Global Observations and Monitoring Division (fund reference 100007298) and the Office of Oceanic and Atmospheric Research of NOAA. Henry C. Bittig has been supported by the BONUS INTEGRAL project (grant no. 03F0773A). This research was also funded by the Initiative and Networking Fund of the Helmholtz Association through the project “Digital Earth” (grant no. ZT-0025)

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., Biogeosciences, 18 (8), ISSN:1726-4170

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. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019. The major changes are: data from 106 more cruises added, extension of time coverage until 2019, and the inclusion of available discrete fugacity of CO2 (fCO2) values in the merged product files. GLODAPv2.2020 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 CCl4) 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 106 new cruises with the data from the 840 quality-controlled cruises of the GLODAPv2.2019 data product. They correct for errors related to measurement, calibration, and data handling practices, while taking into account any 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, depending on region, in pH, and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete fCO2 were not subjected to bias comparison or adjustments. The original data, their documentation and doi codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, last access: 22 June 2020). 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 https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). The bias corrected product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2020 methods and provides a broad overview of the secondary quality control procedures and results.

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.

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.

Early-career scientists came together recently to learn to use a suite of ocean biogeochemical sensors, with the goal of closing the knowledge gap between ocean technology and potential end users.

The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo’s global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities., Sí

The oceans play a key role in global issues such as climate change, food security, and human health. Given their vast dimensions and internal complexity, efficient monitoring and predicting of the planet’s ocean must be a collaborative effort of both regional and global scale. A first and foremost requirement for such collaborative ocean observing is the need to follow well-defined and reproducible methods across activities: from strategies for structuring observing systems, sensor deployment and usage, and the generation of data and information products, to ethical and governance aspects when executing ocean observing. To meet the urgent, planet-wide challenges we face, methods across all aspects of ocean observing should be broadly adopted by the ocean community and, where appropriate, should evolve into “Ocean Best Practices.” While many groups have created best practices, they are scattered across the Web or buried in local repositories and many have yet to be digitized. To reduce this fragmentation, we introduce a new open access, permanent, digital repository of best practices documentation (oceanbestpractices.org) that is part of the Ocean Best Practices System (OBPS). The new OBPS provides an opportunity space for the centralized and coordinated improvement of ocean observing methods. The OBPS repository employs user-friendly software to significantly improve discovery and access to methods. The software includes advanced semantic technologies for search capabilities to enhance repository operations. In addition to the repository, the OBPS also includes a peer reviewed journal research topic, a forum for community discussion and a training activity for use of best practices. Together, these components serve to realize a core objective of the OBPS, which is to enable the ocean community to create superior methods for every activity in ocean observing from research to operations to applications that are agreed upon and broadly adopted across communities. Using selected ocean observing examples, we show how the OBPS supports this objective. This paper lays out a future vision of ocean best practices and how OBPS will contribute to improving ocean observing in the decade to come.

Oceanic emissions of the climate relevant trace gases carbonyl sulfide (OCS) and carbon disulfide (CS2) are a major source to their atmospheric budget. Their current and future emission estimates are still uncertain due to incomplete process understanding and, therefore, inexact quantification across different biogeochemical regimes. Here we present the first concurrent measurements of both gases together with related fractions of the dissolved organic matter (DOM) pool, i.e. solid-phase extractable dissolved organic sulfur (DOSSPE), chromophoric (CDOM) and fluorescent dissolved organic matter (FDOM) from the Eastern Tropical South Pacific (ETSP). These observations are used to estimate in-situ production rates and identify their drivers. We find different limiting factors of marine photoproduction: while OCS production is limited by the humic-like DOM fraction that can act as a photosensitizer, high CS2 production coincides with high DOSSPE concentration. The lack of correlation between OCS production and DOSSPE may be explained by the active cycling of sulfur between OCS and dissolved inorganic sulfide via OCS photoproduction and hydrolysis. In addition, the only existing parameterization for OCS dark production is validated and updated with new rates from the ETSP and the Indian Ocean. Our results will help to predict oceanic concentrations and emissions of both gases on regional and, potentially, global scales.

International audience; Recently, measurements of oxygen concentration in the ocean -one of the most classical parameters in chemical oceanography-are experiencing a revival. This is not surprising, given the key role of oxygen for assessing the status of the marine carbon cycle and feeling the pulse of the biological pump. The revival, however, has to a large extent been driven by the availability of robust optical oxygen sensors and their painstakingly thorough characterization. For autonomous observations, oxygen optodes are the sensors of choice: They are used abundantly on Biogeochemical-Argo floats, gliders and other autonomous oceanographic observation platforms. Still, data quality and accuracy are often suboptimal, in some part because sensor and data treatment are not always straightforward and/or sensor characteristics are not adequately taken into account. Here, we want to summarize the current knowledge about oxygen optodes, their working principle as well as their behavior with respect to oxygen, temperature, hydrostatic pressure, and response time. The focus will lie on the most widely used and accepted optodes made by Aanderaa and Sea-Bird. We revisit the essentials and caveats of in-situ in air calibration as well as of time response correction for profiling applications, and provide requirements for a successful field deployment. In addition, all required steps to post-correct oxygen optode data will be discussed. We hope this summary will serve as a comprehensive, yet concise reference to help people get started with oxygen observations, ensure successful sensor deployments and acquisition of highest quality data, and facilitate post-treatment of oxygen data. In the end, we hope that this will lead to more and higher-quality oxygen observations and help to advance our understanding of ocean biogeochemistry in a changing ocean.

A yet unexplained drift of (some) oxygen optodes during storage/transport and thus significant deviations from factory/laboratory calibrations have been a major handicap for autonomous oxygen observations. Optode drift appears to be systematic and is predominantly a slope effect due to reduced oxygen sensitivity. A small contribution comes from a reduced luminophore lifetime, which causes a small positive offset. A reliable in situ reference is essential to correct such a drift. Traditionally, this called for a ship-based reference cast, which poses some challenges for opportunistic float deployments. This study presents an easily implemented alternative using near-surface/in-air measurements of an Aanderaa optode on a 10-cm stalk and compares it to the more traditional approaches (factory, laboratory, and in situ deployment calibration). In-air samples show a systematic bias depending on the water saturation, which is likely caused by occasional submersions of the standard-height stalk optode. Linear regression of measured in-air supersaturation against in-water supersaturation (using ancillary meteorological data to define the saturation level) robustly removes this bias and thus provides a precise (0.2%) and accurate (1%) in situ correction that is available throughout the entire instrument’s lifetime.

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 dataset of parameters sensed by the autonomous network was collected. The measurements include ocean currents, seawater salinity and temperature, concentration of inorganic nutrients, of 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 under http://doi.org/10.17882/51678, data collected along ship track are available under http://doi.org/10.17882/51691.

The time response behavior of Aanderaa optodes model 3830, 4330, and 4330F, as well as a Sea-Bird SBE63 optode and a JFE Alec Co. Rinko dissolved oxygen sensor was analyzed both in the laboratory and in the field. The main factor for the time response is the dynamic regime, i.e., the water flow around the sensor that influences the boundary layer’s dynamics. Response times can be drastically reduced if the sensors are pumped. Laboratory experiments under different dynamic conditions showed a close to linear relation between response time and temperature. Application of a diffusion model including a stagnant boundary layer revealed that molecular diffusion determines the temperature behavior, and that the boundary layer thickness was temperature independent. Moreover, field experiments matched the laboratory findings, with the profiling speed and mode of attachment being of prime importance. The time response was characterized for typical deployments on shipboard CTDs, gliders, and floats, and tools are presented to predict the response time as well as to quantify the effect on the data for a given water mass profile. Finally, the problem of inverse filtering optode data to recover some of the information lost by their time response is addressed.

Oxygen optode measurements on floats and gliders suffer from a slow time response and sensors that are or eventually run out of calibration. 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 after Bittig et al. (2014). Also their 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 are in the range of 25–40 s, while they are between 60–95 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 minutes 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 −0.4 % yr−1 and −0.2 % yr−1, respectively. Optode in-air measurements are systematically biased high during mid-day 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 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 reference. The overall accuracy that can be achieved following the proposed correction routines is

We present a laboratory calibration setup for the individual multi-point calibration of oxygen sensors. It is based on the electrochemical generation of oxygen in an electrolytic carrier solution. Under thorough control of the conditions, i.e., temperature, carrier solution flow rate, and electrolytic current, the amount of oxygen is strictly given by Faraday's laws and can be controlled to within ± 0.5 μmol L–1 (2 SD). Whereas Winkler samples can be taken for referencing with a reproducibility between triplicates of 0.8 μmol L–1 (2 SD), the calibration setup can provide a Winkler-free way of referencing with an accuracy of ± 1.2 μmol L–1 (2 SD). Thus calibrated oxygen optodes have been deployed in the Southern Ocean and the Eastern Tropical Atlantic both in profiling and underway mode and confirm the validity of the laboratory calibrations to within few μmol L–1. In two cases, the optodes drifted between deployments, which was easily identified using the calibration setup. The electrochemical calibration setup may thus facilitate accurate oxygen measurements on a large scale, and its small size makes it possible to configure as a mobile, sea-going, Winkler-free system for oxygen sensor calibrations.

This study investigated the effect of hydrostatic pressure of up to 6000 dbar on Aanderaa and Sea-Bird oxygen optodes both in the laboratory and in the field. The overall pressure response is a reduction in the O2 reading by 3%–4% per 1000 dbar, which is closely linear with pressure and increases with temperature. Closer inspection reveals two superimposed processes with an opposite effect: an O2-independent pressure response on the luminophore that increases optode O2 readings and an O2-dependent change in luminescence quenching that decreases optode O2 readings. The latter process dominates and is mainly due to a shift in the equilibrium between the sensing membrane and seawater under elevated pressures. If only the dominant O2-dependent process is considered, then the Aanderaa and Sea-Bird optodes differ in their pressure response. Compensation of the O2-independent process, however, yields a uniform O2 dependence for Aanderaa optodes with standard foil and fast-response foil as well as for Sea-Bird optodes. A new scheme to calculate optode O2 from raw data is proposed to account for the two processes. The overall uncertainty of the optode pressure correction amounts to 0.3% per 1000 dbar, which is mainly due to variability between the sensors.

New reactive, divalent lanthanoid formamidinates [Yb(Form)(2)(thf)(2)] (Form=[RNCHNR]; R=o-MeC(6)H(4) (o-TolForm; 1), 2,6-Me(2)C(6)H(3) (XylForm; 2), 2,4,6-Me(3)C(6)H(2) (MesForm; 3), 2,6-Et(2)C(6)H(3) (EtForm; 4), o-PhC(6)H(4) (o-PhPhForm; 5), 2,6-iPr(2)C(6)H(3) (DippForm; 6), o-HC(6)F(4) (TFForm; 7)) and [Eu(DippForm)(2)(thf)(2)] (8) have been prepared by redox transmetallation/protolysis reactions between an excess of a lanthanoid metal, Hg(C(6)F(5))(2) and the corresponding formamidine (HForm). X-ray crystal structures of 2-6 and 8 show them to be monomeric with six-coordinate lanthanoid atoms, chelating N,N'-Form ligands and cis-thf donors. However, [Yb(TFForm)(2)(thf)(2)] (7) crystallizes from THF as [Yb(TFForm)(2)(thf)(3)] (7a), in which ytterbium is seven coordinate and the thf ligands are "pseudo-meridional". Representative complexes undergo C-X (X=F, Cl, Br) activation reactions with perfluorodecalin, hexachloroethane or 1,2-dichloroethane, and 1-bromo-2,3,4,5-tetrafluorobenzene, giving [Yb(EtForm)(2)F](2) (9), [Yb(o-PhPhForm)(2)F](2) (10), [Yb(o-PhPhForm)(2)Cl(thf)(2)] (11), [Yb(DippForm)(2)Cl(thf)] (12) and [Yb(DippForm)(2)Br(thf)] (16). X-ray crystallography has shown 9 to be a six-coordinate, fluoride-bridged dimer, 12 and 16 to be six-coordinate monomers with the halide and thf ligands cis to each other, and 11 to have a seven-coordinate Yb atom with "pseudo-meridional" unidentate ligands and thf donors cis to each other. The analogous terbium compound [Tb(DippForm)(2)Cl(thf)(2)] (13), prepared by metathesis, has a similar structure to 11. C-Br activation also accompanies the redox transmetallation/protolysis reactions between La, Nd or Yb metals, Hg(2-BrC(6)F(4))(2), and HDippForm, yielding [Ln(DippForm)(2)Br(thf)] complexes (Ln=La (14), Nd (15), Yb (16)).