Description: 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.

Description: This work presents two new methods to estimate oceanic alkalinity (AT), dissolved inorganic carbon (CT), pH, and pCO2 from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO2 estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO2 overestimation due to decoupling of the O2 and C cycles by air-sea gas exchange, but global surface pCO2 estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser AT and CT compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor, which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/. Introduction

Description: Over recent decades, observations based on merchant vessels (Ships of Opportunity—SOOP) equipped with sensors measuring the CO2 partial pressure (pCO2) in the surface seawater formed the backbone of the global ocean carbon observation system. However, the restriction to pCO2 measurements alone is one severe shortcoming of the current SOOP observatory. Full insight into the marine inorganic carbon system requires the measurement of at least two of the four measurable variables which are pCO2, total alkalinity (TA), dissolved inorganic carbon (DIC), and pH. One workaround is to estimate TA values based on established temperature-salinity parameterizations, but this leads to higher uncertainties and the possibility of regional and/or seasonal biases. Therefore, autonomous SOOP-based TA measurements are of great interest. Our study describes the implementation of a novel autonomous analyzer for seawater TA, the CONTROS HydroFIAⓇ TA system (-4H-JENA engineering GmbH, Germany) for unattended routine TA measurements on a SOOP line operating in the North Atlantic. We present the installation in detail and address major issues encountered with autonomous measurements using this analyzer, e.g., automated cleaning and stabilization routines, and waste handling. Another issue during long-term deployments is the provision of reference seawater in large-volume containers for quality assurance measurements and drift correction. Hence, a stable large-volume seawater storage had to be found. We tested several container types with respect to their suitability to store seawater over a time period of 30 days without significant changes in TA. Only one gas sampling bag made of polyvinylidene fluoride (PVDF) satisfied the high stability requirement. In order to prove the performance of the entire setup, we compared the autonomous TA measurements with TA from discrete samples taken during the first two trans-Atlantic crossings. Although the measurement accuracy in unattended mode (about ± 5 μmol kg^-1) slightly deteriorated compared to our previous system characterization, its overall uncertainty fulfilled requirements for autonomous TA measurements on SOOP lines. A comparison with predicted TA values based on an established and often used parameterization pointed at regional and seasonal limitations of such TA predictions. Consequently, TA observations with better coverage of spatiotemporal variability are needed, which is now possible with the method described here.

Description: 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.

Description: The European Research Infrastructure Consortium “Integrated Carbon Observation System” (ICOS) aims at delivering high quality greenhouse gas (GHG) observations and derived data products (e.g., regional GHG-flux maps) for constraining the GHG balance on a European level, on a sustained long-term basis. The marine domain (ICOS-Oceans) currently consists of 11 Ship of Opportunity lines (SOOP – Ship of Opportunity Program) and 10 Fixed Ocean Stations (FOSs) spread across European waters, including the North Atlantic and Arctic Oceans and the Barents, North, Baltic, and Mediterranean Seas. The stations operate in a harmonized and standardized way based on community-proven protocols and methods for ocean GHG observations, improving operational conformity as well as quality control and assurance of the data. This enables the network to focus on long term research into the marine carbon cycle and the anthropogenic carbon sink, while preparing the network to include other GHG fluxes. ICOS data are processed on a near real-time basis and will be published on the ICOS Carbon Portal (CP), allowing monthly estimates of CO2 air-sea exchange to be quantified for European waters. ICOS establishes transparent operational data management routines following the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles allowing amongst others reproducibility, interoperability, and traceability. The ICOS-Oceans network is actively integrating with the atmospheric (e.g., improved atmospheric measurements onboard SOOP lines) and ecosystem (e.g., oceanic direct gas flux measurements) domains of ICOS, and utilizes techniques developed by the ICOS Central Facilities and the CP. There is a strong interaction with the international ocean carbon cycle community to enhance interoperability and harmonize data flow. The future vision of ICOS-Oceans includes ship-based ocean survey sections to obtain a three-dimensional understanding of marine carbon cycle processes and optimize the existing network design.

Description: Ocean data synthesis products for specific biogeochemical essential ocean variables have the potential to facilitate today’s biogeochemical ocean data usage and comply with the Findable Accessible Interoperable and Reusable (FAIR) data principles. The products constitute key outputs from the Global Ocean Observation System, laying the observational foundation for information and services regarding climate and environmental status of the ocean. Using the Framework of Ocean Observing (FOO) readiness level concept, we present an evaluation framework for biogeochemical data synthesis products, which enables a systematic assessment of each product’s maturity. A new criteria catalog provides the foundation for assigning scores to the nine FOO readiness levels. As an example, we apply the assessment to four existing biogeochemical essential ocean variables data products. In descending readiness level order these are: The Surface Ocean CO2 Atlas (SOCAT); the Global Ocean Data Analysis Project (GLODAP); the MarinE MethanE and NiTrous Oxide (MEMENTO) data product and the Global Ocean Oxygen Database and ATlas (GO2DAT). Recognizing that the importance of adequate and comprehensive data from the essential ocean variables will grow, we recommend using this assessment framework to guide the biogeochemical data synthesis activities in their development. Moreover, we envision an overarching cross-platform FAIR biogeochemical data management system that sustainably supports the products individually and creates an integrated biogeochemical essential ocean variables data synthesis product; in short a system that provides truly comparable and FAIR data of the entire biogeochemical essential ocean variables spectrum.

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 8 (2016): 605-649, doi:10.5194/essd-8-605-2016.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr−1, ELUC 1.0 ± 0.5 GtC yr−1, GATM 4.5 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 3.1 ± 0.9 GtC yr−1. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr−1 that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr−1, GATM was 6.3 ± 0.2 GtC yr−1, SOCEAN was 3.0 ± 0.5 GtC yr−1, and SLAND was 1.9 ± 0.9 GtC yr−1. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2016).

Description: Australia, Integrated Marine Observing System and Antarctic Climate and Ecosystems CRC BT; European Commission (EC) Copernicus Atmosphere Monitoring Service, European Centre for Medium-Range Weather Forecasts (ECMWF) FC; EC H2020 (AtlantOS; grant no. 633211) NL, AO; EC H2020 (CRESCENDO; grant no. 641816) CD, RS, OA, PF; EC H2020 European Research Council (ERC) (QUINCY; grant no. 647204). SZ; EC H2020 ERC Synergy grant (IMBALANCE-P; grant no. ERC-2013-SyG-610028) PC; France, BNP Paribas Foundation grant to support the Global Carbon Atlas PC; French Institut National des Sciences de l’Univers (INSU) and Institut Paul Emile Victor (IPEV) for OISO cruises NM; French Institut de recherche pour le développement (IRD) NL; German Federal Ministry of Education and Research (grant no. 01LK1224I ICOS-D) MH; German Research Foundation’s Emmy Noether Programme (grant no. PO1751/1-1) JN; German Max Planck Society CR, SZ; Germany, Federal Ministry of Education and Research (BMBF) AK; Germany, Helmholtz Postdoc Programme (Initiative and Networking Fund of the Helmholtz Association) JH; Japan Ministry of Agriculture, Forestry and Fisheries (MAFF) OT; Japan Ministry of Environment SN; Japan Ministry of Environment (grant no. ERTDF S-10) EK; NASA LCLUC programme (grant no. NASA NNX14AD94G) AJ; New Zealand National Institute of Water and Atmospheric Research (NIWA) Core Funding KC; Norway Research Council (grant no. 229752) AMO; Norway Research Council (grant no. 569980) GPP, RMA, JIK; Norway Research Council (project EVA; grant no. 229771) JS; Norwegian Environment Agency (grant no. 16078007) IS; Research Fund – Flanders (FWO; formerly Hercules Foundation) TG; South Africa Council for Scientific and Industrial Research (CSIR) PMSM; UK Natural Environment Research Council (RAGANRAoCC; grant no. NE/K002473/1) US; UK Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil) AJW; US Department of Agriculture, National Institute of Food and Agriculture (grant no. 2015-67003- 23485) DL; US Department of Energy (grant no. DE-FC03-97ER62402/A010) DL; US Department of Energy, Biological and Environmental Research Program, Office of Science (grant no. DE-AC05-00OR22725) APW; US Department of Commerce, NOAA’s Climate Observation Division of the Climate Program Office SRA, AJS; US Department of Energy, Office of Science and BER programme (grant no. DOE DE-SC0016323) AJ; US National Science Foundation (grant no. AGS-1048827) SD; US National Science Foundation (grant no. AOAS-1543457) DRM; US National Science Foundation (grant no. AOAS-1341647) DRM; US NOAA’s Climate Observation Division of the Climate Program Office (grant no. N8R1SE3P00); US NOAA’s Ocean Acidification Program (grant no. N8R3CEAP00) DP, LB; US National Science Foundation (grant no. NSF AGS 12-43071)

Description: 〈jats:p〉Abstract. 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 synthesize 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 fCO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system 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 incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr−1 (10.2±0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr−1, and SLAND was 3.8±0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (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 methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023). 〈/jats:p〉