The Canary Current System (CCS) is a major eastern boundary upwelling system where intense nearshore productivity, dynamic offshore transport, and Saharan dust deposition jointly shape biogeochemical cycling. Understanding how these physical and atmospheric forcings regulate particulate export is crucial for assessing the biological carbon pump under ongoing North Atlantic warming. Here we combine Lagrangian backtracking of satellite-derived chlorophyll-a (Chl-a), particulate inorganic carbon (PIC), and primary production (PP) with one year of sediment trap fluxes of coccolith species, biogenic particles and lithogenic material (proxy for aeolian dust fluxes) from moorings CB (21° N, 20° W) and M1 (12° N, 23° W), representing distinct open-ocean settings offshore of NW Africa. These fluxes are further integrated with data from satellite-derived upwelling indices, sea surface height (SSH), aerosol optical depth (AOD), and in situ water-column observations collected at the trap locations. The results reveal strong seasonal connectivity between coastal upwelling, offshore transport, and deep export. Late winter–spring intensification of mixing, upwelling, and filament/eddy activity sustained elevated Chl-a, PP, PIC, and high CaCO3 fluxes, with sinking assemblages dominated by fast-blooming (r-selected) placolith-bearing coccolithophores – especially at CB. Lagrangian trajectories further show that this connectivity weakens offshore, with strong coast-to-open-ocean declines in Chl-a, PP, and PIC – particularly along pathways to M1 – highlighting reduced cross-shelf transfer and a stronger year-round influence of stratified tropical waters at that site. During summer–autumn, weakened upwelling and intrusions of warm Mauritanian Current waters reduced surface productivity at both trap sites, yet deep organic matter export remained high – most prominently at CB but also at the persistently oligotrophic M1. Across this period, elevated Saharan dust deposition coincided with enhanced particle fluxes. Multivariate statistical analyses show strong negative correlations between dust and all upper photic zone (UPZ) productivity indicators including Chl-a, the ratio of surface-dwelling species to lower photic zone taxa (UPZ / LPZ), and carbonate fluxes associated with the placolith-forming species Emiliania huxleyi and Calcidiscus leptoporus. In contrast, dust showed positive associations with warm, stratified conditions dominated by tropical, non-blooming LPZ species (Florisphaera profunda and Gladiolithus flabellatus), suggesting that mineral ballasting was the dominant seasonal dust effect. At CB, where summer cross-shelf transfer weakened, the persistence of high export regardless of low surface Chl-a suggests that lateral and subsurface supply of previously produced organic matter played a major role, with Saharan dust further enhancing its downward transfer through ballasting. Nonetheless, several dust-associated export pulses also displayed increases in coccolith UPZ / LPZ ratios, suggesting episodic fertilisation responses by fast-blooming taxa superimposed on a broader ballasting-driven regime. Importantly, dust contributed under both windy, high-productivity late winter–spring conditions and during the stratified summer–autumn phase, sustaining downward particle flux even when local surface productivity was low. The weak relationship between AOD and measured dust flux reflects cloud-induced suppression of satellite AOD retrievals during wet deposition rather than reduced dust deposition. Altogether, these results demonstrate a dual physical–atmospheric control on export in the central–southern CCS. Upwelling and cross-shelf transport fuel the winter–spring CaCO3-rich export regime, whereas Saharan dust plays a particularly important role in maintaining organic-matter fluxes under summer–autumn stratification through ballasting, alongside episodic fertilisation responses. These findings contribute to refine the mechanistic understanding of coast-to-ocean and vertical export pathways and help constrain how dust–upwelling interactions will shape the biological carbon pump under future climate forcing.

Phytoplankton Primary Production supports most of the marine ecosystem and is highly sensitive to changing environmental pressures. There is much debate about whether marine primary production is increasing or decreasing and what environmental parameters may be driving these changes. We analysed a 21-year time-series of net primary production (NPP) computed from Ocean Colour Climate Change Initiative (OC-CCI) data spanning September 1997-December 2018, focusing on areas of similar phenology, climatology, and annual NPP in the north-east Atlantic Ocean. Across the entire area, NPP increased from 1998 to 2003, followed by a significant decline until 2018. This pattern was predominant in north-western European coastal waters and specific areas of the English Channel, Irish Sea, North Sea and Norwegian Sea, where it was related to changes in sea surface temperature and mixed layer depth.

The strong control that the emissions of carbon dioxide (CO2) have over Earth's climate identifies the need for accurate quantification of the emitted CO2 and its redistribution within the Earth system. The ocean annually absorbs more than a quarter of all CO2 emissions and this absorption is fundamentally altering the ocean chemistry. The ocean thus provides a fundamental component and powerful constraint within global carbon assessments used to guide policy action for reducing emissions. These carbon assessments rely heavily on satellite observations, but their inclusion is often invisible or opaque to policy. One reason is that satellite observations are rarely used exclusively, but often in conjunction with other types of observations, thereby complementing and expanding their usability yet losing their visibility. This exploitation of satellite observations led by the satellite and ocean carbon scientific communities is based on exciting developments in satellite science that have broadened the suite of environmental data that can now reliably be observed from space. However, the full potential of satellite observations to expand the scientific knowledge on critical processes such as the atmosphere-ocean exchange of CO2 and ocean acidification, including its impact on ocean health, remains largely unexplored. There is clear potential to begin using these observation-based approaches for directly guiding ocean management and conservation decisions, in particular in regions where in situ data collection is more difficult, and interest in them is growing within the environmental policy communities. We review these developments, identify new opportunities and scientific priorities, and identify that the formation of an international advisory group could accelerate policy relevant advancements within both the ocean carbon and satellite communities. Some barriers to understanding exist but these should not stop the exploitation and the full visibility of satellite observations to policy makers and users, so these observations can fulfil their full potential and recognition for supporting society.

AbstractThe long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re‐analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re‐analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic‐scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation‐based approaches.

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality-controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example, for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude- and projection-dependent (e.g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e.g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into “regions of interest”: spatiotemporal cylinders consisting of circles on the Earth's surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and reanalysis datasets to produce a global matchup dataset of carbonate system data, consisting of ∼286 000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 d in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is presented. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example, to enable regional studies. The matchup dataset provides users with a large multi-parameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model–data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022).

Drones are continuing to grow in popularity due to their low cost and flexibility, and as their robustness and simplicity has developed, researchers have realized their value to science and research. They can be employed as tools in many different sectors, both in rural urban developments, and have been successfully used in mapping city landscapes, planning building construction, monitoring wildfires, quantifying animal species populations, and monitoring crop health, to cite a few. Here, we have reviewed drone water quality applications, discussing their potential to compensate for shortcomings in the current water monitoring programs, such as their ability to monitor large areas or access remote or inhospitable locations. We discuss the use of multispectral cameras and drone remote sensing to observe physical and biological parameters of inland water bodies, by exploring their influence in surface color and light reflectance through satellite and region-specific developed algorithms. Also, we discuss the integration of a variety of low-power electronic sensors into drones, which offer a fast and mobile method to collect in situ data for water quality research.

Primary production (PP) is highly sensitive to changes in the ecosystem and can be used as an early warning indicator for disturbance in the marine environment. Historic indicators of good environmental status of the north-east (NE) Atlantic and north-west (NW) European Seas suggested that daily PP should not exceed 2-3 g C m-2 d-1 during phytoplankton blooms and that annual rates should be <300 g C m-2 yr-1. We use 21 years of Copernicus Marine Service (CMEMS) Ocean Colour data from September 1997 to December 2018 to assess areas in the NE Atlantic with similar peak, climatology, phenology and annual PP values. Daily and annual thresholds of the 90th percentile (P90) of PP are defined for these areas and PP values above these thresholds indicate disturbances, both natural and anthropogenic, in the marine environment. Two case studies are used to test the validity and accuracy of these thresholds. The first is the eruption of the volcano Eyjafjallajökull, which deposited large volumes of volcanic dust (and therefore iron) into the NE Atlantic during April and May 2010. A clear signature in both PP and chlorophyll-a (Chl a) was evident from 28th April to 6th May and from 18th to 27th May 2010, when PP exceeded the PP P90 threshold for the region, which was comparatively more sensitive than Chl a P90 as an indicator of this disturbance. The second case study was for the riverine input of total nitrogen and phosphorus, along the Wadden Sea coast in the North Sea. During years when total nitrogen and phosphorus were above the climatology maximum, there was a lag signature in both PP and Chl a when PP exceeded the PP P90 threshold defined for the study area which was slightly more sensitive than Chl a P90. This technique represents an accurate means of determining disturbances in the environment both in the coastal and offshore waters in the NE Atlantic using remotely sensed ocean colour data.

Large rivers play an important role in transferring water and all of its constituents, including carbon in its various forms, from the land to the ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here, we present the University of Exeter (UNEXE) Satellite Oceanographic Datasets for Acidification (OceanSODA) dataset (OceanSODA-UNEXE) time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010–2020) and Congo (2002–2016) rivers. Optimal empirical approaches were used to generate gridded total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a reference matchup database of in situ observations. Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full carbonate system in the surface ocean (which includes pH and the partial pressure of carbon dioxide, pCO2). The algorithm evaluation constitutes a Type-A uncertainty evaluation for TA and DIC, in which model, input and sampling uncertainties are considered. Total combined uncertainties for TA and DIC were propagated through the carbonate system calculation, allowing all variables to be provided with an associated uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was 36 µmol kg−1 (weighted root-mean-squared difference, RMSD, of 35 µmol kg−1 and weighted bias of 8 µmol kg−1 for n = 82), whereas it was 44 µmol kg−1 for DIC (weighted RMSD of 44 µmol kg−1 and weighted bias of −6 µmol kg−1 for n = 70). The spatially averaged propagated combined uncertainties for the pCO2 and pH were 85 µatm and 0.08, respectively, where the pH uncertainty was relative to an average pH of 8.19. In the Congo outflow, the combined uncertainty for TA was identified as 29 µmol kg−1 (weighted RMSD of 28 µmol kg−1 and weighted bias of 6 µmol kg−1 for n = 102), whereas it was 40 µmol kg−1 for DIC (weighted RMSD of 37 µmol kg−1 and weighted bias of −16 µmol kg−1 for n = 77). The spatially averaged propagated combined uncertainties for pCO2 and pH were 74 µatm and 0.08, respectively, where the pH uncertainty was relative to an average pH of 8.21. The combined uncertainties in TA and DIC in the Amazon and Congo outflows are lower than the natural variability within their respective regions, allowing the time-varying regional variability to be evaluated. Potential uses of these data would be the assessment of the spatial and temporal flow of carbon from the Amazon and Congo rivers into the Atlantic and the assessment of the riverine-driven carbonate system variations experienced by tropical reefs within the outflow regions. The data presented in this work are available at https://doi.org/10.1594/PANGAEA.946888 (Sims et al., 2023).

It is widely projected that under future climate scenarios the economic importance of Arctic Ocean fish stocks will increase. The Arctic Ocean is especially vulnerable to ocean acidification and already experiences low pH levels not projected to occur on a global scale until 2100. This paper outlines how ocean acidification must be considered with other potential stressors to accurately predict movement of fish stocks toward, and within, the Arctic and to inform future fish stock management strategies. First, we review the literature on ocean acidification impacts on fish, next we identify the main obstacles that currently preclude ocean acidification from Arctic fish stock projections. Finally, we provide a roadmap to describe how satellite observations can be used to address these gaps: improve knowledge, inform experimental studies, provide regional assessments of vulnerabilities, and implement appropriate management strategies. This roadmap sets out three inter-linked research priorities: (1) Establish organisms and ecosystem physiochemical baselines by increasing the coverage of Arctic physicochemical observations in both space and time; (2) Understand the variability of all stressors in space and time; (3) Map life histories and fish stocks against satellite-derived observations of stressors.

This paper examines how land use affects water quality and how this impacts the viability of shellfish mariculture (marine aquaculture) in the UK through a synthesis of the current literature, stakeholder workshops and targeted engagement of cross-sector organisations across a case study in South West England. We examine the importance of water quality as a constraint for shellfish mariculture in South West England and explore how current and projected future land uses are likely to influence ongoing viability. Currently, faecal material from agricultural runoff and municipal wastewater discharges is the main water quality issue affecting the viability of shellfish mariculture. Most UK Shellfish Waters, including those in SW England (~90%), do not consistently meet regulatory standards for faecal indicator organisms (FIOs) in shellfish, designed to ensure they are safe for direct human consumption. Other pollutants currently impacting shellfish mariculture include persistent organic pollutants and metals, suspended solids and excess nutrient inputs. Emerging pollutants with the potential to impact on mariculture include a range of consumer- industrial- and agri- chemicals, including biocides, pesticides, veterinary and human medicines. We evaluated possible changes in water quality conditions through deriving and exploring a range of future scenarios, considering policies and trends in land use including regenerative and intensive agriculture, renewable energy generation, afforestation, urban development and climate change. Our findings highlight possible trade-offs and synergies between land and water-based food production systems, applicable in SW England and across other regions of the UK and internationally, for helping to inform environmental policy development and implementation.

The Ocean State Report is an annual publication of the Copernicus Marine Service that provides a comprehensive and state-of-the-art report on the current state, natural variations, and ongoing changes in the global ocean and European regional seas.It is meant to act as a reference European Union report for the scientific community, international and national bodies, and the general public.Using satellite data, models and reanalyses, and in situ measurements, the Copernicus Marine Ocean State Report provides a 4-dimensional view (latitude, longitude, depth, and time) of the blue (e.g.hydrography and currents), white (e.g.sea ice) and green (e.g.biogeochemical) ocean.It draws on expert analysis and is written by over 100 scientific experts from more than 30 European institutions.Scientific integrity is assured through a process of independent peer review in collaboration with the Journal of Operational Oceanography.This document is a summary of the fourth issue of the Copernicus Marine Ocean State Report and highlights the current state, natural variations, and ongoing changes in the global ocean.It draws on the Copernicus Marine Ocean Monitoring Indicator (OMI) framework.It approaches the topic from several angles, presenting the state of key ocean variables, examining ongoing changes to the ocean in line with climate change, analysing natural variability and extreme events, and discussing the services that the ocean provides to humanity.Finally, new tools and success stories from the Copernicus Marine Service illustrate how accurate, timely information is key to understanding and adapting to the evolving ocean and seas.Society, a sustainable economy, and the environment -the three pillars of sustainable development-rely on the ocean.This section explores the importance of the ocean in the framework of the UN Sustainable Development Goals, supported by ocean data and information.Page 3. Key indicators are used to track the vital health signs of the global ocean. This section presents the indicators fromCopernicus Marine used to monitor and understand changes already in motion and presents notable changes to the ocean over the last quarter of a century.Page 4.The ocean is undergoing sweeping, severe, and unavoidable changes, with major impacts on marine ecosystems and humanity.The IPCC Special Report on Ocean and Cryosphere, and the Copernicus Marine Service Ocean State Report both show that the global ocean is becoming warmer and more acidic, sea level is rising, and that sea ice is retreating.This section presents the most notable impacts of climate change on the ocean.Page 6.Humans depend heavily upon the ocean through the goods, cultural importance and services provided by marine ecosystems.This section provides an overview and specific examples of ocean ecosystem services and details the key ocean variables that underlie these services.Page 12.The Copernicus Marine Service provides stateof-the-art analyses and ocean forecasts, offering a valuable capability to observe, understand, and anticipate changes and extreme events in the marine environment.This section presents advancements in the Copernicus Marine service, and successful examples of applying these tools in practice.Page 16.

Oceanic internal waves occur within stratified water along the boundary between water layers of different density and are generated when strong tidal currents flow over seabed topography. Their amplitude can exceed 50 m and they transport energy over long distances and cause vertical mixing when the waves break. This study presents the first fully automated methodology for the mapping of internal waves using satellite synthetic aperture radar (SAR) data and applies this to explore their spatial and temporal distribution within UK shelf seas. The new algorithm includes enhanced edge detection and spatial processing to target the appearance of these features on satellite images. We acquired and processed over 7000 ENVISAT ASAR scenes covering the UK continental shelf between 2006 and 2012, to automatically generate detailed maps of internal waves. Monthly and annual internal wave climatology maps of the continental shelf were produced showing spatial and temporal variability, which can be used to predict where internal waves have the most impact on the seabed environment and ecology in UK shelf seas. These observations revealed correlations between the temporal patterns of internal waves and the seasons when the continental shelf waters were more stratified. The maps were validated using well-known seabed topographic features. Concentrations of internal waves were automatically identified at Wyville-Thomson Ridge in June 2008, at the continental shelf break to the east of Rosemary Bank in January 2010 and in the Faroe-Shetland Channel in June 2011. This new automated methodology has been shown to be robust for mapping internal waves using a large SAR dataset and is recommended for studies in other regions worldwide and for SAR data acquired by other sensors.

The flow (flux) of climate-critical gases, such as carbon dioxide (CO2), between the ocean and the atmosphere is a fundamental component of our climate and an important driver of the biogeochemical systems within the oceans. Therefore, the accurate calculation of these air–sea gas fluxes is critical if we are to monitor the oceans and assess the impact that these gases are having on Earth's climate and ecosystems. FluxEngine is an open-source software toolbox that allows users to easily perform calculations of air–sea gas fluxes from model, in situ, and Earth observation data. The original development and verification of the toolbox was described in a previous publication. The toolbox has now been considerably updated to allow for its use as a Python library, to enable simplified installation, to ensure verification of its installation, to enable the handling of multiple sparingly soluble gases, and to enable the greatly expanded functionality for supporting in situ dataset analyses. This new functionality for supporting in situ analyses includes user-defined grids, time periods and projections, the ability to reanalyse in situ CO2 data to a common temperature dataset, and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings, and research cruises. Here we describe these new capabilities and demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO2 fluxes using in situ data from four research cruises from the Surface Ocean CO2 ATlas (SOCAT) database. The second case study calculates air–sea CO2 fluxes using in situ data from a fixed monitoring station in the Baltic Sea. The third case study focuses on nitrous oxide (N2O) and, through a user-defined gas transfer parameterisation, identifies that biological surfactants in the North Atlantic could suppress individual N2O sea–air gas fluxes by up to 13 %. The fourth and final case study illustrates how a dissipation-based gas transfer parameterisation can be implemented and used. The updated version of the toolbox (version 3) and all documentation is now freely available.

Improving our ability to monitor ocean carbonate chemistry has become a priority as the ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is reducing the ocean pH; a process commonly known as ocean acidification. The use of satellite Earth Observation has not yet been thoroughly explored as an option for routinely observing surface ocean carbonate chemistry, although its potential has been high-lighted. We demonstrate the suitability of using empirical algorithms to calculate total alkalinity (A(T)) and total dissolved inorganic carbon (C-T), assessing the relative performance of satellite, interpolated in situ, and climatology datasets in reproducing the wider spatial patterns of these two variables. Both A(T) and C-T in situ data are reproducible, both regionally and globally, using salinity and temperature datasets, with satellite observed salinity from Aquarius and SMOS providing performance comparable to other datasets for the majority of case studies. Global root mean squared difference (RMSD) between in situ validation data and satellite estimates is 17 mu mol kg(-1) with bias < 5 mu mol kg(-1) for A(T) and 30 mu mol kg(-1) with bias < 10 mu mol kg(-1) for C-T. This analysis demonstrates that satellite sensors provide a credible solution for monitoring surface synoptic scale A(T) and C-T. It also enables the first demonstration of observation-based synoptic scale A(T) and C-T temporal mixing in the Amazon plume for 2010-2016, complete with a robust estimation of their uncertainty.

Anthropogenic emissions of carbon dioxide (CO2) levelled out in 2016, but have since begun to increase again. This continued increase in anthropogenic emissions means that it is now critical to monitor ocean carbon uptake. This long-term uptake of carbon dioxide (CO2) by the oceans is reducing the ocean pH, a process commonly known as ocean acidification. The uptake is also altering the ocean chemistry and ecology, impacting marine ecosystems on which we rely. Recent work has begun to investigate the use of satellite Earth observation, exploiting empirical methods and salinity and sea surface temperature data, to monitor surface-ocean carbonate chemistry. These techniques complement in situ approaches by enabling the first synoptic-scale observation-based assessments of the global oceans and are particularly well suited to monitoring large episodic events. The nine year time series (May 2010-present) of observations of ocean salinity from space now provides the potential for time series analyses.

The contemporary air‐sea flux of CO2 is investigated by the use of an air‐sea flux equation, with particular attention to the uncertainties in global values and their origin with respect to that equation. In particular, uncertainties deriving from the transfer velocity and from sparse upper ocean sampling are investigated. Eight formulations of air‐sea gas transfer velocity are used to evaluate the combined standard uncertainty resulting from several sources of error. Depending on expert opinion, a standard uncertainty in transfer velocity of either ~5% or ~10% can be argued and that will contribute a proportional error in air‐sea flux. The limited sampling of upper ocean fCO2 is readily apparent in the Surface Ocean CO2 Atlas databases. The effect of sparse sampling on the calculated fluxes was investigated by a bootstrap method, that is, treating each ship cruise to an oceanic region as a random episode and creating 10 synthetic data sets by randomly selecting episodes with replacement. Convincing values of global net air‐sea flux can only be achieved using upper ocean data collected over several decades but referenced to a standard year. The global annual referenced values are robust to sparse sampling, but seasonal and regional values exhibit more sampling uncertainty. Additional uncertainties are related to thermal and haline effects and to aspects of air‐sea gas exchange not captured by standard models. An estimate of global net CO2 exchange referenced to 2010 of −3.0 ± 0.6 Pg C/year is proposed, where the uncertainty derives primarily from uncertainty in the transfer velocity.