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We have estimated global air–sea CO2 fluxes (fgCO2) from the open ocean to coastal seas. Fluxes and associated uncertainty are computed from an ensemble-based reconstruction of CO2 sea surface partial pressure (pCO2) maps trained with gridded data from the Surface Ocean CO2 Atlas v2020 database. The ensemble mean (which is the best estimate provided by the approach) fits independent data well, and a broad agreement between the spatial distribution of model–data differences and the ensemble standard deviation (which is our model uncertainty estimate) is seen. Ensemble-based uncertainty estimates are denoted by ±1σ. The space–time-varying uncertainty fields identify oceanic regions where improvements in data reconstruction and extensions of the observational network are needed. Poor reconstructions of pCO2 are primarily found over the coasts and/or in regions with sparse observations, while fgCO2 estimates with the largest uncertainty are observed over the open Southern Ocean (44∘ S southward), the subpolar regions, the Indian Ocean gyre, and upwelling systems.Our estimate of the global net sink for the period 1985–2019 is 1.643±0.125 PgC yr-1 including 0.150±0.010 PgC yr-1 for the coastal net sink. Among the ocean basins, the Subtropical Pacific (18–49∘ N) and the Subpolar Atlantic (49–76∘ N) appear to be the strongest CO2 sinks for the open ocean and the coastal ocean, respectively. Based on mean flux density per unit area, the most intense CO2 drawdown is, however, observed over the Arctic (76∘ N poleward) followed by the Subpolar Atlantic and Subtropical Pacific for both open-ocean and coastal sectors. Reconstruction results also show significant changes in the global annual integral of all open- and coastal-ocean CO2 fluxes with a growth rate of +0.062±0.006 PgC yr-2 and a temporal standard deviation of 0.526±0.022 PgC yr-1 over the 35-year period. The link between the large interannual to multi-year variations of the global net sink and the El Niño–Southern Oscillation climate variability is reconfirmed.

Anthropogenic climate change is projected to lead to ocean warming, acidification, deoxygenation, reductions in near-surface nutrients, and changes to primary production, all of which are expected to affect marine ecosystems. Here we assess projections of these drivers of environmental change over the twenty-first century from Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) that were forced under the CMIP6 Shared Socioeconomic Pathways (SSPs). Projections are compared to those from the previous generation (CMIP5) forced under the Representative Concentration Pathways (RCPs). A total of 10 CMIP5 and 13 CMIP6 models are used in the two multi-model ensembles. Under the high-emission scenario SSP5-8.5, the multi-model global mean change (2080–2099 mean values relative to 1870–1899) ± the inter-model SD in sea surface temperature, surface pH, subsurface (100–600 m) oxygen concentration, euphotic (0–100 m) nitrate concentration, and depth-integrated primary production is +3.47±0.78 ∘C, -0.44±0.005, -13.27±5.28, -1.06±0.45 mmol m−3 and -2.99±9.11 %, respectively. Under the low-emission, high-mitigation scenario SSP1-2.6, the corresponding global changes are +1.42±0.32 ∘C, -0.16±0.002, -6.36±2.92, -0.52±0.23 mmol m−3, and -0.56±4.12 %. Projected exposure of the marine ecosystem to these drivers of ocean change depends largely on the extent of future emissions, consistent with previous studies. The ESMs in CMIP6 generally project greater warming, acidification, deoxygenation, and nitrate reductions but lesser primary production declines than those from CMIP5 under comparable radiative forcing. The increased projected ocean warming results from a general increase in the climate sensitivity of CMIP6 models relative to those of CMIP5. This enhanced warming increases upper-ocean stratification in CMIP6 projections, which contributes to greater reductions in upper-ocean nitrate and subsurface oxygen ventilation. The greater surface acidification in CMIP6 is primarily a consequence of the SSPs having higher associated atmospheric CO2 concentrations than their RCP analogues for the same radiative forcing. We find no consistent reduction in inter-model uncertainties, and even an increase in net primary production inter-model uncertainties in CMIP6, as compared to CMIP5.

Diatoms sustain the marine food web and contribute to the export of carbon from the surface ocean to depth. They account for about 40% of marine primary productivity and particulate carbon exported to depth as part of the biological pump. Diatoms have long been known to be abundant in turbulent, nutrient-rich waters, but observations and simulations indicate that they are dominant also in meso- and submesoscale structures such as fronts and filaments, and in the deep chlorophyll maximum. Diatoms vary widely in size, morphology and elemental composition, all of which control the quality, quantity and sinking speed of biogenic matter to depth. In particular, their silica shells provide ballast to marine snow and faecal pellets, and can help transport carbon to both the mesopelagic layer and deep ocean. Herein we show that the extent to which diatoms contribute to the export of carbon varies by diatom type, with carbon transfer modulated by the Si/C ratio of diatom cells, the thickness of the shells and their life strategies; for instance, the tendency to form aggregates or resting spores. Model simulations project a decline in the contribution of diatoms to primary production everywhere outside of the Southern Ocean. We argue that we need to understand changes in diatom diversity, life cycle and plankton interactions in a warmer and more acidic ocean in much more detail to fully assess any changes in their contribution to the biological pump.

A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) the reconstruction ofpCO2 climatology, and (2) the reconstruction ofpCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1∘×1∘ grid for the period from 2001 to 2016. Global ocean pCO2 was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface oceanpCO2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea–air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyze fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. Results indicate a modest degradation in a retrospective prediction test for 2021–2022. The generated fCO2 and fluxes for January–August 2023 show a progressive reduction in the Equatorial Pacific source following the La Niña retreat. A breaking‐record decrease in the northeastern Atlantic CO2 sink has been diagnosed on account of the marine heatwave event in June 2023. Plain Language Summary There is a growing need to monitor carbon emissions and removals over the globe in near real time in order to correctly interpret changes in CO2 concentrations as they unfold. For the oceans, the best information comes from measurements of the surface ocean CO2 fugacity (fCO2) by the international marine carbon research community. So far, this data is mostly available 6 to 18 months behind real time after collection, qualification, harmonization, and processing. Here, we show that a set of biological, chemical, and physical predictors available in near real time, allows the information contained in the “old” fCO2 measurements to be transferred over time. Based on a statistical technique, we combine all these data sources to estimate global monthly maps of fCO2 and of CO2 fluxes at the air‐sea interface within one month behind real time and with good accuracy. Key Points We demonstrate the capacity of statistical models to generate global maps of fCO2 and air‐sea flux with a latency reduced to one month A decrease in the CO2 source for January to August 2023 diagnosed in the tropical Pacific coheres with the retreat of the La Niña event An unusual northeastern Atlantic sink reduction diagnosed for June 2023 is linked to record heat and exceptionally low winds

Purpose of Review The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). Recent Findings The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. Summary Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).

Dissolved manganese (Mn) is a biologically essential element. Moreover, its oxidised form is involved in removing itself and several other trace elements from ocean waters. Here we report the longest thus far (17 500 km length) full-depth ocean section of dissolved Mn in the west Atlantic Ocean, comprising 1320 data values of high accuracy. This is the GA02 transect that is part of the GEOTRACES programme, which aims to understand trace element distributions. The goal of this study is to combine these new observations with new, state-of-the-art, modelling to give a first assessment of the main sources and redistribution of Mn throughout the ocean. To this end, we simulate the distribution of dissolved Mn using a global-scale circulation model. This first model includes simple parameterisations to account for the sources, processes and sinks of Mn in the ocean. Oxidation and (photo)reduction, aggregation and settling, as well as biological uptake and remineralisation by plankton are included in the model. Our model provides, together with the observations, the following insights: – The high surface concentrations of manganese are caused by the combination of photoreduction and sources contributing to the upper ocean. The most important sources are sediments, dust, and, more locally, rivers. – Observations and model simulations suggest that surface Mn in the Atlantic Ocean moves downwards into the southward-flowing North Atlantic Deep Water (NADW), but because of strong removal rates there is no elevated concentration of Mn visible any more in the NADW south of 40° N. – The model predicts lower dissolved Mn in surface waters of the Pacific Ocean than the observed concentrations. The intense oxygen minimum zone (OMZ) in subsurface waters is deemed to be a major source of dissolved Mn also mixing upwards into surface waters, but the OMZ is not well represented by the model. Improved high-resolution simulation of the OMZ may solve this problem. – There is a mainly homogeneous background concentration of dissolved Mn of about 0.10–0.15 nM throughout most of the deep ocean. The model reproduces this by means of a threshold on particulate manganese oxides of 25 pM, suggesting that a minimal concentration of particulate Mn is needed before aggregation and removal become efficient. – The observed distinct hydrothermal signals are produced by assuming both a strong source and a strong removal of Mn near hydrothermal vents.

Mineral dust and marine sediment resuspension are generally considered the primary sources of the nutrient iron to the oceans. Numerical model results suggest that iron released by hydrothermal activity is also an important source of dissolved iron, particularly in the Southern Ocean. Iron limits phytoplankton growth and hence the biological carbon pump in the Southern Ocean 1 . Models assessing the impacts of iron on the global carbon cycle generally rely on dust input and sediment resuspension as the predominant sources 2 , 3 . Although it was previously thought that most iron from deep-ocean hydrothermal activity was inaccessible to phytoplankton because of the formation of particulates 4 , it has been suggested that iron from hydrothermal activity 5 , 6 , 7 may be an important source of oceanic dissolved iron 8 , 9 , 10 , 11 , 12 , 13 . Here we use a global ocean model to assess the impacts of an annual dissolved iron flux of approximately 9×10 8  mol, as estimated from regional observations of hydrothermal activity 11 , 12 , on the dissolved iron inventory of the world’s oceans. We find the response to the input of hydrothermal dissolved iron is greatest in the Southern Hemisphere oceans. In particular, observations of the distribution of dissolved iron in the Southern Ocean 3 (Chever et al. , manuscript in preparation; Bowie et al. , manuscript in preparation) can be replicated in our simulations only when our estimated iron flux from hydrothermal sources is included. As the hydrothermal flux of iron is relatively constant over millennial timescales 14 , we propose that hydrothermal activity can buffer the oceanic dissolved iron inventory against shorter-term fluctuations in dust deposition.

To derive an optimal observation system for surface oceanpCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean, 11 observation system simulation experiments (OSSEs) were completed. Each OSSE is a feedforward neural network (FFNN) that is based on a different data distribution and provides ocean surface pCO2 for the period 2008–2010 with a 5 d time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships, Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical–biogeochemical global ocean model with 0.25∘ nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the Southern Hemisphere with biogeochemical ARGO floats corresponding to least 25 % of the density of active floats (2008–2010) (OSSE 10) would significantly improve the pCO2 reconstruction and reduce the bias of derived estimates of sea–air CO2 fluxes by 74 % compared to ocean model outputs.

The Arctic Ocean is projected to experience not only amplified climate change but also amplified ocean acidification. Modeling future acidification depends on our ability to simulate baseline conditions and changes over the industrial era. Such centennial-scale changes require a global model to account for exchange between the Arctic and surrounding regions. Yet the coarse resolution of typical global models may poorly resolve that exchange as well as critical features of Arctic Ocean circulation. Here we assess how simulations of Arctic Ocean storage of anthropogenic carbon (Cant), the main driver of open-ocean acidification, differ when moving from coarse to eddy-admitting resolution in a global ocean-circulation–biogeochemistry model (Nucleus for European Modeling of the Ocean, NEMO; Pelagic Interactions Scheme for Carbon and Ecosystem Studies, PISCES). The Arctic's regional storage of Cant is enhanced as model resolution increases. While the coarse-resolution model configuration ORCA2 (2∘) stores 2.0 Pg C in the Arctic Ocean between 1765 and 2005, the eddy-admitting versions ORCA05 and ORCA025 (1∕2∘ and 1∕4∘) store 2.4 and 2.6 Pg C. The difference in inventory between model resolutions that is accounted for is only from their divergence after 1958, when ORCA2 and ORCA025 were initialized with output from the intermediate-resolution configuration (ORCA05). The difference would have been larger had all model resolutions been initialized in 1765 as was ORCA05. The ORCA025 Arctic Cant storage estimate of 2.6 Pg C should be considered a lower limit because that model generally underestimates observed CFC-12 concentrations. It reinforces the lower limit from a previous data-based approach (2.5 to 3.3 Pg C). Independent of model resolution, there was roughly 3 times as much Cant that entered the Arctic Ocean through lateral transport than via the flux of CO2 across the air–sea interface. Wider comparison to nine earth system models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) reveals much larger diversity of stored Cant and lateral transport. Only the CMIP5 models with higher lateral transport obtain Cant inventories that are close to the data-based estimates. Increasing resolution also enhances acidification, e.g., with greater shoaling of the Arctic's average depth of the aragonite saturation horizon during 1960–2012, from 50 m in ORCA2 to 210 m in ORCA025. Even higher model resolution would likely further improve such estimates, but its prohibitive costs also call for other more practical avenues for improvement, e.g., through model nesting, addition of coastal processes, and refinement of subgrid-scale parameterizations.