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 datasets 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 first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, 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 datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Abstract. 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.

Abstract. We described new sea surface CO2 observations in the south-western Indian Ocean obtained in January 2020 when a strong bloom event occurred south-east of Madagascar and extended eastward in the oligotrophic Indian Ocean subtropical domain. Compared to previous years (1991–2019) we observed very low fCO2 and dissolved inorganic carbon concentrations (CT) in austral summer 2020, indicative of a biologically driven process. In the bloom, the anomaly of fCO2 and CT reached respectively −33 µatm and −42 µmol kg−1, whereas no change is observed for alkalinity (AT). In January 2020 we estimated a local maximum of air–sea CO2 flux at 27∘ S of −6.9 mmol m−2 d−1 (ocean sink) and −4.3 mmol m−2 d−1 when averaging the flux in the band 26–30∘ S. In the domain 25–30∘ S, 50–60∘ E we estimated that the bloom led to a regional carbon uptake of about −1 TgC per month in January 2020, whereas this region was previously recognized as an ocean CO2 source or near equilibrium during this season. Using a neural network approach that reconstructs the monthly fCO2 fields, we estimated that when the bloom was at peak in December 2019 the CO2 sink reached −3.1 (±1.0) mmol m−2 d−1 in the band 25–30∘ S; i.e. the model captured the impact of the bloom. Integrated in the domain restricted to 25–30∘ S, 50–60∘ E, the region was a CO2 sink in December 2019 of −0.8 TgC per month compared to a CO2 source of +0.12 (±0.10) TgC per month in December when averaged over the period 1996–2018. Consequently in 2019 this region was a stronger CO2 annual sink of −8.8 TgC yr−1 compared to −7.0 (±0.5) TgC yr−1 averaged over 1996–2018. In austral summer 2019–2020, the bloom was likely controlled by a relatively deep mixed-layer depth during the preceding winter (July–September 2019) that would supply macro- and/or micro-nutrients such as iron to the surface layer to promote the bloom that started in November 2019 in two large rings in the Madagascar Basin. Based on measurements in January 2020, we observed relatively high N2 fixation rates (up to 18 nmol N L−1 d−1), suggesting that diazotrophs could play a role in the bloom in the nutrient-depleted waters. The bloom event in austral summer 2020, along with the new carbonate system observations, represents a benchmark case for complex biogeochemical model sensitivity studies (including the N2 fixation process and iron supplies) for a better understanding of the origin and termination of this still “mysterious” sporadic bloom and its impact on ocean carbon uptake in the future.

Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification as well as their impacts on marine organisms and ecosystems. So far ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim at developing a sustainable production chain of observation-derived global ocean carbonate system datasets at high space-time resolution. As the start of the long-term objective, this study introduces a new global 0.25° monthly reconstruction, namely CMEMS-LSCE, for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (DIC), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple linear regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT. 1σ-uncertainty associated with these estimates is also provided. Here, σ stands for either ensemble standard deviation of pCO2 estimates or total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25°-resolution pCO2 product outperforms a coarser spatial resolution (1°) thanks to a higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal-open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-of-mean–square–deviation from observations less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (DIC), and pH. The global average 1σ-uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and DIC, and 0.4 % for pH relative to their global mean values. Both model-observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account in priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification.

<p>Observational networks monitoring marine carbon variables are established to meet the critical need to estimate ocean CO2 uptake, as well as assessing its consequences on ocean health through changes in carbonate chemistry (ocean acidification). Despite considerable efforts over the past decades, data coverage is still sparse over large ocean regions, prompting the implementation of mapping methods to gap-fill carbon datasets over the globe. Different statistical approaches have been proposed with the aim to generate reconstructions of the complete marine CO2 system at high spatial-temporal resolutions. Following this goal, we first introduce a global reconstruction of surface ocean partial pressure of CO2 (pCO2) at monthly and 0.25-degree resolutions over the period 1985-2021. This high-resolution pCO2 product is derived from ensemble neural network models interpolating monthly gridded observation-based data from Surface Ocean CO2 ATlas (SOCAT). We will assess the ability of the proposed pCO2 ensemble (1) to derive long-term time series of pCO2 and associated 1-sigma uncertainty per 0.25-degree grid cell for each month, (2) to reproduce temporal and horizontal gradients of coastal pCO2 observations in comparison with a coarser spatial resolution, (3) to estimate surface ocean pH and air-sea CO2 fluxes. Furthermore, we will present an extension of the ensemble neural network models, which is referred to as a new module extrapolating pCO2 to several years ahead. The extended ensemble-based approach will ultimately be used to project global ocean CO2 uptake and ocean acidification with low latency.</p>

<div> <p><span data-contrast="none">Producing comprehensive information about the ocean has become a top priority to monitor and predict the ocean and climate change.</span><span data-contrast="none"> Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is developed thought the Copernicus Marine Service MultiOBservation Thematic Assembly (</span><span data-contrast="auto">MOB TAC). Recent advances in data fusion techniques and use of machine-learning approach open the possibility of producing estimators of ocean physic and biogeochemistry (BGC) operationally, using input data from diverse sensors, satellites and in-situ programs.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":160,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">MOB TAC provides the following multi observations products at global scale: </span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":60,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">Blue ocean</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":60,"335559740":259}"> </span></p> </div> <div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">3D temperature, salinity, geopotential height and geostrophic current fields, both in near-real-time (NRT) and as long time series (REP=Reprocessing) in delayed-mode;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">2D sea surface salinity and sea surface density fields, both in NRT and as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">2D total surface and near-surface currents, both in NRT and as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="4" data-aria-level="1"><span data-contrast="auto">3D Vertical velocity fields as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="5" data-aria-level="1"><span data-contrast="auto">L2Q and L4 sea surface salinity from SMOS in REP and NRT (only L2Q)</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> </ul> </div> </div> <div> <div> <p><span data-contrast="auto">Green ocean</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559685":0,"335559739":200,"335559740":276}"> </span></p> </div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">2D surface carbon data sets of FCO2, pCO2, DIC, Alkalinity, saturation states of surface waters with respect to calcite and aragonite as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">Nutrient and Carbon vertical distribution (including Nitrates, Phosphates, Silicates, pH, pCO2, Alkalinity, DIC) profiles as REP and NRT;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">3D Particulate Organic Carbon (POC), particulate backscattering coefficient (bbp) and Chlorophyll a (Chl-a) fields as REP.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> </ul> </div> <div> <p><span data-contrast="auto">Parallel to its portfolio, MOB TAC has and will further develop specific expertise about the integration of multiple satellites and in-situ based observations coming from the other CMEMS TACs and projects. </span><span data-contrast="none">Furthermore, MOB TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor and the global ocean carbon sink. </span></p> </div> </div>

The seasonal cycle is the dominant mode of variability in the air-sea CO 2 flux in most regions of the global ocean, yet discrepancies between different seasonality estimates are rather large.As part of the Regional Carbon Cycle Assessment and Processes phase 2 project (RECCAP2), we synthesize surface ocean pCO 2 and air-sea CO 2 flux seasonality from models and observation-based estimates, focusing on both a modern climatology and decadal changes between the 1980s and 2010s. Four main findings emerge: First, global ocean biogeochemistry models (GOBMs) and observation-based estimates (pCO 2 products) of surface pCO 2 seasonality disagree, primarily due to discrepancies in the seasonal variability in surface DIC. Second, the seasonal cycle in pCO 2 has increased in amplitude over the last three decades in both pCO 2 products and GOBMs. Third, decadal increases in pCO 2 seasonal cycle amplitudes in subtropical biomes for both pCO 2 products and GOBMs are driven by increasing DIC concentrations stemming from the uptake of anthropogenic CO 2 (C ant ). In subpolar and Southern Ocean biomes, however, the seasonality change for GOBMs is dominated by C ant invasion, whereas for pCO 2 products an indeterminate combination of C ant invasion and climate change modulates the changes. Fourth, we have shown that biomeaggregated decadal changes in the amplitude of pCO 2 seasonal variability are largely detectable against both mapping uncertainty (reducible) and natural variability uncertainty (irreducible), but not at the gridpoint scale over much of the northern subpolar oceans and over the Southern Ocean, underscoring the importance of sustained high-quality seasonallyresolved measurements over these regions.

Abstract. Data assimilation is a relevant framework to merge a dynamical model with noisy observations. When various models are in competition, the question is to find the model that best matches the observations. This matching can be measured by using the model evidence, defined by the likelihood of the observations given the model. This study explores the performance of model selection based on model evidence computed using data-driven data assimilation, where dynamical models are emulated using machine learning methods. In this work, the methodology is tested with the three-variable Lorenz' model and with an intermediate complexity atmospheric general circulation model (a.k.a. the SPEEDY model). Numerical experiments show that the data-driven implementation of the model selection algorithm performs as well as the one that uses the dynamical model. The technique is able of selecting the best model among a set of possible models and also to characterize the spatio-temporal variability of the model sensitivity. Moreover, the technique is sensitive to differences in the model dynamics which are not reflected in the moments of the climatological probability distribution of the state variables. This suggests the implementation of this technique using available long-term observations and model simulations.