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. The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO 2 ), but this sink has been shown to vary substantially in time. Here we use surface ocean CO 2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i.e. the Surface Ocean CO 2 Atlas (SO-CAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a two-step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO 2 (pCO 2 ) at a resolution of 1 • × 1 • . From those, we compute the air-sea CO 2 flux maps using a standard gas exchange parameterization and high-resolution wind speeds. The neural networks fit the observed pCO 2 data with a root mean square error (RMSE) of about 10 µatm and with almost no bias. A check against independent time-series data and new data from SOCAT v2 reveals a larger RMSE of 22.8 µatm for the entire Atlantic Ocean, which decreases to 16.3 µatm for data south of 40 • N. We estimate a decadal mean uptake flux of −0.45 ± 0.15 Pg C yr −1 for the Atlantic between 44 • S and 79 • N, representing the sum of a strong uptake north of 18 • N (−0.39 ± 0.10 Pg C yr −1 ), outgassing in the tropics (18 • S-18 • N, 0.11 ± 0.07 Pg C yr −1 ), and uptake in the subtropical/temperate South Atlantic south of 18 • S (−0.16 ± 0.06 Pg C yr −1 ), consistent with recent studies. The strongest seasonal variability of the CO 2 flux occurs in the temperature-driven subtropical North Atlantic, with uptake in winter and outgassing in summer. The seasonal cycle is antiphased in the subpolar latitudes relative to the subtropics largely as a result of the biologically driven winter-to-summer drawdown of CO 2 . Over the 10 yr analysis period (1998 through 2007), sea surface pCO 2 increased faster than that of the atmosphere in large areas poleward of 40 • N, while in other regions of the North Atlantic the sea surface pCO 2 increased at a slower rate, resulting in a barely changing Atlantic carbon sink north of the Equator (−0.01 ± 0.02 Pg C yr −1 decade −1 ). Surface ocean pCO 2 increased at a slower rate relative to atmospheric CO 2 over most of the Atlantic south of the Equator, leading to a substantial trend toward a stronger CO 2 sink for the entire South Atlantic (−0.14 ± 0.02 Pg C yr −1 decade −1 ). In contrast to the 10 yr trends, the Atlantic Ocean carbon sink varies relatively little on inter-annual timescales (±0.04 Pg C yr −1 ; 1σ ).

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 methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9  ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Abstract. Using measurements of the surface-ocean CO 2 partial pressure (pCO 2 ) and 14 different pCO 2 mapping methods recently collated by the Surface Ocean pCO 2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea-air CO 2 fluxes are investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO 2 seasonality, in line with previous estimates.

International audienceUsing measurements of the surface-ocean CO₂ partial pressure (pCO₂) and 14 different pCO₂ mapping methods recently collated by the Surface Ocean pCO₂ Mapping intercomparison (SOCOM) initiative, variations in regional and global sea–air CO₂ fluxes have been investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO₂ seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the Eastern equatorial Pacific. Despite considerable spead in the detailed variations, mapping methods with closer match to the data also tend to be more consistent with each other. Encouragingly, this includes mapping methods belonging to complementary types – taking variability either directly from the pCO₂ data or indirectly from driver data via regression. From a weighted ensemble average, we find an IAV amplitude of the global sea–air CO₂ flux of 0.31 PgC yr⁻¹ (standard deviation over 1992–2009), which is larger than simulated by biogeochemical process models. On a decadal perspective, the global CO₂ uptake is estimated to have gradually increased since about 2000, with little decadal change prior to 2000. The weighted mean total ocean CO₂ sink estimated by the SOCOM ensemble is consistent within uncertainties with estimates from ocean-interior carbon data or atmospheric oxygen trends

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Abstract. Air–sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air–sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate vary greatly, enhancing the spread of estimates. Here we introduce the ensemble data product, SeaFlux (Gregor and Fay, 2021, https://doi.org/10.5281/zenodo.5482547​​​​​​​, https://github.com/luke-gregor/pySeaFlux, last access: 9 September 2021​​​​​​​); this resource enables users to harmonize an ensemble of products that interpolate surface ocean CO2 observations to near-global coverage with a common methodology to fill in missing areas in the products. Further, the dataset provides the inputs to calculate fluxes in a consistent manner. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations. We address differences in spatial coverage of the surface ocean CO2 between the mapping products, which ultimately yields an increase in CO2 uptake of up to 17 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of a scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we present an ensemble of global surface ocean pCO2 and air–sea carbon flux estimates. This work aims to support the community effort to perform model–data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.

A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO 2 (fCO 2 ) on a 18 3 18 spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO 2 Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO 2 increase rate of 1.50 matm yr 21 . The rate was used to normalize multiple years' fCO 2 observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO 2 climatology. The model simulates monthly fCO 2 distributions that agree well with observations and yields an anthropogenic CO 2 update of 21.9 to 22.3 PgC yr 21 . The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont-Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO 2 uptake. The method's benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO 2 as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO 2 in all seasons, and (iii) the model can be extended to produce global fCO 2 maps at a higher resolution in time and space as long as the required data for input variables are available.