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 (E_FOS) are based on energy statistics and cement production data, while emissions from land-use change (E_LUC), 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 (G_ATM) is computed from the annual changes in concentration. The ocean CO₂ sink (S_OCEAN) is estimated with global ocean biogeochemistry models and observation-based f CO₂ products. The terrestrial CO₂ sink (S_LAND) 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 (B_IM), 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, E_FOS 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 E_LUC 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, G_ATM was 4.6±0.2 Gt C yr⁻¹ (2.18±0.1 ppm yr⁻¹; ppm denotes parts per million), S_OCEAN was 2.8±0.4 Gt C yr⁻¹, and S_LAND was 3.8±0.8 Gt C yr⁻¹, with a B_IM 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 E_FOS 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).

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 (E_FOS) are based on energy statistics and cement production data, while emissions from land-use change (E_LUC), 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 (G_ATM) is computed from the annual changes in concentration. The ocean CO₂ sink (S_OCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO₂ sink (S_LAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B_IM), 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 E_LUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, E_FOS 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 E_LUC 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, G_ATM was 5.0 ± 0.2 GtC yr⁻¹ (2.4 ± 0.1 ppm yr⁻¹), S_OCEAN was 3.0 ± 0.4 GtC yr⁻¹, and S_LAND was 2.9 ± 1 GtC yr⁻¹, with a B_IM 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 E_FOS 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).

A new version of the general circulation model CNRM-CM has been developed jointly by CNRM-GAME (Centre National de Recherches Météorologiques—Groupe d’études de l’Atmosphère Météorologique) and Cerfacs (Centre Européen de Recherche et de Formation Avancée) in order to contribute to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The purpose of the study is to describe its main features and to provide a preliminary assessment of its mean climatology. CNRM-CM5.1 includes the atmospheric model ARPEGE-Climat (v5.2), the ocean model NEMO (v3.2), the land surface scheme ISBA and the sea ice model GELATO (v5) coupled through the OASIS (v3) system. The main improvements since CMIP3 are the following. Horizontal resolution has been increased both in the atmosphere (from 2.8° to 1.4°) and in the ocean (from 2° to 1°). The dynamical core of the atmospheric component has been revised. A new radiation scheme has been introduced and the treatments of tropospheric and stratospheric aerosols have been improved. Particular care has been devoted to ensure mass/water conservation in the atmospheric component. The land surface scheme ISBA has been externalised from the atmospheric model through the SURFEX platform and includes new developments such as a parameterization of sub-grid hydrology, a new freezing scheme and a new bulk parameterisation for ocean surface fluxes. The ocean model is based on the state-of-the-art version of NEMO, which has greatly progressed since the OPA8.0 version used in the CMIP3 version of CNRM-CM. Finally, the coupling between the different components through OASIS has also received a particular attention to avoid energy loss and spurious drifts. These developments generally lead to a more realistic representation of the mean recent climate and to a reduction of drifts in a preindustrial integration. The large-scale dynamics is generally improved both in the atmosphere and in the ocean, and the bias in mean surface temperature is clearly reduced. However, some flaws remain such as significant precipitation and radiative biases in many regions, or a pronounced drift in three dimensional salinity.

This paper describes the main characteristics of CNRM‐CM6‐1, the fully coupled atmosphere‐ocean general circulation model of sixth generation jointly developed by Centre National de Recherches Météorologiques (CNRM) and Cerfacs for the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6). The paper provides a description of each component of CNRM‐CM6‐1, including the coupling method and the new online output software. We emphasize where model's components have been updated with respect to the former model version, CNRM‐CM5.1. In particular, we highlight major improvements in the representation of atmospheric and land processes. A particular attention has also been devoted to mass and energy conservation in the simulated climate system to limit long‐term drifts. The climate simulated by CNRM‐CM6‐1 is then evaluated using CMIP6 historical and Diagnostic, Evaluation and Characterization of Klima (DECK) experiments in comparison with CMIP5 CNRM‐CM5.1 equivalent experiments. Overall, the mean surface biases are of similar magnitude but with different spatial patterns. Deep ocean biases are generally reduced, whereas sea ice is too thin in the Arctic. Although the simulated climate variability remains roughly consistent with CNRM‐CM5.1, its sensitivity to rising CO₂ has increased: the equilibrium climate sensitivity is 4.9 K, which is now close to the upper bound of the range estimated from CMIP5 models.

SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean. It is mostly based on pre-existing, well-validated scientific models that are continuously improved. The motivation for the building of SURFEX is to use strictly identical scientific models in a high range of applications in order to mutualise the research and development efforts. SURFEX can be run in offline mode (0-D or 2-D runs) or in coupled mode (from mesoscale models to numerical weather prediction and climate models). An assimilation mode is included for numerical weather prediction and monitoring. In addition to momentum, heat and water fluxes, SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. The main principles of the organisation of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally, the main applications of the code are summarised. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage.

This study introduces CNRM‐ESM2‐1, the Earth system (ES) model of second generation developed by CNRM‐CERFACS for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CNRM‐ESM2‐1 offers a higher model complexity than the Atmosphere‐Ocean General Circulation Model CNRM‐CM6‐1 by adding interactive ES components such as carbon cycle, aerosols, and atmospheric chemistry. As both models share the same code, physical parameterizations, and grid resolution, they offer a fully traceable framework to investigate how far the represented ES processes impact the model performance over present‐day, response to external forcing and future climate projections. Using a large variety of CMIP6 experiments, we show that represented ES processes impact more prominently the model response to external forcing than the model performance over present‐day. Both models display comparable performance at replicating modern observations although the mean climate of CNRM‐ESM2‐1 is slightly warmer than that of CNRM‐CM6‐1. This difference arises from land cover‐aerosol interactions where the use of different soil vegetation distributions between both models impacts the rate of dust emissions. This interaction results in a smaller aerosol burden in CNRM‐ESM2‐1 than in CNRM‐CM6‐1, leading to a different surface radiative budget and climate. Greater differences are found when comparing the model response to external forcing and future climate projections. Represented ES processes damp future warming by up to 10% in CNRM‐ESM2‐1 with respect to CNRM‐CM6‐1. The representation of land vegetation and the CO₂‐water‐stomatal feedback between both models explain about 60% of this difference. The remainder is driven by other ES feedbacks such as the natural aerosol feedback.