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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 synthesise datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use data. The global atmospheric CO2 growth rate (GATM) is computed from changes in concentration measured at surface stations. The global net uptake of CO2 by the ocean (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. This year, we introduced corrections on the ELUC, SOCEAN and SLAND estimates. The sum of all sources and sinks results in the carbon budget imbalance (BIM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ± 1σ. For the year 2024, EFOS increased by 1.1 % relative to 2023, with fossil emissions at 10.3 ± 0.5 GtC yr−1 (including the cement carbonation sink, 0.2 GtC yr−1), ELUC was 1.3 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.6 ± 0.9 GtC yr−1 (42.4 ± 3.2 GtCO2 yr−1). Also, for 2024, GATM was 7.9 ± 0.2 GtC yr−1 (3.73 ± 0.1 ppm yr−1), 2.2 GtC above the 2023 growth rate. SOCEAN was 3.4 ± 0.4 GtC yr−1 and SLAND was 1.9 ± 1.1 GtC yr−1, leaving a large negative BIM (−1.7 GtC yr−1), suggesting that the total sink or GATM is strongly overestimated in 2024. The global atmospheric CO2 concentration averaged over 2024 reached 422.8 ± 0.1 ppm. Preliminary data for 2025 suggest an increase in EFOS relative to 2024 of +1.0 % (0.2 % to 1.7 %) globally, and atmospheric CO2 concentration increasing by 2.1 ppm reaching 425.6 ppm, 53 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2024, with a near-zero overall budget imbalance, although discrepancies of up to around 1 GtC yr−1 persist for the representation of annual to 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 extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink. This living data update documents changes in methods and datasets 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-2025 (Friedlingstein et al., 2025c).
The CNES-CLS22 Mean Dynamic Topography (MDT; https://doi.org/10.24400/527896/A01-2023.003, Jousset, 2023) represents an incremental update to previous CNES-CLS solutions, combining altimetry, satellite gravity, and in situ observations (drifters, hydrography profiles, and HF radar data). The main improvement lies in the Arctic, where enhanced Mean Sea Surface (MSS) coverage eliminate artifacts present in CNES-CLS18 and enable a more physically consistent representation of circulation, including the Norwegian Atlantic Front Current along the Mohn Ridge. Globally, CNES-CLS22 remains close to CNES-CLS18, with modest improvements in validation against independent datasets: RMS differences in geostrophic velocities decrease by only ∼ 0.2 %–0.5 % at the global scale and the average variance reduction at the global scale compared to heights derived from profiles is ∼ 0.5 %. Though regional gains are significant in the Arctic and Nordic Seas. HF radar integration in the Mid-Atlantic Bight demonstrates progress but highlights persistent challenges in shelf regions dominated by ageostrophic processes. At very small scales (< 40 km), noise from in situ data may introduce unrealistic kinetic energy, underscoring the need for improved filtering. Overall, CNES-CLS22 consolidates previous advances and provides better representation of key circulation features, but further progress will require enhanced coastal observations and refined processing methods, particularly for high-latitude and shelf areas.
As the North Atlantic Oscillation (NAO) accounts for a dominant share of wintertime weather variability across the North Atlantic, it is a coveted target for seasonal prediction. Yet dynamical forecast systems exhibit limited skill. Here I build on previous results linking November sea-surface temperature (SST) anomalies to the subsequent winter NAO via ocean–atmosphere feedback mechanisms involving baroclinicity and surface heat fluxes. I hypothesise that limited model skill is partly attributable to a deficient representation of these mechanisms. While remote influences such as tropical or stratospheric forcing can affect both SSTs and the NAO, thereby contributing to apparent but non-causal relationships, I find that the seasonal prediction system SEAS5's internal lagged SST–NAO relationship nonetheless correlates with its NAO forecast skill. Since this skill reflects the combined effects of all sources of predictability – including tropical and stratospheric forcing – this correlation is an important finding. Using mediation analysis to contrast the behaviour of SEAS5 with that of the ERA5 reanalysis, I find that SEAS5 produces weaker mediated effects via both fluxes and baroclinicity than those found in ERA5. Critically, the strength of these mediated effects in the model correlates with its NAO forecast skill. This suggests that models reproducing realistic mediation pathways for ocean–atmosphere interactions are likely to achieve higher NAO skill than models that do not.
Storylines are physically plausible scenarios of future climate change, statistically derived from an ensemble of climate model projections and organized according to the magnitude of projected changes in two or more remote drivers that strongly influence the spatial pattern of the climate response. Here, we provide novel insights into the Arctic storylines identified by Levine et al. (2024), where Barents-Kara Sea warming and lower-tropospheric Arctic warming during the extended summer season (May-October) were remote drivers, as we identify a set of models from the Coupled Model Intercomparison Project phase 6 to represent the storylines. We do this by first identifying models that are similar to these storylines in terms of each remote driver response and quantifying this similarity. Second, we evaluate the model's performance in terms of a simple performance score based on the mean normalized root-mean-square error for multiple climate variables of importance for the storylines. The normalized values vary between 0 and 1 for all variables, allowing them to exert a comparable influence on the score. The advantage of the score is that it provides an easily implementable and interpretable way of identifying models that are characterized by large errors relative to the rest of the ensemble. Finally, we combine the similarity estimate and the score to select models to represent the storylines. We focus on the Arctic during the extended summer season for which the storylines were designed, but also consider other seasons and regions. Through this exercise, we also document the methodology, benefits, and limitations of the score.
In the Arctic Ocean, satellite-based sea surface temperature data shows that marine heatwave (MHW) intensity, frequency, duration and coverage have increased significantly in recent decades, raising concern for Arctic ecosystems. A high frequency (more than three events per year) of MHWs has been shown around the Svalbard Archipelago. Based on this, we investigate MHW trends around Svalbard at the surface and subsurface, using a regional reanalysis from TOPAZ (1991–2022). We find an increase in the frequency and duration of MHW events around the Svalbard Archipelago over the last decade. Focussing on a region west of Svalbard, we observe an increase in MHW frequency and duration, associated with a long-term rise in sea surface temperature in the region. Analysis of eight individual summer (June–September) MHW events lasting longer than 10 d west of Svalbard, indicated the presence of four shallow (≤50 m) and four deep (>50 m) MHWs after 2010, with a mean duration of 29 d. Some events extended into the Barents Sea. Heat budget analysis demonstrated a greater contribution of ocean heat transport compared to air-sea heat fluxes in driving the MHW events. Deep and shallow events were associated with ocean heat transport anomalies of up to 9 TW. This new understanding of MHW characteristics, including their horizontal and vertical distribution, is key to assessing ecological impacts.
