<figure><img src="image_61.png" /></figure>

2011 Here, we take the average of our update of both IPCC AR6 and Forster et al. (2025) estimates, giving a remaining carbon (50% likelihood) for limiting global warming to 1.5°C, 1.7°C and 2°C of respectively 2012 45, 145, and 290 GtC (170, 525, 1055 GtCO₂) starting from January 2026. We emphasise the large 2013 uncertainties, particularly when close to the global warming limit of 1.5°C. These 1.5°C, 1.7°C and 2°C 2014 remaining carbon budgets correspond respectively to about 4, 12 and 25 years from the beginning of 2015 2026, at the 2025 level of total anthropogenic CO₂ emissions. Reaching net-zero CO₂ emissions by 2016 2050 entails cutting total anthropogenic CO₂ emissions by about 0.5 GtC (1.7 GtCO₂), 4% of 2025 2017 emissions, each year on average, comparable to the decrease in EFOS observed in 2020 during the 2018 COVID-19 pandemic. However, this would lead to cumulative emissions over 2025-2050 of 140 GtC 2019 (520 GtCO₂), well above the remaining carbon budget of 50 GtC to limit global warming to 1.5°C, but 2020 still within the remaining budget of 145 GtC to limit warming to 1.7°C (in phase with the “well below 2021 2°C” ambition of the Paris Agreement). 2022

## 5 Discussion

2024 Each year when the global carbon budget is published, each flux component is updated for all previous 2025 years to consider corrections that are the result of further scrutiny and verification of the underlying 2026 data in the primary input datasets. Annual estimates may be updated with improvements in data quality 2027 and timeliness (e.g., to eliminate the need for extrapolation of forcing data such as land use). Of all 2028 terms in the global budget, only the fossil CO₂ emissions and the growth rate in atmospheric CO₂ 2029 concentration are based primarily on empirical inputs supporting annual estimates in this carbon 2030 budget. The carbon budget imbalance, yet an imperfect measure, provides a strong indication of the 2031 limitations in observations, in understanding and representing processes in models, and/or in the 2032 integration of the carbon budget components.

2033 The persistent unexplained variability in the carbon budget imbalance limits our ability to verify 2034 reported emissions (Peters et al., 2017a) and suggests we do not yet have a complete understanding of 2035 the underlying carbon cycle dynamics on annual to decadal timescales. Resolving most of this 2036 unexplained variability should be possible through different and complementary approaches. First, as 2037 intended with our annual updates, the imbalance as an error term should be reduced by improvements 2038 of individual components of the global carbon budget that follow from improving the underlying data 2039 and statistics and by improving the models through the resolution of some of the key uncertainties 2040 detailed in Table 10. Second, additional clues to the origin and processes responsible for the variability 2041 in the budget imbalance could be obtained through a closer scrutiny of carbon variability in light of 2042 other Earth system data (e.g., heat balance, water balance), and the use of a wider range of 2043 biogeochemical observations to better understand the land-ocean partitioning of the carbon imbalance 2044 such as the constraint from atmospheric oxygen. Finally, additional information could also be obtained 2045 through better inclusion of process knowledge at the regional level. The limit for reducing the carbon
<figure><img src="image_62.png" /></figure>

2046 budget imbalance is yet unclear, but most certainly not yet reached given the possibilities for
2047 improvements that lie ahead.

2048 Estimates of global fossil CO₂ emissions from different datasets are in relatively good agreement when
2049 the different system boundaries of these datasets are considered (Andrew, 2020a). While estimates of
2050 E<sub>FOS</sub> are derived from reported activity data requiring much fewer complex transformations than some
2051 other components of the budget, uncertainties remain, and one reason for the apparently low variation
2052 between datasets is precisely the reliance on the same underlying reported energy data. The budget
2053 excludes some sources of fossil CO₂ emissions, which available evidence suggests are relatively small
2054 (<1%) (see Supplement S.8.2). We have added emissions from lime production in China and the US,
2055 but these are still absent in reporting from most other non-Annex I countries, and before 1990 in other
2056 Annex I countries.

2057 Estimates of E<sub>LUC</sub> suffer from a range of intertwined issues (Obermeier et al., 2025), including the poor
2058 knowledge of historical land-cover and land-use change (particularly before the 1960s when FAO
2059 reporting started), the rudimentary representation of management processes in most models, and the
2060 diversity in methodologies and boundary conditions used across methods (e.g., Arneth et al., 2017;
2061 Pongratz et al., 2014; Bastos et al., 2021). Uncertainties in current and historical carbon stocks in soils
2062 and vegetation also contribute to uncertainty in E<sub>LUC</sub> estimates. Major efforts are thus necessary to
2063 resolve the different issues concerning E<sub>LUC</sub> (Obermeier et al., 2025). The large uncertainty and limited
2064 reliability of E<sub>LUC</sub> estimates are particularly concerning given the growing importance of E<sub>LUC</sub> for
2065 climate mitigation strategies, and the large issues in the quantification of the cumulative emissions over
2066 the historical period that arise from large uncertainties in E<sub>LUC</sub>.

2067 By adding the natural land sink in managed forests estimated by DGVMs (part of SLAND in this budget)
2068 to the budget E_LUC estimate, we reconcile most of the large gap between our E_LUC estimate and the land-
2069 use flux estimates from NGHGIs (Figure 8). This reconciliation (translation) can be used as potential
2070 adjustment in the policy context, for instance to help assess the collective countries' progress towards
2071 the goal of the Paris Agreement avoiding double-accounting for the natural sink in managed forests. In
2072 the absence of this translation, collective progress would appear better than it is (Grassi et al., 2021). A
2073 clear understanding of these implications is thus essential for policymakers for developing effective
2074 climate targets (Grassi et al., 2025). The application of this translation is also recommended in the
2075 UNFCCC Synthesis report for the first Global Stocktake (UNFCCC, 2022) whenever a comparison
2076 between LULUCF fluxes reported by countries and the global emission estimates of the IPCC is
2077 conducted. However, this translation should be seen as a short-term and pragmatic fix based on existing
2078 data, rather than a definitive solution to bridge the differences between global models and national
2079 inventories. Platforms for dataset comparisons (e.g., Melo et al., 2025) help strengthening the dialogue
2080 across communities and identify the additional steps needed to understand and reconcile the remaining
<figure><img src="image_63.png" /></figure>

2081 differences, some of which are relevant at the country level (Grassi, et al., 2023, Schwingshackl, et al., 2022).

2083 The comparison of GOBMs, $/CO_2$-products, and inversions highlights discrepancies in the temporal evolution of $SOCEAN$ in the Southern Ocean and northern high-latitudes (Figure 14, Hauck et al., 2023a) and in the mean $SOCEAN$ in the tropics. A large part of the uncertainty in the mean fluxes stems from the regional distribution of the river flux adjustment term. The current distribution simulates the largest share of the outgassing to occur in the tropics (Lacroix et al., 2020). The long-standing sparse data coverage of $/CO_2$ observations in the Southern compared to the Northern Hemisphere (e.g., Takahashi et al., 2009) continues to exist (Bakker et al., 2016, 2025a, Figure S5) and to lead to substantially higher uncertainty in the $SOCEAN$ estimate for the Southern Hemisphere (Watson et al., 2020, Gloege et al., 2021, Hauck et al., 2023a). This discrepancy, which also hampers model improvement, points to the need for increased high-quality $/CO_2$ observations especially in the Southern Ocean. At the same time, model uncertainty is illustrated by the large spread of individual GOBM estimates (indicated by shading in Figure 14) and highlights the need for model improvement, now also supported by the IOMB benchmarking. The issue of diverging trends in $SOCEAN$ from different methods remains a matter of concern. Recent and on-going work suggests that the $/CO_2$-products may overestimate the trend although biases in the $/CO_2$-product ensemble may partly cancel out (Supplement section S3.4). A data-constrained model approach suggests that the GOBMs underestimate the amplitude of decadal variability, but that the $/CO_2$-products overestimate the trend (Mayot et al., 2024). The various methods, now also including ocean interior observation-based estimates, agree within uncertainties on the mean ocean sink in the last decade, although the independent estimate from atmospheric oxygen measurements still shows the largest sink estimate for the past decade and has a steeper trend (Table 6). However, the estimate is consistent within uncertainties with $SOCEAN$, with the relatively larger ocean sink in the $/CO_2$-products and some of the GOBMs. The assessment of the net land-atmosphere exchange from DGVMs and atmospheric inversions also shows substantial discrepancy, particularly for the estimate of the net land flux over the northern extra-tropic. This discrepancy highlights the difficulty to quantify complex processes ($CO_2$ fertilisation, nitrogen deposition and fertilisers, climate change and variability, land management, etc.) that collectively determine the net land $CO_2$ flux. Resolving the differences in the Northern Hemisphere land sink will require the consideration and inclusion of larger volumes of observations.

2111 The adjustments introduced in this budget for $SLAND$ and $SOCEAN$ to account for known biases (RSS for DGVMs, cold skin/warm layer for $/CO_2$ products, ocean sink underestimation for GOBMs) lead to a smaller net land sink ($SLAND-ELUC$) and a larger ocean sink ($SOCEAN$), more in line with the atmospheric inversions and independent oxygen based estimates (Table 7), and also consistent with a recent study based on satellite derived changes in aboveground biomass over the 2000-2019 period (Randerson et al., 2025).
<figure><img src="image_64.png" /></figure>

2117 We provide metrics for the evaluation of the ocean and land models and the atmospheric inversions (Figure S6-S8 and S13, Table S11). These metrics expand the use of observations in the global carbon budget, helping 1) to support improvements in the ocean and land carbon models that produce the sink estimates, and 2) to constrain the representation of key underlying processes in the models and to allocate the regional partitioning of the CO₂ fluxes. The use of process-based metrics in an objective evaluation framework (IOMB) targeted to evaluate the simulation of SOCEAN in the ocean biogeochemistry models is an important advance. This is another step in the endeavour to use a broader range of observations and more stringent model evaluation that we hope will support continued improvements in the models and in the annual estimates of the global carbon budget.

2126 We assessed before that a sustained decrease of –1% in global emissions could be detected at the 66% likelihood level after a decade only (Peters et al., 2017a). Similarly, a change in behaviour of the land and/or ocean carbon sink would take as long to detect, and much longer if it emerges more slowly. To continue reducing the carbon imbalance on annual to decadal time scales, regionalising the carbon budget, and integrating multiple variables are powerful ways to shorten the detection limit and ensure the research community can rapidly identify issues of concern in the evolution of the global carbon cycle under the current rapid and unprecedented changing environmental conditions.

## 6 Conclusions

2134 The estimation of global CO₂ emissions and sinks is a major effort by the carbon cycle research community that requires a careful compilation and synthesis of measurements, statistical estimates, and model results. The delivery of an annual carbon budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the datasets associated with the annual carbon budget including scientists, policy makers, businesses, journalists, and non-governmental organisations engaged in adapting to and mitigating human-driven climate change. Second, over the last decades we have seen unprecedented changes in the human and biophysical environments (e.g., changes in the growth of fossil fuel emissions, impact of COVID-19 pandemic, Earth's warming, extreme events, and strength of the carbon sinks), which call for frequent assessments of the state of the planet, a better quantification of the causes of changes in the contemporary global carbon cycle, and an improved capacity to anticipate its evolution in the future. Building this scientific understanding to meet the extraordinary climate mitigation challenge requires frequent, robust, transparent, and traceable datasets and methods that can be scrutinised and replicated. This paper via 'living data' helps to keep track of new budget updates.
<figure><img src="image_65.png" /></figure>

2149 7 Data availability

2150 The data presented here are made available in the belief that their wide dissemination will lead to
2151 greater understanding and new scientific insights of how the carbon cycle works, how humans are
2152 altering it, and how we can mitigate the resulting human-driven climate change. Full contact details and
2153 information on how to cite the data shown here are given at the top of each page in the accompanying
2154 database and summarised in Table 2.

2155 The accompanying database includes three Excel files organised in the following spreadsheets:

2156 File Global_Carbon_Budget_2025v1.0.xlsx includes the following:

2157 1. Summary

2158 2. The global carbon budget (1959-2024);

2159 3. The historical global carbon budget (1750-2024);

2160 4. Global CO₂ emissions from fossil fuels and cement production by fuel type, and the per-capita emissions (1850-2024);

2161 5. CO₂ emissions from land-use change from the individual bookkeeping models (1959-2024);

2163 6. Ocean CO₂ sink from the individual global ocean biogeochemistry models and fCO₂-products (1959-2024);

2164 7. Terrestrial CO₂ sink from the individual DGVMs (1959-2024);

2165 8. Cement carbonation CO₂ sink (1959-2024).

2167 File National_Fossil_Carbon_Emissions_2025v1.0.xlsx includes the following:

2168 1. Summary

2169 2. Territorial country CO₂ emissions from fossil fuels and cement production (1850-2024);

2170 3. Consumption country CO₂ emissions from fossil fuels and cement production and emissions transfer from the international trade of goods and services (1990-2020) using CDIAC/UNFCCC data as reference;

2171 4. Emissions transfers (Consumption minus territorial emissions; 1990-2020);

2172 5. Country definitions.

2175 File National_LandUseChange_Carbon_Emissions_2024v1.0.xlsx includes the following:
<figure><img src="image_66.png" /></figure>

2176 1. Summary

2177 2. Territorial country CO₂ emissions from Land Use Change (1850-2024) from three bookkeeping

2178 models;

2179 All three spreadsheets are published by the Integrated Carbon Observation System (ICOS) Carbon

2180 Portal and are available at https://doi.org/10.18160/GCP-2025 (Friedlingstein et al., 2025c). National

2181 emissions data are also available at https://doi.org/10.5281/zenodo.5569234 (Andrew and Peters, 2025),

2182 from the Global Carbon Atlas (http://www.globalcarbonatlas.org/, last access: 23 October 2025) and

2183 from Our World in Data (https://ourworldindata.org/co2-emissions, last access: 23 October 2025).

2184 Author contributions

2185 PF, MOS, MWJ, DCEB, RMA, JH, PL, CLQ, HL, ITL, GPP, WP, JP, CSc, and SS designed the study,

2186 conducted the analysis, and wrote the paper with input from JGC and PC. RMA, GPP, JIK and TK

2187 produced the fossil CO₂ emissions and their uncertainties and analysed the emissions data. ME and GM

2188 provided fossil fuel emission data. ZL provided the Carbon Monitor fossil emission projection. JP, CSc,

2189 TG and ZQ provided the bookkeeping land-use change emissions with synthesis by JP and CSc. KH

2190 and JDM provided the estimates of non-vegetation CDR fluxes. LB, MAC, ÖG, NG, TI, TJ, LR, JS,

2191 RS, and HTs provided an update of the global ocean biogeochemical models; LD, DJF, MG, LG, YI,

2192 AJ, GMK, CR, JZ, and PC provided an update of the ocean fCO₂-data products, with synthesis on both

2193 streams by JH, PL and AR. SRA, LBa, NRB, AB, CFB, MC, MPE, KE, WE, RAF, TGk, CL, NM,

2194 DRM, SN, AO, AMO, DP, GR, IS, AJS, CSw, ST, BT, SJvdV, EVO, and RW provided ocean fCO₂

2195 measurements for the year 2024, with synthesis by DCEB and SDJ. KA, PA, THC, JHE, AFo, BG, JG,

2196 AI, AKJ, EK, JK, EM, JM, LM, TN, QS, TLS, HT, APW, WY, XYa, XYu and CK provided an update

2197 of the Dynamic Global Vegetation Models, with synthesis by SS and MOS. RB, FF, HL, VS, DS, and

2198 HiT provided estimates of land and ocean sinks from Earth System Models, as well as a projection of

2199 the atmospheric growth rate for 2025, with synthesis by HL. FC, NC, LF, ARJ, FJ, ITL, JL, LRN, YN,

2200 CR, XT, YK, and ZL provided an updated atmospheric inversion, WP, FC, and ITL developed the

2201 protocol and produced the synthesis and evaluation of the atmospheric inversions. EJM and RFK

2202 provided the atmospheric oxygen estimate of surface net carbon sinks. RMA provided projections of

2203 the 2025 fossil emissions and atmospheric CO₂ growth rate. PL provided the predictions of the 2025
<figure><img src="image_67.png" /></figure>

ocean sink. IBMB, LPC, KKG, GCH, TMR and GRvdW provided forcing data for land-use change. FT and GG provided data for the land-use change NGHGI harmonisation. XL provided the atmospheric CO₂ data. SP provided the satellite OCO-2 atmospheric CO₂ growth rate. MWJ provided the historical atmospheric CO₂ concentration and growth rate. IH provided the climate forcing data for the DGVMs. NP provided the nitrogen fertilizer forcing data for the DGVMs. MOS and NB produced the aerosol diffuse radiative forcing for the DGVMs. PCM provided the iLAMB evaluation of the DGVMs. NOC and MGS provided the iOMB evaluation of the GOBMs. PR provided the historical land carbon export estimate, MWJ provided the emissions prior for use in the inversion systems. XD provided seasonal emissions data, based on GRACED (Global gridded daily CO₂ emissions dataset), for most recent years for the emission prior. PF, MO and MWJ coordinated the effort, revised all figures, tables, text and numbers to ensure the update was clear from the 2024 edition and in line with the globalcarbonatlas.org.

2216

## Competing interests.

At least one of the authors is a member of the editorial board of Earth System Science Data

2219

## Acknowledgements

First, we wish to thank Richard 'Skee' Houghton for his tremendous contribution to the Global Carbon Budget since its inception, pioneering the estimate of land use change emissions with bookkeeping models. We thank all people and institutions who provided the data used in this global carbon budget 2025 and the Global Carbon Project members for their input throughout the development of this publication. We thank Christian Ethé, Xavier Perrot, Damian Loher, Fatemeh Chegini, Fabrice Lacroix, Yangyang Zhao, Paridhi Rustogi, Sarah Berthet, Aurore Voldoire, Laurent Oziel, T. Toyoda, Y. Kitamura, K. Toyama, H. Nakano, L. S. Urakawa, Philip Townsend, Nathaniel O. Collier, Min Xu, James D. Shutler, Andrew J. Watson, T. Holding, I.G.C. Ashton, D. K. Woolf, Lonneke Goddijn-Murphy, Richard Sims, Stefanie Falk, Pengyue Du, Peter Lawrence, Sean Swenson, Daniel Kennedy, Sam Levis, Erik Kluzek, Lachlan Whyborn, Drew Holzworth, Ian Harman, Naiqing Pan, Shufen Pan for their involvement in the development, use, and analysis of the models and data products used here. We thank Kim Currie, Hannelore Theetaert, and Coraline Leseurre who contributed to the provision of surface
<figure><img src="image_68.png" /></figure>

ocean CO₂ observations for the year 2024 (see Table A5). We also thank Kevin O'Brien and Eugene Burger of NOAA's Pacific Marine Environmental Laboratory and Alex Kozyr of NOAA's National Centers for Environmental Information, for their contribution to surface ocean CO₂ data and metadata management. We thank the scientists, institutions, and funding agencies responsible for the collection and quality control of the data in SOCAT as well as the International Ocean Carbon Coordination Project (IOCCP) and the Surface Ocean Lower Atmosphere Study (SOLAS) program for their support. ITL and WP thank the CarbonTracker Europe team at Wageningen University, including Remco de Kok, Joram Hooghiem and Auke van der Woude. The inverse modelling team thanks all data providers of atmospheric CO₂ data provided through multiple ObsPack products. HL thanks Tatiana Ilyina for supporting the coordination of ESMs' contributions to GCBs, and Sebastian Brune and Kristina Frölich for helping with the MPI-ESM1-2-LR assimilation. Zhangcai Qin thanks Ziwang Fu and Dr. Yakun Zhu for contributing to LUCE modeling. We thank Ram Alkama and Simone Rossi for helping in filtering S<sub>LAND</sub> data from DGVMs with managed forest area and Joana Melo for extracting and processing data from NGHGIs. FT acknowledges support of member countries to the FAO regular budget and FAOSTAT. The conclusions in this paper are the authors' only and do not represent FAO views and position on the subject matter. This is PMEL contribution 5802. MC thanks Tobias Steinhoff (MACID), Lukasz Pawlikowski, Ian Murphy and Gordon Furey (P&O Maritime Services), and Aodhan Fitzgerald (Marine Institute) for their support. TG and the VLIZ team are thankful to the management group and crew of the research vessels Simon Stevin, Skagerak and Neil Armstrong for all the support and help they provided. pCO₂ measurements on the Marion Dufresne were collected in the frame of the French COOL/OISO long-term monitoring program with support from the National Institute of Sciences of the Universe (INSU), the French Polar Institute (IPEV) and the French Oceanographic Fleet (FOF). AMO thanks the captain and crew on the Sea-Cargo Express for their help. DP would like to thank Kevin Sullivan for processing and quality controlling AOML data and facilitating submission to SOCATv2025. GR thanks the "Finnmaid science and tech-teams" at IOW and SYKE for their support, and is indebted to the crew of the ship and the people involved at Finnlines. IS thanks the captain and crew on the G. O. Sars for their help. BT and EvO thank Australia's Integrated Marine Observing System (IMOS) for sourcing CO₂ data. IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS). SJV thanks Andrew Marriner for maintaining the pCO₂ underway system on the RV Tangaroa. PCM thanks Tristan Quaife, Eddy Robertson, Emily Black and Anthony Walker for support. FC thanks Adrien Martinez who maintained the atmospheric transport model for the CAMS inversion. We acknowledge the contributions of Jeongmin Yun, Anthony Bloom, and Kevin Bowman to the CMS-Flux inversion submission. YN thanks Suman Maity who performed the NISMON-GOSAT inversion. LF thanks Paul I. Palmer and acknowledges ongoing support from the National Centre for Earth Observation. Xiangjun Tian thanks Zhe Jin, Yilong Wang, Hongqin Zhang, Min Zhao, Tao Wang, Jinzhi Ding, and Shilong Piao for their contributions to the GONGGA inversion system. We thank Ning Zeng, Yun Liu, Eugenia Kalnay, and Gassem Asrar for their contributions to the COLA system. CarbonTracker CT2025
<figure><img src="image_69.png" /></figure>

results provided by NOAA GML, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. FJ thanks Weimin Ju for running the BEPS model. LN thanks Shamil Maksyutov, Rajesh Janardanan and Tsuneo Matsunaga for contributing to NTFVAR inverse model study. YK acknowledges Bo Zheng from Tsinghua University for his contribution to the THU inversion system. RSA thanks William Merryfield, Woosung Lee, Jason Cole for their help to set up and produce the model runs. The IPSL contribution was achieved thanks to the valuable contributions of Patricia Cadule, Juliette Mignot and Olivier Torres. GAM acknowledges contributions from Amanda Fay to updating the LDEO-HPD fCO2 product. JRM thanks Gesa Meyer for assistance setting up the DGVM simulations. LMD thanks Pedro. M. S. Monteiro (SCS-SU) and Sandy Thomalla (SOCCO-CSIR) for their continued support to make the CSIR-ML6 fCO2-product up-to-date. CFB is grateful to the technicians and crew of the research vessel Victor Angelescu for all the support and assistance they provided. This is INIDEP contribution 2460.

2282

2283 Financial and computing support

This research was supported by the following sources of funding: Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP) (Argentina); Joint Technical Commission of the Maritime Front (CTMFM, Argentina-Uruguay) (Argentina); Integrated Marine Observing System (IMOS) (Australia); National Environmental Science Program (NESP) (Australia); Research Foundation Flanders (grant no. I001821N) (Belgium); ICOS (Integrated Carbon Observing System) Belgium (Belgium); Tula Foundation (Canada); National Key Research and Development Program of China (grant no. 2023YFF0805400) (China); Horizon Europe (NextGenCarbon: grant no. 101184989) (EC); Horizon Europe (RESCUE: grant no. 101056939) (EC); Horizon 2020 (ESM2025: grant no. 101003536) (EC); Horizon Europe (TRICUSO: grant no. 101199028) (EC); Horizon Europe (GreenFeedBack: grant no. 101056921) (EC); H2020 (JERICO-S3: grant no. 871153) (EC); Horizon Europe (GEORGE: grant no. 101094716) (EC); Copernicus Atmosphere Monitoring Service, implemented by ECMWF on behalf of the European Commission (Grant no. CAMS2 55 bis) (EC); MOB TAC project of the European Copernicus Marine Environment Monitoring Service (EC); Horizon Europe (TipESM: grant no. 101137673) (EC); Horizon Europe (OceanICU: grant no. 101083922) (EC); Climate Space RECCAP-2 (grant no. 4000144908/24/1-LR) (European Space Agency); EO-LINCS (European Space Agency); Ocean Carbon for Climate (grant no. 3-18399/24/1-NB) (European Space Agency); Satellite-based observations of Carbon in the Ocean: Pools, Fluxes and Exchanges (grant no. 4000142532/23/1-DT) (European Space Agency); THRAC3E (grant no. 4000149569/25/1-AG) (European Space Agency); ICOS Germany (Germany); Federal Ministry of Education and Research (BMBF) (STEPSEC: grant no. 01LS2102A) (Germany); Marine Institute (Ireland); Japan Meteorological Agency (Japan); Global Environmental Research Coordination System, Ministry of the Environment (grant nos. E2152 and
<figure><img src="image_70.png" /></figure>

2305 E2252) (Japan); NIES GOSAT Project (Japan); the Environment Research and Technology
2306 Development Fund (JPMEERF24S12200) of the Environmental Restoration and Conservation Agency
2307 provided by Ministry of the Environment of Japan (Japan); Environment Research and Technology
2308 Development Fund (grant no. JPMEERF24S12205, Co-PI: P. K. Patra) of the Environmental Restoration
2309 and Conservation Agency of Japan, and the Arctic Challenge for Sustainability phase III project (ArCS-
2310 3; grant no. JPMXD1720251001; Co-PI: P. K. Patra) (Japan); Environment Research and Technology
2311 Development Fund (grant no. JPMEERF24S12206) of the Environmental Restoration and Conservation
2312 Agency of Japan (Japan); Ministry of Business, Innovation and Employment (Strategic Science
2313 Investment Fund via the NIWA Ocean-Climate Interaction programme) (New Zealand); Research
2314 Council of Norway (ICOS-3, grant no. 350341) (Norway); Research Council of Norway (grant no.
2315 352474) (Norway); Research Council of Norway (grant no. 352204) (Norway); South African
2316 Department of Science, Technology and Innovation (grant no. DSI/CON C3184/2023) (South Africa);
2317 UK Research and Innovation (Horizon Europe GreenFeedBack: grant no. 10040851) (UK); NCAS at the
2318 University of Reading (UK); NERC NE/V011103/1 "Frontiers of instability in marine ecosystems and
carbon export"UK Natural Environment Research Council (NE/V01417X/1) (UK); Department of
2319 Energy, Office of Science, Office of Biological and Environmental Research (USA); NOAA (Global
2320 Ocean Monitoring and Observing Program: Open Funder Registry no. 100018302) (USA); NOAA
2321 (Ocean Acidification Program: Open Funder Registry no. 100018228) (USA); NOAA (Cooperative
2322 Agreement NA20OAR4320472) (USA); NOAA (Cooperative Agreement NA-03-AR4320179) (USA);
2323 Alaska Ocean Observing System (USA); NASA Carbon Monitoring System (grant no.
2324 80NSSC25K7221) (USA); NASA Early Career Investigator Program in Earth Science (grant no.
2325 80NSSC24K1632) (USA); NASA Land Cover and Land Use Change Program (Grants:
2326 80NSSC24K0920 and 80NSSC25K7497) (USA); NSF (Cooperative Agreement No. 1852977) (USA);
2327 NASA Orbiting Carbon Observatory Science Team Program (grant no. NNH23ZDA001N-OCOST)
2328 (USA); NASA Carbon Monitoring System (grant no.NNH22ZDA001N-CMS ) (USA); Work of J.L. and
2329 S.P. was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under a contract
2330 with the National Aeronautics and Space Administration (80NM0018D0004) (USA); NOAA
2331 (Cooperative Institute for Marine, Earth and Atmospheric Systems, Award No. NA25NMFX432C0003-
2332 T1-01) (USA); NSF (OPP-2329254) (USA); ORNL is managed by UT-Battelle, LLC, for the DOE (grant
2333 no. DE-AC05-1008 00OR22725) (USA); Schmidt Sciences (VESRI CALIPSO) (USA); Schmidt
2334 Sciences (OBVI InMOS) (USA); NSF (OPP-1922922) (USA); NOAA grant NA24NESX432C0001
2335 (Cooperative Institute for Satellite Earth System Studies - CISESS) (USA); NOAA cooperative
2336 agreement NA22OAR4320151 (CIRES: Andrew Jacobson and Xin Lan) (USA); Schmidt Sciences
2337 (CLARITy project, part of the Virtual Institute for the Carbon Cycle) (USA ); Schmidt Sciences (COCO2
2338 project, part of the Virtual Institute for the Carbon Cycle) (USA ); NSF (LEAP STC Award no 2019625)
2339 (USA ); NASA (grant no. 80NSSC22K0150) (USA); NOAA (GOMO, grant no. NA24OARX431G0151-
2340 T1-01) (USA).
2341
<figure><img src="image_71.png" /></figure>

2342 The US National Science Foundation (grant nos. OPP-1922922, OPP-2329254), a NOAA cooperative agreement, the Cooperative Institute for Marine, Earth and Atmospheric Systems (CIMEAS, grant nos. NA20OAR4320278-T1-01 and NA25NMFX432C0003-T3-01S022); although NSF grants 1922922 and 2329254 and NOAA grants NA20OAR4320278-T1-01 and NA25NMFX432C0003-T3-01S022 have been identified for conflict of interest management based on the overall scope of the project and its potential benefit to the Keeling Curve Foundation, the research findings included in this particular publication may not necessarily relate to the interests of Keeling Curve Foundation. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

2351 We also acknowledge support from the following computing facilities: Australian Community Climate and Earth System Simulator - National Research Infrastructure (ACCESS-NRI) (Australia); National Computational Infrastructure (NCI) (Australia); High-Performance Computing Center (HPCC) of Nanjing University for doing the inversions on its blade cluster system (China); HPC computing and storage resources by GENCI at IDRIS and TGCC thanks to the grant 2024-2201 on the supercomputers Jean Zay's V100 and Joliot Curie's SKL and V100 partitions. (France); The ORCHIDEE simulations were performed using HPC resources from GENCi-TGCC on grant 2024-00006328 (France); IPSL Climate Modelling Centre (https://mesocentre.ipsl.fr) (France); HPC resources from GENCI - TGCC (Grant A005-017403) (France); Deutsches Klimarechenzentrum (DKRZ) (Projects: bm0891, bm1124 and bg1446) (Germany); NIES Supercomputer system, GOSAT Supercomputing Facility (GOCF) (Japan); NEC SX-Aurora TSUBASA at NIES (Japan); Fugaku provided by the RIKEN Center for Computational Science (Project ID: hp250024) (Japan); JAMSTEC's ES4 Super Computer system (Japan); Sigma2 - the National Infrastructure for High-Performance Computing and Data Storage in Norway (Norway); NICIS Centre for High-Performance Computing (South Africa); UK CEDA JASMIN supercomputer (UK); Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory (USA); LEAP Pangeo cloud platform (USA); NASA Ames Supercomputers (USA).
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