George Hurtt

· Professor, Distinguished Scholar-TeacherVerified

University of Maryland, College Park · Geography

Active 1994–2026

h-index77

Citations59.1k

Papers36969 last 5y

Funding—

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About

Professor George Hurtt received his Ph.D. from Princeton University in 1997. He has held academic and research positions at the University of New Hampshire, where he worked from 1998 to 2010, serving as Chair of the Natural Resources and Earth System Science Ph.D. Program and Director of the Complex Systems Research Center. In 2010, he joined the University of Maryland Department of Geography as Professor and Research Director, and in 2011 he was named Associate Director of the Joint Global Change Research Institute and the National Socio-Environmental Synthesis Center (SESYNC). Dr. Hurtt's research focuses on the structure, function, and dynamics of ecological systems within the context of global change. His primary approach combines mathematics and data to develop models for scientific understanding and prediction, particularly in areas such as the global carbon cycle, climate change, biodiversity, land-use practices, terrestrial carbon sequestration, and ecosystem services. He is a founding Science Team Leader of the NASA Carbon Monitoring System and a member of the Mission Team for NASA's Global Ecosystem Dynamics Investigation. Dr. Hurtt has contributed to numerous national and international scientific assessments, including the IPCC and the Millennium Ecosystem Assessment, and has authored over 100 peer-reviewed publications. His work includes developing and applying mathematical models to address sustainability, natural disasters, and interactions between the biosphere, hydrosphere, atmosphere, and society.

Research topics

Environmental science
Ecology
Geography
Geology
Climatology
Biology
Computer Science
Remote sensing
Astronomy
Optics
Physical geography
Physics
Environmental resource management
Algorithm
Atmospheric sciences
Geodesy
Oceanography

Selected publications

Reply on CC1
2026-02-04
peer-reviewOpen access
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 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 statistics and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use and land-use change data. Atmospheric CO2 concentration is measured at surface stations, and the global atmospheric CO2 growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. 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), 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 +1.1 % (0.2 % to 2.2 %) globally, and atmospheric CO2 concentration increasing by 2.3 ppm reaching 425.7 ppm, 52 % 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 <a href="https://doi.org/10.18160/GCP-2025" target="_blank" rel="noopener">https://doi.org/10.18160/GCP-2025</a> (Friedlingstein et al., 2025c).
Publisher DOI
Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models
Geoscientific model development · 2026-03-25
articleOpen access
Abstract. Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.
Publisher OA PDF DOI
Comment on essd-2025-659
2026-02-02
peer-reviewOpen access
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 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 statistics and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use and land-use change data. Atmospheric CO2 concentration is measured at surface stations, and the global atmospheric CO2 growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. 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), 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 +1.1 % (0.2 % to 2.2 %) globally, and atmospheric CO2 concentration increasing by 2.3 ppm reaching 425.7 ppm, 52 % 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 <a href="https://doi.org/10.18160/GCP-2025" target="_blank" rel="noopener">https://doi.org/10.18160/GCP-2025</a> (Friedlingstein et al., 2025c).
Publisher DOI
Remote Sensing–Derived Forest Regrowth Canopy Height, Biomass, and Age Across the Americas
Zenodo (CERN European Organization for Nuclear Research) · 2026-01-30
datasetOpen accessSenior author
OverviewThis dataset provides spatially explicit observations of secondary forest regrowth derived from the integration of remote sensing–based canopy height, aboveground biomass (AGB), and regrowth age. The dataset is designed to support analyses of forest structural and biomass recovery chronosequences, including comparisons between natural regeneration and commercial plantations. Each record represents a spatially joined point where canopy height (or AGB) observations are matched with regrowth age and forest type. Spatial CoverageThe dataset covers three broad regions: the Contiguous United States, Central America, and South America (southward to approximately 50°S). Forest Type StratificationEach data point is classified as either natural regeneration or commercial plantation. Forest type classification is based on spatial overlays with remote sensing–derived plantation maps (Fagan et al., 2022; Richter et al., 2024). Canopy Height Data SourcesCanopy height is derived from multiple sources: 1) GEDI Level 2A (GEDI_L2A v002; April 2019–March 2023; 2) global canopy height map from Potapov et al. 2021 (GLAD_HT); 3) global canopy height map from Lang et al. 2023 (ETH_HT). For GEDI L2A, only shots meeting conservative quality thresholds were retained, including quality_flag = 1, beam sensitivity greater than 0.95, and canopy height between 5 and 60 m. Aboveground Biomass (AGB) Data SourcesAGB are derived from GEDI Level 4A products (GEDI_L4A, version 1; April 2019–March 2023) and the ESA Climate Change Initiative (ESA_CCI) AGB map for 2021. For GEDI L4A, only shots meeting conservative quality thresholds were retained, including quality_flag = 1, l4a_quality_flag = 1, beam sensitivity greater than 0.95, and AGB values between 20 and 1000 Mg ha⁻¹. Regrowth Age EstimationForest regrowth age is calculated as the number of years since the most recent transition from non-forest to forest, inferred from Landsat-based land-cover change products spanning 1985–2021. Region-specific data sources include the National Land Cover Database (NLCD) for the United States (1985–2021; Sohl et al., 2025), the Tropical Moist Forests (TMF) dataset for Central America (1990–2021; Vancutsem et al., 2021), and MapBiomas national datasets for South America, including Bolivia, Brazil, Colombia, Ecuador, Paraguay, Uruguay, and Venezuela (1985–2021), Argentina (1998–2021), and Chile (2000–2021) (Souza et al., 2020). Spatial Overlay and Data IntegrationFor each region, GEDI footprints or wall-to-wall raster grids were spatially overlaid with regrowth-age maps and remote sensing–derived plantation maps. This integration assigns each observation a regrowth age and a forest-type classification, distinguishing between natural regeneration and commercial plantations. File Organization and FormatSix compressed (.zip) files are provided, containing canopy height and aboveground biomass (AGB) data for each of the three regions. The dataset is organized to facilitate regional and source-specific analyses while remaining compatible with common geospatial and statistical workflows. File StructureWithin each archive, data are stored as comma-separated values (CSV) files and spatially tiled into 10° × 10° geographic tiles. File names follow the convention {region}_{age_source}_{variable_source}_{tile}.csv, which encodes the geographic region, the source of regrowth age information, and the source of the canopy height or AGB data. CSV ContentsEach CSV file contains point-level observations with geographic coordinates recorded as latitude and longitude in decimal degrees. Associated attributes include regrowth age (in years) and indicator fields identifying whether each observation corresponds to natural regeneration or a commercial plantation. Intended UseThis dataset is intended to support analyses of forest structural and biomass recovery, including the derivation of canopy height and AGB chronosequences, comparisons between natural regeneration and plantations, and benchmarking of ecosystem and carbon-cycle models across broad spatial scales. Contact For questions or additional information about this dataset, please contact Lei Ma at leima578542312@gmail.com.
Publisher DOI
Reply on CC1
2026-02-04
peer-reviewOpen access
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 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 statistics and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use and land-use change data. Atmospheric CO2 concentration is measured at surface stations, and the global atmospheric CO2 growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. 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), 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 +1.1 % (0.2 % to 2.2 %) globally, and atmospheric CO2 concentration increasing by 2.3 ppm reaching 425.7 ppm, 52 % 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 <a href="https://doi.org/10.18160/GCP-2025" target="_blank" rel="noopener">https://doi.org/10.18160/GCP-2025</a> (Friedlingstein et al., 2025c).
Publisher DOI
Next-Generation Harmonized Land-Use Forcing Datasets for Global Carbon and Climate Models
2026-03-14
articleOpen accessCorresponding
Land-use change is an essential forcing dataset for climate and carbon cycle models, prescribing both the biogeophysical boundary conditions of the land surface as well as the land-based carbon sinks and sources. These datasets are built upon model requirements of a consistent set of variables and formats throughout the historical period as well as into future scenarios. Over the past two decades, both the ability of carbon and climate models to simulate land-use change, and the land-use datasets themselves, have advanced from relatively simple representations of 4 land-use types and their related transitions, to datasets that represent 13 land-use types, their transitions, as well as multiple data layers describing the detailed management of those land-use types. The Land-Use Harmonization (LUH) dataset has been used in both CMIP5 and CMIP6 experiments, as well as over 10 Global Carbon Budgets (GCBs), ISIMIP, IPBES, and will be used again in CMIP7 with several new features and new future scenarios, all provided at a resolution of 0.25 degrees for the years 850-2100 and beyond. In this presentation we will provide an overview of this data product, including recent updates to the historical dataset developed for GCB and comparisons with previous versions. We will present details of the 7 new harmonized future land-use scenarios developed for ScenarioMIP, including new variables used to model land-based Carbon Dioxide Removal (CDR) technologies such as BECCS and Re/Afforestation. Finally we will discuss our plans for new land-use datasets within the “Combining LAnd-use, modeling and Remote-sensing to Transform carbon budgets” (CLARiTy) project, which seeks to reduce the persistently high uncertainties in land carbon flux estimates and will include the development of new LUH products built upon high resolution remote sensing data to inform historical forest disturbances and areas of forest plantations.
Publisher DOI
Digi-Sylva Dataset
Harvard Dataverse · 2026-04-24
datasetOpen access
Digi-Sylva: a plot-level dataset of forest attributes integrating field and airborne laser scanning measurements across the boreal-temperate ecotone of the northeastern United States
Publisher DOI
Comment on essd-2025-659
2026-02-27
peer-reviewOpen access
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 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 statistics and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use and land-use change data. Atmospheric CO2 concentration is measured at surface stations, and the global atmospheric CO2 growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. 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), 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 +1.1 % (0.2 % to 2.2 %) globally, and atmospheric CO2 concentration increasing by 2.3 ppm reaching 425.7 ppm, 52 % 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 <a href="https://doi.org/10.18160/GCP-2025" target="_blank" rel="noopener">https://doi.org/10.18160/GCP-2025</a> (Friedlingstein et al., 2025c).
Publisher DOI
Remote Sensing–Derived Forest Regrowth Canopy Height, Biomass, and Age Across the Americas
Open MIND · 2026-01-30
datasetSenior author
OverviewThis dataset provides spatially explicit observations of secondary forest regrowth derived from the integration of remote sensing–based canopy height, aboveground biomass (AGB), and regrowth age. The dataset is designed to support analyses of forest structural and biomass recovery chronosequences, including comparisons between natural regeneration and commercial plantations. Each record represents a spatially joined point where canopy height (or AGB) observations are matched with regrowth age and forest type. Spatial CoverageThe dataset covers three broad regions: the Contiguous United States, Central America, and South America (southward to approximately 50°S). Forest Type StratificationEach data point is classified as either natural regeneration or commercial plantation. Forest type classification is based on spatial overlays with remote sensing–derived plantation maps (Fagan et al., 2022; Richter et al., 2024). Canopy Height Data SourcesCanopy height is derived from multiple sources: 1) GEDI Level 2A (GEDI_L2A v002; April 2019–March 2023; 2) global canopy height map from Potapov et al. 2021 (GLAD_HT); 3) global canopy height map from Lang et al. 2023 (ETH_HT). For GEDI L2A, only shots meeting conservative quality thresholds were retained, including quality_flag = 1, beam sensitivity greater than 0.95, and canopy height between 5 and 60 m. Aboveground Biomass (AGB) Data SourcesAGB are derived from GEDI Level 4A products (GEDI_L4A, version 1; April 2019–March 2023) and the ESA Climate Change Initiative (ESA_CCI) AGB map for 2021. For GEDI L4A, only shots meeting conservative quality thresholds were retained, including quality_flag = 1, l4a_quality_flag = 1, beam sensitivity greater than 0.95, and AGB values between 20 and 1000 Mg ha⁻¹. Regrowth Age EstimationForest regrowth age is calculated as the number of years since the most recent transition from non-forest to forest, inferred from Landsat-based land-cover change products spanning 1985–2021. Region-specific data sources include the National Land Cover Database (NLCD) for the United States (1985–2021; Sohl et al., 2025), the Tropical Moist Forests (TMF) dataset for Central America (1990–2021; Vancutsem et al., 2021), and MapBiomas national datasets for South America, including Bolivia, Brazil, Colombia, Ecuador, Paraguay, Uruguay, and Venezuela (1985–2021), Argentina (1998–2021), and Chile (2000–2021) (Souza et al., 2020). Spatial Overlay and Data IntegrationFor each region, GEDI footprints or wall-to-wall raster grids were spatially overlaid with regrowth-age maps and remote sensing–derived plantation maps. This integration assigns each observation a regrowth age and a forest-type classification, distinguishing between natural regeneration and commercial plantations. File Organization and FormatSix compressed (.zip) files are provided, containing canopy height and aboveground biomass (AGB) data for each of the three regions. The dataset is organized to facilitate regional and source-specific analyses while remaining compatible with common geospatial and statistical workflows. File StructureWithin each archive, data are stored as comma-separated values (CSV) files and spatially tiled into 10° × 10° geographic tiles. File names follow the convention {region}_{age_source}_{variable_source}_{tile}.csv, which encodes the geographic region, the source of regrowth age information, and the source of the canopy height or AGB data. CSV ContentsEach CSV file contains point-level observations with geographic coordinates recorded as latitude and longitude in decimal degrees. Associated attributes include regrowth age (in years) and indicator fields identifying whether each observation corresponds to natural regeneration or a commercial plantation. Intended UseThis dataset is intended to support analyses of forest structural and biomass recovery, including the derivation of canopy height and AGB chronosequences, comparisons between natural regeneration and plantations, and benchmarking of ecosystem and carbon-cycle models across broad spatial scales. Contact For questions or additional information about this dataset, please contact Lei Ma at leima578542312@gmail.com.
DOI
Reply on CC2
2026-02-05
peer-reviewOpen access
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 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 statistics and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use and land-use change data. Atmospheric CO2 concentration is measured at surface stations, and the global atmospheric CO2 growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. 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), 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 +1.1 % (0.2 % to 2.2 %) globally, and atmospheric CO2 concentration increasing by 2.3 ppm reaching 425.7 ppm, 52 % 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 <a href="https://doi.org/10.18160/GCP-2025" target="_blank" rel="noopener">https://doi.org/10.18160/GCP-2025</a> (Friedlingstein et al., 2025c).
Publisher DOI

Frequent coauthors

Paul Leadley
141 shared
Henrique M. Pereira
Martin Luther University Halle-Wittenberg
124 shared
Louise Chini
University of Maryland, College Park
117 shared
Rob Alkemade
Wageningen University & Research
115 shared
Juan F. Fernández‐Manjarrés
Écologie, Systématique et Évolution
109 shared
Thierry Oberdorff
Centre de Recherche sur la Biodiversité et l'Environnement
106 shared
Jörn P. W. Scharlemann
University of Sussex
105 shared
Robert J. Scholes
105 shared

Labs

Global Ecology LabPI

Education

Ph.D., Geography
University of California, Santa Barbara
1992
M.S., Geography
University of California, Santa Barbara
1988
B.A., Geography
University of California, Santa Barbara
1985

Awards & honors

University of Maryland Distinguished Scholar-Teacher Award

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