Suchergebnisse

This is the final version. Available on open access from Frontiers Media via the DOI in this record ; Data Availability Statement: The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author. ; The two major Brazilian biomes, the Amazonia and the Cerrado (savanna), are increasingly exposed to fires. The Amazonian Forest is a fire sensitive ecosystem where fires are a typically rare disturbance while the Cerrado is naturally fire-dependent. Human activities, such as landscape fragmentation and land-use management, have modified the fire regime of the Cerrado and introduced fire into the Amazonian Forest. There is limited understanding of the role of landscape fragmentation on fire occurrence in the Amazonia and Cerrado biomes. Due to differences in vegetation structure, composition, and land use characteristics in each biome, we hypothesize that the emerging burned area (BA) patterns will result from biome-specific fire responses to fragmentation. The aim of this study was to test the general relationship between BA, landscape fragmentation, and agricultural land in the Amazonia and the Cerrado biomes. To estimate the trends and status of landscape fragmentation a Forest Area Density (FAD) index was calculated based on the MapBiomas land cover dataset for both biomes between 2002 and 2018. BA fraction was analyzed within native vegetation against the FAD and agricultural land fraction. Our results showed an increase in landscape fragmentation across 16% of Amazonia and 15% of Cerrado. We identified an opposite relationship between BA fraction, and landscape fragmentation and agricultural fraction contrasting the two biomes. For Amazonia, both landscape fragmentation and agricultural fraction increased BA fraction due to an increase of human ignition activities. For the Cerrado, on the other hand, an increase in landscape fragmentation and agricultural fraction caused a decrease in BA fraction within the native vegetation. For both biomes, we found that during drought years BA increases whilst the divergent trends driven by fragmentation in the two contrasting global biomes is maintained. This understanding will be critical to informing the representation of fire dynamics in fire-enable Dynamic Global Vegetation Models and Earth System Models for climate projection and future ecosystem service provision. ; Newton Fund ; ESA Climate Change Initiative ; European Union Horizon 2020 ; Natural Environment Research Council (NERC)

This is the final version. Available on open access from EGU via the DOI in this record ; Data availability. The JULES model can be downloaded from the Met Office Science Repository Service (https://code.metoffice.gov. uk/trac/jules, last access: 11 September 2017 – see here for a helpful how-to http://jules.jchmr.org/content/getting-started, last access: 11 September 2017). Model output data presented in this paper and the exact version of JULES with name lists are available upon request from the corresponding author. ; The capacity of the terrestrial biosphere to sequester carbon and mitigate climate change is governed by the ability of vegetation to remove emissions of CO2 through photosynthesis. Tropospheric O3, a globally abundant and potent greenhouse gas, is, however, known to damage plants, causing reductions in primary productivity. Despite emission control policies across Europe, background concentrations of tropospheric O3 have risen significantly over the last decades due to hemispheric-scale increases in O3 and its precursors. Therefore, plants are exposed to increasing background concentrations, at levels currently causing chronic damage. Studying the impact of O3 on European vegetation at the regional scale is important for gaining greater understanding of the impact of O3 on the land carbon sink at large spatial scales. In this work we take a regional approach and update the JULES land surface model using new measurements specifically for European vegetation. Given the importance of stomatal conductance in determining the flux of O3 into plants, we implement an alternative stomatal closure parameterisation and account for diurnal variations in O3 concentration in our simulations. We conduct our analysis specifically for the European region to quantify the impact of the interactive effects of tropospheric O3 and CO2 on gross primary productivity (GPP) and land carbon storage across Europe. A factorial set of model experiments showed that tropospheric O3 can suppress terrestrial carbon uptake across Europe over the period 1901 to 2050. By 2050, simulated GPP was reduced by 4 to 9 % due to plant O3 damage and land carbon storage was reduced by 3 to 7 %. The combined physiological effects of elevated future CO2 (acting to reduce stomatal opening) and reductions in O3 concentrations resulted in reduced O3 damage in the future. This alleviation of O3 damage by CO2-induced stomatal closure was around 1 to 2 % for both land carbon and GPP, depending on plant sensitivity to O3. Reduced land carbon storage resulted from diminished soil carbon stocks consistent with the reduction in GPP. Regional variations are identified with larger impacts shown for temperate Europe (GPP reduced by 10 to 20 %) compared to boreal regions (GPP reduced by 2 to 8 %). These results highlight that O3 damage needs to be considered when predicting GPP and land carbon, and that the effects of O3 on plant physiology need to be considered in regional land carbon cycle assessments. ; Rebecca J. Oliver and Lina M. Mercado were supported by the EU FP7 (ECLAIRE, 282910) and JWCRP (UKESM, NEC05816). This work was also supported by EMEP under UNECE. Stephen Sitch and Lina M. Mercado acknowledge the support of the NERC SAMBBA project (NE/J010057/1). The UK Met Office contribution was funded by BEIS under the Hadley Centre Climate Programme (GA01101). Gerd A. Folberth also acknowledges funding from the EU's Horizon 2020 research and innovation programme (CRESCENDO, 641816).

This is the final version. Available on open access from IOP Publishing via the DOI in this record ; Data availability statement: The data that support the findings of this study are openly available at the following URL/DOI:10.5281/zenodo.5658676 ; Current estimates of the global land carbon sink contain substantial uncertainties on interannual timescales which contribute to a non-closure in the global carbon budget (GCB) in any given year. This budget imbalance (BIM) partly arises due to the use of imperfect models which are missing or misrepresenting processes. One such omission is the separate treatment of downward direct and diffuse solar radiation on photosynthesis. Here we evaluate and use an improved high-resolution (6-hourly), gridded dataset of surface solar diffuse and direct fluxes, over 1901-2017, constrained by satellite and ground-level observations, to drive two global land models. Results show that tropospheric aerosol-light interactions have the potential for substantial land carbon impacts (up to 0.4 PgCyr-1 enhanced sink) at decadal timescales, however large uncertainties remain, with models disagreeing on the direction of change in carbon uptake. On interannual timescales, results also show an enhancement of the land carbon sink (up to 0.9 PgCyr-1) and subsequent reduction in BIM by 55% in years following volcanic eruptions. We therefore suggest GCB assessments include this dataset in order to improve land carbon sink estimates. ; European Union Horizon 2020 ; Newton Fund

This is the author accepted manuscript. The final version is available from European Geosciences Union (EGU) via the DOI in this record. ; Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in terrestrial biosphere models (TBMs). The published results of these models are used in major assessments of processes and impacts of global environmental change, such as the reports of the Intergovernmental Panel on Climate Change (IPCC). Fully coupled models of climate, land use and biogeochemical cycles to explore land use-climate interactions across spatial scales are currently not available. Instead, information on land use is provided as exogenous data from the land-use change modules of integrated assessment models (IAMs) to TBMs. In this article, we discuss, based on literature review and illustrative analysis of empirical and modeled LULCC data, three major challenges of this current LULCC representation and their implications for land use-climate interaction studies: (I) provision of consistent, harmonized, land-use time series spanning from historical reconstructions to future projections while accounting for uncertainties associated with different land-use modeling approaches, (II) accounting for sub-grid processes and bidirectional changes (gross changes) across spatial scales, and (III) the allocation strategy of independent land-use data at the grid cell level in TBMs. We discuss the factors that hamper the development of improved land-use representation, which sufficiently accounts for uncertainties in the land-use modeling process. We propose that LULCC data-provider and user communities should engage in the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories, to improve the representation of land use in TBMs. Moreover, we suggest concentrating on the development of integrated modeling frameworks that may provide further understanding of possible land-climate-society feedbacks. ; The research in this paper has been supported by the European Research Council under the European Union's Seventh Framework Programme project LUC4C (Grant No. 603542), ERC grant GLOLAND (No. 311819) and BiodivERsA project TALE (No. 832.14.006) funded by the Dutch National Science Foundation (NWO). This research contributes to the Global Land Project (www.globallandproject.org). This is paper number 26 of the Birmingham Institute of Forest Research.

This is the final version. Available on open access from Copernicus Publications via the DOI in this record ; Code availability; The source code used to produce the core LUH2 datasets and the LUH2-GCB datasets, along with the sources and citations of necessary inputs, are archived at https://doi.org/10.5281/zenodo.3954113 (Chini et al., 2020a). ; Data availability: The data produced in this study are archived and publicly available at the NASA Oak Ridge National Laboratory Distributed Active Archive Center: https://doi.org/10.3334/ORNLDAAC/1851 (Chini et al, 2020b). ; Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-The-Art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100g000gkm2) and in the year 2009 for cropland (difference of 77g000gkm2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies. ; NASA ; US Department of Energy, Office of Science, Office of Biological and Environmental Research ; European Union Horizon 2020

This is the final version. Available on open access from AGU via the DOI in this record ; The dataset associated with this article is located in ORE at: https://doi.org/10.24378/exe.2883 ; Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation-based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light-use efficiency model (RS-LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP amongst the products, with exceptions in tropical and high northern latitudes. On longer timescales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture – atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasise the need for more direct long-term observations of GPP along with an extension of in-situ networks in underrepresented regions (e.g. tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP. ; European Union ; European Space Agency ; Natural Environment Research Council (NERC) ; NASA

This is the final version. Available on open access from the European Geosciences Union via the DOI in this record ; Code and data availability. The benchmarking code is archived at https://zenodo.org/record/3879161#.Xtq-py-z2fU (last access: 5 June 2020) (https://doi.org/10.5281/zenodo.3879161, Kelley, 2020), which also contains the code to produce the figures presented here. The FireMIP model output is archived at https://zenodo.org/record/3555562#.Xell3C2ZOcY (last access: 22 November 2019) (https://doi.org/10.5281/zenodo.3555562, Hantson et al., 2019). Data availability for each reference dataset is provided in Table A1. ; Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. The two older fire models included in the FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less well globally than other models, but it is difficult to distinguish between the remaining ensemble members; some of these models are better at representing certain aspects of the fire regime; none clearly outperforms all other models across the full range of variables assessed. ; European Union FP7 ; German Federal Ministry of Education and Research (BMBF) ; European Research Council (ERC) ; Natural Environment Research Council (NERC) ; Deutsche Forschungsgemeinschaft (DFG) ; National Natural Science Foundation of China ; Strategic Research Area MERGE (ModElling the Regional and Global Earth system) ; Lund University Centre for Studies of Carbon Cycle and Climate Interactions (LUCCI)

This is the final version. Available on open access from the American Association for the Advancement of Science via the DOI in this record ; In summer 2018, central and northern Europe were stricken by extreme drought and heat (DH2018). The DH2018 differed from previous events in being preceded by extreme spring warming and brightening, but moderate rainfall deficits, yet registering the fastest transition between wet winter conditions and extreme summer drought. Using 11 vegetation models, we show that spring conditions promoted increased vegetation growth, which, in turn, contributed to fast soil moisture depletion, amplifying the summer drought. We find regional asymmetries in summer ecosystem carbon fluxes: Increased (reduced) sink in the northern (southern) areas affected by drought. These asymmetries can be explained by distinct legacy effects of spring growth and of water-use efficiency dynamics mediated by vegetation composition, rather than by distinct ecosystem responses to summer heat/drought. The asymmetries in carbon and water exchanges during spring and summer 2018 suggest that future land-management strategies could influence patterns of summer heat waves and droughts under long-term warming. ; European Union Horizon 2020 ; SNSF ; Helmholtz Association ; US Department of Energy

This is the final version. Available on open access from Frontiers media via the DOI in this record ; Data Availability Statement; The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The TRENDY-v8 ensemble of simulation outputs is available upon request at https://sites.exeter.ac.uk/trendy. The PML-v2 product script is available online from https://github.com/gee-hydro/gee_PML. The SMOS-IC V2 L-VOD product was provided by Jean-Pierre Wigneron. Processing code is available at https://doi.org/10.5281/zenodo.5511724. Google Earth Engine Repository is available at: https://earthengine.googlesource.com/users/dfawcett/DRIVING_C_RS_publication (requires Google account to access). ; Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson's r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson's r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle. ; Natural Environment Research Council (NERC) ; ESA ; ANR CLAND Convergence Institute ; European Union Horizon 2020

This is the author accepted manuscript. The final version is available from EGU via the DOI in this record. ; The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model. ; S. Rabin was supported by a National Science Foundation Graduate Research Fellowship and by the Carbon Mitigation Initiative, and along with S. Hantson and A. Arneth would like to acknowledge support by the EU FP7 projects BACCHUS (grant agreement no. 603445) and LUC4C (grant agreement no. 603542). This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF), through the Helmholtz Association and its research programme ATMO, and the HGF Impulse and Networking 5 fund. F. Li was funded by the National Natural Science Foundation of China under Grant No. 41475099 and the CAS Youth Innovation Promotion Association Fellowship. The UK Met Office contribution was funded by BEIS under the Hadley Centre Climate Programme contract (GA01101). G. A. Folberth also wishes to acknowledge funding received from the European Union's Horizon 2020 research and innovation programme under grant agreement No 641816 (CRESCENDO). J. O. Kaplan was supported by the European Research Council (COEVOLVE, 313797).

Open access journal ; This is the final version of the article. Available from Copernicus Publications via the DOI in this record. ; Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002-2011), EFF was 8.3 ± 0.4 PgC yr-1, ELUC 1.0 ± 0.5 PgC yr-1, GATM 4.3 ± 0.1PgC yr-1, SOCEAN 2.5 ± 0.5 PgC yr-1, and SLAND 2.6 ± 0.8 PgC yr-1. For year 2011 alone, EFF was 9.5 ± 0.5 PgC yr -1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 ± 0.5 PgC yr-1, approximately constant throughout the decade; GATM was 3.6 ± 0.2 PgC yr-1, SOCEAN was 2.7 ± 0.5 PgC yr-1, and SLAND was 4.1 ± 0.9 PgC yr-1. GATM was low in 2011 compared to the 2002-2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9-3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future. © 2013 Author(s). ; We thank all people and institutions who provided data used in this carbon budget, in particular, G. Hurt, L. Chini, and I. Harris. The observations and modelling analysis were possible thanks to funding from multiple agencies around the world. The UK Natural Environment Research Council provided funding to CLQ and the GCP though their International Opportunities Fund specifically to support this publication (project NE/103002X/1). CLQ, PC, SZ, and JS thank the EU FP7 for funding through projects GEOCarbon (283080), COMBINE (226520) and CARBOCHANGE (264879). GPP and RMA acknowledge support from the Norwegian Research Council (221355/E10). SCD acknowledges support from the US National Science Foundation (NSF AGS-1048827). JH was supported by a Leverhulme Research Fellowship and the Cabot Institute, University of Bristol. RJA and TAB were sponsored by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programs and performed at Oak Ridge National Laboratory (ORNL) under US Department of Energy contract DE-AC05-00OR22725. CH was supported by the Centre for Ecology and Hydrology "Science Budget". EK was supported by the Global Environment Research Fund (S-10) of the Ministry of Environment of Japan. GrvdW was supported by the European Research Council. BDS was supported by the Swiss National Science Foundation. AA acknowledges the Mistra-SWECIA programme and the strategic research areas MERGE, BECC and LUCCI. AKJ is funded by the NASA LCLUC Program (No. NNX08AK75G) and the Office of Science (BER), US Department of Energy (DOE-DE-SC0006706).

Journal Article ; Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil-fuel combustion and cement production (EFF) are based on energy statistics, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (G ATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2 and land cover change (some including nitrogen-carbon interactions). All uncertainties are reported as ±1σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2003-2012), EFF was 8.6±0.4 GtC yr-1, ELUC 0.9±0.5 GtC yr-1, GATM 4.3±0.1 GtC yr-1, SOCEAN 2.5±0.5 GtC yr -1, and SLAND 2.8±0.8 GtC yr-1. For year 2012 alone, EFF grew to 9.7±0.5 GtC yr-1, 2.2% above 2011, reflecting a continued growing trend in these emissions, G ATM was 5.1±0.2 GtC yr-1, SOCEAN was 2.9±0.5 GtC yr-1, and assuming an ELUC of 1.0±0.5 GtC yr-1 (based on the 2001-2010 average), S LAND was 2.7±0.9 GtC yr-1. GATM was high in 2012 compared to the 2003-2012 average, almost entirely reflecting the high EFF. The global atmospheric CO2 concentration reached 392.52±0.10 ppm averaged over 2012. We estimate that EFF will increase by 2.1% (1.1- 3.1 %) to 9.9±0.5 GtC in 2013, 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy.With this projection, cumulative emissions ofCO2 will reach about 535±55 GtC for 1870-2013, about 70% from EFF (390±20 GtC) and 30% from ELUC (145±50 GtC). This paper also documents any changes in the methods and data sets used in this new carbon budget from previous budgets (Le Quéré et al., 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP-2013-V2.3). © 2014 Author(s) CC Attribution 3.0 License. ; International Ocean Carbon Coordination Project (IOCCP) ; Surface Ocean Lower Atmosphere Study (SOLAS) ; Integrated Marine Biogeochemistry and Ecosystem Research program (IMBER) ; NERC ; International Opportunities Fund ; US Department of Energy, Office of Science, Biological and Environmental Research (BER) ; Norwegian Research Council ; EU FP7 for funding through projects GEOCarbon, COMBINE, CARBOCHANGE, EMBRACE, and LUC4C ; US National Science Foundation ; NASA LCLUC program ; Swiss National Science Foundation ; Joint UK DECC/Defra Met Office Hadley Centre Climate Programme ; Environment Research and Technology Development Fund (S-10) of the Ministry of Environment of Japan ; Australian Climate Change Science Program ; Leverhulme Research Fellowship

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere - the "global carbon budget" - is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E-FF) 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) and terrestrial CO2 sink (S-LAND) are estimated with global process models constrained by observations. 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 +/- 1 sigma. For the last decade available (2009-2018), E-FF was 9.5 +/- 0.5 GtC yr 1, E-LUC 1.5 +/- 0.7 GtC yr 1, G(ATM) 4.9 +/- 0.02 GtC yr(-1) (2.3 +/- 0.01 ppm yr(-1)), S-OCEAN 2.5 +/- 0.6 GtC yr(-1), and S-LAND 3.2 +/- 0.6 GtC yr(-1), with a budget imbalance B-IM of 0.4 GtC yr(-1) indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E-FF was about 2.1% and fossil emissions increased to 10.0 +/- 0.5 GtC yr 1, reaching 10 GtC yr(-1) for the first time in history, E-LUC was 1.5 +/- 0.7 GtC yr(-1), for total anthropogenic CO2 emissions of 11.5 +/- 0.9 GtC yr(-1) (42.5 +/- 3.3 GtCO(2)). Also for 2018, G(ATM) was 5.1 +/- 0.2 GtC yr(-1) (2.4 +/- 0.1 ppm yr(-1)), S-OCEAN was 2.6 +/- 0.6 GtC yr(-1), and S-LAND was 3.5 +/- 0.7 GtC yr(-1), with a B-IM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38 +/- 0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6-10 months indicate a reduced growth in E-FF of +0.6% (range of -0.2% to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959-2018, but discrepancies of up to 1 GtC yr(-1) persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. 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 (Le Quere et al., 2018a, b, 2016, 2015a, b, 2014, 2013).

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use and 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) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. 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 last decade available (2008–2017), EFF was 9.4±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.7±0.02 GtC yr−1, SOCEAN 2.4±0.5 GtC yr−1, and SLAND 3.2±0.8 GtC yr−1, with a budget imbalance BIM of 0.5 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr−1. Also for 2017, ELUC was 1.4±0.7 GtC yr−1, GATM was 4.6±0.2 GtC yr−1, SOCEAN was 2.5±0.5 GtC yr−1, and SLAND was 3.8±0.8 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models, originating outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018.