Search

Climatologies, which depict mean fields of oceanographic variables on a regular geographic grid, and atlases, which provide graphical depictions of specific areas, play pivotal roles in comprehending the societal vulnerabilities linked to ocean acidification (OA). This significance is particularly pronounced in coastal regions where most economic activities, such as commercial and recreational fisheries and aquaculture industries, occur. In this paper, we unveil a comprehensive data product featuring coastal ocean acidification climatologies and atlases, encompassing the fugacity of carbon dioxide, pH on the total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle factor, total dissolved inorganic carbon content, and total alkalinity content. These variables are provided on 1° × 1° spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 m, along the North American ocean margins, defined as the region between the coastline and a distance of 200 nautical miles (∼370 km) offshore. The climatologies and atlases were developed using the World Ocean Atlas (WOA) gridding methods of the NOAA National Centers for Environmental Information (NCEI) based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA), along with the 2021 update to the Global Ocean Data Analysis Project version 2 (GLODAPv2.2021) data product. The relevant variables were adjusted to the index year of 2010. The data product is available in NetCDF (https://doi.org/10.25921/g8pb-zy76 , Jiang et al., 2022b) on the NOAA Ocean Carbon and Acidification Data System: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0270962.html (last access: 15 July 2024). It is recommended to use the objectively analyzed mean fields (with "_an" suffix) for each variable. The atlases can be accessed at https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/nacoastal.html (last access: 15 July 2024). [ABSTRACT FROM AUTHOR]

As the vegetation in the Arctic changes, tundra ecosystems along the southern border of the Arctic are becoming greener and gradually giving way to boreal ecosystems. This change is affecting local populations, wildlife, energy exchange processes between environmental compartments, and the carbon cycle. To understand the progression and the implications of this change in vegetation, satellite measurements and surface models can be employed. However, in situ observational data are required to validate these measurements and models. This paper presents observational data from two nearby sites in the forest–tundra ecotone in the Tasiapik Valley near Umiujaq in Northern Quebec, Canada. One site is on a mixture of lichen and shrub tundra. The associated data set comprises 9 years of meteorological, soil and snow data as well as 3 years of eddy covariance data. The other site, 850 m away, features vegetation consisting mostly of tall shrubs and black spruce. For that location, 6 years of meteorological, soil and snow data are available. In addition to the data from the automated stations, profiles of snow density and specific surface area were collected during field campaigns. The data are available at 10.1594/PANGAEA.964743 (Domine et al., 2024). [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero greenhouse gas (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and gross domestic product (GDP) make this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2 O) in Africa over 3 decades (1990–2018). We compare bottom-up (BU) approaches, including United Nations Convention Framework on Climate Change (UNFCCC) national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO 2 fluxes in the land use, land use change and forestry (LULUCF) sector and global atmospheric inversions. For inversions, we applied different methods to separate anthropogenic CH 4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO 2 emissions. With a mean of 1373 Mt CO 2 yr -1 , total African fossil CO 2 emissions over 2010–2018 represent only 4 % of global fossil emissions. However, these emissions grew by +34% from 1990–1999 to 2000–2009 and by +31% from 2000–2009 to 2010–2018, which represents more than a doubling in 30 years. This growth rate is more than 2 times faster than the global growth rate of fossil CO 2 emissions. The anthropogenic emissions of CH 4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N 2 O emissions grew by 19.5 % from 1990–1999 to 2000–2009 and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of the estimates from UNFCCC reports (including the land use sector) with corrections from outliers, Africa was a mean source of greenhouse gases of 262221863239 Mt CO 2 eq. yr -1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO 2 eq. yr -1 from top-down (TD) methods during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of TD estimates is larger than the one of the BU estimates, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty in BU estimates. The main source of uncertainty comes from CO 2 fluxes in the LULUCF sector, for which the spread across inversions is larger than 50 %, especially in central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022). The medians of CH 4 emissions from inversions based on satellite retrievals and surface station networks are consistent with each other within 2 % at the continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH 4 emissions with BU inventories such as PRIMAP-hist. For N 2 O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N 2 O sources cannot be separated accurately from anthropogenic ones in inversions or because BU estimates ignore indirect emissions and underestimate emission factors. Future improvements can be expected thanks to a denser network of monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows us to compute budgets thanks to recombinations of those data products. Our results allow us to understand uncertainty and trends in emissions and removals in a region of the world where few observations exist and where most inventories are based on default IPCC guideline values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to the figures are available at 10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [ABSTRACT FROM AUTHOR]

Aviation emission accounting is the key to establishing market measures to control aviation pollutant emissions. Based on the fuel percentage method (FPM), this paper applies the improved BFFM2-FOA-FPM (Boeing Fuel Flow Method 2–First Order Approximation FPM) to calculate the emissions of six pollutants (CO 2 , CO, HC, NO x , SO 2 , and PM 2.5) between Chinese and foreign cities from 2014 to 2019, including CCD (climbing, cruising, and descending) emissions and LTO (landing and take-off) emissions. The error rate between the calculated results and the official data is about 2.75 %. The results show that the emissions of six pollutants changed before and after the proposal of the "Carbon Neutral Growth 2020" strategy (CNG2020 strategy). Although the total amount has increased, the average emission per tonne-kilometer of CO 2 , CO, HC, NO x , SO 2 , and PM 2.5 has decreased by 17.77 %, 17.26 %, 25.15 %, 14.32 %, 17.77 %, and 16.35 %, respectively. The results of this paper can provide a data basis and method reference for implementing the CNG2020 strategy and realizing global carbon emission reduction goals. The dataset is available from https://doi.org/10.6084/m9.figshare.20071751.v1 (Cui., 2022). [ABSTRACT FROM AUTHOR]

A series of attenuated atmospheric backscatter profiles were measured at 1572 nm by the CO 2 Sounder lidar during the eight flights of the 2017 ASCENDS/ABoVE (Active Sensing of CO 2 Emission over Nights, Days, and Seasons, Arctic Boreal Vulnerability Experiment) airborne campaign. In addition to measuring the column average CO 2 mixing ratio from the laser signals reflected by the ground, the CO 2 Sounder lidar also recorded the height-resolved attenuated atmospheric backscatter profiles beneath the aircraft. We have recently processed these vertical profiles with a 15 m vertical spacing and 1 s integration time along the flight path (∼ 200 m) for all the 2017 flights and have posted the results at NASA Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics 10.3334/ORNLDAAC/2051 (Sun et al., 2022). This paper describes the measurement principles, the data processing technique, and the signal to noise ratios. [ABSTRACT FROM AUTHOR]

We describe the instrumentation, calibration, and uncertainty of the network of ground-based, in situ, cavity ring down spectroscopy (CRDS) greenhouse gas (GHG) measurements deployed in the Permian Basin. The primary goal of the network is to be used in conjunction with atmospheric transport modeling to determine methane emissions of the Delaware sub-basin of the Permian Basin oil and natural gas extraction area in Texas and New Mexico. Four of the measurements are based on tall communications towers, while one is on a building on a mountain ridge, with the recent addition of a small tower at that site. Although methane (CH4) is the primary species of interest, carbon dioxide (CO2), hydrogen sulfide (H2S), and the isotopic ratio of methane (δ13CH4) are also reported for a subset of the sites. Measurements were reported following the WMO X2004A scale for CH4 and the WMO X2019 scale for CO2. CRDS instruments were calibrated for CH4 and CO2 in the laboratory prior to deployment. For H2S , data were offset-corrected using the minimum 40 min running mean value of the day, and for δ13CH4 , calibrations were based on laboratory data. We describe the characteristics of the dataset with a set of illustrative analyses. Methane and carbon dioxide showed strong seasonality, with a well-defined diurnal cycle during the summer, which was opposed to the winter, when a diurnal cycle was absent. CH4 enhancements to the background, during the winter, are up to twice the summer values, which is attributed to the changes in boundary layer depth and wind speed. The largest CH4 enhancements occurred when winds blow from the center of the Delaware sub-basin, where most of the methane emissions come from. The magnitude of enhancements of CO2 did not present seasonality. H2S enhancements indicated a potential source northeast of the tower (Hobbs, New Mexico) where the inlet is installed. Isotopic ratios of methane indicated that oil and natural gas extraction is the source of local methane in the region. The hourly-averaged data, starting on 1 March 2020 and described in this paper, are archived at The Pennsylvania State University Data Commons at 10.26208/98y5-t941 (Monteiro et al., 2021). [ABSTRACT FROM AUTHOR]

Accurate assessment of the size and distribution of carbon dioxide (CO 2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO 2 (XCO 2) have been widely used to infer spatial and temporal variations in carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO 2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were - 2.22 ± 0.75 and - 2.32 ± 0.18 Pg C yr -1 , absorbing approximately 23 % and 24 % of contemporary fossil fuel CO 2 emissions, respectively. The annual mean global atmospheric CO 2 growth rate was 5.17 ± 0.68 Pg C yr -1 , which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 Pg C yr -1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO 2 simulations with Total Carbon Column Observing Network (TCCON) retrievals as well as Observation Package (ObsPack) surface flask observations and aircraft observations. Compared with CO 2 simulations using the unoptimized fluxes, the bias and root mean square error (RMSE) in posterior CO 2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO 2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at 10.5281/zenodo.8368846 (Jin et al., 2023a). [ABSTRACT FROM AUTHOR]

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO 2 e yr -1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here. [ABSTRACT FROM AUTHOR]

The late Taro Takahashi (Lamont-Doherty Earth Observatory (LDEO), Columbia University) and colleagues provided the first near-global monthly air–sea CO 2 flux climatology in Takahashi et al. (1997), based on available surface water partial pressure of CO 2 measurements. This product has been a benchmark for uptake of CO 2 in the ocean. Several versions have been provided since, with improvements in procedures and large increases in observations, culminating in the authoritative assessment in Takahashi et al. (2009a, b). Here we provide and document the last iteration using a greatly increased dataset (SOCATv2022) and determining fluxes using air–sea partial pressure differences as a climatological reference for the period 1980–2021 (Fay et al., 2023, 10.25921/295g-sn13). The resulting net flux for the open ocean region is estimated as -1.79±0.7 Pg C yr -1 , which compares well with other global mean flux estimates. While global flux results are consistent, differences in regional means and seasonal amplitudes are discussed. Consistent with other studies, we find the largest differences in the data-sparse southeast Pacific and Southern Ocean. [ABSTRACT FROM AUTHOR]

Since the beginning of the industrial revolution, atmospheric carbon dioxide (CO 2) concentrations have risen steadily and have induced a decrease of the averaged surface ocean pH by 0.1 units, corresponding to an increase in ocean acidity of about 30 %. In addition to ocean warming, ocean acidification poses a tremendous challenge to some marine organisms, especially calcifiers. The need for long-term oceanic observations of pH and temperature is a key element to assess the vulnerability of marine communities and ecosystems to these pressures. Nearshore productive environments, where a large majority of shellfish farming activities are conducted, are known to present pH levels as well as amplitudes of daily and seasonal variations that are much larger than those observed in the open ocean. Yet, to date, there are very few coastal observation sites where these parameters are measured simultaneously and at high frequency. To bridge this gap, an observation network was initiated in 2021 in the framework of the CocoriCO 2 project. Six sites were selected along the French Atlantic and Mediterranean coastlines based on their importance in terms of shellfish production and the presence of high- and low-frequency monitoring activities. At each site, autonomous pH sensors were deployed, both inside and outside shellfish production areas, next to high-frequency CTD (conductivity–temperature–depth) probes operated through two operating monitoring networks. pH sensors were set to an acquisition rate of 15 min, and discrete seawater samples were collected biweekly in order to control the quality of pH data (laboratory spectrophotometric measurements) as well as to measure total alkalinity and dissolved inorganic carbon concentrations for full characterization of the carbonate system. While this network has been up and running for more than 2 years, the acquired dataset has already revealed important differences in terms of pH variations between monitored sites related to the influence of diverse processes (freshwater inputs, tides, temperature, biological processes). Data are available at 10.17882/96982 (Petton et al., 2023a). [ABSTRACT FROM AUTHOR]

Ocean surface pCO 2 estimates are of great interest for the calculation of air–sea CO 2 fluxes, oceanic uptake of anthropogenic CO 2 , and eventually the Global Carbon Budget. They are accessible from direct observations, which are discrete in space and time and thus always sparse, or from biogeochemical models, which only approximate reality. Here, a combined method for the extrapolation of pCO 2 observations is presented that uses (1) model-based patterns of variability from an empirical orthogonal function (EOF) analysis of variability with (2) observational data to constrain EOF pattern amplitudes in (3) an ensemble approach, which locally adjusts the spatial scale of the mapping to the density of the observations. Thus, data-constrained, gap- and discontinuity-free mapped fields including local error estimates are obtained without the need for or dependence on ancillary data (e.g. satellite sea surface temperature maps). This extrapolation approach is generic in that it can be applied to any oceanic or coastal region covered by a suitable model and observations. It is used here to establish a regional pCO 2 climatology of the Baltic Sea (: 10.1594/PANGAEA.961119), largely based on ICOS-DE ship of opportunity (SOOP) Finnmaid surface pCO 2 observations between Lübeck-Travemünde (Germany) and Helsinki (Finland). The climatology can serve as improved input for atmosphere–ocean CO 2 flux estimation in this coastal environment. [ABSTRACT FROM AUTHOR]

Gridded bottom-up inventories of CO 2 emissions are needed in global CO 2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO 2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1 ∘ × 0.1 ∘) monthly emissions fluxes of CO 2 fossil fuel (CO 2 ff, long cycle) and CO 2 biofuel (CO 2 bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO 2 ff (36.7 Gt) and CO 2 bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389 , last access: 15 May 2023; Urraca Valle et al., 2023). [ABSTRACT FROM AUTHOR]

The reduction of CO 2 emissions and the enhancement of CO 2 removals related to land use are considered essential for future pathways towards net-zero emissions and mitigating climate change. With the growing pressure under global climate treaties, country-level land-use CO 2 flux data are becoming increasingly important. So far, country-level estimates are mainly available through official country reports, such as the greenhouse gas inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Recently, different modelling approaches, namely dynamic global vegetation models (DGVMs) and bookkeeping models, have moved to higher spatial resolutions, which makes it possible to obtain model-based country-level estimates that are globally consistent in their methodology. To progress towards a largely independent assessment of country reports using models, we analyse the robustness of country-level CO 2 flux estimates from different modelling approaches in the period 1950–2021 and compare them with estimates from country reports. Our results highlight the general ability of modelling approaches to estimate land-use CO 2 fluxes at the country level and at higher spatial resolution. Modelled land-use CO 2 flux estimates generally agree well, but the investigation of multiple DGVMs and bookkeeping models reveals that the robustness of their estimates strongly varies across countries, and substantial uncertainties remain, even for top emitters. Similarly, modelled land-use CO 2 flux estimates and country-report-based estimates agree reasonably well in many countries once their differing definitions are accounted for, although differences remain in some other countries. A separate analysis of CO 2 emissions and removals from land use using bookkeeping models also shows that historical peaks in net fluxes stem from emission peaks in most countries, whereas the long-term trends are more connected to removal dynamics. The ratio of the net flux to the sum of CO 2 emissions and removals from land use (the net-to-gross flux ratio) underlines the spatio-temporal heterogeneity in the drivers of net land-use CO 2 flux trends. In many tropical regions, net-to-gross flux ratios of about 50 % are due to much larger emissions than removals; in many temperate countries, ratios close to zero show that emissions and removals largely offset each other. Considering only the net flux thus potentially masks large emissions and removals and the different timescales upon which they act, particularly if averaged over countries or larger regions, highlighting the need for future studies to focus more on the gross fluxes. Data from this study are openly available via the Zenodo portal at 10.5281/zenodo.8144174. [ABSTRACT FROM AUTHOR]

We present a high-resolution global emission catalogue of CO 2 and co-emitted species (NO x , SO 2 , CO, CH 4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO 2 and emission ratios for co-emitted species (e.g. the amount of NO x emitted relative to CO 2 : NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO 2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO 2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO 2 Monitoring and Verification Support capacity (CO2MVS). [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from 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) is estimated with global ocean biogeochemistry models and observation-based f CO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (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 incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr -1 (10.2±0.5 Gt C yr -1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr -1 , for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr -1 (40.7±3.2 Gt CO2 yr -1). Also, for 2022, GATM was 4.6±0.2 Gt C yr -1 (2.18±0.1 ppm yr -1 ; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr -1 , and SLAND was 3.8±0.8 Gt C yr -1 , with a BIM of -0.1 Gt C yr -1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr -1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at 10.18160/GCP-2023 (Friedlingstein et al., 2023). [ABSTRACT FROM AUTHOR]

We present a 12-year time series of continuous atmospheric measurements of O 2 and CO 2 at the Weybourne Atmospheric Observatory in the United Kingdom. These measurements are combined into the term atmospheric potential oxygen (APO), a tracer that is invariant to terrestrial biosphere fluxes. The CO 2 , O 2 and APO datasets discussed are hourly averages between May 2010 and December 2021. We include details of our measurement system and calibration procedures, and describe the main long-term and seasonal features of the time series. The 2 min repeatability of the measurement system is approximately ±3 per meg for O 2 and approximately ±0.005 ppm for CO 2. The time series shows average long-term trends of 2.40 ppm yr -1 (2.38 to 2.42) for CO 2 , -24.0 per meg yr -1 for O 2 (-24.3 to -23.8) and -11.4 per meg yr -1 (-11.7 to -11.3) for APO, over the 12-year period. The average seasonal cycle peak-to-peak amplitudes are 16 ppm for CO 2 , 134 per meg for O 2 and 68 per meg for APO. The diurnal cycles of CO 2 and O 2 vary considerably between seasons. The datasets are publicly available at 10.18160/Z0GF-MCWH (Adcock et al., 2023) and have many current and potential scientific applications in constraining carbon cycle processes, such as investigating air–sea exchange of CO 2 and O 2 and top-down quantification of fossil fuel CO 2. [ABSTRACT FROM AUTHOR]

The main contributor to the greenhouse gas (GHG) footprint of the cement industry is the decomposition of alkaline carbonates during clinker production. However, systematic accounts for the reverse of this process – namely carbonation of calcium oxide and other alkaline oxides and/or hydroxides within cement materials during cements' life cycles – have only recently been undertaken. Here, adopting a comprehensive analytical model, we provide the most updated estimates of CO 2 uptake by cement carbonation. The accumulated amount of global CO 2 uptake by cements produced from 1930 to 2021 is estimated to be 22.9 Gt CO 2 (95 % confidence interval, CI: 19.6–26.6 Gt CO 2). This amount includes the CO 2 uptake by concrete, mortar, construction waste and kiln dust, accounting for 30.1 %, 58.5 %, 4.0 % and 7.1 % respectively. The cumulative carbon uptake by cement materials from 1930 to 2021 offsets 55.1 % of the emissions from cement production (41.6 Gt CO 2 , 95 % CI: 38.7–47.2 Gt CO 2) over the same period, with the greater part coming from mortar (58.5 % of the total uptake). China has the highest cement carbon uptake, with cumulative carbonation of 7.06 Gt CO 2 (95 % CI: 5.22–9.44 Gt CO 2) since 1930. In addition, the carbon uptake amounts of the USA, EU, India and the rest of the world took 5.0 %, 23.2 %, 5.6 % and 34.8 % separately. As a result of rapidly increased production in recent years, over three-quarters of the cement carbon uptake has occurred since 1990. Additionally, our results show little impact by the COVID-19 pandemic on cement production and use, with carbon uptake reaching about 0.92 Gt CO 2 (95 % CI: 0.78–1.10 Gt CO 2) in 2020 and 0.96 Gt CO 2 (95 % CI: 0.81–1.15 Gt CO 2) in 2021. Our uniformly formatted and most updated cement uptake inventories provide coherent data-based support for including cement carbon uptake into future carbon budgets from the local to global scale. The latest version contains the uptake data till 2021, showing the global uptake's increasing pattern and offering more usable and relevant data for evaluating cement's carbon uptake capacity. All the data described in this study are accessible at 10.5281/zenodo.7516373 (Bing et al., 2023). [ABSTRACT FROM AUTHOR]

Quantification of land surface–atmosphere fluxes of carbon dioxide (CO 2) and their trends and uncertainties is essential for monitoring progress of the EU27 + UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO 2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO 2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27 + UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27 + UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO 2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO 2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO 2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27 + UK accounts for 926 ± 13 Tg C yr -1 , while eight other BU sources report a mean value of 948 [ 937,961 ] Tg C yr -1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO 2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for - 91 ± 32 Tg C yr -1 , while six other BU approaches reported a mean sink of - 62 [ -117,-49 ] Tg C yr -1 , and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported - 69 [ -152,-5 ] Tg C yr -1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of - 73 Tg C yr -1 has a slightly smaller spread (0th–100th percentiles of [ -135,+45 ] Tg C yr -1), and it was calculated after removing net land–atmosphere CO 2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO 2 fossil and net CO 2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27 + UK. We conclude that CO 2 emissions from fossil sources have decreased over the past 30 years in the EU27 + UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (- 0.61) Tg C yr -2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr -1 between CO 2 fossil emissions and net CO 2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO 2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at 10.5281/zenodo.8148461 (McGrath et al., 2023). [ABSTRACT FROM AUTHOR]

A substantial amount of CO 2 is released into the atmosphere from the process of the high-temperature decomposition of limestone to produce lime. However, during the lifecycle of lime production, the alkaline components of lime will continuously absorb CO 2 from the atmosphere during use and waste disposal. Here, we adopt an analytical model describing the carbonation process to obtain regional and global estimates of carbon uptake from 1930 to 2020 using lime lifecycle use-based material data. The results reveal that the global uptake of CO 2 by lime increased from 9.16 Mt C yr -1 (95 % confidence interval, CI: 1.84–18.76 Mt C) in 1930 to 34.84 Mt C yr -1 (95 % CI: 23.50–49.81 Mt C) in 2020. Cumulatively, approximately 1444.70 Mt C (95 % CI: 1016.24–1961.05 Mt C) was sequestered by lime produced between 1930 and 2020, corresponding to 38.83 % of the process emissions during the same period, mainly contributed from the utilization stage (76.21 % of the total uptake). We also fitted the missing lime output data of China from 1930 to 2001, thus compensating for the lack of China's lime production (cumulative 7023.30 Mt) and underestimation of its carbon uptake (467.85 Mt C) in the international data. Since 1930, lime-based materials in China have accounted for the largest proportion (about 63.95 %) of the global total. Our results provide data to support including lime carbon uptake into global carbon budgets and scientific proof for further research of the potential of lime-containing materials in carbon capture and storage. The data utilized in the present study can be accessed at 10.5281/zenodo.7896106 (Ma et al., 2023). [ABSTRACT FROM AUTHOR]

Large rivers play an important role in transferring water and all of its constituents, including carbon in its various forms, from the land to the ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here, we present the University of Exeter (UNEXE) Satellite Oceanographic Datasets for Acidification (OceanSODA) dataset (OceanSODA-UNEXE) time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010–2020) and Congo (2002–2016) rivers. Optimal empirical approaches were used to generate gridded total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a reference matchup database of in situ observations. Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full carbonate system in the surface ocean (which includes pH and the partial pressure of carbon dioxide, pCO2). The algorithm evaluation constitutes a Type-A uncertainty evaluation for TA and DIC, in which model, input and sampling uncertainties are considered. Total combined uncertainties for TA and DIC were propagated through the carbonate system calculation, allowing all variables to be provided with an associated uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was 36 µmolkg-1 (weighted root-mean-squared difference, RMSD, of 35 µmolkg-1 and weighted bias of 8 µmolkg-1 for n = 82), whereas it was 44 µmolkg-1 for DIC (weighted RMSD of 44 µmolkg-1 and weighted bias of - 6 µmolkg-1 for n = 70). The spatially averaged propagated combined uncertainties for the pCO2 and pH were 85 µatm and 0.08, respectively, where the pH uncertainty was relative to an average pH of 8.19. In the Congo outflow, the combined uncertainty for TA was identified as 29 µmolkg-1 (weighted RMSD of 28 µmolkg-1 and weighted bias of 6 µmolkg-1 for n = 102), whereas it was 40 µmolkg-1 for DIC (weighted RMSD of 37 µmolkg-1 and weighted bias of - 16 µmolkg-1 for n = 77). The spatially averaged propagated combined uncertainties for pCO2 and pH were 74 µatm and 0.08, respectively, where the pH uncertainty was relative to an average pH of 8.21. The combined uncertainties in TA and DIC in the Amazon and Congo outflows are lower than the natural variability within their respective regions, allowing the time-varying regional variability to be evaluated. Potential uses of these data would be the assessment of the spatial and temporal flow of carbon from the Amazon and Congo rivers into the Atlantic and the assessment of the riverine-driven carbonate system variations experienced by tropical reefs within the outflow regions. The data presented in this work are available at 10.1594/PANGAEA.946888 (Sims et al., 2023). [ABSTRACT FROM AUTHOR]

The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO 2 (p CO 2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface p CO 2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface p CO 2 in the SCS with a reasonably high spatial resolution (0.05 ∘ × 0.05 ∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of p CO 2 constrained by EOF, atmospheric p CO 2 , and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed p CO 2 field successfully simulated the spatial and temporal patterns of sea surface p CO 2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm , which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed p CO 2 and in situ data ranged from -10 to 4 µatm (-2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface p CO 2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface p CO 2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface p CO 2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO 2 , the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at 10.57760/sciencedb.02050 (Wang and Dai, 2022). [ABSTRACT FROM AUTHOR]

Accurate accounting of emissions and removals of CO 2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on "top-down" NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO 2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with "bottom-up" estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1 ∘ × 1 ∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: 10.48588/npf6-sw92. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO2yr-1 (0.90–1.25 PgCyr-1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems. [ABSTRACT FROM AUTHOR]

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2 ∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2 ∘ , hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2 ∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2 , the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5– 10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes") originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2. [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO 2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO 2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO 2 sink (SLAND) is estimated with dynamic global vegetation models. 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 ± 1 σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr -1 (9.9 ± 0.5 GtC yr -1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr -1 , for a total anthropogenic CO 2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr -1 (40.0 ± 2.9 GtCO 2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr -1 (2.5 ± 0.1 ppm yr -1), SOCEAN was 2.9 ± 0.4 GtC yr -1 , and SLAND was 3.5 ± 0.9 GtC yr -1 , with a BIM of - 0.6 GtC yr -1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO 2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of + 1.0 % (0.1 % to 1.9 %) globally and atmospheric CO 2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr -1 persist for the representation of annual to semi-decadal variability in CO 2 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 CO 2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and 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. The data presented in this work are available at 10.18160/GCP-2022 (Friedlingstein et al., 2022b). [ABSTRACT FROM AUTHOR]

A global gridded net ecosystem exchange (NEE) of CO 2 dataset is vital in global and regional carbon cycle studies. Top-down atmospheric inversion is one of the major methods to estimate the global NEE; however, the existing global NEE datasets generated through inversion from conventional CO 2 observations have large uncertainties in places where observational data are sparse. Here, by assimilating the GOSAT ACOS v9 XCO 2 product, we generate a 10-year (2010–2019) global monthly terrestrial NEE dataset using the Global Carbon Assimilation System, version 2 (GCASv2), which is named GCAS2021. It includes gridded (1∘×1∘), globally, latitudinally, and regionally aggregated prior and posterior NEE and ocean (OCN) fluxes and prescribed wildfire (FIRE) and fossil fuel and cement (FFC) carbon emissions. Globally, the decadal mean NEE is -3.73±0.52 PgC yr -1 , with an interannual amplitude of 2.73 PgC yr -1. Combining the OCN flux and FIRE and FFC emissions, the net biosphere flux (NBE) and atmospheric growth rate (AGR) as well as their inter-annual variabilities (IAVs) agree well with the estimates of the Global Carbon Budget 2020. Regionally, our dataset shows that eastern North America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia are carbon sinks, while the western United States, African grasslands, Brazilian plateaus, and parts of South Asia are carbon sources. In the TRANSCOM land regions, the NBEs of temperate N. America, northern Africa, and boreal Asia are between the estimates of CMS-Flux NBE 2020 and CT2019B, and those in temperate Asia, Europe, and Southeast Asia are consistent with CMS-Flux NBE 2020 but significantly different from CT2019B. In the RECCAP2 regions, except for Africa and South Asia, the NBEs are comparable with the latest bottom-up estimate of Ciais et al. (2021). Compared with previous studies, the IAVs and seasonal cycles of NEE of this dataset could clearly reflect the impacts of extreme climates and large-scale climate anomalies on the carbon flux. The evaluations also show that the posterior CO 2 concentrations at remote sites and on a regional scale, as well as on vertical CO 2 profiles in the Asia-Pacific region, are all consistent with independent CO 2 measurements from surface flask and aircraft CO 2 observations, indicating that this dataset captures surface carbon fluxes well. We believe that this dataset can contribute to regional- or national-scale carbon cycle and carbon neutrality assessment and carbon dynamics research. The dataset can be accessed at 10.5281/zenodo.5829774 (Jiang, 2022). [ABSTRACT FROM AUTHOR]

A common strategy for calculating the direction and rate of carbon dioxide gas (CO 2) exchange between the ocean and atmosphere relies on knowledge of the partial pressure of CO 2 in surface seawater (p CO 2(sw)), a quantity that is frequently observed by autonomous sensors on ships and moored buoys, albeit with significant spatial and temporal gaps. Here we present a monthly gridded data product of p CO 2(sw) at 0.25 ∘ latitude by 0.25 ∘ longitude resolution in the northeastern Pacific Ocean, centered on the California Current System (CCS) and spanning all months from January 1998 to December 2020. The data product (RFR-CCS; Sharp et al., 2022; 10.5281/zenodo.5523389) was created using observations from the most recent (2021) version of the Surface Ocean CO 2 Atlas (Bakker et al., 2016). These observations were fit against a variety of collocated and contemporaneous satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from sensors on moored buoys, and find that the data product effectively captures seasonal p CO 2(sw) cycles at nearshore sites. This result is notable because global gridded p CO 2(sw) products do not capture local variability effectively in this region, suggesting that RFR-CCS is a better option than regional extractions from global products to represent p CO 2(sw) in the CCS over the last 2 decades. Lessons learned from the construction of RFR-CCS provide insight into how global p CO 2(sw) products could effectively characterize seasonal variability in nearshore coastal environments. We briefly review the physical and biological processes – acting across a variety of spatial and temporal scales – that are responsible for the latitudinal and nearshore-to-offshore p CO 2(sw) gradients seen in the RFR-CCS reconstruction of p CO 2(sw). RFR-CCS will be valuable for the validation of high-resolution models, the attribution of spatiotemporal carbonate system variability to physical and biological drivers, and the quantification of multiyear trends and interannual variability of ocean acidification. [ABSTRACT FROM AUTHOR]

We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990–2019. We find that in 2019, global agri-food system emissions were 16.5 (95 %; CI range: 11–22) billion metric tonnes (Gt CO 2 eq. yr -1), corresponding to 31 % (range: 19 %–43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate – from crop and livestock production processes including on-farm energy use – were 7.2 Gt CO 2 eq. yr -1 ; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO 2 eq. yr -1 ; and emissions from pre- and post-production processes – manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal – were 5.8 Gt CO 2 eq. yr -1. Over the study period 1990–2019, agri-food system emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAOSTAT data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased 9 %. In 2019, in terms of individual greenhouse gases (GHGs), pre- and post-production processes emitted the most CO 2 (3.9 Gt CO 2 yr -1), preceding land use change (3.3 Gt CO 2 yr -1) and farm gate (1.2 Gt CO 2 yr -1) emissions. Conversely, farm gate activities were by far the major emitter of methane (140 Mt CH 4 yr -1) and of nitrous oxide (7.8 Mt N 2 O yr -1). Pre- and post-production processes were also significant emitters of methane (49 Mt CH 4 yr -1), mostly generated from the decay of solid food waste in landfills and open dumps. One key trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along the agri-food system, at global, regional and national scales. In fact, our data show that by 2019, pre- and post-production processes had overtaken farm gate processes to become the largest GHG component of agri-food system emissions in Annex I parties (2.2 Gt CO 2 eq. yr -1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO 2 eq. yr -1), becoming larger than emissions from land use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO 2 eq. yr -1), the USA (700 Mt CO 2 eq. yr -1) and the EU-27 (600 Mt CO 2 eq. yr -1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO 2 gases within the farm gate and on CO 2 mitigation from land use change. The information used in this work is available as open data with DOI 10.5281/zenodo.5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (https://www.fao.org/faostat/en/#data/EM ; FAO, 2021a), with annual updates. [ABSTRACT FROM AUTHOR]

We present 20-year flask sample records of atmospheric CO 2 , δ (O2/N2), and atmospheric potential oxygen (APO) from the stations Lutjewad (the Netherlands) and Mace Head (Ireland), and a 3-year record from Halley station (Antarctica). We include details of our calibration procedures and the stability of our calibration scale over time, which we estimate to be 3 per meg over the 11 years of calibration, and our compatibility with the international Scripps O 2 scale. The measurement records from Lutjewad and Mace Head show similar long-term trends during the period 2002–2018 of 2.31 ± 0.07 ppm yr -1 for CO 2 and - 21.2 ± 0.8 per meg yr -1 for δ (O2/N2) at Lutjewad, and 2.22 ± 0.04 ppm yr -1 for CO 2 and - 21.3 ± 0.9 per meg yr -1 for δ (O2/N2) at Mace Head. They also show a similar δ (O2/N2) seasonal cycle with an amplitude of 54 ± 4 per meg at Lutjewad and 61 ± 5 per meg at Mace Head, while the CO 2 seasonal amplitude at Lutjewad (16.8 ± 0.5 ppm) is slightly higher than that at Mace Head (14.8 ± 0.3 ppm). We show that the observed long-term trends and seasonal cycles are in good agreement with the measurements from various other stations, especially the measurements from the Weybourne Atmospheric Observatory (United Kingdom). However, there are remarkable differences in the progression of annual trends between the Mace Head and Lutjewad records for δ (O2/N2) and APO, which might in part be caused by sampling differences, but also by environmental effects, such as North Atlantic Ocean oxygen ventilation changes to which Mace Head is more sensitive. The Halley record shows clear trends and seasonality in δ (O2/N2) and APO, the latter agreeing especially well with continuous measurements at the same location made by the University of East Anglia (UEA), while CO 2 and δ (O2/N2) present slight disagreements, most likely caused by small leakages during sampling. From our 2002–2018 records, we find a good agreement with Global Carbon Budget 2021 (Friedlingstein et al. (2021) for the global ocean carbon sink: 2.1 ± 0.8 PgC yr -1 , based on the Lutjewad record. The data presented in this work are available at 10.18160/qq7d-t060 (Nguyen et al., 2021). [ABSTRACT FROM AUTHOR]

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, 10.3334/ORNLDAAC/1934). [ABSTRACT FROM AUTHOR]

The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns, and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under 10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under 10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping. [ABSTRACT FROM AUTHOR]