Global Carbon Project Research Articles

A simple model for the prediction of CO2 concentrations in the atmosphere, depending on global CO2 emissions

We present a very simple model for estimating time dependent atmospheric CO2 concentrations c(t) from global carbon emission scenarios, serving as single input data. We derive a single linear differential equation of 1st order, based on parameters which are estimated from quantitative data of the global carbon project and Mauna Loa data for CO2 concentrations. The model is tested first by comparing it to the 1960–2021 period with reasonably good quantitative agreement and, second to two of the typical current IPCC scenarios with good qualitative agreement. Finally, some new emission scenarios are modelled. Despite several drawbacks concerning absolute quantitative predictions, there are two important advantages of the model. First, it can be easily executed by students already with simple programmable spreadsheet programs such as Excel. Second input emission scenarios can be changed easily and expected changes are immediately seen for discussion during undergraduate and graduate courses on the carbon cycle and climate change.

Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity

Wetland methane (CH4) emissions over the Boreal–Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002–2021) of Boreal–Arctic wetland CH4 emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH4 emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH4 emissions (22.3 Tg CH4 yr−1) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH4 emission driven by amplified Boreal–Arctic warming and greening.

Cold‐Season Methane Fluxes Simulated by GCP‐CH4 Models

AbstractCold‐season methane (CH4) emissions may be poorly constrained in wetland models. We examined cold‐season CH4 emissions simulated by 16 models participating in the Global Carbon Project model intercomparison and analyzed temporal and spatial patterns in simulation results using prescribed inundation data for 2000–2020. Estimated annual CH4 emissions from northern (>60°N) wetlands averaged 10.0 ± 5.5 Tg CH4 yr−1. While summer CH4 emissions were well simulated compared to in‐situ flux measurement observations, the models underestimated CH4 during September to May relative to annual total (27 ± 9%, compared to 45% in observations) and substantially in the months with subzero air temperatures (5 ± 5%, compared to 27% in observations). Because of winter warming, nevertheless, the contribution of cold‐season emissions was simulated to increase at 0.4 ± 0.8% decade−1. Different parameterizations of processes, for example, freezing–thawing and snow insulation, caused conspicuous variability among models, implying the necessity of model refinement.

Urbanization associated changes in biogeochemical cycles.

Urbanization associated changes in biogeochemical cycles.

Reconstructions and predictions of the global carbon budget with an emission-driven Earth system model

Abstract. The global carbon budget (GCB) – including fluxes of CO2 between the atmosphere, land, and ocean and its atmospheric growth rate – show large interannual to decadal variations. Reconstructing and predicting the variable GCB is essential for tracing the fate of carbon and understanding the global carbon cycle in a changing climate. We use a novel approach to reconstruct and predict the variations in GCB in the next few years based on our decadal prediction system enhanced with an interactive carbon cycle. By assimilating physical atmospheric and oceanic data products into the Max Planck Institute Earth System Model (MPI-ESM), we are able to reproduce the annual mean historical GCB variations from 1970–2018, with high correlations of 0.75, 0.75, and 0.97 for atmospheric CO2 growth, air–land CO2 fluxes, and air–sea CO2 fluxes, respectively, relative to the assessments from the Global Carbon Project (GCP). Such a fully coupled decadal prediction system, with an interactive carbon cycle, enables the representation of the GCB within a closed Earth system and therefore provides an additional line of evidence for the ongoing assessments of the anthropogenic GCB. Retrospective predictions initialized from the simulation in which physical atmospheric and oceanic data products are assimilated show high confidence in predicting the following year's GCB. The predictive skill is up to 5 years for the air–sea CO2 fluxes, and 2 years for the air–land CO2 fluxes and atmospheric carbon growth rate. This is the first study investigating the GCB variations and predictions with an emission-driven prediction system. Such a system also enables the reconstruction of the past and prediction of the evolution of near-future atmospheric CO2 concentration changes. The Earth system predictions in this study provide valuable inputs for understanding the global carbon cycle and informing climate-relevant policy.

A multivariate dynamic statistical model of the global carbon budget 1959–2020

AbstractWe propose a multivariate dynamic statistical model of the global carbon budget (GCB) as represented in the annual data set made available by the Global Carbon Project, covering the sample period 1959–2020. The model connects four main objects of interest: atmospheric carbon dioxide (CO2) concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink), and by the ocean and marine biosphere (ocean sink). The model captures the GCB equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World Gross Domestic Product while sink activities depend on the level of atmospheric concentrations and the Southern Oscillation Index. We derive the time series properties of atmospheric concentrations from the model, showing that they contain one unit root and a near-second unit root. The statistical system allows for the estimation of key parameters of the global carbon cycle and for the assessment of estimation uncertainty. It also allows for the estimation and the uncertainty assessment of related variables such as the airborne fraction and the sink rate. We provide short-term forecasts of the components of the GCB.

Reconciling the bottom-up and top-down estimates of the methane chemical sink using multiple observations

Abstract. The methane chemical sink estimated by atmospheric chemistry models (bottom-up method) is significantly larger than estimates based on methyl chloroform (MCF) inversions (top-down method). The difference is partly attributable to large uncertainties in hydroxyl radical (OH) concentrations simulated by the atmospheric chemistry models used to derive the bottom-up estimates. In this study, we propose a new approach based on OH precursor observations and a chemical box model. This approach contributes to improving the 3D distributions of tropospheric OH radicals obtained from atmospheric chemistry models and reconciling bottom-up and top-down estimates of the chemical loss of atmospheric methane. By constraining simulated OH precursors with observations, the global mean tropospheric column-averaged air-mass-weighted OH concentration ([OH]trop-M) is ∼10×105 molec. cm−3 (which is 2×105 molec. cm−3 lower than the original model-simulated global [OH]trop-M) and agrees with that obtained by the top-down method based on MCF inversions. With OH constrained by precursor observations, the methane chemical loss is 471–508 Tg yr−1, averaged from 2000 to 2009. The new adjusted estimate is in the range of the latest top-down estimate of the Global Carbon Project (GCP) (459–516 Tg yr−1), contrary to the bottom-up estimates that use the original model-simulated OH fields (577–612 Tg yr−1). The overestimation of global [OH]trop-M and methane chemical loss simulated by the atmospheric chemistry models is caused primarily by the models' underestimation of carbon monoxide and total ozone column, and overestimation of nitrogen dioxide. Our results highlight that constraining the model-simulated OH fields with available OH precursor observations can help improve bottom-up estimates of the global methane sink.

Estimating emissions of methane consistent with atmospheric measurements of methane and δ13C of methane

Abstract. We have constructed an atmospheric inversion framework based on TM5-4DVAR to jointly assimilate measurements of methane and δ13C of methane in order to estimate source-specific methane emissions. Here we present global emission estimates from this framework for the period 1999–2016. We assimilate a newly constructed, multi-agency database of CH4 and δ13C measurements. We find that traditional CH4-only atmospheric inversions are unlikely to estimate emissions consistent with atmospheric δ13C data, and assimilating δ13C data is necessary to derive emissions consistent with both measurements. Our framework attributes ca. 85 % of the post-2007 growth in atmospheric methane to microbial sources, with about half of that coming from the tropics between 23.5∘ N and 23.5∘ S. This contradicts the attribution of the recent growth in the methane budget of the Global Carbon Project (GCP). We find that the GCP attribution is only consistent with our top-down estimate in the absence of δ13C data. We find that at global and continental scales, δ13C data can separate microbial from fossil methane emissions much better than CH4 data alone, and at smaller scales this ability is limited by the current δ13C measurement coverage. Finally, we find that the largest uncertainty in using δ13C data to separate different methane source types comes from our knowledge of atmospheric chemistry, specifically the distribution of tropospheric chlorine and the isotopic discrimination of the methane sink.

Climate change - a review of the mass balance of biogenic and fossil carbon

Trying to understand the causes of climate change can be confusing. On the one hand, methane (CH4) emissions from cattle, and methane emissions from food wastes in landfills, are said to contribute to greenhouse gases (GHGs) that drive climate change. People are working on feed additives for dairy cows to reduce their methane emissions. But, at the same time, cattle manure and food wastes can be fed into anaerobic digesters to convert these organic wastes to biogas; the resulting “renewable methane” or “renewable natural gas” (RNG), can be used in place of fossil natural gas and avoid extra GHG emissions and stop global warming. Can we have it both ways? Burning gasoline in our cars and trucks generates carbon dioxide (CO2), which is said to contribute to climate change. But more than 8 billion people on planet Earth, breathe in oxygen and exhale carbon dioxide every minute of the day. And so do all the other animals who live on this planet, breathe in oxygen and exhale carbon dioxide. Is our breathing also contributing to climate change, just as the emissions from our automobile tailpipes? It is time to step back from all the hype, evaluate the various sources of CO2 and CH4 being generated and review the “mass balance” of these gases in our atmosphere. Some of these are part of the natural biogenic carbon cycle and some are simply adding to the overall mass balance. What is driving climate change - excess GHGs from the biogenic carbon cycle, excess emissions from other sources, or both? Let’s take a fresh look at the available data. (Of course, water vapor also plays a part in the climate change story, as a “positive feedback” effect. As non-condensable GHGs rise in concentration and slightly warm the planet, slightly warming oceans add a bit more water vapor to the story and push the warming up a bit more). During the past few decades, a great deal of pertinent data has been carefully observed and recorded, by world-class scientists and engineers. This data is available in the public domain and is now easily available on the internet. These data sources come from reputable organizations, such as NASA (National Atmospheric and Space Administration), NOAA (National Oceanic and Atmospheric Administration), USGS (United States Geological Survey) and international organizations such as the “Global Carbon Project” and “Our-World-in-Data”. How much of the growing concentration of CO2 (and CH4) in our atmosphere can be attributed to out-of-control emissions of biogenic carbon? How much of the CO2 rise can be attributed to fossil fuel emissions: 10%, 50%, 90%, or more? The available data appear to show that the emissions from burning fossil fuels, more than account for all of the rise of carbon dioxide in our atmosphere during the past 60 years, or longer. In comparison, the biogenic carbon cycle data seem to be very consistent, year after year, decade after decade and have little or no impact on climate change. But, let’s have a closer look at the available data, and let the data speak for itself.

High-resolution inverse modelling of European CH4 emissions using the novel FLEXPART-COSMO TM5 4DVAR inverse modelling system

Abstract. We present a novel high-resolution inverse modelling system (“FLEXVAR”) based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (“baselines”) consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (“FLExKF”) system and with TM5-4DVAR inversions at 1∘×1∘ resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km×7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1∘×1∘. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

Estimating Global Anthropogenic CO2 Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model

The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO2 emission-reduction goals. This study built a data-driven stacked random forest regression model for estimating gridded global fossil fuel CO2 emissions. The driving variables include the annual features of column-averaged CO2 dry-air mole fraction (XCO2) anomalies based on their ecofloristic zone, night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS), terrestrial carbon fluxes, and vegetation parameters. A two-layer stacked random forest regression model was built to fit 1° gridded inventory of open-source data inventory for anthropogenic CO2 (ODIAC). Then, the model was trained using the 2014–2018 dataset to estimate emissions in 2019, which provided a higher accuracy compared with a single-layer model with an R2 of 0.766 and an RMSE of 0.359. The predicted gridded emissions are consistent with Global Carbon Grid at 1° scale with an R2 of 0.665, and the national total emissions provided a higher R2 at 0.977 with the Global Carbon Project (GCP) data, as compared to the ODIAC (R2 = 0.956) data, in European countries. This study demonstrates that data-driven random forest regression models are capable of estimating anthropogenic CO2 emissions at a grid scale.

Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution

In recent studies, proxy XCH4 retrievals from the Japanese Greenhouse gases Observing SATellite (GOSAT) have been used to constrain top-down estimation of CH4 emissions. Still, the resulting interannual variations often show significant discrepancies over some of the most important CH4 source regions, such as China and Tropical South America, by causes yet to be determined. This study compares monthly CH4 flux estimates from two parallel assimilations of GOSAT XCH4 retrievals from 2010 to 2019 based on the same Ensemble Kalman Filter (EnKF) framework but with the global chemistry transport model (GEOS-Chem v12.5) being run at two different spatial resolutions of 4° × 5° (R4, lon × lat) and 2° × 2.5° (R2, lon × lat) to investigate the effects of resolution-related model errors on the derived long-term global and regional CH4 emission trends. We found that the mean annual global methane emission for the 2010s is 573.04 Tg yr−1 for the inversion using the R4 model, which becomes about 4.4 Tg yr−1 less (568.63 Tg yr−1) when a finer R2 model is used, though both are well within the ensemble range of the 22 top-down results (2008–17) included in the current Global Carbon Project (from 550 Tg yr−1 to 594 Tg yr−1). Compared to the R2 model, the inversion based on the R4 tends to overestimate tropical emissions (by 13.3 Tg yr which is accompanied by a general underestimation (by 8.9 Tg yr−1) in the extratropics. Such a dipole reflects differences in tropical-mid-latitude air exchange in relation to the model’s convective and advective schemes at different resolutions. The two inversions show a rather consistent long-term CH4 emission trend at the global scale and over most of the continents, suggesting that the observed rapid increase in atmospheric methane can largely be attributed to the emission growth from North Africa (1.79 Tg yr−2 for R4 and 1.29 Tg yr−2 for R2) and South America Temperate (1.08 Tg yr−2 for R4 and 1.21 Tg yr−2 for R2) during the first half of the 2010s, and from Eurasia Boreal (1.46 Tg yr−2 for R4 and 1.63 Tg yr−2 for R2) and Tropical South America (1.72 Tg yr−2 for R4 and 1.43 Tg yr−2 for R2) over 2015–19. In the meantime, emissions in Europe have shown a consistent decrease over the past decade. However, the growth rates by the two parallel inversions show significant discrepancies over Eurasia Temperate, South America Temperate, and South Africa, which are also the places where recent GOSAT inversions usually disagree with one other.

Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?

AbstractThe Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.

Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions

Abstract. In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N2O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).

“Green Kazakhstan”: its relevance to the domestic and international climate ambitions of the Republic of Kazakhstan

Climate change makes and shapes the globalized world in important ways. As the COVID-19 pandemic brings the health of the human race and the health of the Earth to the forefront of world politics, urgent action to combat climate change and its impacts has been called for. Given the pledge of President Kassym-Jomart Tokayev that Kazakhstan is to reach carbon neutrality by 2060, this research paper aims to investigate the Republic’s domestic and international climate ambitions with specific attention paid on the national development project “Green Kazakhstan.” The research question is: to what extent are Kazakhstan’s domestic and international climate ambitions complementary, or disparate? Constructivism is employed as the theoretical framework to unveil the domestic challenges faced by President Tokayev to pursue a low-carbon transition in referral to Kazakhstan’s latest foreign policy concept, supplemented by the use of quantitative results produced by the Climate Change Performance Index and the Global Carbon Project to assess the Republic’s capability and responsibility to display a climate ambition. The findings indicate that Kazakhstan’s domestic and international climate ambitions are considered disparate on most occasions but nonetheless have undeniable domestic and international significance. Key words: Kazakhstan; capability; climate; emissions; energy; environment; green; responsibility

Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)

Abstract. Regional land carbon budgets provide insights into the spatial distribution of the land uptake of atmospheric carbon dioxide and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields, or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions due to different definitions and component fluxes being reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers that connect CO2 uptake in one area with its release in another also requires better definitions and protocols to reach harmonized regional budgets that can be summed up to a globe scale and compared with the atmospheric CO2 growth rate and inversion results. In this study, using the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update to regional carbon budgets over the last 2 decades based on observations for 10 regions covering the globe with a better harmonization than the precursor project, we provide recommendations for using atmospheric inversion results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes, and land use fluxes.

The Variability of Air-sea O2 Flux in CMIP6: Implications for Estimating Terrestrial and Oceanic Carbon Sinks

The measurement of atmospheric O2 concentrations and related oxygen budget have been used to estimate terrestrial and oceanic carbon uptake. However, a discrepancy remains in assessments of O2 exchange between ocean and atmosphere (i.e. air-sea O2 flux), which is one of the major contributors to uncertainties in the O2-based estimations of the carbon uptake. Here, we explore the variability of air-sea O2 flux with the use of outputs from Coupled Model Intercomparison Project phase 6 (CMIP6). The simulated air-sea O2 flux exhibits an obvious warming-induced upward trend (∼1.49 Tmol yr−2) since the mid-1980s, accompanied by a strong decadal variability dominated by oceanic climate modes. We subsequently revise the O2-based carbon uptakes in response to this changing air-sea O2 flux. Our results show that, for the 1990–2000 period, the averaged net ocean and land sinks are 2.10±0.43 and 1.14±0.52 GtC yr−1 respectively, overall consistent with estimates derived by the Global Carbon Project (GCP). An enhanced carbon uptake is found in both land and ocean after year 2000, reflecting the modification of carbon cycle under human activities. Results derived from CMIP5 simulations also investigated in the study allow for comparisons from which we can see the vital importance of oxygen dataset on carbon uptake estimations.

Airborne quantification of net methane and carbon dioxide fluxes from European Arctic wetlands in Summer 2019.

Arctic wetlands and surrounding ecosystems are both a significant source of methane (CH4) and a sink of carbon dioxide (CO2) during summer months. However, precise quantification of this regional CH4 source and CO2 sink remains poorly characterized. A research flight using the UK Facility for Airborne Atmospheric Measurement was conducted in July 2019 over an area (approx. 78 000 km2) of mixed peatland and forest in northern Sweden and Finland. Area-averaged fluxes of CH4 and carbon dioxide were calculated using an aircraft mass balance approach. Net CH4 fluxes normalized to wetland area ranged between 5.93 ± 1.87 mg m−2 h−1 and 4.44 ± 0.64 mg m−2 h−1 (largest to smallest) over the region with a meridional gradient across three discrete areas enclosed by the flight survey. From largest to smallest, net CO2 sinks ranged between −513 ± 74 mg m−2 h−1 and −284 ± 89 mg m−2 h−1 and result from net uptake of CO2 by vegetation and soils in the biosphere. A clear gradient of decreasing bulk and area-averaged CH4 flux was identified from north to south across the study region, correlated with decreasing peat bog land area from north to south identified from CORINE land cover classifications. While N2O mole fraction was measured, no discernible gradient was measured over the flight track, but a minimum flux threshold using this mass balance method was calculated. Bulk (total area) CH4 fluxes determined via mass balance were compared with area-weighted upscaled chamber fluxes from the same study area and were found to agree well within measurement uncertainty. The mass balance CH4 fluxes were found to be significantly higher than the CH4 fluxes reported by many land-surface process models compiled as part of the Global Carbon Project. There was high variability in both flux distribution and magnitude between the individual models. This further supports previous studies that suggest that land-surface models are currently ill-equipped to accurately capture carbon fluxes inthe region.This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000–2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top‐down (TD) (atmospheric and transport model‐based) and bottom‐up (BU) (inventory‐ and process model‐based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions—China, Southeast Asia, USA, South Asia, and Brazil—account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4 yr−1 in 2008–2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

Towards near real-time, monthly fossil CO2 emissions estimates for the European Union with current-year projections

With the international goals of the Paris Agreement and the growing number of time-bound national goals for emissions reductions, reliable estimates of CO2 emissions are becoming more and more important. In particular, reducing the time lag of these estimates and producing short-term projections are gaining importance as the remaining time until mitigation deadlines becomes shorter. The Global Carbon Project has been producing a current-year projection of global CO2 emissions since 2012, introducing a sub-projection for the European Union in 2018. The success of this EU projection has been variable, and in this article I explore how the projections in 2019 were made along with some of the reasons why the projections have high uncertainty and bias. About 84% of the total error in the projection of EU emissions in 2019 was because of a poor projection for coal consumption, which was a result of poor estimates of sub-annual observations, a misunderstanding of conflicting information, and poor assumptions applied to the remainder of the year. The correction of the errors identified here will go some way to improving future short-term projections of the European Union's CO2 emissions, paving the way for a low-maintenance, operational system.