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Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH4 yr−1) and the mean of the BU CH4 emissions (17.8 (16–19) Tg CH4 yr−1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH4 yr−1 and 24 (22–25) Tg CH4 yr−1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH4 yr−1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024).

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr−1 in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750–2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions have increased 40 % (or 1.9 Tg N yr−1) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr−1, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1) . For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr−1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr−1. For the 2010–2019 period, the annual BU decadal-average emissions for both natural and anthropogenic sources were 18.2 (10.6–25.9) Tg N yr−1 and TD emissions were 17.4 (15.8–19.20) Tg N yr−1. The once top emitter Europe has reduced its emissions by 31 % since the 1980s, while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N2O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the importance of reducing anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose the establishment of a global network for monitoring and modeling N2O from the surface through to the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

Satellite-based estimations of dry-air column-averaged mixing ratios of methane (XCH4) contribute to a better understanding of changes in CH4 emission sources and variations in its atmospheric growth rates. High accuracy of the satellite measurements is required, and therefore, extensive validation is performed, mainly against the Total Carbon Column Observing Network (TCCON). However, validation opportunities at open-ocean areas outside the coastal regions are sparse. We propose a new approach to assess the accuracy of satellite-derived XCH4 trends and variations. We combine various ship and aircraft observations with the help of atmospheric chemistry models, mainly used for the stratospheric column, to derive observation-based XCH4 (obs. XCH4). Based on our previously developed approach for the application to XCO2, we investigated three different advancements, from a simple approach to more elaborate approaches (approaches 1, 2, and 3), to account for the higher tropospheric and stratospheric variability in CH4 as compared to CO2. Between 2014 and 2018, at 20–40° N of the western Pacific, we discuss the uncertainties in the approaches and the derived obs. XCH4 within 10° by 20° latitude–longitude boxes. Uncertainties were 22 ppb (parts per billion) for approach 1, 20 ppb for approach 2, and 16 ppb for approach 3. We analyzed the consistency with the nearest TCCON stations and found agreement of approach 3 with Saga of 1±12 ppb and -1±11 ppb with Tsukuba for the northern and southern latitude box, respectively. Furthermore, we discuss the impact of the modeled stratospheric column on the derived obs. XCH4 by applying three different models in our approaches. Depending on the models, the difference can be more than 12 ppb (0.6 %), showing the importance for the appropriate choice. We show that our obs. XCH4 dataset accurately captures seasonal variations in CH4 over the ocean. Using different retrievals of the Greenhouse Gases Observing Satellite (GOSAT) from the National Institute for Environmental Studies (NIES), the RemoTeC full-physics retrieval operated at the Netherlands Institute for Space Research (SRON), and the full-physics retrieval of the University of Leicester (UoL-OCFP), we demonstrate the applicability of the dataset for satellite evaluation. The comparison with results of approach 3 revealed that NIES showed a difference of −0.04 ± 13 ppb and strong scatter at 20–30° N, while RemoTeC and OCFP have a rather systematic negative bias of −12.1 ± 8.1 and −10.3 ± 9.6 ppb. Our new approach to derive XCH4 reference datasets over the ocean can contribute to the validation of existing and upcoming satellite missions in future.

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 (CO2), methane (CH4) and nitrous oxide (N2O) 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 CO2 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 CH4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO2 emissions. With a mean of 1373 Mt CO2 yr−1, total African fossil CO2 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 CO2 emissions. The anthropogenic emissions of CH4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N2O 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 CO2 eq. yr−1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO2 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 CO2 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 CH4 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 CH4 emissions with BU inventories such as PRIMAP-hist. For N2O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N2O 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 https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022).

Methane (CH4) is the second major greenhouse gas after carbon dioxide (CO2) which has substantially increased during recent decades in the atmosphere, raising serious sustainability and climate change issues. Here, we develop a data assimilation system for in situ and column-averaged concentrations using a local ensemble transform Kalman filter (LETKF) to estimate surface emissions of CH4. The data assimilation performance is tested and optimized based on idealized settings using observation system simulation experiments (OSSEs), where a known surface emission distribution (the truth) is retrieved from synthetic observations. We tested three covariance inflation methods to avoid covariance underestimation in the emission estimates, namely fixed multiplicative (FM), relaxation-to-prior spread (RTPS), and adaptive multiplicative. First, we assimilate the synthetic observations at every grid point at the surface level. In such a case of dense observational data, the normalized root mean square error (RMSE) in the analyses over global land regions is smaller by 10 %–15 % in the case of RTPS covariance inflation method compared to FM. We have shown that integrated estimated flux seasonal cycles over 15 regions using RTPS inflation are in reasonable agreement between true and estimated flux, with 0.04 global normalized annual mean bias. We then assimilated the column-averaged CH4 concentration by sampling the model simulations at Greenhouse Gases Observing Satellite (GOSAT) observation locations and time for another OSSE. Similar to the case of dense observational data, the RTPS covariance inflation method performs better than FM for GOSAT synthetic observation in terms of normalized RMSE (2 %–3 %) and integrated flux estimation comparison with the true flux. The annual mean averaged normalized RMSE (normalized mean bias) in LETKF CH4 flux estimation in the case of RTPS and FM covariance inflation is found to be 0.59 (0.18) and 0.61 (0.23), respectively. The χ2 test performed for GOSAT synthetic observations assimilation suggests high underestimation of background error covariance in both RTPS and FM covariance inflation methods; however, the underestimation is much higher (>100 % always) for FM compared to RTPS covariance inflation method.

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

Accurate accounting of emissions and removals of CO2 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 CO2 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: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−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.

Global and regional sources and sinks of carbon across the earth's surface have been studied extensively using atmospheric carbon dioxide (CO2) observations and atmospheric chemistry-transport model (ACTM) simulations (top-down/inversion method). However, the uncertainties in the regional flux distributions remain unconstrained due to the lack of high-quality measurements, uncertainties in model simulations, and representation of data and flux errors in the inversion systems. Here, we assess the representation of data and flux errors using a suite of 16 inversion cases derived from a single transport model (MIROC4-ACTM) but different sets of a priori (bottom-up) terrestrial biosphere and oceanic fluxes, as well as prior flux and observational data uncertainties (50 sites) to estimate CO2 fluxes for 84 regions over the period 2000–2020. The inversion ensembles provide a mean flux field that is consistent with the global CO2 growth rate, land and ocean sink partitioning of −2.9 ± 0.3 (± 1σ uncertainty on the ensemble mean) and −1.6 ± 0.2 PgC yr−1, respectively, for the period 2011–2020 (without riverine export correction), offsetting about 22 %–33 % and 16 %–18 % of global fossil fuel CO2 emissions. The rivers carry about 0.6 PgC yr−1 of land sink into the deep ocean, and thus the effective land and ocean partitioning is −2.3 ± 0.3 and −2.2 ± 0.3, respectively. Aggregated fluxes for 15 land regions compare reasonably well with the best estimations for the 2000s (∼ 2000–2009), given by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all regions appeared as a carbon sink over 2011–2020. Interannual variability and seasonal cycle in CO2 fluxes are more consistently derived for two distinct prior fluxes when a greater degree of freedom (increased prior flux uncertainty) is given to the inversion system. We have further evaluated the inversion fluxes using meridional CO2 distributions from independent (not used in the inversions) aircraft and surface measurements, suggesting that the ensemble mean flux (model–observation mean ± 1σ standard deviation = −0.3 ± 3 ppm) is best suited for global and regional CO2 flux budgets than an individual inversion (model–observation 1σ standard deviation = −0.35 ± 3.3 ppm). Using the ensemble mean fluxes and uncertainties for 15 land and 11 ocean regions at 5-year intervals, we show promise in the capability to track flux changes toward supporting the ongoing and future CO2 emission mitigation policies.

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.

Emissions of greenhouse gases (GHGs) from the Indian subcontinent have increased during the last 20 years along with rapid economic growth; however, there remains a paucity of GHG measurements for policy-relevant research. In northern India and Bangladesh, agricultural activities are considered to play an important role in GHG concentrations in the atmosphere. We performed weekly air sampling at Nainital (NTL) in northern India and Comilla (CLA) in Bangladesh from 2006 and 2012, respectively. Air samples were analyzed for dry-air gas mole fractions of CO2, CH4, CO, H2, N2O, and SF6 and carbon and oxygen isotopic ratios of CO2 (δ13C-CO2 and δ18O-CO2). Regional characteristics of these components over the Indo-Gangetic Plain are discussed compared to data from other Indian sites and Mauna Loa, Hawaii (MLO), which is representative of marine background air. We found that the CO2 mole fraction at CLA had two seasonal minima in February–March and September, corresponding to crop cultivation activities that depend on regional climatic conditions. Although NTL had only one clear minimum in September, the carbon isotopic signature suggested that photosynthetic CO2 absorption by crops cultivated in each season contributes differently to lower CO2 mole fractions at both sites. The CH4 mole fraction of NTL and CLA in August–October showed high values (i.e., sometimes over 4000 ppb at CLA), mainly due to the influence of CH4 emissions from the paddy fields. High CH4 mole fractions sustained over months at CLA were a characteristic feature on the Indo-Gangetic Plain, which were affected by both the local emission and air mass transport. The CO mole fractions at NTL were also high and showed peaks in May and October, while CLA had much higher peaks in October–March due to the influence of human activities such as emissions from biomass burning and brick production. The N2O mole fractions at NTL and CLA increased in June–August and November–February, which coincided with the application of nitrogen fertilizer and the burning of biomass such as the harvest residues and dung for domestic cooking. Based on H2 seasonal variation at both sites, it appeared that the emissions in this region were related to biomass burning in addition to production from the reaction of OH and CH4. The SF6 mole fraction was similar to that at MLO, suggesting that there were few anthropogenic SF6 emission sources in the district. The variability of the CO2 growth rate at NTL was different from the variability in the CO2 growth rate at MLO, which is more closely linked to the El Niño–Southern Oscillation (ENSO). In addition, the growth rates of the CH4 and SF6 mole fractions at NTL showed an anticorrelation with those at MLO, indicating that the frequency of southerly air masses strongly influenced these mole fractions. These findings showed that rather large regional climatic conditions considerably controlled interannual variations in GHGs, δ13C-CO2, and δ18O-CO2 through changes in precipitation and air mass.

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b).

Satellite observations provide spatially resolved global estimates of column-averaged mixing ratios of CO2 (XCO2) over the Earth's surface. The accuracy of these datasets can be validated against reliable standards in some areas, but other areas remain inaccessible. To date, limited reference data over oceans hinder successful uncertainty quantification or bias correction efforts and preclude reliable conclusions about changes in the carbon cycle in some regions. Here, we propose a new approach to analyze and evaluate seasonal, interannual, and latitudinal variations of XCO2 over oceans by integrating cargo-ship (Ship Of Opportunity – SOOP) and commercial aircraft (Comprehensive Observation Network for Trace gases by Airliner – CONTRAIL) observations with the aid of state-of-the art atmospheric chemistry-transport model calculations. The consistency of the “observation-based column-averaged CO2” dataset (obs. XCO2) with satellite estimates was analyzed over the western Pacific between 2014 and 2017, and its utility as a reference dataset evaluated. Our results demonstrate that the new dataset accurately captures seasonal and interannual variations of CO2. Retrievals of XCO2 over the ocean from GOSAT (Greenhouse Gases Observing Satellite: National Institute for Environmental Studies – NIES v02.75; Atmospheric CO2 Observation from Space – ACOS v7.3) and OCO-2 (Orbiting Carbon Observatory, v9r) observations show a negative bias of about 1 part per million (ppm) in northern midlatitudes, which was attributed to measurement uncertainties of the satellite observations. The NIES retrieval had higher consistency with obs. XCO2 at midlatitudes as compared to the other retrievals. At low latitudes, it shows many fewer valid data and high scatter, such that ACOS and OCO-2 appear to provide a better representation of the carbon cycle. At different times, the seasonal cycles of all three retrievals show positive phase shifts of 1 month relative to the observation-based data. The study indicates that even if the retrievals complement each other, remaining uncertainties limit the accurate interpretation of spatiotemporal changes in CO2 fluxes. A continuous long-term XCO2 dataset with wide latitudinal coverage based on the new approach has great potential as a robust reference dataset for XCO2 and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.

The measurement of solar radiation and its forecasting at any particular location is a difficult task as it depends on various input parameters. So, intelligent modeling approaches with advanced techniques are always necessary for this challenging activity. Adaptive neuro-fuzzy inference system (ANFIS) based on modeling plays a vital role in the selection of relevant input parameters for undertaking precise solar radiation prediction. Numerous literature works focusing on ANFIS-based techniques have been reviewed during the estimation of solar energy incidents in the eastern part of India. During solar forecasting, the input parameters considered for this model are the duration of the sunshine, temperature, and humidity whereas the clearness index value has been considered as an output parameter for calculation. For designing the model, practical data sets have been prepared for some specified locations. Finally, the outcome is compared with several other techniques. During this course of analysis, several studies have been reviewed for a comprehensive literature survey work.

Variations in the atmospheric oxidative capacity, largely determined by variations in the hydroxyl radical (OH), form a key uncertainty in many greenhouse and other pollutant budgets, such as that of methane (CH4). Methyl chloroform (MCF) is an often-adopted tracer to indirectly put observational constraints on large-scale variations in OH. We investigated the budget of MCF in a 4DVAR inversion using the atmospheric transport model TM5, for the period 1998–2018, with the objective to derive information on large-scale, interannual variations in atmospheric OH concentrations. While our main inversion did not fully converge, we did derive interannual variations in the global oxidation of MCF that bring simulated mole fractions of MCF within 1 %–2 % of the assimilated observations from the NOAA-GMD surface network at most sites. Additionally, the posterior simulations better reproduce aircraft observations used for independent validation compared to the prior simulations. The derived OH variations showed robustness with respect to the prior MCF emissions and the prior OH distribution over the 1998 to 2008 period. Although we find a rapid 8 % increase in global mean OH concentrations between 2010 and 2012 that quickly declines afterwards, the derived interannual variations were typically small (< 3 %/yr), with no significant long-term trend in global mean OH concentrations. The inverse system found strong adjustments to the latitudinal distribution of OH, relative to widely used prior distributions, with systematic increases in tropical and decreases in extra-tropical OH concentrations (both up to 30 %). These spatial adjustments were driven by intrahemispheric biases in simulated MCF mole fractions, which have not been identified in previous studies. Given the large amplitude of these adjustments, which exceeds spread between literature estimates, and a residual bias in the MCF intrahemispheric gradients, we suggest a reversal in the extratropical ocean sink of MCF in response to declining atmospheric MCF abundance (as hypothesized in Wennberg et al., 2004). This ocean source provides a more realistic explanation for the biases, possibly complementary to adjustments in the OH distribution. We identified significant added value in the use of a 3D transport model, since it implicitly accounts for variable transport and optimizes the observed spatial gradients of MCF, which is not possible in simpler models. However, we also found a trade-off in computational expense and convergence problems. Despite these convergence problems, the derived OH variations do result in an improved match with MCF observations relative to an interannually repeating prior for OH. Therefore, we consider that variations in OH derived from MCF inversions with 3D models can add value to budget studies of long-lived gases like CH4.

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009–2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80 % since TransCom 3 and 70 % since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009–2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004–2014, 2001–2004, 1992–1996) and re-evaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean–land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean–land partitioning from the model ensemble.

We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.

Methane (CH4) is one of the most important short-lived climate forcers for its critical roles in greenhouse warming and air pollution chemistry in the troposphere, and the water vapor budget in the stratosphere. It is estimated that up to about 8 % of global CH4 emissions occur from South Asia, covering less than 1 % of the global land. With the availability of satellite observations from space, variability in CH4 has been captured for most parts of the global land with major emissions, which were otherwise not covered by the surface observation network. The satellite observation of the columnar dry-air mole fractions of methane (XCH4) is an integrated measure of CH4 densities at all altitudes from the surface to the top of the atmosphere. Here, we present an analysis of XCH4 variability over different parts of India and the surrounding cleaner oceanic regions as measured by the Greenhouse gases Observation SATellite (GOSAT) and simulated by an atmospheric chemistry-transport model (ACTM). Distinct seasonal variations of XCH4 have been observed over the northern (north of 15° N) and southern (south of 15° N) parts of India, corresponding to the peak during the southwestern monsoon (July–September) and early autumn (October–December) seasons, respectively. Analysis of the transport, emission, and chemistry contributions to XCH4 using ACTM suggests that a distinct XCH4 seasonal cycle over northern and southern regions of India is governed by both the heterogeneous distributions of surface emissions and a contribution of the partial CH4 column in the upper troposphere. Over most of the northern Indian Gangetic Plain regions, up to 40 % of the peak-to-trough amplitude during the southwestern (SW) monsoon season is attributed to the lower troposphere ( ∼ 1000–600 hPa), and ∼ 40 % to uplifted high-CH4 air masses in the upper troposphere ( ∼ 600–200 hPa). In contrast, the XCH4 seasonal enhancement over semi-arid western India is attributed mainly ( ∼ 70 %) to the upper troposphere. The lower tropospheric region contributes up to 60 % in the XCH4 seasonal enhancement over the Southern Peninsula and oceanic region. These differences arise due to the complex atmospheric transport mechanisms caused by the seasonally varying monsoon. The CH4 enriched air mass is uplifted from a high-emission region of the Gangetic Plain by the SW monsoon circulation and deep cumulus convection and then confined by anticyclonic wind in the upper tropospheric heights ( ∼ 200 hPa). The anticyclonic confinement of surface emission over a wider South Asia region leads to a strong contribution of the upper troposphere in the formation of the XCH4 peak over northern India, including the semi-arid regions with extremely low CH4 emissions. Based on this analysis, we suggest that a link between surface emissions and higher levels of XCH4 is not always valid over Asian monsoon regions, although there is often a fair correlation between surface emissions and XCH4. The overall validity of ACTM simulation for capturing GOSAT observed seasonal and spatial XCH4 variability will allow us to perform inverse modeling of XCH4 emissions in the future using XCH4 data.

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by top-down inversions at 558 Tg CH4 yr−1, range 540–568. About 60 % of global emissions are anthropogenic (range 50–65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 Tg CH4 yr−1, range 596–884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (∼ 64 % of the global budget, 4 yr−1, range 51–72, −14 %) and higher emissions in Africa (86 Tg CH4 yr−1, range 73–108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30–40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model–TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Using MIPAS-based stratospheric CH4 fields in place of model simulations improves the model–TCCON XCH4 agreement for all models. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 to 4.3 ppb) and LMDz (Laboratoire de Météorologie Dynamique model with zooming capability; from 6.8 to 4.3 ppb). Replacing model simulations with MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduces the average XCH4 bias for ACTM (3.3 ppb), but increases the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that model errors in simulating stratospheric CH4 contribute to model biases. Current satellite instruments cannot definitively measure stratospheric CH4 to sufficient accuracy to eliminate these biases. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. Therefore, it would be worthwhile to analyze how individual model components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) impact the simulation of stratospheric CH4 and XCH4.

The first concerted multi-model intercomparison of halogenated very short-lived substances (VSLS) has been performed, within the framework of the ongoing Atmospheric Tracer Transport Model Intercomparison Project (TransCom). Eleven global models or model variants participated (nine chemical transport models and two chemistry–climate models) by simulating the major natural bromine VSLS, bromoform (CHBr3) and dibromomethane (CH2Br2), over a 20-year period (1993–2012). Except for three model simulations, all others were driven offline by (or nudged to) reanalysed meteorology. The overarching goal of TransCom-VSLS was to provide a reconciled model estimate of the stratospheric source gas injection (SGI) of bromine from these gases, to constrain the current measurement-derived range, and to investigate inter-model differences due to emissions and transport processes. Models ran with standardised idealised chemistry, to isolate differences due to transport, and we investigated the sensitivity of results to a range of VSLS emission inventories. Models were tested in their ability to reproduce the observed seasonal and spatial distribution of VSLS at the surface, using measurements from NOAA's long-term global monitoring network, and in the tropical troposphere, using recent aircraft measurements – including high-altitude observations from the NASA Global Hawk platform. The models generally capture the observed seasonal cycle of surface CHBr3 and CH2Br2 well, with a strong model–measurement correlation (r ≥ 0.7) at most sites. In a given model, the absolute model–measurement agreement at the surface is highly sensitive to the choice of emissions. Large inter-model differences are apparent when using the same emission inventory, highlighting the challenges faced in evaluating such inventories at the global scale. Across the ensemble, most consistency is found within the tropics where most of the models (8 out of 11) achieve best agreement to surface CHBr3 observations using the lowest of the three CHBr3 emission inventories tested (similarly, 8 out of 11 models for CH2Br2). In general, the models reproduce observations of CHBr3 and CH2Br2 obtained in the tropical tropopause layer (TTL) at various locations throughout the Pacific well. Zonal variability in VSLS loading in the TTL is generally consistent among models, with CHBr3 (and to a lesser extent CH2Br2) most elevated over the tropical western Pacific during boreal winter. The models also indicate the Asian monsoon during boreal summer to be an important pathway for VSLS reaching the stratosphere, though the strength of this signal varies considerably among models. We derive an ensemble climatological mean estimate of the stratospheric bromine SGI from CHBr3 and CH2Br2 of 2.0 (1.2–2.5) ppt, ∼ 57 % larger than the best estimate from the most recent World Meteorological Organization (WMO) Ozone Assessment Report. We find no evidence for a long-term, transport-driven trend in the stratospheric SGI of bromine over the simulation period. The transport-driven interannual variability in the annual mean bromine SGI is of the order of ±5 %, with SGI exhibiting a strong positive correlation with the El Niño–Southern Oscillation (ENSO) in the eastern Pacific. Overall, our results do not show systematic differences between models specific to the choice of reanalysis meteorology, rather clear differences are seen related to differences in the implementation of transport processes in the models.

Warmer temperatures and elevated atmospheric CO2 concentrations over the last several decades have been credited with increasing vegetation activity and photosynthetic uptake of CO2 from the atmosphere in the high northern latitude ecosystems: the boreal forest and arctic tundra. At the same time, soils in the region have been warming, permafrost is melting, fire frequency and severity are increasing, and some regions of the boreal forest are showing signs of stress due to drought or insect disturbance. The recent trends in net carbon balance of these ecosystems, across heterogeneous disturbance patterns, and the future implications of these changes are unclear. Here, we examine CO2 fluxes from northern boreal and tundra regions from 1985 to 2012, estimated from two atmospheric inversions (RIGC and Jena). Both used measured atmospheric CO2 concentrations and wind fields from interannually variable climate reanalysis. In the arctic zone, the latitude region above 60° N excluding Europe (10° W–63° E), neither inversion finds a significant long-term trend in annual CO2 balance. The boreal zone, the latitude region from approximately 50–60° N, again excluding Europe, showed a trend of 8–11 Tg C yr−2 over the common period of validity from 1986 to 2006, resulting in an annual CO2 sink in 2006 that was 170–230 Tg C yr−1 larger than in 1986. This trend appears to continue through 2012 in the Jena inversion as well. In both latitudinal zones, the seasonal amplitude of monthly CO2 fluxes increased due to increased uptake in summer, and in the arctic zone also due to increased fall CO2 release. These findings suggest that the boreal zone has been maintaining and likely increasing CO2 sink strength over this period, despite browning trends in some regions and changes in fire frequency and land use. Meanwhile, the arctic zone shows that increased summer CO2 uptake, consistent with strong greening trends, is offset by increased fall CO2 release, resulting in a net neutral trend in annual fluxes. The inversion fluxes from the arctic and boreal zones covering the permafrost regions showed no indication of a large-scale positive climate–carbon feedback caused by warming temperatures on high northern latitude terrestrial CO2 fluxes from 1985 to 2012.

About 70 % of the anthropogenic carbon dioxide (CO2) is emitted from the megacities and urban areas of the world. In order to draw effective emission mitigation policies for combating future climate change as well as independently validating the emission inventories for constraining their large range of uncertainties, especially over major metropolitan areas of developing countries, there is an urgent need for greenhouse gas measurements over representative urban regions. India is a fast developing country, where fossil fuel emissions have increased dramatically in the last three decades and are predicted to continue to grow further by at least 6 % per year through to 2025. The CO2 measurements over urban regions in India are lacking. To overcome this limitation, simultaneous measurements of CO2 and carbon monoxide (CO) have been made at Ahmedabad, a major urban site in western India, using a state-of-the-art laser-based cavity ring down spectroscopy technique from November 2013 to May 2015. These measurements enable us to understand the diurnal and seasonal variations in atmospheric CO2 with respect to its sources (both anthropogenic and biospheric) and biospheric sinks. The observed annual average concentrations of CO2 and CO are 413.0 ± 13.7 and 0.50 ± 0.37 ppm respectively. Both CO2 and CO show strong seasonality with lower concentrations (400.3 ± 6.8 and 0.19 ± 0.13 ppm) during the south-west monsoon and higher concentrations (419.6 ± 22.8 and 0.72 ± 0.68 ppm) during the autumn (SON) season. Strong diurnal variations are also observed for both the species. The common factors for the diurnal cycles of CO2 and CO are vertical mixing and rush hour traffic, while the influence of biospheric fluxes is also seen in the CO2 diurnal cycle. Using CO and CO2 covariation, we differentiate the anthropogenic and biospheric components of CO2 and found significant contributions of biospheric respiration and anthropogenic emissions in the late night (00:00–05:00 h, IST) and evening rush hours (18:00–22:00 h) respectively. We compute total yearly emissions of CO to be 69.2 ± 0.07 Gg for the study region using the observed CO : CO2 correlation slope and bottom-up CO2 emission inventory. This calculated emission of CO is 52 % larger than the estimated emission of CO by the emissions database for global atmospheric research (EDGAR) inventory. The observations of CO2 have been compared with an atmospheric chemistry-transport model (ACTM), which incorporates various components of CO2 fluxes. ACTM is able to capture the basic variabilities, but both diurnal and seasonal amplitudes are largely underestimated compared to the observations. We attribute this underestimation by the model to uncertainties in terrestrial biosphere fluxes and coarse model resolution. The fossil fuel signal from the model shows fairly good correlation with observed CO2 variations, which supports the overall dominance of fossil fuel emissions over the biospheric fluxes in this urban region.

Land biosphere uptake of carbon is important in mitigating the anthropogenic increase in atmospheric CO2and its climate forcing. Here, the authors show that land biosphere uptake of carbon in Asia has increased substantially since the mid 1990s, likely owing to reforestation and regional climate change.

Atmospheric methane (CH4) increased from ~900 ppb (parts per billion, or nanomoles per mole of dry air) in 1900 to ~1800 ppb in 2010 at a rate unprecedented in any observational records. However, the contributions of the various methane sources and sinks to the CH4 increase are poorly understood. Here we use initial emissions from bottom-up inventories for anthropogenic sources, emissions from wetlands and rice paddies simulated by a~terrestrial biogeochemical model, and an atmospheric general circulation model (AGCM)-based chemistry-transport model (i.e. ACTM) to simulate atmospheric CH4 concentrations for 1910–2010. The ACTM simulations are compared with the CH4 concentration records reconstructed from Antarctic and Arctic ice cores and firn air samples, and from direct measurements since the 1980s at multiple sites around the globe. The differences between ACTM simulations and observed CH4 concentrations are minimized to optimize the global total emissions using a mass balance calculation. During 1910–2010, the global total CH4 emission doubled from ~290 to ~580 Tg yr−1. Compared to optimized emission, the bottom-up emission data set underestimates the rate of change of global total CH4 emissions by ~30% during the high growth period of 1940–1990, while it overestimates by ~380% during the low growth period of 1990–2010. Further, using the CH4 stable carbon isotopic data (δ13C), we attribute the emission increase during 1940–1990 primarily to enhancement of biomass burning. The total lifetime of CH4 shortened from 9.4 yr during 1910–1919 to 9 yr during 2000–2009 by the combined effect of the increasing abundance of atomic chlorine radicals (Cl) and increases in average air temperature. We show that changes of CH4 loss rate due to increased tropospheric air temperature and CH4 loss due to Cl in the stratosphere are important sources of uncertainty to more accurately estimate the global CH4 budget from δ13C observations.

This study examines N2O emission estimates from five different atmospheric inversion frameworks based on chemistry transport models (CTMs). The five frameworks differ in the choice of CTM, meteorological data, prior uncertainties and inversion method but use the same prior emissions and observation data set. The posterior modelled atmospheric N2O mole fractions are compared to observations to assess the performance of the inversions and to help diagnose problems in the modelled transport. Additionally, the mean emissions for 2006 to 2008 are compared in terms of the spatial distribution and seasonality. Overall, there is a good agreement among the inversions for the mean global total emission, which ranges from 16.1 to 18.7 TgN yr−1 and is consistent with previous estimates. Ocean emissions represent between 31 and 38% of the global total compared to widely varying previous estimates of 24 to 38%. Emissions from the northern mid- to high latitudes are likely to be more important, with a consistent shift in emissions from the tropics and subtropics to the mid- to high latitudes in the Northern Hemisphere; the emission ratio for 0–30° N to 30–90° N ranges from 1.5 to 1.9 compared with 2.9 to 3.0 in previous estimates. The largest discrepancies across inversions are seen for the regions of South and East Asia and for tropical and South America owing to the poor observational constraint for these areas and to considerable differences in the modelled transport, especially inter-hemispheric exchange rates and tropical convective mixing. Estimates of the seasonal cycle in N2O emissions are also sensitive to errors in modelled stratosphere-to-troposphere transport in the tropics and southern extratropics. Overall, the results show a convergence in the global and regional emissions compared to previous independent studies.

We present a comprehensive estimate of nitrous oxide (N2O) emissions using observations and models from 1995 to 2008. High-frequency records of tropospheric N2O are available from measurements at Cape Grim, Tasmania; Cape Matatula, American Samoa; Ragged Point, Barbados; Mace Head, Ireland; and at Trinidad Head, California using the Advanced Global Atmospheric Gases Experiment (AGAGE) instrumentation and calibrations. The Global Monitoring Division of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) has also collected discrete air samples in flasks and in situ measurements from remote sites across the globe and analyzed them for a suite of species including N2O. In addition to these major networks, we include in situ and aircraft measurements from the National Institute of Environmental Studies (NIES) and flask measurements from the Tohoku University and Commonwealth Scientific and Industrial Research Organization (CSIRO) networks. All measurements show increasing atmospheric mole fractions of N2O, with a varying growth rate of 0.1–0.7% per year, resulting in a 7.4% increase in the background atmospheric mole fraction between 1979 and 2011. Using existing emission inventories as well as bottom-up process modeling results, we first create globally gridded a priori N2O emissions over the 37 years since 1975. We then use the three-dimensional chemical transport model, Model for Ozone and Related Chemical Tracers version 4 (MOZART v4), and a Bayesian inverse method to estimate global as well as regional annual emissions for five source sectors from 13 regions in the world. This is the first time that all of these measurements from multiple networks have been combined to determine emissions. Our inversion indicates that global and regional N2O emissions have an increasing trend between 1995 and 2008. Despite large uncertainties, a significant increase is seen from the Asian agricultural sector in recent years, most likely due to an increase in the use of nitrogenous fertilizers, as has been suggested by previous studies.

We present a comparison of chemistry-transport models (TransCom-N2O) to examine the importance of atmospheric transport and surface fluxes on the variability of N2O mixing ratios in the troposphere. Six different models and two model variants participated in the inter-comparison and simulations were made for the period 2006 to 2009. In addition to N2O, simulations of CFC-12 and SF6 were made by a subset of four of the models to provide information on the models' proficiency in stratosphere–troposphere exchange (STE) and meridional transport, respectively. The same prior emissions were used by all models to restrict differences among models to transport and chemistry alone. Four different N2O flux scenarios totalling between 14 and 17 TgN yr−1 (for 2005) globally were also compared. The modelled N2O mixing ratios were assessed against observations from in situ stations, discrete air sampling networks and aircraft. All models adequately captured the large-scale patterns of N2O and the vertical gradient from the troposphere to the stratosphere and most models also adequately captured the N2O tropospheric growth rate. However, all models underestimated the inter-hemispheric N2O gradient by at least 0.33 parts per billion (ppb), equivalent to 1.5 TgN, which, even after accounting for an overestimate of emissions in the Southern Ocean of circa 1.0 TgN, points to a likely underestimate of the Northern Hemisphere source by up to 0.5 TgN and/or an overestimate of STE in the Northern Hemisphere. Comparison with aircraft data reveal that the models overestimate the amplitude of the N2O seasonal cycle at Hawaii (21° N, 158° W) below circa 6000 m, suggesting an overestimate of the importance of stratosphere to troposphere transport in the lower troposphere at this latitude. In the Northern Hemisphere, most of the models that provided CFC-12 simulations captured the phase of the CFC-12, seasonal cycle, indicating a reasonable representation of the timing of STE. However, for N2O all models simulated a too early minimum by 2 to 3 months owing to errors in the seasonal cycle in the prior soil emissions, which was not adequately represented by the terrestrial biosphere model. In the Southern Hemisphere, most models failed to capture the N2O and CFC-12 seasonality at Cape Grim, Tasmania, and all failed at the South Pole, whereas for SF6, all models could capture the seasonality at all sites, suggesting that there are large errors in modelled vertical transport in high southern latitudes.

Air–sea CO2 fluxes over the Pacific Ocean are known to be characterized by coherent large-scale structures that reflect not only ocean subduction and upwelling patterns, but also the combined effects of wind-driven gas exchange and biology. On the largest scales, a large net CO2 influx into the extratropics is associated with a robust seasonal cycle, and a large net CO2 efflux from the tropics is associated with substantial interannual variability. In this work, we have synthesized estimates of the net air–sea CO2 flux from a variety of products, drawing upon a variety of approaches in three sub-basins of the Pacific Ocean, i.e., the North Pacific extratropics (18–66° N), the tropical Pacific (18° S–18° N), and the South Pacific extratropics (44.5–18° S). These approaches include those based on the measurements of CO2 partial pressure in surface seawater (pCO2sw), inversions of ocean-interior CO2 data, forward ocean biogeochemistry models embedded in the ocean general circulation models (OBGCMs), a model with assimilation of pCO2sw data, and inversions of atmospheric CO2 measurements. Long-term means, interannual variations and mean seasonal variations of the regionally integrated fluxes were compared in each of the sub-basins over the last two decades, spanning the period from 1990 through 2009. A simple average of the long-term mean fluxes obtained with surface water pCO2 diagnostics and those obtained with ocean-interior CO2 inversions are −0.47 ± 0.13 Pg C yr−1 in the North Pacific extratropics, +0.44 ± 0.14 Pg C yr−1 in the tropical Pacific, and −0.37 ± 0.08 Pg C yr−1 in the South Pacific extratropics, where positive fluxes are into the atmosphere. This suggests that approximately half of the CO2 taken up over the North and South Pacific extratropics is released back to the atmosphere from the tropical Pacific. These estimates of the regional fluxes are also supported by the estimates from OBGCMs after adding the riverine CO2 flux, i.e., −0.49 ± 0.02 Pg C yr−1 in the North Pacific extratropics, +0.41 ± 0.05 Pg C yr−1 in the tropical Pacific, and −0.39 ± 0.11 Pg C yr−1 in the South Pacific extratropics. The estimates from the atmospheric CO2 inversions show large variations amongst different inversion systems, but their median fluxes are consistent with the estimates from climatological pCO2sw data and pCO2sw diagnostics. In the South Pacific extratropics, where CO2 variations in the surface and ocean interior are severely undersampled, the difference in the air–sea CO2 flux estimates between the diagnostic models and ocean-interior CO2 inversions is larger (0.18 Pg C yr−1). The range of estimates from forward OBGCMs is also large (−0.19 to −0.72 Pg C yr−1). Regarding interannual variability of air–sea CO2 fluxes, positive and negative anomalies are evident in the tropical Pacific during the cold and warm events of the El Niño–Southern Oscillation in the estimates from pCO2sw diagnostic models and from OBGCMs. They are consistent in phase with the Southern Oscillation Index, but the peak-to-peak amplitudes tend to be higher in OBGCMs (0.40 ± 0.09 Pg C yr−1) than in the diagnostic models (0.27 ± 0.07 Pg C yr−1).

The Indian Ocean (44° S–30° N) plays an important role in the global carbon cycle, yet it remains one of the most poorly sampled ocean regions. Several approaches have been used to estimate net sea–air CO2 fluxes in this region: interpolated observations, ocean biogeochemical models, atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Indian Ocean sea–air CO2 fluxes between 1990 and 2009. Using all of the models and inversions, the median annual mean sea–air CO2 uptake of −0.37 ± 0.06 PgC yr−1 is consistent with the −0.24 ± 0.12 PgC yr−1 calculated from observations. The fluxes from the southern Indian Ocean (18–44° S; −0.43 ± 0.07 PgC yr−1 are similar in magnitude to the annual uptake for the entire Indian Ocean. All models capture the observed pattern of fluxes in the Indian Ocean with the following exceptions: underestimation of upwelling fluxes in the northwestern region (off Oman and Somalia), overestimation in the northeastern region (Bay of Bengal) and underestimation of the CO2 sink in the subtropical convergence zone. These differences were mainly driven by lack of atmospheric CO2 data in atmospheric inversions, and poor simulation of monsoonal currents and freshwater discharge in ocean biogeochemical models. Overall, the models and inversions do capture the phase of the observed seasonality for the entire Indian Ocean but overestimate the magnitude. The predicted sea–air CO2 fluxes by ocean biogeochemical models (OBGMs) respond to seasonal variability with strong phase lags with reference to climatological CO2 flux, whereas the atmospheric inversions predicted an order of magnitude higher seasonal flux than OBGMs. The simulated interannual variability by the OBGMs is weaker than that found by atmospheric inversions. Prediction of such weak interannual variability in CO2 fluxes by atmospheric inversions was mainly caused by a lack of atmospheric data in the Indian Ocean. The OBGM models suggest a small strengthening of the sink over the period 1990–2009 of −0.01 PgC decade−1. This is inconsistent with the observations in the southwestern Indian Ocean that shows the growth rate of oceanic pCO2 was faster than the observed atmospheric CO2 growth, a finding attributed to the trend of the Southern Annular Mode (SAM) during the 1990s.

Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

Atmospheric CO2 inversions estimate surface carbon fluxes from an optimal fit to atmospheric CO2 measurements, usually including prior constraints on the flux estimates. Eleven sets of carbon flux estimates are compared, generated by different inversions systems that vary in their inversions methods, choice of atmospheric data, transport model and prior information. The inversions were run for at least 5 yr in the period between 1990 and 2010. Mean fluxes for 2001–2004, seasonal cycles, interannual variability and trends are compared for the tropics and northern and southern extra-tropics, and separately for land and ocean. Some continental/basin-scale subdivisions are also considered where the atmospheric network is denser. Four-year mean fluxes are reasonably consistent across inversions at global/latitudinal scale, with a large total (land plus ocean) carbon uptake in the north (−3.4 Pg C yr−1 (±0.5 Pg C yr−1 standard deviation), with slightly more uptake over land than over ocean), a significant although more variable source over the tropics (1.6 ± 0.9 Pg C yr−1) and a compensatory sink of similar magnitude in the south (−1.4 ± 0.5 Pg C yr−1) corresponding mainly to an ocean sink. Largest differences across inversions occur in the balance between tropical land sources and southern land sinks. Interannual variability (IAV) in carbon fluxes is larger for land than ocean regions (standard deviation around 1.06 versus 0.33 Pg C yr−1 for the 1996–2007 period), with much higher consistency among the inversions for the land. While the tropical land explains most of the IAV (standard deviation ~ 0.65 Pg C yr−1), the northern and southern land also contribute (standard deviation ~ 0.39 Pg C yr−1). Most inversions tend to indicate an increase of the northern land carbon uptake from late 1990s to 2008 (around 0.1 Pg C yr−1, predominantly in North Asia. The mean seasonal cycle appears to be well constrained by the atmospheric data over the northern land (at the continental scale), but still highly dependent on the prior flux seasonality over the ocean. Finally we provide recommendations to interpret the regional fluxes, along with the uncertainty estimates.

A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

No abstract available.

Atmospheric column-averaged mole fractions of carbon dioxide (XCO2) at 53 locations around the world were derived from aircraft measurements covering the altitude range of about 1–10 km. We used CO2 vertical profile measurements from three major carbon cycle programs, a global climatological data set of air number density profiles and tropopause height for calculating XCO2 for the period of 2007–2009. Vertical profiles of the CO2 mixing ratio are complemented by tall tower data up to 400 m from the earth's surface and by simulated profiles in the stratosphere from a chemistry-transport model. The amplitude of the seasonal cycle of calculated XCO2 values shows clear latitudinal dependence, and the amplitude decreases from about 10 ppm at high latitudes in the Northern Hemisphere to at most 2 ppm in the tropics and the Southern Hemisphere. The uncertainties of XCO2 were estimated from assumptions about CO2 profiles for each flight. Typically, uncertainties were less than 1 ppm; thus, this data set is within the level of uncertainty needed for primary validation of XCO2 measurements by the Greenhouse gases Observing SATellite (GOSAT) and by future satellite missions for monitoring greenhouse gases.

We present the first intercalibration of dry-air column-averaged mole fractions of methane (XCH4) retrieved from solar Fourier transform infrared (FTIR) measurements of the Network for the Detection of Atmospheric Composition Change (NDACC) in the mid-infrared (MIR) versus near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON). The study uses multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch, Germany (47.48° N, 11.06° E, 743 m a.s.l.), and Wollongong, Australia (34.41° S, 150.88° E, 30 m a.s.l.). Direct comparison of the retrieved MIR and NIR XCH4 time series for Garmisch shows a quasi-periodic seasonal bias leading to a standard deviation (stdv) of the difference time series (NIR–MIR) of 7.2 ppb. After reducing time-dependent a priori impact by using realistic site- and time-dependent ACTM-simulated profiles as a common prior, the seasonal bias is reduced (stdv = 5.2 ppb). A linear fit to the MIR/NIR scatter plot of monthly means based on same-day coincidences does not show a y-intercept that is statistically different from zero, and the MIR/NIR intercalibration factor is found to be close to ideal within 2-σ uncertainty, i.e. 0.9996(8). The difference time series (NIR–MIR) do not show a significant trend. The same basic findings hold for Wollongong. In particular an overall MIR/NIR intercalibration factor close to the ideal 1 is found within 2-σ uncertainty. At Wollongong the seasonal cycle of methane is less pronounced and corresponding smoothing errors are not as significant, enabling standard MIR and NIR retrievals to be used directly, without correction to a common a priori. Our results suggest that it is possible to set up a harmonized NDACC and TCCON XCH4 data set which can be exploited for joint trend studies, satellite validation, or the inverse modeling of sources and sinks.

A modified cumulus convection parametrisation scheme is presented. This scheme computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by a reanalysis dataset. The representation of vertical transport within the scheme includes entrainment and detrainment processes in convective updrafts and downdrafts. Output from the proposed parametrisation scheme is employed in the National Institute for Environmental Studies (NIES) global chemical transport model driven by JRA-25/JCDAS reanalysis. The simulated convective precipitation rate and mass fluxes are compared with observations and reanalysis data. A simulation of the short-lived tracer 222Rn is used to further evaluate the performance of the cumulus convection scheme. Simulated distributions of 222Rn are evaluated against observations at the surface and in the free troposphere, and compared with output from models that participated in the TransCom-CH4 Transport Model Intercomparison. From this comparison, we demonstrate that the proposed convective scheme in general is consistent with observed and modeled results.

The source and sinks of carbon dioxide (CO2) and methane (CH4) due to anthropogenic and natural biospheric activities were estimated for the South Asian region (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). Flux estimates were based on top-down methods that use inversions of atmospheric data, and bottom-up methods that use field observations, satellite data, and terrestrial ecosystem models. Based on atmospheric CO2 inversions, the net biospheric CO2 flux in South Asia (equivalent to the Net Biome Productivity, NBP) was a sink, estimated at −104 ± 150 Tg C yr−1 during 2007–2008. Based on the bottom-up approach, the net biospheric CO2 flux is estimated to be −191 ± 193 Tg C yr−1 during the period of 2000–2009. This last net flux results from the following flux components: (1) the Net Ecosystem Productivity, NEP (net primary production minus heterotrophic respiration) of −220 ± 186 Tg C yr−1 (2) the annual net carbon flux from land-use change of −14 ± 50 Tg C yr−1, which resulted from a sink of −16 Tg C yr−1 due to the establishment of tree plantations and wood harvest, and a source of 2 Tg C yr−1 due to the expansion of croplands; (3) the riverine export flux from terrestrial ecosystems to the coastal oceans of +42.9 Tg C yr−1; and (4) the net CO2 emission due to biomass burning of +44.1 ± 13.7 Tg C yr−1. Including the emissions from the combustion of fossil fuels of 444 Tg C yr−1 for the 2000s, we estimate a net CO2 land–atmosphere flux of 297 Tg C yr−1. In addition to CO2, a fraction of the sequestered carbon in terrestrial ecosystems is released to the atmosphere as CH4. Based on bottom-up and top-down estimates, and chemistry-transport modeling, we estimate that 37 ± 3.7 Tg C yr−1 were released to atmosphere from South Asia during the 2000s. Taking all CO2 and CH4 fluxes together, our best estimate of the net land–atmosphere CO2-equivalent flux is a net source of 334 Tg C yr−1 for the South Asian region during the 2000s. If CH4 emissions are weighted by radiative forcing of molecular CH4, the total CO2-equivalent flux increases to 1148 Tg C yr−1 suggesting there is great potential of reducing CH4 emissions for stabilizing greenhouse gases concentrations.

We present the mixing ratio, δ13C and δD of atmospheric CH4 using commercial aircraft in the upper troposphere (UT) over the Western Pacific for the period December 2005–September 2010. The observed results were compared with those obtained using commercial container ships in the lower troposphere (LT) over the same region. In the Northern Hemisphere (NH), the UT CH4 mixing ratio shows high values in the boreal summer–autumn, when the LT CH4 mixing ratio reaches a seasonal minimum. From tagged tracer experiments made using an atmospheric chemistry transport model, we found that such high CH4 values are due to rapid transport of air masses influenced by CH4 sources in South Asia and East Asia. The observed isotopic ratio data imply that these areas have CH4 sources with relatively low δ13C and δD signatures such as biogenic sources. Latitudinal distributions of the annual average UT and LT CH4 mixing ratio intersect each other in the tropics; the mixing ratio value is lower in the UT than in the LT in the NH and the situation is reversed in the Southern Hemisphere (SH), due mainly to the NH air intrusion into the SH through the UT. Such intersection of the latitudinal distributions is observable in δD but not in δ13C, implying an additional contribution from reaction of CH4 with active chlorine in the marine boundary layer. δ13C and δD show low values in the NH and high values in the SH both in the UT and in the LT. We also observed an increase in the CH4 mixing ratio and decreases in δ13C and δ

We present a comparison of an atmospheric general circulation model (AGCM)-based chemistry-transport model (ACTM) simulation with total column measurements of CO2, CH4 and N2O from the Total Carbon Column Observing Network (TCCON). The model is able to capture observed trends, seasonal cycles and inter hemispheric gradients at most sampled locations for all three species. The model-observation agreements are best for CO2, because the simulation uses fossil fuel inventories and an inverse model estimate of non-fossil fuel fluxes. The ACTM captures much of the observed seasonal variability in CO2 and N2O total columns (~81 % variance, R>0.9 between ACTM and TCCON for 19 out of 22 cases). These results suggest that the transport processes in troposphere and stratosphere are well represented in ACTM. Thus the poor correlation between simulated and observed CH4 total columns, particularly at tropical and extra-tropical sites, have been attributed to the uncertainties in surface emissions and loss by hydroxyl radicals. While the upward-looking total column measurements of CO2 contains surface flux signals at various spatial and temporal scales, the N2O measurements are strongly affected by the concentration variations in the upper troposphere and stratosphere.

Numerical simulation and validation of three-dimensional structure of atmospheric carbon dioxide (CO2) is necessary for quantification of transport model uncertainty and its role on surface flux estimation by inverse modeling. Simulations of atmospheric CO2 were performed using four transport models and two sets of surface fluxes compared with an aircraft measurement dataset of Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL), covering various latitudes, longitudes, and heights. Under this transport model intercomparison project, spatiotemporal variations of CO2 concentration for 2006–2007 were analyzed with a three-dimensional perspective. Results show that the models reasonably simulated vertical profiles and seasonal variations not only over northern latitude areas but also over the tropics and southern latitudes. From CONTRAIL measurements and model simulations, intrusion of northern CO2 in to the Southern Hemisphere, through the upper troposphere, was confirmed. Furthermore, models well simulated the vertical propagation of seasonal variation in the northern free troposphere. However, significant model-observation discrepancies were found in Asian regions, which are attributable to uncertainty of the surface CO2 flux data. In summer season, differences in latitudinal gradients by the fluxes are comparable to or greater than model-model differences even in the free troposphere. This result suggests that active summer vertical transport sufficiently ventilates flux signals up to the free troposphere and the models could use those for inferring surface CO2 fluxes.

A chemistry-transport model (CTM) intercomparison experiment (TransCom-CH4) has been designed to investigate the roles of surface emissions, transport and chemical loss in simulating the global methane distribution. Model simulations were conducted using twelve models and four model variants and results were archived for the period of 1990–2007. All but one model transports were driven by reanalysis products from 3 different meteorological agencies. The transport and removal of CH4 in six different emission scenarios were simulated, with net global emissions of 513 ± 9 and 514 ± 14 Tg CH4 yr−1 for the 1990s and 2000s, respectively. Additionally, sulfur hexafluoride (SF6) was simulated to check the interhemispheric transport, radon (222Rn) to check the subgrid scale transport, and methyl chloroform (CH3CCl3) to check the chemical removal by the tropospheric hydroxyl radical (OH). The results are compared to monthly or annual mean time series of CH4, SF6 and CH3CCl3 measurements from 8 selected background sites, and to satellite observations of CH4 in the upper troposphere and stratosphere. Most models adequately capture the vertical gradients in the stratosphere, the average long-term trends, seasonal cycles, interannual variations (IAVs) and interhemispheric (IH) gradients at the surface sites for SF6, CH3CCl3 and CH4. The vertical gradients of all tracers between the surface and the upper troposphere are consistent within the models, revealing vertical transport differences between models. An average IH exchange time of 1.39 ± 0.18 yr is derived from SF6 time series. Sensitivity simulations suggest that the estimated trends in exchange time, over the period of 1996–2007, are caused by a change of SF6 emissions towards the tropics. Using six sets of emission scenarios, we show that the decadal average CH4 growth rate likely reached equilibrium in the early 2000s due to the flattening of anthropogenic emission growth since the late 1990s. Up to 60% of the IAVs in the observed CH4 concentrations can be explained by accounting for the IAVs in emissions, from biomass burning and wetlands, as well as meteorology in the forward models. The modeled CH4 budget is shown to depend strongly on the troposphere-stratosphere exchange rate and thus on the model's vertical grid structure and circulation in the lower stratosphere. The 15-model median CH4 and CH3CCl3 atmospheric lifetimes are estimated to be 9.99 ± 0.08 and 4.61 ± 0.13 yr, respectively, with little IAV due to transport and temperature.

Fourteen global atmospheric transport models were evaluated by comparing the simulation of 222Rn against measurements at three continental stations in Germany: Heidelberg, Freiburg and Schauinsland. Hourly concentrations simulated by the models using a common 222Rn-flux without temporal variations were investigated for 2002 and 2003. We found that the mean simulated concentrations in Heidelberg are related to the diurnal amplitude of boundary layer height in each model. Summer mean concentrations simulated by individual models were negatively correlated with the seasonal mean of diurnal amplitude of boundary layer height, while in winter the correlation was positive. We also found that the correlations between simulated and measured concentrations at Schauinsland were higher when the simulated concentrations were interpolated to the station altitude in most models. Temporal variations of the mismatch between simulated and measured concentrations suggest that there are significant interannual variations in the 222Rn exhalation rate in this region. We found that the local inversion layer during daytime in summer in Freiburg has a significant effect on 222Rn concentrations. We recommend Freiburg concentrations for validation of models that resolve local stable layers and those at Heidelberg for models without this capability.

Quantifying the fluxes of carbon dioxide (CO2) between the atmosphere and terrestrial ecosystems in all their diversity, across the continents, is important and urgent for implementing effective mitigating policies. Whereas much is known for Europe and North America for instance, in comparison, South Asia, with 1.6 billion inhabitants and considerable CO2 fluxes, remained terra incognita in this respect. We use regional measurements of atmospheric CO2 aboard a Lufthansa passenger aircraft between Frankfurt (Germany) and Chennai (India) at cruise altitude, in addition to the existing network sites for 2008, to estimate monthly fluxes for 64-regions using Bayesian inversion and transport model simulations. The applicability of the model's transport parameterization is confirmed using SF6, CH4 and N2O simulations for the CARIBIC datasets. The annual amplitude of carbon flux obtained by including the aircraft data is twice as large as the fluxes simulated by a terrestrial ecosystem model that was applied to prescribe the fluxes used in the inversions. It is shown that South Asia sequestered carbon at a rate of 0.37 ± 0.20 Pg C yr−1 (1 Pg C = 1015 g of carbon in CO2) for the years 2007 and 2008. The seasonality and the strength of the calculated monthly fluxes are successfully validated using independent measurements of vertical CO2 profiles over Delhi and spatial variations at cruising altitude over Asia aboard Japan Airlines passenger aircraft.

Extensive observations were made during the late Southwest Monsoon of 2004 over the Indian and Omani shelves, and along a transect that extended from the southern coast of Oman to the central west coast of India, tracking the southern leg of the US JGOFS expedition (1994–1995) in the west. The data are used, in conjunction with satellite-derived data, to investigate long-term trends in chlorophyll and sea surface temperature, indicators of upwelling intensity, and to understand factors that control primary production (PP) in the Arabian Sea, focussing on the role of iron. Our results do not support an intensification of upwelling in the western Arabian Sea, reported to have been caused by the decline in the winter/spring Eurasian snow cover since 1997. We also noticed, for the first time, an unexpected development of high-nutrient, low-chlorophyll condition off the southern Omani coast. This feature, coupled with other characteristics of the system, such as a narrow shelf and relatively low iron concentrations in surface waters, suggest a close similarity between the Omani upwelling system and the Peruvian and California upwelling systems, where PP is limited by iron. Iron limitation of PP may complicate simple relationship between upwelling and PP assumed by previous workers, and contribute to the anomalous offshore occurrence of the most severe oxygen (O2) depletion in the region. Over the much wider Indian shelf, which experiences large-scale bottom water O2-depletion in summer, adequate iron supply from reducing bottom-waters and sediments seems to support moderately high PP; however, such production is restricted to the thin, oxygenated surface layer, probably because of the unsuitability of the O2-depleted environment for the growth of oxygenic photosynthesizers.

In-situ observations of atmospheric CO2 and CH4 at Hateruma Island (24.05° N, 123.80° E, 47 m a.s.l), Japan shows large synoptic scale variations during a 6-month period from November to April, when the sampled air is predominantly of continental origin due to the Asian winter monsoon. Synoptic scale variations are extracted from the daily averaged values for the years between 1996 and 2007, along with the annual standard deviations (σCO2 and σCH4 for CO2 and CH4, respectively) for the relevant 6-month period. During this 6-month period the absolute mixing ratios of CO2 and CH4 at Hateruma are also elevated compared to those at two sites in the central North Pacific Ocean. The temporal change in σCO2 shows a systematic increase over the 12-year period, with elevated excursions in 1998 and 2003; there is no clear increase in σCH4. We also find that the σCO2/σCH4 ratio increases gradually from 1996 to 2002 and rapidly after 2002 without any extreme deviations that characterised σCO2. The σCO2/σCH4 ratio correlates closely with the recent rapid increase in fossil carbon emissions from China, as indicated in the Carbon Dioxide Information Analysis Center (CDIAC) database. This methodology can be applied to multiple chemical tracers of sufficient lifetime, for tracking overall changes in regional emissions.

We use an atmospheric general circulation model (AGCM) driven chemistry-transport model (ACTM) to simulate the evolution of sulfur hexafluoride (SF6) in the troposphere. The model results are compared with continuous measurements at 6 sites over 71° N–90° S. These comparisons demonstrate that the ACTM simulations lie within the measurement uncertainty over the analysis period (1999–2006) and capture salient features of synoptic, seasonal and interannual SF6 variability. To understand transport timescales of SF6 within the troposphere, transport times of air parcels from the surface to different regions of the troposphere ("age") are estimated from a simulation of an idealized tracer. The age estimation error and its sensitivity to the selection of reanalysis meteorology for ACTM nudging or the tracer transport by deep cumulus convection as represented in the model are discussed. Monthly-mean, 2-box model exchange times (τex) are calculated from both the observed and simulated SF6 time series at the 6 observing sites and show favorable agreement, suggesting that the ACTM adequately represents large-scale interhemispheric transport. The simulated SF6 variability is further investigated through decomposition of the mixing ratio time-tendency into advective, convective, and vertical diffusive components. The transport component analysis illustrates the role of each process in SF6 synoptic variability at the site level and provides insight into the seasonality of τex.