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2008
2019
2009
Inverse modelling is employed to reconcile greenhouse gas (GHG) emission inventories, based on bottom-up methods, with the observed atmospheric GHG concentrations. The Community Inversion Framework (CIF) was created to unify inverse-model developments and simplify the generation of inversions. It makes atmospheric transport models and inversion algorithms easily interchangeable and facilitates the comparison of inversion results obtained using such diverse components.After several years of development and the coupling of CIF with a wide range of transport models used by the inversion community, we present the first intercomparison study conducted with CIF. This exercise focuses on Europe and aims to refine CO₂ natural emissions for the year 2019, following a strict protocol. It involves five transport models (CHIMERE, ICON-ART, LMDz, STILT, and WRF-CHEM) and two inversion algorithms (variational and ensemble-based). Two additional transport models, TM5 and FLEXPART, will be incorporated in the near future.The results show a good agreement, both across transport models, and inversion algorithms. It paves the way towards using CIF as an operational tool for intercomparison studies. It also highlights its strong potential to support the systematic derivation of GHG budgets with multiple transport models, enable a proper and easy quantification of the modelling uncertainty, and improve the robustness of emission estimates, for any relevant atmospheric species, at any scale.
2026
The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies
Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.
2021
2022
Traveling planetary waves surrounding sudden stratospheric warming events can result from direct propagation from below or in situ generation. They can have significant impacts on the circulation in the mesosphere and lower thermosphere. Our study runs a series of ensembles initialized from the Whole Atmosphere Community Climate Model, Version 4, nudged up to 50 km by six-hourly Modern-Era Retrospective Analysis for Research and Application, Version 2, reanalysis to compile a library of sudden stratospheric warming events. To our knowledge, we present the first composite or ensemble study that attempts to link direct propagation and in situ generation by evaluating the wave geometries associated with the overreflection perspective, a framework used to describe how planetary waves interact with critical and turning levels. The present study looks at the evolution of these interactions through the onset of sudden stratospheric warmings with an elevated stratopause or ES-SSWs. Robust and unique features of ES-SSWs are determined by employing an ensemble study that compares ES-SSWs with normal winters. Our study evaluates the production and impacts of westward-propagating, quasi-stationary, and eastward-propagating planetary waves surrounding ES-SSWs. Our results show that eastward-propagating planetary waves are generated within the westward stratospheric wind layer after ES-SSW onset which aids in restoring the eastward stratospheric wind. The interaction of quasi-stationary and westward-propagating waves with the westward stratospheric wind is explored from an overreflection perspective and reaffirms that westward-propagating planetary waves are produced from instabilities at the top of the westward stratospheric wind reversal.
2023
2017
The concept of essential use for determining when uses of PFASs can be phased out
Because of the extreme persistence of per- and polyfluoroalkyl substances (PFASs) and their associated risks, the Madrid Statement argues for stopping their use where they are deemed not essential or when safer alternatives exist. To determine when uses of PFASs have an essential function in modern society, and when they do not, is not an easy task. Here, we: (1) develop the concept of “essential use” based on an existing approach described in the Montreal Protocol, (2) apply the concept to various uses of PFASs to determine the feasibility of elimination or substitution of PFASs in each use category, and (3) outline the challenges for phasing out uses of PFASs in society. In brief, we developed three distinct categories to describe the different levels of essentiality of individual uses. A phase-out of many uses of PFASs can be implemented because they are not necessary for the betterment of society in terms of health and safety, or because functional alternatives are currently available that can be substituted into these products or applications. Some specific uses of PFASs would be considered essential because they provide for vital functions and are currently without established alternatives. However, this essentiality should not be considered as permanent; rather, constant efforts are needed to search for alternatives. We provide a description of several ongoing uses of PFASs and discuss whether these uses are essential or non-essential according to the three essentiality categories. It is not possible to describe each use case of PFASs in detail in this single article. For follow-up work, we suggest further refining the assessment of the use cases of PFASs covered here, where necessary, and expanding the application of this concept to all other uses of PFASs. The concept of essential use can also be applied in the management of other chemicals, or groups of chemicals, of concern.
2019
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)
2021
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. ...
2023
Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011–2015, the CO2 land sources and sinks from NGHGI estimates report −90 Tg C yr−1 ± 30 Tg C yr−1 while all other BU approaches report a mean sink of −98 Tg C yr−1 (± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr−1 ± 400 Tg C yr−1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO2 flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).
2021
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. ...
2023
2003
The Covid-19 pandemic and environmental stressors in Europe: synergies and interplays
This report provides an overview of the potential impacts of Covid-19 restrictions, in particular, focusing on review and assessment of Covid-19 impacts on air quality, for the year 2020. Complementary analyses address compliance with the National Emission reductions Commitments (NEC) Directive and noise. This expands the initial analysis of impacts of the pandemic-related restrictions on air quality based on data for the first months of 2020, presented in the EEA Air quality report for 2020. The results show a clear decline in NO2 short-term levels and annual average throughout Europe. Results for other pollutants are less uniform, and mostly do not show significant changes in annual average or other relevant metrics . The results regarding air quality are robust, obtained by a wealth of methods and consistent also with literature findings. The noise analysis shows a general decline in noise levels related to road traffic, though some areas show an increase. An analysis of policies and measures reported by Member States in 2021 for base year 2019 shows that additional measures related to emissions of NH3 are expected to be negatively impacted to the greatest extent by the Covid-19 related restrictions.
ETC/ATNI
2022
2008
The deposition and fate of perfluorinated alkyl substances (PFAS) in the Norwegian Arctic snowpack. NILU F
2012
2012
2005
The dietary footprints and transition priorities of over 12 000 urban centers
Urban consumption plays a central role in driving global carbon emissions and is associated with a wide range of environmental pressures, including deforestation, water use, and biodiversity loss. As cities and networks of cities work to define and achieve quantitative sustainability goals, the lack of insight into their unique food footprints, shaped by each city’s specific dietary patterns and demographic composition, remains a significant obstacle. Here we combine demographic, group-specific diet, and supply-chain environmental footprint data to estimate the dietary footprints in 12 228 urban centers across 159 countries. We show that diet-related greenhouse gas emissions (GHGE) are highly concentrated in a small number of cities: The 200 urban centers with the largest footprints account for 39.8% of global urban population and 45.6% of total global urban dietary emissions. Overall, adoption of the EAT-Lancet 2.0 diet across all studied centers delivers a modest net reduction in total footprints (≈5%–16%). However, this aggregate outcome masks substantial variability: high-income centers achieve considerable reductions through lower meat and dairy consumption, while lower-income centers presently consuming below EAT-Lancet recommended levels experience footprint increases, partially offsetting global gains. Within transnational city networks (C40 Cities, Eurocities, and Milan Urban Food Policy Pact members), where member centers are predominantly high-income, EAT-Lancet 2.0 adoption delivers substantially larger reductions of 22%–50% in GHGE, underscoring the policy relevance of targeted urban-level dietary interventions. Complementary supply-side interventions, including decarbonization of agricultural production, processing, and distribution, offer additional reductions, indicating that dietary and supply-side levers must be deployed jointly. Our results highlight the need for context-specific urban dietary strategies. The resulting database, MATILDA-City, provides a globally harmonized evidence base to benchmark, rank, and monitor urban-level dietary footprints, supporting municipalities to coordinate actions toward sustainable food systems.
2026