The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017

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. (Petrescu et al., 2020b)

The consolidated European synthesis of CH₄ and N₂O emissions for the European Union and United Kingdom: 1990–2019

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 yrc (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)

Drifting snow sublimation: A high-resolution 3-D model with temperature and moisture feedbacks

The snow transport model of Alpine3D is augmented with a drifting snow sublimation routine. Contrary to other three-dimensional high-resolution snow transport models, Alpine3D now accounts for feedback mechanisms on the air temperature, humidity, and snow mass concentration in three dimensions. Results show that the negative feedbacks of sublimation on the snow mass concentration, temperature, and humidity are, in general, small but relevant on the slope scale. We analyzed the deposition on a leeward slope for simulations including sublimation and compared these to a reference simulation of the model without sublimation. Including sublimation, but neglecting sublimation feedbacks, leads to a reduction in deposition of approximately 12% on this slope. In a simulation including sublimation and its feedbacks, the reduction in snow deposition on the same slope was 10%. The feedbacks thus reduced the loss of snow due to sublimation by 2%. The sublimation process is therefore quite important for a leeward slope influenced by drifting snow. However, we also show that the spatial variability is large and that drifting snow sublimation will mainly affect small regions within a catchment. Averaged over our model domain (2.4 km(2)) in the Swiss Alps, drifting snow sublimation causes a reduction in deposition of 2.3% during a 43 h test period, which is comparable to the sublimation loss from the snow cover during the same time

Event-driven deposition of snow on the Antarctic Plateau: analyzing field measurements with SNOWPACK

Antarctic surface snow has been studied by means of continuous measurements and observations over a period of 3 yr at Dome C. Snow observations include solid deposits in form of precipitation, diamond dust, or hoar, snow temperatures at several depths, records of deposition and erosion on the surface, and snow profiles. Together with meteorological data from automatic weather stations, this forms a unique dataset of snow conditions on the Antarctic Plateau. Large differences in snow amounts and density exist between solid deposits measured 1m above the surface and deposition at the surface. We used the snow-cover model SNOWPACK to simulate the snow-cover evolution for different deposition parameterizations. The main adaptation of the model described here is a new event-driven deposition scheme. The scheme assumes that snow is added to the snow cover permanently only during periods of strong winds. This assumption followed from the comparison between observations of solid deposits and daily records of changes in snow height: solid deposits could be observed on tables 1m above the surface on 94 out of 235 days (40 %) while deposition at the surface occurred on 59 days (25 %) during the same period, but both happened concurrently on 33 days (14 %) only. This confirms that precipitation is not necessarily the driving force behind non-temporary snow height changes. A comparison of simulated snow height to stake farm measurements over 3 yr showed that we underestimate the total accumulation by at least 33 %, when the total snow deposition is constrained by the measurements of solid deposits on tables 1m above the surface. During shorter time periods, however, we may miss over 50% of the deposited mass. This suggests that the solid deposits measured above the surface and used to drive the model, even though comparable to ECMWF forecasts in its total magnitude, should be seen as a lower boundary. As a result of the new deposition mechanism, we found a good agreement between model results and measurements of snow temperatures and recorded snow profiles. In spite of the underestimated deposition, the results thus suggest that we can obtain quite realistic simulations of the Antarctic snow cover by the introduction of event-driven snow deposition

Effects of extreme meteorological conditions in 2018 on European methane emissions estimated using atmospheric inversions

The effect of the 2018 extreme meteorological conditions in Europe on methane (CH4) emissions is examined using estimates from four atmospheric inversions calculated for the period 2005-2018. For most of Europe, we find no anomaly in 2018 compared to the 2005-2018 mean. However, we find a positive anomaly for the Netherlands in April, which coincided with positive temperature and soil moisture anomalies suggesting an increase in biogenic sources. We also find a negative anomaly for the Netherlands for September-October, which coincided with a negative anomaly in soil moisture, suggesting a decrease in soil sources. In addition, we find a positive anomaly for Serbia in spring, summer and autumn, which coincided with increases in temperature and soil moisture, again suggestive of changes in biogenic sources, and the annual emission for 2018 was 33 +/- 38% higher than the 2005-2017 mean. These results indicate that CH4 emissions fromareas where the natural source is thought to be relatively small can still vary due to meteorological conditions. At the European scale though, the degree of variability over 2005-2018 was small, and there was negligible impact on the annual CH4 emissions in 2018 despite the extreme meteorological conditions.Peer reviewe

Modelling Small-Scale Drifting Snow with a Lagrangian Stochastic Model Based on Large-Eddy Simulations

Observations of drifting snow on small scales have shown that, in spite of nearly steady winds, the snow mass flux can strongly fluctuate in time and space. Most drifting snow models, however, are not able to describe drifting snow accurately over short time periods or on small spatial scales as they rely on mean flow fields and assume equilibrium saltation. In an attempt to gain understanding of the temporal and spatial variability of drifting snow on small scales, we propose to use a model combination of flow fields from large-eddy simulations (LES) and a Lagrangian stochastic model to calculate snow particle trajectories and so infer snow mass fluxes. Model results show that, if particle aerodynamic entrainment is driven by the shear stress retrieved from the LES, we can obtain a snow mass flux varying in space and time. The obtained fluctuating snow mass flux is qualitatively compared to field and wind-tunnel measurements. The comparison shows that the model results capture the intermittent behaviour of observed drifting snow mass flux yet differences between modelled turbulent structures and those likely to be found in the field complicate quantitative comparisons. Results of a model experiment show that the surface shear-stress distribution and its influence on aerodynamic entrainment appear to be key factors in explaining the intermittency of drifting snow

Temporal and spatial variability of Icelandic dust emissions and atmospheric transport

Icelandic dust sources are known to be highly active, yet there exist few model simulations of Icelandic dust that could be used to assess its impacts on the environment. We here present estimates of dust emission and transport in Iceland over 27 years (1990–2016) based on FLEXDUST and FLEXPART simulations and meteorological re-analysis data. Simulations for the year 2012 based on high-resolution operational meteorological analyses are used for model evaluation based on PM2. 5 and PM10 observations in Iceland. For stations in Reykjavik, we find that the spring period is well predicted by the model, while dust events in late fall and early winter are overpredicted. Six years of dust concentrations observed at Stórhöfði (Heimaey) show that the model predicts concentrations of the same order of magnitude as observations and timing of modelled and observed dust peaks agrees well. Average annual dust emission is 4.3 ± 0.8 Tg during the 27 years of simulation. Fifty percent of all dust from Iceland is on average emitted in just 25 days of the year, demonstrating the importance of a few strong events for annual total dust emissions. Annual dust emission as well as transport patterns correlate only weakly to the North Atlantic Oscillation. Deposition amounts in remote regions (Svalbard and Greenland) vary from year to year. Only limited dust amounts reach the upper Greenland Ice Sheet, but considerable dust amounts are deposited on Icelandic glaciers and can impact melt rates there. Approximately 34 % of the annual dust emission is deposited in Iceland itself. Most dust (58 %), however, is deposited in the ocean and may strongly influence marine ecosystems

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

International audienceAbstract. 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