The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2018

Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout Francesco N. Tubiello, Peter Bergamaschi, Dominik 5 Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Orregioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann and Han Dolman 10


Introduction
The global atmospheric concentrations of methane (CH4) has increased by 160% and that of nitrous oxide (N2O) by 22% since the pre-industrial period (WMO, 2019) and are well documented as observed by long-term icecore records (Etheridge et al., 1998, CSIRO). According to the NOAA atmospheric data (https://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/ last access June 2020) the CH4 concentration in the atmosphere 75 continues to increase and, after a small dip in 2017, has an average growth of 10 ppb / year, representing the highest rate observed since the 1980s 1 (Nisbet et al. 2016(Nisbet et al. , 2019. This increase was attributed to anthropogenic emissions from agriculture (livestock enteric fermentation and rice cultivation) and fossil fuel related activities, combined with a contribution from natural tropical wetlands (Saunois et al., 2020, Thompson et al. 2018, Nisbet et al., 2019. The recent increase in atmospheric N2O is more linked to agriculture in particular due to the application of nitrogen 80 fertilizers and livestock manure on agricultural land (FAO, 2020(FAO, , 2015IPCC, 2019b, Tian et al., 2020. National GHG inventories (NGHGI) are prepared and reported on annual basis by Annex I countries 2 based on IPCC Guidelines using national activity data and different levels of sophistication (tiers) for well-defined sectors.
1 The 1980s rapid development of gas industry in former USSR.
2 Annex I Parties include the industrialized countries that were members of the OECD (Organization for Economic Co-operation and Development) in 1992 plus countries with economies in transition (the EIT Parties), including the Russian Federation, the Baltic States, and several central and eastern European states (UNFCCC, https://unfccc.int/parties-observers, last access: February 2020).
countries, that will continue to submit also on an annual basis. Some developing countries will face challenges to provide and then update inventories.
The work presented here represents dozens of distinct datasets and models, in addition to the individual country submissions to the UNFCCC for all European countries (NGHGIs), which while following the general guidance laid out in IPCC (2006) still differ in specific approaches, models, and parameters, in addition to differences 130 underlying activity datasets. A comprehensive investigation of detailed differences between all datasets is beyond the scope of this paper, though attempts have been previously made for specific subsectors (e.g. agriculture Petrescu et al., 2020) and in dedicated gas-specific follow-ups to this manuscript. As this is the most comprehensive comparison of NGHGIs and research datasets (including both TD and BU approaches) for the European continent to date, we focus here on the rich set of questions that such a comparison raises without necessarily yet offering detailed solutions:

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How to compare the detailed sectoral NGHGI to the observation-based estimates? Which new information the observation-based estimates are likely to bring (mean fluxes, trend, ensemble variability)? What to expect from such a complex study and how to proceed forward?
We compare official anthropogenic NGHGI emissions with research datasets, and wherever needed harmonizing research data on total emissions to ensure consistent comparisons of anthropogenic emissions. We 140 analyze differences and inconsistencies between emissions, and make recommendations towards future actions to evaluate NGHGI data. While NGHGI include uncertainty estimates, individual spatially disaggregated research datasets of emissions often lack quantification of uncertainty. Here, we use the median 4 and minimum/maximum (min/max) range of different research products of the same type to get a first estimate of uncertainty.

CH4 and N2O data sources and estimation approaches
We analyze CH4 and N2O emissions in the EU27+UK from inversions (TD) and anthropogenic emissions from various BU approaches that cover specific sectors. These data (Table 2) (Etiope et al., 2019;Hmiel et al., 2020) and for lakes and reservoirs (Del Sontro et al., 2018). Emissions from gas hydrates and termites are not included as they are close to zero in the EU27+UK (Saunois et al., 2020). Emissions from LULUCF biomass burning emissions of CH4 account for 3 % of the total emissions in EU27+UK. These estimates are described in 2.2. From TD approaches, we used both regional and global inversions, the latter having a coarser spatial resolution.

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These estimates are described in 2.3.
For N2O emissions, we used the same global BU inventories as for CH4, and natural emissions from inland waters (rivers, lakes and reservoirs) from Lauerwald et al., 2019). According to Yuanzhi Yao (pers. comm.), about 66 % of the N2O emitted by Europe's natural rivers are considered anthropogenic indirect emissions, caused by leaching and run-off of N-fertilizers from the agriculture sector. We did not account for natural 165 N2O emissions from unmanaged soils (Tian et al., 2019, estimated pre-industrial soil emissions in Europe at a third of the level of the most recent decade -emissions that in pre-industrial times may have been influenced by human management activities, or based on natural processes that have been abolished since). For N2O inversions, we used one regional inversion FLEXINVERT_NILU and three global inversions (Friedlingstein et al., 2019;Tian et al., 2020). Agricultural sector emissions of N2O were presented in detail by Petrescu et al., 2020. In this current study 170 these emissions belong to CAPRI model and FAOSTAT, with the latter additionally covering non-CO2 emissions from biomass fires in LULUCF. Fossil fuel related and industrial emissions were obtained from GAINS (see Appendix A1). Table AA in Appendix A presents the methodological differences of current study with respect to Petrescu et al., 2020. 175 Table 1: Sectors used in this study and data sources providing estimates for these sectors.  The units used in this paper are metric tonne (t) [1kt = 10 9 g; 1Mt = 10 12 g] of CH4 and N2O. The referenced data used for the figures' replicability purposes are available for download at https://doi.org/10.5281/ zenodo.4288969 (Petrescu et al., 2020). We focus herein on EU27+UK. In the VERIFY project, we have constructed in addition a web tool which allows for the selection and display of all plots show in this paper (as well as the companion paper on CO2), not only for the regions shown here but for a total of 79 countries and groups of countries in Europe. The website, 185 located on the VERIFY project website: http://webportals.ipsl.jussieu.fr/VERIFY/FactSheets/, is accessible with a username and password distributed by the project. Figure 1 includes also data from countries outside the EU but located within geographical Europe (Switzerland, Norway, Belarus, Ukraine and Rep. of Moldova).

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UNFCCC NGHGI (2019) emissions are country estimates covering the period 1990-2017. They were kept separate to be compared with other BU and TD data. We supplemented the NGHGI estimates with the NRT -Near Real Time (EEA, 2019) to capture one additional year with preliminary estimates 7 . NRT represents the approximated GHG inventory (also referred to as "proxy estimates") with an early estimate of the GHG emissions for the preceding year, as required by Regulation (EU) 525/2013 of the European Parliament and of the Council.

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Anthropogenic CH4 emissions from the four UNFCCC sectors (Table 1, excl. LULUCF) were grouped together. As anthropogenic NGHGI CH4 emissions from the LULUCF sector are very small for EU27+UK (2.6 % in 2017 incl. biomass burning) we exclude them in Figures 4 but include them in the total UNFCCC estimates from Figure 1,2,3,5 and 6. Only a few countries 8 under the NGHGI volunteered to report "wetland" emissions, following 7 t-1 refers to an early estimate of the GHG emissions for the preceding year, as required by Regulation (EU) 525/2013 of the European Parliament and of the Council. 8 Denmark, Finland, Germany, Ireland, Latvia, Sweden, France, Estonia and Spain. In total these nine countries report in 2017 11.2 kton CH4 from The N2O anthropogenic emissions from BU datasets belong predominantly to two main categories, as presented in Table 5: 1) direct emissions from the agricultural sector where synthetic fertilizers and manure were 235 applied, and from manure management and 2) indirect emissions on non-agricultural land and water receiving anthropogenic N through atmospheric N deposition, leaching and run-off (also from agricultural land). Furthermore, emissions from industrial processes are declining over time but originate from fossil fuel combustion, air pollution abatement devices, specific chemical reactions, wastewater treatment and land use change. In this study, we do not consider the natural emissions from soils, since these emissions are relatively small for temperate regions, including 240 Europe and cannot be singled out in landscapes largely dominated by human activities. Therefore, the only "natural" fluxes considered in this study are emissions from inland waters (lakes, rivers and reservoirs, Maavara et al., 2019;Lauerwald et al., 2019, Appendix A3) even if, more than half of the emissions (56 % globally, Tian et al., 2020, and 66 % for Europe, Yao pers. comm.) are due to eutrophication following N-fertilizer leaching to inland waters.
Emissions from natural soils, in this study are considered as "anthropogenic" because, according country specific 245 NIRs, all land in EU27+UK is considered to be managed, except 5% of France EU territory.

CH4 and N2O emission data from top-down inversions
Inversions combine atmospheric observations, transport and chemistry models and prior estimates of GHG sources all with their uncertainties, to estimate emissions. Emission estimates from inversions depend on the data set 250 of atmospheric measurements and the choice of the atmospheric model, as well as on other settings (e.g. prior emissions and their uncertainties). Inversions outputs were taken from original publications without evaluation of their performance through specific metrics (e.g. fit to independent cross validation atmospheric measurements (Bergamaschi et al., 2013(Bergamaschi et al., , 2018Patra et al., 2016). Some of the inversions solve explicitly for sectors, others solve for all fluxes in each grid cell and separate sectors using prior grid-cell fractions (see details in Saunois et al. 2020 for 255 global inversions).
For CH4, we use nine regional TD inversions and 22 global TD inversions listed in Table 2. These inversions are not independent from each other: some are variants from the same modeling group, many use the same transport model, and most of them use the same atmospheric data. Different prior data is generally used in models, which produces a greater range of posterior emission estimates (Appendix B, Table B4). The subset of InGOS inversions 260 (Bergamaschi et al., 2018a) belongs to a project where all models used the same atmospheric data over Europe covering the period 2006-2012. The global inversions from Saunois et al. 2020 were all updated to 2017.
The regional inversion uses a higher resolution transport model for Europe, with atmospheric N2O concentration 275 boundary conditions taken from global fields. As all inversions derived total rather than anthropogenic emissions, emissions from inland waters (lakes, rivers and reservoirs) estimated by Maavara et al. (2019) and Lauerwald et al. (2019) were subtracted from the total emissions. Note that the estimates of Maavara et al. (2019) and Lauerwald et al. (2019) include anthropogenic emissions from N-fertilizer leaching accounting for 66% of the inland water emissions in EU27+UK. The natural N2O emissions are small, but should be better quantified in the future to allow for a more 280 accurate comparison between BU (anthropogenic sources only) and TD estimates.
The largest share of N2O emissions comes from the agricultural soils (direct and indirect emissions from the applications of fertilizers, whether synthetic or manure) contributing in 2017 69 % of the total N2O emissions (excl. LULUCF) in EU27+UK. In Table 3 we present the allocation of emissions by activity type covering all agricultural activities and natural emissions, following the IPCC classification. We notice that each data product has its own 285 particular way of grouping emissions, and does not necessarily cover all emissions activities. Main inconsistencies between models and inventories are observed with activity allocation in the two models (ECOSSE and DayCent).
ECOSSE only estimates direct N2O emissions, and does not estimate downstream emissions of N20, for example indirect emissions from nitrate leached into water courses, which also contributes to an underestimation of total N2O emissions. Field burning emissions are as well not included by most of the data sources.  n/a n/a n/a Natural (unmanaged) N2O emissions n/a n/a n/a n/a n/a n/a n/a n/a Emissio ns from lakes, rivers and estuaries https://doi.org/10.5194/essd-2020-367

Results and discussion 295
3.1. Comparing CH4 anthropogenic emissions estimates from different approaches\

Estimates of European and regional total CH4 fluxes
We present results of total CH4 fluxes from EU27+UK and five main regions in Europe: North, West, Central, East (non-EU) and South. The countries included in these regions are listed in Appendix A, table A. Figure 1 shows 300 the total CH4 fluxes from NGHGI for both base year 1990 and mean of 2011-2015 period. This period was the common denominator for which data was available, including 2 years of the Kyoto Protocol first reporting period (2011)(2012) and reaching the year of the Paris Agreement was adopted. We aim with the selection of this period to bring together all information over a 5-year period for which values are known in 2018. In fact, this can be seen as a reference for what we can achieve in 2023, the year of the first global stocktake, where for most UN Parties the reported inventories 305 will be known until 2021. Given that the global stocktake is only repeated every 5 years, a five-year average is clearly of interest.
The total NGHGI estimates include emissions from all sectors and we plot and compare them with fluxes from global datasets, BU models and inversions. We note that for all five regions, the NGHGI reported CH4 emissions decreased, by 21 % in South Europe, by up to 54 % in East Europe, and by 35% for the European Union with respect 310 to the 1990 value. This is encouraging in the context of meeting EUs commitments under the PA (at least 50% and towards 55% compared with 1990 levels stated by the amended proposal for a regulation of the European parliament and of the council on establishing the framework for achieving climate neutrality and amending Regulation (EU) 2018/1999 (European Climate Law) (https://ec.europa.eu/clima/sites/clima/files/eu-climateaction/docs/prop_reg_ecl_en.pdf) and reaching carbon neutrality by 2050). It also shows that not only at EU27+UK 315 level, but also at regional European level, the emissions from BU (anthropogenic and natural) and TD estimates agree well with reported NGHGI data despite the high uncertainty observed in the TD models. This uncertainty is represented here by the variability in the model ensembles and denotes the range of the extremes (min and max) of estimates within each model ensemble. From Figure 1  In line with Bergamaschi et al., 2018a we highlight the potential significant contribution from natural unmanaged sources (peatlands, geological and inland water), which for EU27+UK accounted for 5.24 Tg CH4 yr -1 ( Figure 1). Taking into account these natural unmanaged CH4 emissions, and adding it to the range of the 345 anthropogenic estimates (19 -21 Tg CH4 yr -1 ) the total BU estimates become broadly consistent for all European regions with the range of the TD estimates (23 -28 Tg CH4 yr -1 ).
The data in Figure 2 shows anthropogenic CH4 emissions and their change from one decade to the next, from UNFCCC NGHGI (2019), with the contribution from different UNFCCC sectors. In 2017, NGHGI report CH4 from agricultural activities to be 52 % (± 10 %) of the total EU27+UK CH4 emissions, followed by emissions from waste,   (1990-1999, 2000-2009 and 2010-2017) and percentages 375 represent the contribution of each sector to the total reduction percentages (black arrows) between periods.

NGHGI estimates compared with bottom-up inventories
The data in Figure 3     for material in the pipelines (in the case of gas transport) and the activity data). EDGAR v5.0, for example, uses the 410 gas pipeline length as a proxy for the activity data however this may not be appropriate for the case of the official data, which could consider the total amount of gas being transported or both methods according to the countries. Using pipeline length may overestimate the emissions because the pipeline is not always at 100% capacity thus a larger amount of methane is assumed to be leaked. For coal mining, emissions are a function of the different types of processes being modelled.

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The IPPU sector (Figure 3c), which has only a small share of the total emissions, is not reported in GAINS,  method. The main differences between the two datasets come from i) sources for total waste generated per person, ii) assumption for the fraction composted and iii) the oxidation. The two inventories may have used different strategies to complete the waste database when inconsistencies were observed in the EUROSTAT database or in the waste trends 440 in UNFCCC.

Regional inversions
Figure 5 compares TD regional estimates with NGHGI anthropogenic data for CH4 and with natural BU emissions. We present TD estimates of total emissions (Fig. 5a) as well as estimates of anthropogenic emissions only   For the common period 2006-2012, the four inverse models give a total CH4 emissions mean of 25.8 (24.0-465 27.4) Tg CH4 yr -1 compared to anthropogenic total of 20.3 ± 1.9 Tg CH4 yr -1 in NGHGI (Fig. 4a). The large positive difference between TD and NGHGI suggests a potentially significant contribution from natural sources (peatlands, geological sources and inland waters), which for the same period report a total mean of 5.2 Tg CH4 yr -1 . However, it needs to be emphasized that wetland emission estimates have large uncertainties and show large variability in the spatial (seasonal) distribution of CH4 emissions but for Europe their inter-annual variability is not very strong (mean 470 of 13 years from JSBACH-HIMMELI peatland emissions 1.4 ± 0.1 Tg CH4 yr -1 ). Overall, they do represent an important source and could dominate the budget assessments in some regions such as Northern Europe ( Figure 1). We also note that the TD trends do not necessarily match those of NGHGIs and this might be due to strong seasonality of emissions coming from the natural priors in the inversions (Saunois et al., 2020) The natural emissions from inland waters (based on Lauerwald et al., 2019, see appendix A2) contribute 2.53 475 Tg CH4 yr -1 , or 48 % of the total natural CH4 emissions (sum of lakes and reservoirs, geological and peatlands emissions). Peatlands (Raivonen et al. 2017 andSusiluoto et al. 2018) account for 1.38 Tg CH4 yr -1 , i.e. 27 % of the total natural CH4 emissions, and geological sources sum up to 1.27 Tg CH4 yr -1 , i.e. 25 % of the total natural CH4 emissions. It should be noted that geological emissions are an important component of the EU27+UK emissions budget, although not of concern for climate warming if their source strength has not changed since pre-industrial times 480 (Hmiel et al., 2020) In an attempt to quantify the anthropogenic CH4 component in the European TD estimates, in Figure 4b we 485 subtract from the total TD emissions the BU peatland emissions from the regional JSBACH-HIMMELI model and those from geological and inland water sources. It remains however uncertain to perform these corrections due to the prior inventory data allocation of emissions to different sectors (e.g. anthropogenic or natural), which can induce uncertainty of up to 100 % if for example an inventory allocates all emissions to natural emissions and the correction https://doi.org/10.5194/essd-2020-367 show no significant trend. From this attempt we clearly note that not so many of the inversions showed the clear decline of NGHGI. As NGHGI emissions are dominated by anthropogenic fluxes and decline with almost 30% 495 compared to 1990, this should be seen as well in the corrected anthropogenic inversions. Therefore, we need to further investigate how well the NGHGI reflect reality or how well the TD estimates capture the trends.

Global inversions estimates
Figures 5 compares TD global estimates, with NGHGI data and gives for information the wetland emissions 500 from global wetland models (Saunois et al., 2020). We present TD estimates of total emissions (Fig. 5a) as well as estimates of anthropogenic emissions (Fig. 5b). The global inversion models were split according to the type of observations used, 11 of them using satellites (GOSAT) and 11 using surface stations (SURF). Wetlands emissions provided by 22 global TD inversions from the Global Methane Budget (Saunois et al., 2020) are post-processed with prior ratios estimates for wetlands CH4 emissions (Appendix B, Table B4).

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For the common period 2010-2016 for the EU27+UK, the two ensembles of regional and global models give a total CH4 emission mean (Figure 5a) of 22.6 Tg CH4 yr -1 (GOSAT) and 23.7 Tg CH4 yr -1 (SURF) compared to 19.0 ± 1.7 Tg CH4 yr -1 for NGHGI ( Figure 5a). The mean of the natural wetland emissions from the global inversions is 1.3 Tg yr -1 and partly explains the positive difference between total emissions from inversions and NGHGI anthropogenic emissions.

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In an attempt to quantify the European TD anthropogenic CH4 component, in Figure 5b we subtract from the total TD CH4 emissions once again the peatland emissions from the regional JSBACH-HIMMELI model and those from geological and inland waters sources. The reason for correcting both regional and global inversions with the European peatland emissions from the JSBACH-HIMMELI model, lays in the fact that they are in the range of the global wetland emissions estimates for Europe (Saunois et al., 2020). Their median for all years (1.43 Tg CH4 yr −1 , yr -1 compared to -1.5 % yr -1 for the NGHGI over 2000-2016, while the GOSAT ensemble shows a decreasing trend of -0.8 % yr -1 compared to -0.9 % yr -1 for the NGHGI over 2010-2017.

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Similarly, as done for CH4 (section 3.1.1. and Figure 1), we present results of total N2O fluxes from EU27+UK and five main regions in Europe. Figure  The total UNFCCC estimates include emissions from all sectors. We plot these and compare them with fluxes 550 from global datasets, BU models and TD inversions. We note that for all five regions, the N2O emissions decreased between 29 % (Northern Europe) to 43 % (Western Europe) and for EU27+UK 37 % with respect to NGHGI 1990 value. It also shows that at regional European level, the emissions from BU (anthropogenic and natural) and TD estimates agree well with reported NGHGI data within the high uncertainty reported by UNFCCC (~80%) or observed in the TD model range. This TD uncertainty is represented here by the variability in the model ensembles and denotes 555 the range of the extremes (min and max) of estimates within each model ensemble. There is significant uncertainty in Northern Europe, where the TD estimates indicate either a source or a sink ( Figure 6). The current observation network is sparse, which currently limits the capability of inverse models to quantify GHG emissions at country or regional scale.
For all other regions BU anthropogenic emissions agree well with NGHGI given uncertainties, though we 560 note consistently higher estimates from TD regional and global models estimates. The difference is too high to be attributed to the natural emission, which is related here to inland waters as only source, and which ranges for all five regions between 0.2 -1.3 kton N2O yr -1 . The blue bar representing the natural emissions has a lower value estimates
France, UK and Germany contributed together 41% of total N2O emissions, respectively slightly higher than for CH4 (Appendix B, Figure B1b).
The data in Figure 7 shows anthropogenic CH4 emissions and their change from one decade to the next, from 585 UNFCCC NGHGI (2019) emissions from the energy sector with 12 % (± 23 %). We exclude fire emissions as they only account for 1.8 % of the total N2O emissions in EU27+UK.

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Between the 1990s and the 2000s, the net -17.7 % reduction originates largely from IPPU and agriculture sectors, which contributed -13.5 % and -4.2 % respectively. For the period between the 2000s and 2010-2017, the net -15.2 % reduction was again mainly attributed to the IPPU sector (-14.1 %), despite very small increases from the LULUCF and waste sectors (+0.6 %).
We note that in 2017, the amount of emissions from the IPPU sector had already decreased by 98 % compared 595 to 1990 and was only 3.5 kton N2O yr -1 . Although the IPPU sector contributes in 2017 only 4% to total N2O emissions, it was the sector with the largest reduction. IPPU sector emissions are mainly linked to the production of nitric acid (e.g. used in fertilizer production) and adipic acid (e.g. used in nylon production).   (1990-1999, 2000-2009 and 2010-2017) and percentages represent the contribution of each sector to the total reduction percentages between periods.  For agriculture (Figure 8d) five models/inventories, show a very good match with the NGHGI. Over 1990-2015, we found linear trends of -0.7 % yr -1 in NGHGI, GAINS and EDGAR v5.0. This provides further evidence that the sources rely on the same basic activity data from FAOSTAT and follow the IPCC EF Tier 1 or 2 approach (Petrescu et al., 2020). In contrast, ECOSSE estimates do not use the FAO fertilizer application rate data base, but instead calculates 620 ideal fertilizer application rates from the nitrogen demand of the crops. This means that it can severely under-estimate the applied fertilizer amounts for some areas (e.g. Netherlands, Denmark or North-West Germany), and the results are more indicative of emissions under idealized fertilizer application rates. Additionally, as mentioned above, the model simulates only the direct emissions. In the NGHGI (2018) submissions, the EU27+UK Tier 1 total uncertainty (based on the IPCC chapter 3 error 635 propagation method described in detail by Petrescu et al., 2020) for the waste sector was 626 %. The sectoral activity responsible for this high uncertainty was the wastewater treatment and discharge (913%) and this remains one of the most uncertain sources of N2O having the highest emissions in the waste sector. Emissions are known to vary markedly in space and time even within a single wastewater treatment plant (Gruber et al., 2020), a fact that only recently has been properly accounted for in the inventory guidelines (IPCC, 2019a). However, the total emissions from the waste 640 sector account for only 4.4 % of the total EU27+UK N2O emissions (excl. LULUCF). For the N2O we do not present the corrected anthropogenic value because the only natural flux, from inland waters, is very low (2.7 kton N2O yr -1 ) and when subtracted from the 4 inversions the change is almost negligible. Part of the inland water natural estimate is considered anthropogenic in Europe and is due to the leaching of N-fertilizers

Data availability
All raw data files reported in this work which were used for calculations and figures are available for public 670 download at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020). The data we submitted are reachable with one click (without the need for entering login and password), with a second click to download the data, consistent with the two click access principle for data published in ESSD (Carlson and Oda, 2018). The data and the DOI number are subject to future updates and only refers to this version of the paper.

Summary and concluding remarks
This study represents the first comprehensive European verification that compares total and sectoral European CH4 and N2O emission estimates from BU (anthropogenic and natural) with TD estimates in order to assess their use for verification purposes with the UNFCCC NGHGI reporting. Above, in the results sections, we discussed https://doi.org/10.5194/essd-2020-367 differences between estimates. Identification of source specific uncertainty is key in understanding these differences 680 and will lead to the reduction of the overall uncertainty in GHG inventories. More specifically, we present the first EU27+UK and European regional 2011-2015 averaged results for CH4 ( Figure 1) and N2O ( Figure 6) compared to the NGHGI emissions (incl. LULUCF) for the same period and the one reported in 1990, in the framework of the future global stock-take estimate.
Regarding sources of inconsistencies between CH4 BU estimates and NGHGI data ( Regarding the TD estimates, our exercise shows that comparison between CH4 inversions estimates and NGHGI is highly uncertain because of the large spread in the inversion results. As TD inversions do not fully distinguish between all emission sectors used by NGHGI and report either total emissions or a coarse sectorial 700 partitioning, their comparison to NGHGI is only possible for total emissions. It is also necessary to make an adjustment for natural emissions, which are included in TD inversions but not reported by the NGHGIs. However, the natural N2O emissions does not explain the difference between BU and TD (452 kton N2O) and more research is needed to identify the source of discrepancies.
Some studies (Fronzek et al., 2018) showed that ensembles work well in simulating variables with high 705 uncertainty. In general regional inversions show less spread then the global inversions as they used recent updates of transport models and higher resolution transport. Total CH4 from regional inversions show a min-max range of 8.7 Tg CH4 yr -1 compared to 12.4 Tg CH4 yr -1 from global GOSAT inversions and 13.5 Tg CH4 yr -1 from global SURF inversions (Figures 4 and 5). The global models are less well constrained as they have lower resolution (hence larger representation errors) and often use fewer observations.

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A key challenge for the inversion CH4 community remains the separation of emissions in specific source sectors, as derived total emissions may also include natural emissions (or removals), while in the case of N2O this won't be possible due to the use of definitions (e.g. "natural" N2O emissions are defined as the level of emission in the pre-industrial period). It is therefore not possible to separate between N2O from natural or anthropogenic sources We provide for EU27+UK a consolidated synthesis of the relative uncertain CH4 and N2O emissions making use of consistently derived BU and TD estimates over the region of Europe, which might illustrate the importance of Determined Contributions" and to assess collective progress towards achieving the purpose of this Agreement and its long-term goals (stocktake). As this will be mainly achieved and build upon BU methodologies developed by the IPCC, we need to take into consideration the potential to quantify GHG emissions by using "top-down" methods ("inverse modelling") (Bergamaschi et al., 2018b). One advantage of the inverse estimate is that it provides total 730 emission estimates. Therefore, the capability to quantify anthropogenic emissions depends on the magnitude of natural sources and sinks and the capability to quantify them.
As stated in the introduction, our aim was to identify in this synthesis the issues which cause the differences between NGHGI, BU and TD to further improve and build a pathway to a verification system (BU use of activity data, emission factor and emission allocation (CO2 fossil and CH4), very large NGHGI reported uncertainties which need 735 to be reassessed (N2O) and higher TD estimates then inventories (CH4 and N2O).

VERIFY project
VERIFY's primary aim is to develop scientifically robust methods to assess the accuracy and potential biases 745 in national inventories reported by the parties through an independent pre-operational framework. The main concept is to provide observation-based estimates of anthropogenic and natural GHG emissions and sinks as well as associated uncertainties. The proposed approach is based on the integration of atmospheric measurements, improved emission inventories, ecosystem data, and satellite observations, and on an understanding of processes controlling GHG fluxes (ecosystem models, GHG emission models).

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Two complementary approaches relying on observational data-streams will be combined in VERIFY to quantify GHG fluxes: 2) bottom-up activity data (e.g. fuel use and emission factors) and ecosystem measurements (bottom-up models).

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For CO2, a specific effort will be made to separate fossil fuel emissions from ecosystems fluxes. For CH4 and N2O, we will separate agricultural from fossil fuel and industrial emissions. Finally, trends in the budget of the three GHGs will be analysed in the context of NDC targets.
The objectives of VERIFY are: Objective 4. Produce periodic scientific syntheses of observation-based GHG balance of EU countries and practical policy-oriented assessments of GHG emission trends, and apply these methodologies to other countries.
For more information on project team and products/results check https://verify.lsce.ipsl.fr/.

UNFCCC NGHGI (2019)
Under the UNFCCC convention and its Kyoto Protocol national greenhouse gas (GHG) inventories are the most important source of information to track progress and assess climate protection measures by countries. In order to build mutual trust in the reliability of GHG emission information provided, national GHG inventories are subject 795 to standardized reporting requirements, which have been continuously developed by the Conference of the Parties The UNFCCC NGHGI anthropogenic CH4 emissions include estimates from 4 key sectors for the EU27+UK: 1 Energy, 2 Industrial processes and product use (IPPU), 3 Agriculture and 5 Waste. The tiers method a country applies depends on the national circumstances and the individual conditions of the land, which explains the variability 810 of uncertainties among the sector itself as well as among EU countries. The LULUCF CH4 emissions are very small but are included in some figures (see Table 1). waste emissions were quantified using the First-Order-Decay method, combining nationally defined inputs (for waste generation rates and compositions) and IPCC's regional default values for parameters associated with waste degradation processes (specific mass of biodegradable organic carbon, the methane volumetric fraction in the obtained landfill gas and the half life time for each waste component). The total landfilled waste was split into six streams: food and organic waste type, paper and cardboard, textiles, rubber, wood, sludge and similar effluents. This 835 was done, making use of the EUROSTAT waste database (Eurostat 2020) but also by employing data from waste composition for municipal/household type waste from Silpa (Silpa et al., 2018).   Table A1.

Uncertainty
Uncertainties are not available for the CAPRI estimates.

FLEXINVERT
The FlexInvert framework is based on Bayesian statistics and optimizes surface-atmosphere fluxes using the maximum probability solution (Rodgers, 2000). Atmospheric transport is modelled using the Lagrangian model 930 FLEXPART (Stohl et al., 2005;Pisso et al., 2019) run in the backwards time mode to generate a so-called Source-Receptor Matrix (SRM). The SRM describes the relationship between the change in mole fraction and the fluxes discretized in space and time (Seibert and Frank, 2004) and was calculated for 8 days prior to each observation. For use in the inversions, FLEXPART was driven using ECWMF operational analysis wind fields. The state vector consisted of prior fluxes discretized on an irregular grid based on the SRMs (Thompson et al. 2014). This grid has 935 finer resolution (in this case the finest was 0.25°×0.25°) where the fluxes have a strong influence on the observations and coarser resolution where the influence is only weak (the coarsest was 2°×2°). The fluxes were solved at 10-days temporal resolution. The state vector also included scalars for the background contribution. The background mixing ratio, i.e., the contribution to the mixing ratio that is not accounted for in the 8-day SRMs, was estimated by coupling the termination points of backwards trajectories (modelled using virtual particles) to initial fields of methane simulated Most models are driven by meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (Dee et al., 2011). In the case of STILT, the operational ECMWF analyses were used, while for NAME meteorological analyses of the Met Office Unified Model (UM) were employed. The regional models use boundary conditions (background CH4 mole fractions) from inversions of the global models (STILT from 960 TM3, COMET from TM5-4DVAR, CHIMERE from LMDZ) or estimate the boundary conditions in the inversions (NAME) using baseline observations at MaceHead as prior estimates. In the case of NAME and CHIMERE, the boundary conditions are further optimised in the inversion. The inverse modelling systems applied in this study use different inversion techniques. TM5-4DVAR, LMDZ, and TM3-STILT use 4DVAR variational techniques, which allow optimisation of emissions of individual grid cells. These 4DVAR techniques employ an adjoint model in order 965 to iteratively minimise the cost function using a quasi-Newton (Gilbert and Lemaréchal, 1989) or conjugate gradient (Rödenbeck, 2005) algorithm. The NAME model applies a simulated annealing technique, a probabilistic technique for approximating the global minimum of the cost function. In CHIMERE and COMET, the inversions are performed analytically after reducing the number of parameters to be optimised by aggregating individual grid cells before the inversion. TM5-CTE applies an ensemble Kalman filter (EnKF) (Evensen, 2003), with a fixed-lag smoother (Peters 970 et al., 2005).

Uncertainty:
In general, the estimated model uncertainties depend on the type of station and for some models (TM5-4DVAR and NAME) also on the specific synoptic situation. In InGOS the uncertainty of the ensemble was calculated as 1σ estimate. Bergamaschi et al. (2015) showed that the range of the derived total CH4 emissions from north-western and eastern Europe using four different inverse modelling systems was considerably larger than the uncertainty

InTEM -NAME
The Inverse Technique for Emission Modelling (InTEM) (Arnold et al., 2018) uses the NAME (Numerical Atmospheric dispersion Modelling Environment) (Jones et al, 2007) atmospheric Lagrangian transport model. NAME is driven by analysis 3-D meteorology from the UK Met Office Unified Model (Cullen, 1993). The horizontal and vertical resolution of the meteorology has improved over the modelled period from 40 km to 12 km (1.5 km over the 985 UK). InTEM is a Bayesian system that minimises the mismatch between the model and the atmospheric observations  Uncertainty: This random removal of observations allows a greater exploration of the uncertainty, given the potential for some of the emission sources to be intermittent within the time-period of the inversion.

CTE-CH4 Europe, CTE-SURF and CTE-GOSAT
CarbonTracker Europe CH4 (CTE-CH4) (Tsuruta et al., 2017) applies an ensemble Kalman filter (Peters et al. 2005) in combination with the Eulerian transport model TM5 (Krol et al. 2005). It optimizes surface fluxes weekly 1005 , and assimilates atmospheric CH4 observations. TM5 was run at 1° x 1° resolution over Europe and 6° x 4° resolution globally, constrained by 3-hourly ECMWF ERA-Interim meteorological data. The photochemical sink of CH4 due to tropospheric and stratospheric OH, and stratospheric Cl and O( 1 D) was pre-calculated based on Houweling et al. (2014) and Brühl and Crutzen (1993) and not adjusted in the optimization scheme.
Three experiments were conducted, which differ in (1) sets of prior fluxes, (2) sets of assimilated

MIROC4-ACTM:
The MIROC4-ACTM time dependent inversions solve for emissions from 53 regions for CH4 and 84 regions for N2O. is performed online. The hydroxyl (OH) radical concentration for reaction with CH4 vary monthly but without any interannual variations. The simulated mole fractions for the total a priori fluxes are subtracted from the observed concentrations before running the inversion calculation (as in Patra et al., 2016 for CH4 inversion). Both the inversion 1040 results are contributed to the GCP-CH4 and GCP-N2O activities (Saunois et al., 2020;Thompson et al., 2019;Tian et al., 2020).

Uncertainties:
The posterior fluxes are subject to systematic errors primarily from: 1) errors in the modelled atmospheric transport; 2) aggregation errors, i.e. errors arising from the way the flux variables are discretized in space (84 regions) and time (monthly-means); 3) errors in the background mole fractions (assumed to be a minor factor);

1045
and 4) the incomplete information from the sparse observational network and hence the dependence on the prior fluxes.
In addition, there is, to a much smaller extent, some error due to calibration offsets between observing instruments, which is more pertinent for N2O than for other GHGs. We have validated model transport in troposphere using SF6 for the inter-hemispheric exchange time, and the using SF6 and CO2 for the age of air in the stratosphere. The simulated N2O concentrations are also compared with aircraft measurements in the upper troposphere and lower stratosphere for 1065 each group selected their constraining observations. More information can be found in Saunois et al. (2020) in particular in their Table 6 and S6.
Uncertainties: currently there are no uncertainties reported for the GMB models. This study uses the median and the min/mas as uncertainty range estimation from the 22 models ensemble. In general uncertainties might be due to factors like: different transport models, physical parametrizations, prior fluxes, observation data sets etc.

Bottom-up CH4 emissions estimates CH4 emissions from inland waters 1075
The CH4 estimate from inland waters represents a climatology of average annual diffusive and ebulitive CH4 emissions from lakes and reservoirs at the spatial resolution of 0.1°. The climatology is based on five alternative estimates, all relying on the high-resolution HydroLAKES database (Messager et al., 2016), and of which we report the mean and the standard deviation as a measure of uncertainty. Four of these estimates are based on predictions of CH4 emission rates from N and P concentrations. These concentrations were computed for each lake and reservoir of 1080 the HydroLAKES dataset (> 1.4 millions), using the mechanistic-stochastic model (MSM) of Maavara et al. (2017Maavara et al. ( , 2019 and Lauerwald et al. (2019); see methodology for inland water N2O emissions for further details. The four estimates result from two empirical equations relating CH4 emissions to chlorophyll-a concentrations (Deemer et al., 2016;DelSontro et al., 2018) and two equations relating chlorophyll-a concentrations to nutrient concentrations (both from McCauley et al., 1989) in lakes and reservoirs. The fifth estimate is based on direct upscaling from observed 1085 CH4 emission rates (155 lakes and reservoirs), which we have classified into rates reported for small lakes (<0.3 km 2 ), larger (>0.3 km 2 ) lakes, and reservoirs. In addition, we applied a coarse regionalization distinguishing the Boreal (>54°N) from the Temperate to Sub-Tropical (<54°N) zone. The model framework, JSBACH-HIMMELI (Raivonen et al., 2017;Susiluoto et al., 2018) is used to estimate wetland and mineral soil emissions, and an empirical model is used to estimate the emissions from inland water bodies.

JSBACH-HIMMELI
JSBACH-HIMMELI is a combination of two models, JSBACH, that is the land-surface model of MPI-ESM (Reick et al., 2013), and HIMMELI, that is a specific model for northern peatland emissions of CH4 (Raivonen et al., 2017). HIMMELI (HelsinkI Model of MEthane buiLd-up and emIssion for peatlands) has been developed especially 1095 for estimating CH4 production and transport in northern peatlands. It simulates both CH4 and CO2 fluxes and can be used as a module within different modelling environments (Raivonen et al., 2017;Susiluoto et al., 2018). HIMMELI is driven with soil temperature, water table depth, the leaf area index and anoxic respiration. These parameters are provided to HIMMELI from JSBACH, which models hydrology, vegetation and soil carbon input from litter and root exudates. CH4 emission and uptake of mineral soils are calculated applying the method by  based 1100 on soil moisture estimated by JSBACH.
The distribution of terrestrial vegetation types in JSBACH-HIMMELI is adopted from CORINE land cover data and from native JSBACH land cover for the areas that CORINE does not cover. The HIMMELI methane model is applied for peatlands and the mineral soil approach for the rest. The map of inland water CH4 emissions has been combined with JSBACH-HIMMELI land use map so that the map of inland waters is preserved and JSBACH grid-1105 based fractions of different land use categories adjusted accordingly. In order to avoid double-counting the terrestrial CH4 flux estimates have been normalized by the ratio of the two inland water body distributions.
Uncertainties: As in any process modeling the uncertainties of the bottom up modeling of CH4 arise from three primary sources: parameters, forcing data (including spatial and temporal resolution), and model structure. An important source of uncertainty in the case of terrestrial CH4 flux modeling is the spatial distribution of peatlands.

1110
The uncertainties of JSBACH-HIMMELI peatland emissions were estimated by comparing the annual totals of measured and simulated methane fluxes at five European observation sites. Two of the sites are located in Finnish Lapland, one in middle Sweden, one in southern Finland and one in Poland.
For the sensitivity of mineral soil fluxes  tested two soil moisture thresholds, 85% or 95% of water holding capacity, below which mineral soils were assumed to be only CH4 sinks, above which sources. We 1115 used the higher value, 95% of water holding capacity. The uncertainty was estimated using CH4 flux simulations of one year (2005). We did two new model runs, using moisture thresholds 95±15%, and derived the uncertainty from the resulting range in the annual emission sum.

Geological fluxes 1120
To calculate geological CH4 emissions we used literature data for geological emissions on land (excluding marine seepage) (Etiope et al., 2019;Hmiel et al., 2020). Geological emissions were calculated by scaling the regional

ECOSSE
ECOSSE is a biogeochemical model that is based on the carbon model ROTH-C (Jenkinson and Rayner, 1977;Jenkinson et al. 1987;Coleman and Jenkinson, 1996) and the nitrogen-model SUNDIAL (Bradbury et al. 1993;Smith et al. 1996). All processes of the carbon and nitrogen dynamics are considered (Smith et al., 2010a,b).
Additionally, in ECOSSE processes of minor relevance for mineral arable soils are implemented as well (e.g. methane 1150 emissions) to have a better representation of processes that are relevant for other soils (e.g. organic soils). ECOSSE can run in different modes and for different time steps. The two main modes are site specific and limited data. In the later version, basis assumptions/estimates for parameters can be provided by the model. This increases the uncertainty but makes ECOSSE a universal tool that can be applied for large scale simulations even if the data availability is limited. To increase the accuracy in the site-specific version of the model, detailed information about soil properties, 1155 plant input, nutrient application and management can be added as available.
During the decomposition process, material is exchanged between the SOM pools according to first order rate equations, characterised by a specific rate constant for each pool, and modified according to rate modifiers dependent on the temperature, moisture, crop cover and pH of the soil. The N content of the soil follows the decomposition of the SOM, with a stable C:N ratio defined for each pool at a given pH, and N being either mineralised 1160 or immobilised to maintain that ratio. Nitrogen released from decomposing SOM as ammonium (NH4+) or added to the soil may be nitrified to nitrate (NO3-). For spatial simulations the model is implemented in a spatial model platform. This allows us to aggregate the input parameter for the needed resolution. ECOSSE is a one-dimensional model and the model platform provides the input data in a spatial distribution and aggregates the model outputs for further analysis. While climate data are 1165 interpolated, soil data are represented by the dominant soil type or by the proportional representation of the different soil types in the spatial simulation unit (this is in VERIFY a grid cell).
Uncertainties in ECOSSE arise from three primary sources: parameters, forcing data (including spatial and temporal resolution), and model structure.

DayCent
DayCent was designed to simulate soil C dynamics, nutrient flows (N, P, S) and trace gas fluxes (CO2, CH4, N2O, NOx, N2) between soil, plants and the atmosphere at daily time-step. Submodels include soil water content and temperature by layer, plant production and allocation of net primary production (NPP), decomposition of litter and soil organic matter, mineralization of nutrients, N gas emissions from nitrification and denitrification, and CH4 1175 oxidation in non-saturated soils.
The DayCent modelling application at the EU level is a consolidated model framework running on LUCAS point (Orgiazzi, 2018) which was extensively explained in previous works (Lugato et al., 2017(Lugato et al., , 2018Quemada et al., 2020) where a detailed description of numerical and geographical datasets and uncertainty estimations is reported.
Information directly derived from LUCAS (2009)(2010)(2011)(2012)(2013)(2014)(2015) included the soil organic carbon content (SOC), 1180 particle size distribution and pH. Hydraulic properties and bulk density was also calculated with an empiricallyderived pedotransfer. Management information was derived from official statistics (Eurostat, 2019) and included crop shares at NUTS2 level. The amount of mineral N was partitioned according to the regional crop rotations and agronomic crop requirements. Organic fertilization and irrigated areas were derived from the 'Gridded Livestock of the World' FAO dataset and the FAO-AQUASTAT product.

1185
Meteorological data were downloaded from the E-OBS gridded dataset (http://www.ecad.eu) at 0.1° resolution. For the climatic projection, the gridded data from CORDEX database (https://esgfnode.ipsl.upmc.fr/search/cordex-ipsl/) were used. The average annual (2006)(2007)(2008)(2009)(2010) atmospheric N deposition from the EMEP model (rv 4.5) were also implemented into the simulations.  in a consistent manner at 0.5° resolution, which were then downscaled to 0.1° using the spatial distribution of European 1200 inland water bodies. The procedure to calculate the cascading loads of N and P delivered to each water body along the river-reservoir-estuary continuum and to topologically connect 1.4 million lakes (extracted from the HYDROLAKES database) is described in Maavara et al., 2019 andLauerwald et al., 2019. The methodology to quantify N2O emissions is based on the application of a mechanistic stochastic model (MSM) to estimate inland water C-N-P cycling as well as N2O production and emission generated by nitrification and denitrification. Using a Monte Carlo analysis, the MSM 1205 allows to generate relationships relating N processes and N2O emissions to N and P loads and water residence time from the mechanistic model outputs, which are subsequently applied for the spatially resolved upscaling. For the estimation of N2O emission, we ran two distinct model configurations relying on EFs scaling to denitrification and nitrification rates: one assuming that N2O production equals N2O emissions, the other taking into account the kinetic limitation on N2O gas transfer and progressive N2O reduction to N2 during denitrification in water bodies with 1210 increasing residence time . The model outputs from the two scenarios are used to constrain uncertainties in N2O emission estimates.

GAINS
Specific sectors and abatement technologies in GAINS vary by the specific emitted compound, with source 1215 sector definition and emission factors largely following the IPCC methodology at the Tier 1 or Tier 2 level. GAINS includes in general all anthropogenic emissions to air, but does not cover emissions from forest fires, savannah burning and land use / land use change. Emissions are estimated for 174 countries/regions, with the possibility to aggregate to a global emission estimate, and spanning a timeframe from 1990 to 2050 in five-year intervals. Activity drivers for macroeconomic development, energy supply and demand, and agricultural activities are entered externally, GAINS 1220 extends with knowledge required to estimate "default" emissions (emissions occurring due to an economic activity without emission abatement) and emissions and costs of situations under emission control (Amann et al., 2001).
Emissions of nitrous oxide derive from energy, industry, agriculture, and waste. Land use change emissions are not included. In the energy sector, certain technologies implemented to improve air quality affect N2O emission factors (like catalytic converters in vehicles), sometimes also negatively. That is also the case for non-selective parameter. In the waste sector, composting and wastewater treatment are considered relevant sources. For wastewater treatment, GAINS also considers a specific emission reduction option when optimizing processes towards N2O reduction (e.g. via favoring the anammox process). All details have been reported by Winiwarter et al. (2018) in their supplementary material.

Uncertainties:
The same paper provides full information on the uncertainty of N2O emissions in the GAINS model, which is a consequence of uncertainty provided in the activity data, in the emission factors, and in the actual structure of the respective management strategies that also include the share of abatement technology already implemented.

Top-down N2O emission estimates FLEXINVERT
The FlexInvert framework is based on Bayesian statistics and optimizes surface-atmosphere fluxes using the 1265 maximum probability solution (Rodgers 2000). Atmospheric transport is modelled using the Lagrangian model FLEXPART (Stohl et al. 2005;Pisso et al. 2019) run in the backwards time mode to generate a so-called Source-Receptor Matrix (SRM). The SRM describes the relationship between the change in mole fraction and the fluxes discretized in space and time (Seibert and Frank, 2004) and was calculated for 7 days prior to each observation. For use in the inversions, FLEXPART was driven using ECMWF Era Interim wind fields.

1270
The state vector consisted of flux increments (i.e. offsets to the prior fluxes) discretized on an irregular grid based on the SRMs (Thompson et al. 2014). This grid has finer resolution (in this case the finest was 0.5°×0.5°) where the fluxes have a strong influence on the observations and coarser resolution where the influence is only weak (the https://doi.org/10.5194/essd-2020-367 coarsest was 2°×2°). The flux increments were solved at 2-weekly temporal resolution. The state vector also included scalars for the background mole fractions. The optimal (posterior) fluxes were found using the Conjugate Gradient 1275 method (e.g. Paige and Saunders, 1975).
The background mole fractions, i.e., the contribution to the modelled mole fractions that is not accounted for in the 7-day SRMs, was estimated by coupling the termination points of backwards trajectories (modelled using virtual particles) to initial fields of mole fractions from the optimized Eulerian model LMDz (i.e. the CAMS N2O mole fraction product v18r1) following the method of Thompson et al. 2014.

TOMCAT-INVICAT 1320
TOMCAT-INVICAT  is a variational inverse transport model, which is based on the global chemical transport model TOMCAT, and its adjoint. It uses a 4-D variational (4D-VAR) optimization framework based on Bayesian theory which seeks to minimize model-observation differences by altering surface fluxes, while allowing for prior knowledge of these fluxes to be retained. TOMCAT (Monks et al., 2017) is an offline chemical transport model, in which meteorological data is taken from ECMWF ERA-Interim reanalyses (Dee et al.,  and 4) the incomplete information from the sparse observational network and hence the dependence on the prior fluxes.

1350
In addition, there is, to a much smaller extent, some error due to calibration offsets between observing instruments, which is more pertinent for N2O than for other GHGs. We have validated model transport in the troposphere using SF6 for the inter-hemispheric exchange time, and the using SF6 and CO2 for the age of air in the stratosphere. The          Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM, Biogeosciences, 8, 1925-1953, doi:10.5194/bg-8-1925