number of measurements worldwide and related spatial (activity) and emission factor statistics.

For the other geological sources categories, i.e., onshore seeps (OS; including mud volcanoes), geothermal manifestations (GM) and submarine seepage (SS), the masks of the European territories (for the EU27 + UK and EU49, and the EEZ for the marine areas) on the global 1° × 1° emission grid of Etiope et al. (2019) were applied. The result is summarized in Table A4.

Table A5 compares the new results with previous European estimates (Etiope, 2009; Petrescu et al., 2021).

The overall uncertainties of the spatial distribution of the geo-CH₄ sources and CH₄ emissions depend on individual uncertainties of the four categories of seepage, which are discussed in Etiope et al. (2019). Compared to the global grid model, the uncertainty of the microseepage at European scale by refining the activity (microseepage area) has been reduced.

The new EU27 + UK geo-CH₄ emission estimate is lower than the one derived by Petrescu et al. (2021) using the global gridding but higher than the scaled-down value (Eq. A1). The EU49 (onshore + offshore) emission is higher than, but of the same order of magnitude of, the preliminary, rough estimate of geographic Europe, which included Azerbaijan, by Etiope (2009). Onshore geo-CH₄ emissions occur mostly in Azerbaijan, Italy and Romania, which are actually the EU49 countries with major onshore oil-gas reserves and production (BP, 2020) and thus with greater natural seepage potential. Offshore emissions are dominated by the large estimates published for the UK shelf (Judd et al., 1997, revised by Tizzard, 2008). These estimates need to be verified and improved. Beyond the emission values, our gridding provides, however, the first detailed map of natural geological methane emission in Europe, which can be used for continental-scale methane budget modeling.

## A2.3 Top-down natural CH₄ emission estimates

### Global Carbon Project – Global Methane Budget (Saunois et al., 2020)

For this study, none of the global inversions were updated.

GMB uses an ensemble of 13 monthly gridded estimates of wetland emissions based on different land surface models as calculated for Saunois et al. (2020). Each model conducted a 30-year spin-up and then simulated net methane emissions from wetland ecosystems over 2000–2017. The models were forced by CRU JRA (University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA)) reconstructed climate fields (Harris, 2019) and by the remote-sensing-based wetland dynamical area dataset WAD2M (Wetland Area Dynamics for Methane Modeling). This dataset provides monthly global areas over 2000–2017 based on a combination of microwave remote sensing data from Schroeder et al. (2015) and various regional inven-

tory datasets. More information is available in Saunois et al. (2020), and more details will be presented in a future publication led by Poulter et al. (2017) and colleagues.

*Uncertainty*. As described by Saunois et al. (2020) uncertainties are reported as minimum and maximum values of the available studies, in brackets. They do not take into account the uncertainty of the individual estimates but rather express the uncertainty as the range of available mean estimates, i.e., the standard error across measurements/methodologies considered.

## A3 Anthropogenic and natural N₂O emissions

### A3.1 Bottom-up N₂O emission estimates

### UNFCCC GHG1 (2019), EDGARv6.0 and CAPRI

Descriptions are found in Appendix A1.

### 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 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, plant input, nutrient application and management can be added as available.

During the decomposition process, material is exchanged between the soil organic matter (SOM) pools according to first-order rate equations; characterized 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 with N being either mineralized or immobilized to maintain that ratio. Nitrogen released from decomposing SOM as ammonium ($NH_4^+$) or added to the soil may be nitrified to nitrate ($NO_3^-$).

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
<table><caption>**Table A4.** Gridded European geo-CH₄ emissions (t yr⁻¹).</caption><thead><tr><th></th><th>Microseepage (MS)</th><th>Onshore seeps (OS)</th><th>Geothermal (GM)</th><th>Submarine seeps (SS)</th><th>Total</th></tr></thead><tbody><tr><td>EU49</td><td>2 985 570</td><td>2 162 539</td><td>404 205</td><td>1 653 049</td><td>7 205 363</td></tr><tr><td>EU27 + UK</td><td>2 183 733</td><td>69 618</td><td>206 705</td><td>863 368</td><td>3 323 424</td></tr></tbody></table>

<table><caption>**Table A5.** European geo-CH₄ emission estimates (Tg yr⁻¹).</caption><thead><tr><th></th><th>Etiope (2009)</th><th>Petrescu et al. (2021)</th><th>This work</th></tr></thead><tbody><tr><td>Geographic Europe (onshore + offshore, including Azerbaijan)</td><td>3</td><td></td><td></td></tr><tr><td>EU27 + UK onshore</td><td></td><td>8.8 (from global grid model)<br>1.3 (scaled down as Hmiel et al., 2020)</td><td>2.4</td></tr><tr><td>EU27 + UK onshore + offshore</td><td></td><td></td><td>3.3</td></tr><tr><td>EU49 onshore</td><td></td><td></td><td>5.5</td></tr><tr><td>EU49 onshore + offshore</td><td></td><td></td><td>7.2</td></tr></tbody></table>

outputs for further analysis. While climate data are interpo-
lated, 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 a grid cell in VERIFY).

Uncertainties in ECOSSE arise from three primary
sources: parameters, forcing data (including spatial and tem-
poral resolution), and model structure.

## DayCent

DayCent was designed to simulate soil C dynamics, nutri-
ent flows (N, P, S) and trace gas fluxes (CO₂, CH₄, N₂O,
NOₓ, N₂) between soil, plants and the atmosphere at a daily
time step. Submodels include soil water content and temper-
ature 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 oxidation in non-
saturated soils.

The DayCent modeling application at the EU level is a
consolidated model framework running on LUCAS point
(Orgiazzi et al., 2018), which was extensively explained in
previous works (Lugato et al., 2017, 2018; Quemada et al.,
2020), where a detailed description of numerical and geo-
graphical datasets and uncertainty estimations is reported.

Information directly derived from LUCAS (2009–2015)
included the soil organic carbon content (SOC), particle size
distribution and pH. Hydraulic properties and bulk density
were also calculated with an empirically derived pedotransfer
function. Management information was derived from official
statistics (EUROSTAT, 2019) and included crop shares at the
NUTS2 level. The amount of mineral N was partitioned ac-

cording to the regional crop rotations and agronomic crop re-
quirements. Organic fertilization and irrigated areas were de-
rived from the Gridded Livestock of the World FAO dataset
and the FAO-AQUASTAT (2020) product.

Meteorological data were downloaded from the E-OBS
gridded dataset (http://www.ecad.eu, last access: June 2019)
at 0.1° resolution. For the climatic projection, the gridded
data from CORDEX database (https://esgf-node.ipsl.upmc.
fr/search/cordex-ipsl/, last access: June 2019; CORDEX,
2020) were used. The average annual (2006–2010) atmo-
spheric N deposition from the EMEP model (v4.5) were also
implemented into the simulations. For current study, the re-
sults were updated and use the 2015–2019 mean.

*Uncertainty.* The starting year of the simulation was set to 2009 and projected in the future. The uncertainty analysis, based on the Monte Carlo approach, was done running the model 52 times in each point and, contemporary, randomly sampling model inputs from probability density functions for SOC pool partition, irrigation, and both mineral and organic fertilization rates. The model outputs (including uncertainties) at point level were upscaled regionally at 1 km resolution by a machine learning approach based on random forest regression.

## N₂O emissions from inland waters

The N₂O estimate represents a climatology of average annual N₂O emissions from rivers, lakes, reservoirs and estuaries at the spatial resolution of 0.1°. Based on a spatially explicit representation of water bodies and point and non-point sources of N and P, this model quantifies the global-scale spatial patterns in inland water N₂O emissions in a
consistent manner at 0.5° resolution, which were then down-scaled to 0.1° using the spatial distribution of European 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) and Lauerwald et al. (2019). The methodology to quantify N₂O emissions is based on the application of a 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 allows us 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 and the other taking into account the kinetic limitation on N₂O gas transfer and progressive N₂O reduction to N₂ during denitrification in water bodies with increasing residence time (Maavara et al., 2019). The model outputs from the two scenarios are used to constrain uncertainties in N₂O emission estimates.

For this study, the upscaled RECCAP2 estimates were used. This approach synthesizes existing inland water emission estimates for N₂O (Yao et al., 2020; Marzadri et al., 2021; Maavara et al., 2019; Lauerwald et al., 2019; Del Sontro et al., 2018; Deemer et al., 2016), homogenized and rescaled to a consistent set of inland water surface area (Messager et al., 2016; Allen and Pavelsky, 2018) and corrected for the effects of seasonal ice cover (Yang et al., 2020). It produced average annual N₂O emissions fluxes for Europe.

## GAINS

Specific sectors and abatement technologies in GAINS vary by the specific emitted compound, with source 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 time frame from 1990 to 2050 in 5-year intervals. Activity drivers for macroeconomic development, energy supply and demand, and agricultural activities are entered externally; GAINS 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., 2011).

Emissions of nitrous oxide derive from energy, industry, agriculture and waste. Land use change emissions are not included. In the energy sector, certain technologies imple-

mented to improve air quality affect N₂O emission factors (like catalytic converters in vehicles), sometimes also negatively. That is also the case for non-selective catalytic reduction devices for NOₓ abatement in power plants or for fluoridized bed combustion. Relevant industrial processes cover nitric acid and adipic acid, with other processes (glyoxal, if relevant, or caprolactam) included. Both processes allow for two different levels of abatement technologies, and both are relatively easily accessible and low cost. The use of N₂O in gaseous form, often as an anesthetic for medical purposes, is associated with population numbers and scaled by availability of hospital beds. Marked emission reductions (at low costs) and a complete phaseout of emissions (high costs) are implemented as technologies. Agricultural emissions in part derive from manure handling, where different management strategies have repercussions on emissions. The larger fraction of emission is from application of nitrogen compounds in different forms to grassland, crops and rice, with rice using a different emission factor. Application of manure and of mineral fertilizer in GAINS can be reduced by advanced computer technology such as automatic steering and variable rate application or by agrochemistry (nitrification inhibitors). Costs of implementation are considered to depend on the size of a farm; hence, farm size is an important 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. Further parameters also described (on uncertainty of future projections and on costs) are not relevant here.

## FAOSTAT

The Statistics Division of the Food and Agriculture Organization of the United Nations provides N₂O emissions from agriculture (https://www.fao.org/faostat/en/#data/GT, last access: April 2022) and its sub-domains, as well as N₂O emissions from land use linked to biomass burning. The FAOSTAT emissions database is computed following Tier 1 of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html, last access: January 2022). Country reports to FAO on crops, livestock and agriculture use of fertilizers are the source of activity data. Geospatial data are the source of AD for the estimates from cultivation of organic soils, biomass and peat fires. N₂O emissions are provided by country, regions and special groups, with global coverage, relative
to the period of 1961–present (with annual updates, currently 2019) and with projections for 2030 and 2050 for agriculture only, expressed in both CO₂e and N₂O by underlying agricultural and land use emission sub-domain and by aggregate (agriculture total, agriculture total plus energy, agricultural soils). The main N₂O emissions are reported for the following agricultural activities: manure management, synthetic fertilizers, manure applied to the soils, manure left in pasture, crop residues, cultivation of organic soils and burning crop residues. LULUCF emissions consist of N₂O associated with burning biomass and peat fires, as well as from the drainage of organic soils. Comparison to the UNFCCC submissions is also provided.

Uncertainties were computed by Tubiello et al. (2013) but are not available in the FAOSTAT database.

## A3.2 Top-down N₂O emission 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 modeled 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 d prior to each observation. For use in the inversions, FLEXPART was driven using ECMWF ERA-Interim wind fields.

The state vector consisted of flux increments (i.e., offsets to the prior fluxes) discretized on an irregular grid based on the SRMs (Thompson and Stohl, 2014). This grid has a finer resolution (in this case the finest was 0.5° × 0.5°) where the fluxes have a strong influence on the observations and a coarser resolution where the influence is only weak (the 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 method (e.g., Paige and Saunders, 1975).

The background mole fractions, i.e., the contribution to the modeled mole fractions that is not accounted for in the 7 d SRMs, were estimated by coupling the termination points of backward trajectories (modeled 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 and Stohl (2014).

**Uncertainties.** The posterior fluxes are subject to systematic errors primarily from (1) errors in the modeled atmospheric transport; (2) aggregation errors, i.e., errors arising from the way the flux variables are discretized in space and time; (3) errors in the background mole fractions; and (4) the incomplete information from the observations and hence the

dependence on the prior fluxes. In addition, there is, to a smaller extent, some error due to calibration offsets between observing instruments, which is more pertinent for N₂O than for other GHGs. Random uncertainties are calculated from a Monte Carlo ensemble of inversions following Chevallier et al. (2007), and uncertainties in the observation space were inflated to take into account the model representation errors.

### VERIFY Community Inversion Framework (CIF)

In the current study, new CIF results are presented (Berchet et al., 2021). For N₂O, inversions were run with CHIMERE and FLEXPART (NILU). The results using the two models were very comparable at the level of the EU27 plus Norway, Switzerland and the UK, with annual mean emissions for 2005–2018.

CHIMERE (see https://www.lmd.polytechnique.fr/chimere/, last access: May 2022) is a non-hydrostatic Eulerian chemistry-transport model. Its area-limited domains can be designed to cover the hemispheric to the urban scales, with horizontal resolutions from several degrees to 1 km. The time steps usually cover a few minutes, depending on the CFL and choices made by the user for minimizing computation costs. For the purpose of flux inversions, the tangent-linear and the adjoint codes have been developed and parallelized only for trace gases (see Fortems-Cheiney et al., 2021, their Sect. 3.2, for more details). The required input data are meteorological 3D and 2D fields (e.g., temperature, wind speed), boundary conditions for concentrations at the four sides and at the top of the domain, and emission fluxes. The comparison to surface measurements is done by extracting from the model the simulated concentrations in the grid cell matching the stations' locations for the time steps matching the measurement date and time. If the measurement covers a longer time (e.g., hourly means), the simulated concentrations for the matching time steps are averaged. For this deliverable, CHIMERE was run using meteorological data from the ECMWF's IFS operational forecast (every 3 h) retrieved at 0.25° × 0.25° and interpolated onto the model's grid (0.5° × 0.5°). CHIMERE extends from the surface to 200 hPa with 17 sigma-pressure levels.

For the inversions provided by NILU, FLEXPARTv10.4 was run using ECMWF's operational IFS forecast with backward trajectory lengths of 7 d for N₂O. The footprints were saved at 0.5° × 0.5° for N₂O inversions. For N₂O anthropogenic emissions were taken from EDGARv6.0. An estimate for the natural N₂O flux was provided by the O-CN land surface model – a monthly climatology of the pre-industrial (or baseline) fluxes (Zaehle et al., 2011) and an estimate for the ocean N₂O flux was provided by the coupled ocean-biogeochemistry model, PlankTOM-v10 – also a monthly climatology (Buitenhuis et al., 2018).

**Uncertainties.** In the CIF, the uncertainties are described by matrices, with variances on the diagonal and covariances on the off-diagonal. They are called the prior error (for the
error statistics on the prior controlled variables) and the ob-
servation error (for the error statistics on the difference be-
tween the observed and modeled mixing ratios). Observation
errors were assumed to be independent from each other in all
inversions; i.e., the observation error covariance matrix is di-
agonal. For CHIMERE, measurement errors were provided
from observation files. Transport, representation and aggre-
gation errors were deduced from the CH₄-based errors. For
FLEXPART (NILU), the observation errors were calculated
in the same way as for CH₄, but a minimum error of 0.3 ppb
was used.

A3.3 Global N₂O Budget – GCP (Tian et al., 2020)

CAMS-N₂O

Within the GCP 2019 results, N₂O fluxes are estimated us-
ing the atmospheric inversion framework, CAMS-N₂O. At-
mospheric inversions use observations of atmospheric mix-
ing ratios, in this case, of N₂O, and provide the fluxes that
best explain the observations while at the same time being
guided by a prior estimate of the fluxes. In other words, the
fluxes are optimized to fit the observations within the limits
of the prior and observation uncertainties. To produce the op-
timized (a posteriori) fluxes, a number of steps are involved:
first, the observations are pre-processed; second, a prior flux
estimate is prepared; third, mixing ratios are simulated us-
ing the prior fluxes and are used to estimate the model rep-
resentation error; and fourth, the inversion is performed. In
total, 140 ground-based sites, ship and aircraft transects are
included in the inversion. The term "site" refers to locations
where there is a long-term record of observations and in-
cludes ground-based measurements, both from discrete sam-
ples (or "flasks") and quasi-continuous sampling by in situ
instruments, as well as aircraft measurements. A prior esti-
mate of the total N₂O flux with monthly resolution and inter-
annually varying fluxes is prepared from a number of models
and inventories. For the soil fluxes (including anthropogenic
and natural) an estimate from the land surface model OCN-
v1.2 is used, which is driven by observation-based climate
data, N-fertilizer statistics and modeled N deposition (Zaehle
et al., 2011). For the ocean fluxes, an estimate from the ocean
biogeochemistry model PlankTOM-v10.2 is used, which is
a prognostic model (Buitenhuis et al., 2018). Atmospheric
transport is modeled using an offline version of the Labo-
ratoire de Météorologie Dynamique model, LMDZ5, which
computes the evolution of atmospheric compounds using
archived fields of winds, convection mass fluxes and plan-
etary boundary layer (PBL) exchange coefficients that have
been calculated using the online version nudged to ECMWF
ERA-Interim winds.

CAMS-N₂O uses the Bayesian inversion method to find
the optimal fluxes of N₂O, given prior information about the
fluxes and their uncertainty, and observations of atmospheric
N₂O mole fractions. The method is the same as that used in

Thompson and Stohl (2014). For this study, inversions were
not updated.

*Uncertainty.* Uncertainties in CAMS-N₂O simulations pertain to observation space and to state space. Uncertainty in the observation space is calculated as the quadratic sum of the measurement and transport uncertainties. The measurement uncertainty is assumed to be 0.3 ppb (approximately 0.1 %) based on the recommendations of data providers. The transport uncertainty includes estimates of uncertainties in advective transport (based on the method of Rödenbeck et al., 2003) and from a lack of subgrid-scale variability (based on the method of Bergamaschi et al., 2010). For the error in each land grid cell, the maximum magnitude of the flux in the cell of interest and its eight neighbors is used, while for ocean grid cells the magnitude of the cell of interest only is used. Posterior flux uncertainties are calculated from a Monte Carlo ensemble of inversions, based on the method of Chevallier et al. (2005).

TOMCAT-INVICAT

TOMCAT-INVICAT (Wilson et al., 2014) is a variational in-
verse transport model, which is based on the global chemical
transport model TOMCAT and its adjoint. It uses a 4D varia-
tional (4DVAR) optimization framework based on Bayesian
theory which seeks to minimize model–observation differ-
ences 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 are taken from ECMWF ERA-Interim
reanalyses (Dee et al., 2011). The model grid resolution and
therefore the optimized surface flux estimates have a hori-
zontal resolution of 5.6 × 5.6°. The model has 60 vertical
levels running from the surface to 0.1 hPa. For each individ-
ual year’s fluxes, which are optimized on a monthly basis, 30
minimization iterations are carried out. For this study, inver-
sions were not updated.

*Uncertainty.* Uncertainties in TOMCAT-INVICAT N₂O inversions are described as follows and further in Thompson et al. (2019). Uncertainty in the observations is calculated as the quadratic sum of the measurement and transport uncertainties. The measurement uncertainty for each observation is assumed to be 0.4 ppb, while the transport error of each observation is assumed to be the mean difference between the observation grid cell and its eight neighbors. Prior flux errors are assumed to be 100% or the same as those of the prior estimates and are uncorrelated in space and time. Posterior flux uncertainties are not currently able to be calculated.

MIROC4-ACTM

The MIROC4-ACTM time-dependent inversion for the 84-
region (TDI84) framework is based on Bayesian statistics
and optimizes surface-atmosphere fluxes using the max-
imum probability solution (Rodgers, 2000). Atmospheric
transport is modeled using the atmospheric chemistry–transport model (MIROC4-ACTM) based on JAMSTEC’s Model for Interdisciplinary Research on Climate, version 4 (Watanabe et al., 2008; Patra et al., 2018, 2022). The source–receptor matrix (SRM) is calculated by simulating unitary emissions from 84 basis regions, for which the fluxes are optimized. The SRM describes the relationship between the change in mole fraction at the measurement locations for the unitary basis region fluxes (similar to Rayner et al., 1999). The MIROC4-ACTM meteorology was nudged to the JMA 55-year reanalysis (JRA55) horizontal wind fields and temperature. 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).

In this study, the simulations have been updated to 2019 (Patra et al., 2022).

*Uncertainties*. The posterior fluxes are subject to systematic errors primarily from (1) errors in the modeled 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); 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 the troposphere using SF6 for the inter-hemispheric exchange time and 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 evaluating the stratosphere–troposphere exchange rates. Comparisons with ACE-FTS vertical profiles in the stratosphere and mesosphere indicate good parameterization of N₂O loss by photolysis and chemical reactions, and thus the lifetime, which affect the global total N₂O budgets.

Random uncertainties are calculated by the inverse model depending on the prior flux uncertainties and the observational data density and data uncertainty. Only 37 sites are used in the inversion, and thus the reduction in the prior flux uncertainties has been minimal. The net fluxes from the inversion from individual basis regions are less reliable compared to the anomalies in the estimated fluxes over a period of time.
# Appendix B

## B1 Overview figures

<figure><img src="image_21.png" /><figcaption>**Figure B1.** EU27 + UK total CH₄ emissions (a) and N₂O emissions (b) time series per sector as reported by UNFCCC NGHGI (2021).</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**Figure B2.** EU27 + UK natural CH₄ emissions from Fig. 4b, as follows: two estimates for inland waters (lake, river and reservoir process-based models, blue, and upscaled emissions, cyan), peatlands and mineral soils (from JSBACH-HIMMELI, light green and dark green), geological emissions (orange), and biomass burning (from GFEDv4.1, brown).</figcaption></figure>
## B2 Source-specific methodology: AD, EF and uncertainties

<table><caption>**Table B1.** Source-specific activity data (AD), emission factors (EF) and uncertainty methodology for the current VERIFY and non-VERIFY 2021 data product collection.</caption><thead><tr><th colspan="6">CH<sub>4</sub> bottom-up anthropogenic emissions</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th><th></th></tr></thead><tbody><tr><td>UNFCCC (2021)</td><td>NGHGI<br/>Country-specific information consistent with the IPCC GLs.</td><td>IPCC GLs/country-specific information for higher tiers.</td><td>IPCC GLs (https://www.ipcc-nggip.iges.or.jp/public/2006gl/, last access: December 2019) for calculating the uncertainty of emissions based on the uncertainty of AD and EF, two different approaches: (1) error propagation and (2) Monte Carlo simulation.<br/>The EU GHG inventory team provided yearly harmonized and gap-filled uncertainties.</td><td>NGHGI official data (CRFs) are found at https://unfccc.int/ghg-inventories-annex-i-parties/2021 (last access: March 2022)</td><td></td></tr><tr><td>EDGARv6.0</td><td>International Energy Agency (IEA) for fuel combustion<br/>Food and Agriculture Organization (FAO) for agriculture<br/>US Geological Survey (USGS) for industrial processes (e.g., cement, lime, ammonia and ferroalloys)<br/>GGFR/NOAA for gas flaring<br/>World Steel Association for iron and steel production<br/>International Fertilizer Association (IFA) for urea consumption and production<br/>Complete description of the data sources can be found in Janssens-Maenhout et al. (2019) and in Crippa et al. (2021).</td><td>IPCC (2006), Tier 1 or Tier 2 depending on the sector.</td><td>Tier 1 with error propagation by sectors for CH<sub>4</sub>.</td><td>https://edgar.jrc.ec.europa.eu/dataset_ghg60 (last access: November 2022)</td><td></td></tr><tr><td>CAPRI</td><td>Farm and market balances, economic parameters, crop areas, livestock population and yields from EUROSTAT, parameters for input-demand functions at the regional level from FADN (EC), data on trade between world regions from FAOSTAT, policy variables from OECD.</td><td>IPCC (2006): Tier 2 for emissions from enteric fermentation of cattle and from manure management of cattle.<br/>Tier 1 for all other livestock types and emission categories.<br/>N flows through agricultural systems (including N excretion) calculated endogeneously.</td><td>Spatial uncertainties computed for 2014, 2016 and 2018.</td><td>Detailed gridded data CH<sub>4</sub> and N<sub>2</sub>O emissions can be obtained by contacting the data provider: adrian.leip@ec.europa.eu</td><td></td></tr></tbody></table>
<table><caption>Table B1. Continued.</caption><thead><tr><th colspan="5">CH<sub>4</sub> bottom-up anthropogenic emissions</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th></tr></thead><tbody><tr><td>GAINS</td><td>Livestock numbers by animal type (FAOSTAT, 2010; EUROSTAT, 2009; UNFCCC, 2010)<br/>Growth in livestock numbers from FAO (2003), CAPRI model (2009)<br/>Rice cultivation land area for rice cultivation (FAOSTAT, 2010)<br/>Projections for EU are taken from the CAPRI model</td><td>Country-specific information and Livestock - Implied EFs reported to UNFCCC and IPCC Tier 1 (2006, vol. 4, chap. 10) default factors<br/>Rice cultivation - IPCC Tier 1-2 (2006, vol. 4, p. 5.49<br/>Agricultural waste burning - IPCC Tier 1 (2006, vol. 5, p. 520</td><td>IPCC (2006, vol. 4, p. 10.33) uncertainty range</td><td>Detailed gridded data CH<sub>4</sub> and N<sub>2</sub>O emissions can be obtained by contacting the data providers: for CH<sub>4</sub>, contact Lena Höglund-Isaksson (hoglund@iiasa.ac.at); for N<sub>2</sub>O, contact Wilfried Winiwarter (winiwart@iiasa.ac.at).</td></tr><tr><td>FAOSTAT</td><td>FAOSTAT crop and livestock production domains from country reporting; FAOSTAT land use domain; Harmonized World Soil Database; ESA CCI and Copernicus Global Land Cover Service (C3S) maps; MODIS MCD12Q1 v6; FAO Gridded Livestock of the World; MODIS MCD64A1.006burned area products</td><td>IPCC guidelines Tier 1</td><td>IPCC (2006, vol. 4, p. 10.33)<br/>Uncertainties in estimates of GHG emissions are due to uncertainties in emission factors and activity data. They may be related to, inter alia, natural variability, partitioning fractions, lack of spatial or temporal coverage, or spatial aggregation.</td><td>Agriculture total and subdomain-specific GHG emissions are found for download at http://www.fao.org/faostat/en/#data/GT (last access: April 2022).</td></tr><tr><th colspan="5">CH<sub>4</sub> bottom-up natural emissions</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th></tr><tr><td>Mechanistic-stochastic model CH<sub>4</sub> emissions from inland waters</td><td>HydroSHEDS 15 s (Lehner et al., 2008) and Hydro1K (USGS, 2000) for river network, HydroLAKES for lake and reservoir network and surface area (Messager et al., 2016); worldwide typology of estuaries by Dürr et al. (2011)</td><td>NA</td><td>Four model configurations for CH<sub>4</sub></td><td>Detailed gridded data can be obtained by contacting the data providers: Ronny Lauerwald ronny.lauerwald@inrae.fr Pierre Regnier pierre.regnier@ulb.ac.be</td></tr><tr><td>JSBACH-HIMMELI</td><td>JSBACH vegetation and soil carbon and physical parameters provided to HIMMELI to simulate wetland methane fluxes<br/>HydroLAKES database (Messager et al., 2016).<br/>CORINE land cover data<br/>VERIFY climate drivers 0.1° × 0.1°.</td><td>CH<sub>4</sub> fluxes from peatlands and mineral soils</td><td>The standard deviation and the resulting range in the annual emission sum represents a measure of uncertainty.</td><td>Detailed gridded data CH<sub>4</sub> emissions can be obtained by contacting the data providers: tuula.aalto@fmi.fi tiina.markkanen@fmi.fi</td></tr></tbody></table>
<table><caption>**Table B1. Continued.**</caption><thead><tr><th colspan="5">CH<sub>4</sub> bottom-up natural emissions</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th></tr></thead><tbody><tr><td>Geological emissions, including marine and land geological</td><td>Areal distribution activity: 1° × 1° maps include the four main categories of natural geo-CH<sub>4</sub> emission: (a) onshore hydrocarbon macro-seeps, including mud volcanoes; (b) submarine (offshore) seeps; (c) diffuse microseepage; and (d) geothermal manifestations.</td><td>CH<sub>4</sub> fluxes, measurements and estimates based on size and activity</td><td>95% confidence interval of the median emission-weighted mean sum of individual regional values</td><td>Etiope et al. (2019) with updated activity for current study<br>Detailed gridded data on geological CH<sub>4</sub> emissions can be obtained by contacting the data providers:<br>Giuseppe Etiope<br>giuseppe.etiope@ingv.it<br>Giancarlo Ciotoli<br>giancarlo.ciotoli@gmail.com</td></tr></tbody><thead><tr><th colspan="5">CH<sub>4</sub> Top-down inversions</th></tr><tr><th colspan="5">Regional inversions over Europe (high transport model resolution)</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th></tr></thead><tbody><tr><td>FLEXPART – FLEXFKF</td><td>Extended Kalman filter in combination with backward Lagrangian transport simulations using the model FLEXPART atmospheric observations<br>ECMWF ERA-Interim meteorological fields</td><td>FLEXFKF-CAMSV19R_EMPA specific background</td><td>The random uncertainties are represented by the posterior error covariance matrix provided by the Kalman filter, which combines errors in the prior fluxes with errors in the observations and model representation (see description in Appendix A1).</td><td>Detailed gridded data can be obtained by contacting the data provider:<br>dominik.brunner@empa.ch</td></tr><tr><td>TM5-4DVAR</td><td>Global Eulerian models with a zoom over Europe, ERA-Interim reanalysis</td><td>4DVAR variational techniques</td><td>Uncertainty was calculated as 1σ estimate. See descriptions in Appendix A1.</td><td>Detailed gridded data can be obtained by contacting the data provider:<br>peter.bergamaschi@ext.ec.europa.eu</td></tr><tr><td>FLEXINVERT</td><td>Bayesian statistics Atmospheric transport is modeled using the Lagrangian model FLEXPART</td><td>Prior fluxes from LPX-Bern DYPTOP, EDGARv4.2 FT2010 GFEDv4 Termites and ocean fluxes ground-based surface CH<sub>4</sub> observations. Background fields based on nudged FLEXPART-CTM simulations (Groot Zwaaitink et al., 2018).</td><td></td><td>Detailed gridded data CH<sub>4</sub> emissions can be obtained by contacting the data provider:<br>Christine Groot Zwaaitink<br>cgz@nilu.no</td></tr><tr><td>InTEM-NAME</td><td>Atmospheric Lagrangian transport model analysis 3D meteorology from the UK Met Office Unified Model.</td><td>(a) the UK National Atmospheric Emissions Inventory (NAEI) 2015 within the UK. (b) Outside the UK – EDGAR 2010 emissions distributed uniformly over land (excluding the UK).</td><td>Derived from the variability of the observations within each 2h period: (a) 40%; (b) 50%.</td><td>Detailed gridded data can be obtained by contacting the data provider:<br>Alistair Manning (alistair.manning@metoffice.gov.uk).</td></tr></tbody></table>
<table><caption>**Table B1. Continued.**</caption><thead><tr><th colspan="6">CH<sub>4</sub> Top-down inversions</th></tr><tr><th colspan="6">Regional inversions over Europe (high transport model resolution)</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th><th></th></tr></thead><tbody><tr><td>CTE-FMI</td><td>Ensemble Kalman filter Eulerian transport model TM5 ECMWF ERA-Interim meteorological data</td><td>Prior fluxes from LPX-Bern DYPTOP, EDGARv4.2 FT2010 GFEDv4 Termites and ocean fluxes ground-based surface CH<sub>4</sub> observations GOSAT XCH<sub>4</sub> retrievals from NIESv2.72</td><td>The prior uncertainty is assumed to be a Gaussian probability distribution function. The posterior uncertainty is calculated as standard deviation of the ensemble members, where the posterior error covariance matrix is driven by the ensemble Kalman filter.</td><td>Detailed gridded data can be obtained by contacting the data provider: aki.tsuruta@fmi.fi</td><td></td></tr><tr><td>InGOS</td><td>18 European monitoring stations EDGARv4.2FT-InGOS wetland inventory of J.Kaplan and LPX-Bern v1.0 ERA-Interim reanalysis Met Office Unified Model</td><td>For priors please see Table B4</td><td>The uncertainty of the model ensemble was calculated as 1σ estimate. Individual models use Bayes' theorem to calculate the reduction of assumed a priori emission uncertainties by assimilating measurements.</td><td>Detailed gridded data can be obtained by contacting the data provider: peter.bergamaschi@ext.ec.europa.eu</td><td></td></tr><tr><td>VERIFY Community Inversion Framework (CIF): CHIMERE, FLEXPARTv10.4 (NILU) and FLEXPART (EMPA) (only CH<sub>4</sub>)</td><td>Extended Kalman filter in combination with backward Lagrangian transport simulations using the model FLEXPART atmospheric observations ECMWF ERA-Interim meteorological fields CHIMERE is a non-hydrostatic Eulerian chemistry-transport model</td><td>For priors please see Table B4</td><td>The uncertainty in each grid cell (0.25° × 0.25° for CH<sub>4</sub> and 0.5° × 0.5° for N<sub>2</sub>O) includes that due to the spatial disaggregation plus that due to emission-weighted uncertainty of a specific process.</td><td>Detailed gridded data can be obtained by contacting the data providers: Antoine Berchet antoine.berchet@lsce.ipsl.fr Dominik Brunner dominik.brunner@empa.ch Rona Thompson rlt@nilu.no Gregoire Broquet gregoire.broquet@lsce.ipsl.fr</td><td></td></tr><tr><th colspan="6">Global inversions from the Global Carbon Project CH<sub>4</sub> budget (Saunois et al., 2020)</th></tr><tr><td>GCP-CH<sub>4</sub> 2019 anthropogenic and natural partitions from inversions</td><td>Ensemble of inversions gathering various chemistry transport models surface or satellite data</td><td>For priors please see Table B4</td><td>Uncertainties are reported as minimum and maximum values of the available studies, as the range of available mean estimates, i.e., the standard error across measurements/methodologies considered. Posterior uncertainty mostly uses Monte Carlo methods.</td><td>Detailed gridded data can be obtained by contacting the data provider: Marielle Saunois marielle.saunois@lsce.ipsl.fr</td><td></td></tr></tbody></table>
<table><caption>**Table B1. Continued.**</caption><thead><tr><th colspan="6">N<sub>2</sub>O bottom-up anthropogenic emissions</th></tr><tr><th>Data source</th><th>AD/tier</th><th>EFs/tier</th><th>Uncertainty assessment method</th><th>Emission data availability</th><th></th></tr></thead><tbody><tr><td colspan="6">UNFCCC NGHGI (2021), EDGARv6.0, CAPRI, GAINS and FAOSTAT; see above</td></tr><tr><td>ECOSSE</td><td>The model is a point model, which provides spatial results by using spatial distributed input data (lateral fluxes are not considered). The model is a Tier 3 approach that is applied on grid map data, polygon organized input data or study sites.</td><td>IPCC (2006): Tier 3 The simulation results will be allocated due to the available information (size of spatial unit, representation of considered land use, etc.).</td><td>NA</td><td>Detailed gridded data can be obtained by contacting the data provider: Matthias Kuhnert matthias.kuhnert@abdn.ac.uk</td><td></td></tr><tr><td>DayCent</td><td>Spatial explicit simulations at point level, upscaled at 1 km for agricultural areas.</td><td>Tier 3; land management and input factors for the cropland remaining cropland category based on datasets covering the 2005–2015 period.</td><td>Monte Carlo</td><td>Detailed gridded data can be obtained by contacting the data provider: emanuele.lugato@ec.europa.eu</td><td></td></tr><tr><td colspan="6">N<sub>2</sub>O bottom-up natural emissions</td></tr><tr><td>Mechanistic-stochastic model for N<sub>2</sub>O emissions from inland waters</td><td>HydroSHEDS 15 s (Lehner et al., 2008) and Hydro1K (USGS, 2000) for river network, HydroLAKES for lake and reservoir network and surface area (Mesa-sager et al., 2016); worldwide typology of estuaries by Dürr et al. (2011); terrestrial N and P loads by Global-NEWS (Van Drecht et al., 2009; Bouwman et al., 2009), redistributed at 0.5° resolution by Maavara et al. (2019).</td><td>EFs applied to denitrification and nitrification rates for N<sub>2</sub>O emissions. Values constrained from the range reported in Beaulieu et al. (2011).</td><td>Upscaled emission estimates from RECCAP2</td><td>Detailed gridded data can be obtained by contacting the data providers: Ronny Lauerwald ronny.lauerwald@inrae.fr Pierre Regnier pierre.regnier@ulb.ac.be</td><td></td></tr><tr><td colspan="6">Regional N<sub>2</sub>O inversions over Europe (high transport model resolution)</td></tr><tr><td>FLEXINVERT</td><td>Bayesian statistics Atmospheric transport is modeled using the Lagrangian model FLEXPART</td><td>Background mole fractions</td><td>Random uncertainties are calculated from a Monte Carlo ensemble of inversions</td><td>Detailed gridded N<sub>2</sub>O data can be obtained by contacting the data provider: Rona Thompson rlt@nilu.no</td><td></td></tr><tr><td colspan="6">Global N<sub>2</sub>O inversions over Europe from GN<sub>2</sub>OB (Tian et al., 2020)</td></tr></tbody></table>
<table><caption>Table B1. Continued.</caption><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>CAMS-N<sub>2</sub>O</td><td>Bayesian inversion method observations of atmospheric mixing ratios fluxes from ground-based sites, ship and aircraft transects soil fluxes OCN-v1.2 ocean biogeochemistry model PlankTOM-v10.2 GFEDv4.1s EDGARv4.32 ECMWF ERA-Interim</td><td>Fires emission factors from Akagi et al. (2011)</td><td>Uncertainty in the observation space is calculated as the quadratic sum of the measurement and transport uncertainties. For the error in each land grid cell, the maximum magnitude of the flux in the cell of interest and its eight neighbors is used; for ocean grid cells the magnitude of the cell of interest only is used.</td></tr><tr><td>TOMCAT-INVICAT</td><td>Variational Bayesian inverse model assimilating surface flask observations of atmospheric mixing ratios. ECMWF ERA-Interim meteorological driving data.</td><td>Prior emission estimates are from OCN-v1.1 model (soils), EDGARv4.2 FT2010 (anthropogenic non-soil), PlankTOM5 (oceans) and GFEDv4.1s (biomass burning).</td><td>Uncertainty in the observation space is calculated as the quadratic sum of the measurement and transport uncertainties. For the error in each land grid cell, the maximum magnitude of the flux in the cell of interest and its eight neighbors is used. Prior emission uncertainties are 100% and uncorrelated.</td></tr><tr><td>MIROC4-ACTM</td><td>Matrix inversion for calculation of fluxes from 53 and 84 partitions of the globe for CH<sub>4</sub> and N<sub>2</sub>O, respectively. Forward model transport is nudged to JRA-55 horizontal winds and temperature.</td><td>Fire emissions for CH<sub>4</sub> are taken from GFEDv4.1s</td><td>A posteriori uncertainties are obtained from the Bayesian statistics model. A priori emission uncertainties are uncorrelated.</td></tr></tbody></table>

Detailed gridded N₂O data can be obtained by contacting the data provider: Rona Thompson rlt@nilu.no

Detailed gridded N₂O data can be obtained by contacting the data provider: Christopher Wilson (GEO) c.wilson@leeds.ac.uk

Detailed gridded data can be obtained by contacting the data provider: Prabir Patra prabir@jamstec.go.jp
<table><caption>**Table B2.** Biogeochemical models that computed wetland emissions used in this study. Runs were performed for the whole period of 2000–2017. Models run with prognostic (using their own calculation of wetland areas) and/or diagnostic (using WAD2M) wetland surface areas (see Sect. 3.2.1). From Saunois et al. (2020).</caption><thead><tr><th>Model</th><th>Institution</th><th>Prognostic</th><th>Diagnostic</th><th>References</th></tr></thead><tbody><tr><td>CLASS-CTEM</td><td>Environment and Climate Change Canada</td><td>y</td><td>y</td><td>Arora et al. (2018) Melton and Arora (2016)</td></tr><tr><td>DLEM</td><td>Auburn University</td><td>n</td><td>y</td><td>Tian et al. (2010, 2015)</td></tr><tr><td>ELM</td><td>Lawrence Berkeley National Laboratory</td><td>y</td><td>y</td><td>Riley et al. (2011)</td></tr><tr><td>JSBACH</td><td>MPI</td><td>n</td><td>y</td><td>Kleinen et al. (2020)</td></tr><tr><td>JULES</td><td>UKMO</td><td>y</td><td>y</td><td>Hayman et al. (2014)</td></tr><tr><td>LPJ GUESS</td><td>Lund University</td><td>n</td><td>y</td><td>McGuire et al. (2012)</td></tr><tr><td>LPJ MPI</td><td>MPI</td><td>n</td><td>y</td><td>Kleinen et al. (2012)</td></tr><tr><td>LPJ-WSL</td><td>NASA GSFC</td><td>y</td><td>y</td><td>Zhang et al. (2016)</td></tr><tr><td>LPX-Bern</td><td>University of Bern</td><td>y</td><td>y</td><td>Spahni et al. (2011)</td></tr><tr><td>ORCHIDEE</td><td>LSCE</td><td>y</td><td>y</td><td>Ringeval et al. (2011)</td></tr><tr><td>TEM-MDM</td><td>Purdue University</td><td>n</td><td>y</td><td>Zhuang et al. (2004)</td></tr><tr><td>TRIPLEX_GHG</td><td>UQAM</td><td>n</td><td>y</td><td>Zhu et al. (2014, 2015)</td></tr><tr><td>VISIT</td><td>NIES</td><td>y</td><td>y</td><td>Ito and Inatomi (2012)</td></tr></tbody></table>
<table><caption>**Table B3.** Top-down studies used in our new analysis, with their contribution to the decadal and yearly estimates noted. For decadal means, top-down studies have to provide at least 8 years of data over the decade to contribute to the estimate, from Saunois et al. (2020).</caption><thead><tr><th>Model</th><th>Institution</th><th>Observation used</th><th>Time period</th><th>Number of inversions</th><th>References</th></tr></thead><tbody><tr><td>CarbonTracker Europe CH<sub>4</sub></td><td>FMI</td><td>Surface stations</td><td>2000–2017</td><td>1</td><td>Tsuruta et al. (2017)</td></tr><tr><td>CarbonTracker Europe CH<sub>4</sub></td><td>FMI</td><td>GOSAT NIES L2 v2.72</td><td>2010–2017</td><td>1</td><td>Tsuruta et al. (2017)</td></tr><tr><td>GELCA</td><td>NIES</td><td>Surface stations</td><td>2000–2015</td><td>1</td><td>Ishizawa et al. (2016)</td></tr><tr><td>LMDZ-PYVAR</td><td>LSCE/CEA</td><td>Surface stations</td><td>2010–2016</td><td>2</td><td>Yin et al. (2021)</td></tr><tr><td>LMDZ-PYVAR</td><td>LSCE/CEA</td><td>GOSAT Leicester v7.2</td><td>2010–2016</td><td>4</td><td>Yin et al. (2021)</td></tr><tr><td>LMDZ-PYVAR</td><td>LSCE/CEA</td><td>GOSAT Leicester v7.2</td><td>2010–2017</td><td>2</td><td>Zheng et al. (2018a, b)</td></tr><tr><td>MIROC4-ACTM</td><td>JAMSTEC</td><td>Surface stations</td><td>2000–2016</td><td>1</td><td>Patra et al. (2016, 2018)</td></tr><tr><td>NICAM-TM</td><td>NIES</td><td>Surface stations</td><td>2000–2017</td><td>1</td><td>Niwa et al. (2017a, b)</td></tr><tr><td>NIES-TM-FLEXPART-VAR (NTFVAR)</td><td>NIES</td><td>Surface stations</td><td>2000–2017</td><td>1</td><td>Maksyutov et al. (2020), Wang et al. (2019)</td></tr><tr><td>NIES-TM-FLEXPART-VAR (NTFVAR)</td><td>NIES</td><td>GOSAT NIES L2 v2.72</td><td>2010–2017</td><td>1</td><td>Maksyutov et al. (2020); Wang et al. (2019)</td></tr><tr><td>TM5-CAMS</td><td>TNO/VU</td><td>Surface stations</td><td>2000–2017</td><td>1</td><td>Segers and Houweling (2018), Bergamaschi et al. (2010, 2013), Pandey et al. (2016)</td></tr><tr><td>TM5-CAMS</td><td>TNO/VU</td><td>GOSAT ESA/CCI v2.3.8 (combined with surface observations)</td><td>2010–2017</td><td>1</td><td>Segers and Houweling (2018, report); Bergamaschi et al. (2010, 2013), Pandey et al. (2016)</td></tr><tr><td>TM5-4DVAR</td><td>EC-JRC</td><td>Surface stations</td><td>2000–2017</td><td>2</td><td>Bergamaschi et al. (2013, 2018)</td></tr><tr><td>TM5-4DVAR</td><td>EC-JRC</td><td>GOSAT OCPR v7.2 (combined with surface observations)</td><td>2010–2017</td><td>2</td><td>Bergamaschi et al. (2013, 2018)</td></tr><tr><td>TOMCAT</td><td>Univ. of Leeds</td><td>Surface stations</td><td>2003–2015</td><td>1</td><td>McNorton et al. (2018)</td></tr></tbody></table>
<table><caption>Table B4. List of prior datasets for natural CH₄ and N₂O emissions used by all inverse models.</caption><thead><tr><th rowspan="2">Project</th><th rowspan="2">Model</th><th colspan="7">Prior</th></tr><tr><th>Wetlands</th><th>Geological</th><th>Fire</th><th>Termites</th><th>Soil sink</th><th>Ocean/lakes</th><th>Wild animals</th></tr></thead><tbody><tr><td>VERIFY</td><td>CTE_FMI S5</td><td>JSBACH-HIMMELI</td><td>GCP_CH4 (Etiope et al., 2019)</td><td>RCO plus GFEDv4.1s</td><td>Castaldi as GCP_CH4</td><td>LPX-Bern DYPTOP (Stocker et al., 2014)</td><td>Weber et al. (2019) for oceans and ULB for lakes in Europe, 0 for the rest of the world</td><td></td></tr><tr><td>VERIFY</td><td>FLEXPART(FLExKF-CAMSv19r)_EMPA</td><td>JSBACH-HIMMELI</td><td></td><td></td><td>GCP</td><td>Ridgwell /GCP</td><td>GCP/ULB</td><td></td></tr><tr><td>VERIFY</td><td>FLEXINVERT</td><td>LPX-Bern DYPTOP (Stocker et al., 2014)</td><td></td><td>GFEDv4.1s</td><td>Ito and Inatomi (2012)</td><td>LPX-Bern DYPTOP (Stocker et al., 2014)</td><td>(Tsuruta et al., 2017)</td><td></td></tr><tr><td>VERIFY</td><td>TM5_4DVAR JRC</td><td>GCP_CH4_2019 (global total: 15 Tg CH4 yr<sup>-1</sup>)</td><td></td><td></td><td>GCP_CH4_2019</td><td>GCP_CH4_2019</td><td>GCP_CH4_2019</td><td></td></tr><tr><td>VERIFY-CIF</td><td>FLEXPART-NILU FLEXPART-EMPA CHIMERE</td><td>JSBACH-HIMMELI</td><td>Etiope et al. (2019)</td><td>EDGARv6.0 (biofuel) and GFEDv4.1s (biomass)</td><td>Saunois et al. (2020) (GCP-CH4)</td><td></td><td>Weber et al. (2019)</td><td></td></tr><tr><td>InGOS</td><td>INGOS-CTE-S4_EC</td><td>LPX-Bern v1.0 (Spahni et al., 2013)</td><td></td><td>GFEDv4</td><td>Ito and Inatomi (2012)</td><td>LPX-Bern v1.0 (Spahni et al., 2013)</td><td>Tsuruta et al. (2015)</td><td></td></tr><tr><td>InGOS</td><td>INGOS-LMDZEU-S4_EC</td><td>wetland inventory of J. Kaplan (Bergamaschi et al., 2007)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>InGOS</td><td>INGOS-TM3STILT-S4_EC</td><td>wetland inventory of Jed Kaplan (Bergamaschi et al., 2007)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>InGOS</td><td>INGOS-TM5VAR-S4_EC</td><td>wetland inventory of Jed Kaplan (Bergamaschi et al., 2007)</td><td></td><td></td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td>Oslson climatology</td></tr><tr><td>InGOS</td><td>INGOS-NAME-S4_EC</td><td>wetland inventory of Jed Kaplan (Bergamaschi et al., 2007)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GCP</td><td>GELCA-SURF_NIES</td><td>VISIT (Ito and Inatomi, 2012)</td><td>NA</td><td>GFEDv3.1 then GFASv1.2 after 2011</td><td>Sanderson (TransCom-CH4/GCP)</td><td>VISIT (Ito and Inatomi, 2012)</td><td>NA</td><td></td></tr><tr><td>GCP</td><td>MIROCV4-SURF_JAMASTEC</td><td>VISIT (Ito and Inatomi, 2012) (global total range: 173-197 Tg CH4 yr<sup>-1</sup>)</td><td>Etiope and Milkov (2004) (global total: 7.5 Tg CH4 yr<sup>-1</sup>)</td><td>GFEDv4.1s (global total range: 14-35 Tg CH4 yr<sup>-1</sup>)</td><td>Sanderson (TransCom-CH4) (global total: 20.5 Tg CH4 yr<sup>-1</sup>)</td><td>VISIT (Ito and Inatomi, 2012)</td><td>Lambert/Houweling (TransCom-CH4) (global total: 18.5 Tg CH4 yr<sup>-1</sup>)</td><td></td></tr><tr><td>GCP</td><td>NICAM-SURF_NIES</td><td>VISIT (Ito and Inatomi, 2012)</td><td>GCP based on Etiope (2015)</td><td>GFEDv4.1s/GCP</td><td>Sanderson (TransCom-CH4/GCP)</td><td>VISIT (Ito and Inatomi, 2012)</td><td>Lambert/Houweling (TransCom-CH4/GCP)</td><td></td></tr></tbody></table>
<table><caption>Table B4. Continued.</caption><thead><tr><th rowspan="2">Project</th><th rowspan="2">Model</th><th colspan="8">Prior</th></tr><tr><th>Wetlands</th><th>Geological</th><th>Fire</th><th>Termites</th><th>Soil sink</th><th>Ocean/lakes</th><th>Wild animals</th></tr></thead><tbody><tr><td>GCP</td><td>TOMCAT-SURF_ECMWF</td><td>JULES emissions from McNorton et al. (2016)</td><td>TOMCAT 2006 (McNorton et al., 2016)</td><td>GFEDv4</td><td>Matthews and Fung (1987)</td><td>Patra et al. (2011)</td><td>TOMCAT (2006), Matthews and Fung (1987) – all emission total rescaled to Schwietzke et al. (2016)</td><td></td></tr><tr><td>GCP</td><td>NTFVAR-GOSAT_NIES</td><td>VISIT (Ito and Inatomi, 2012)</td><td>Etiope and Milkov (2004)</td><td>GFASv1.2</td><td>Ito and Inatomi (2012)</td><td>VISIT (Ito and Inatomi, 2012)</td><td>TransCom-CH₄</td><td></td></tr><tr><td>GCP</td><td>NTFVAR-SURF_NIES</td><td>VISIT (Ito and Inatomi, 2012)</td><td>Etiope and Milkov (2004)</td><td>GFASv1.2</td><td>Ito and Inatomi (2012)</td><td>VISIT (Ito and Inatomi, 2012)</td><td>TransCom-CH₄</td><td></td></tr><tr><td>GCP</td><td>LMDZ-GOSAT1_LSCE</td><td>Bloom 2017</td><td>NA</td><td>GFEDv4.1</td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td></td></tr><tr><td>GCP</td><td>LMDZ-GOSAT2_LSCE</td><td>GCP ensemble mean ESSD Saunois et al. (2016)</td><td>GCP based on Etiope (2015)</td><td>GFEDv4.1</td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td></td></tr><tr><td>GCP</td><td>LMDZ-GOSAT3 CALTECH LMDZ-GOSAT4 CALTECH LMDZ-GOSAT5 CALTECH LMDZ-GOSAT6 CALTECH LMDZ-SURF1 CALTECH LMDZ-SURF2 CALTECH</td><td>Kaplan (2002) rescaled by Bergamaschi et al. (2007)</td><td>NA</td><td>GFEDv4.1</td><td>Sanderson 1996/GCP</td><td>Ridgwell/GCP</td><td>Lambert and Schmidt (1993)</td><td></td></tr><tr><td>GCP</td><td>TM5-CAMS-GOSAT_TNO</td><td>Kaplan climatology</td><td>NA</td><td>GFEDv3.1 climatology after 2011</td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td>Oslson climatology</td></tr><tr><td>GCP</td><td>TM5-GOSAT1_EC</td><td>WETCHIMP ensemble mean;</td><td>GCP_CH₄ 2019 (global total: 15 Tg CH₄ yr⁻¹)</td><td></td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td>Oslson climatology</td></tr><tr><td>GCP</td><td>TM5-GOSAT2_EC</td><td>GCP_CH₄_2019</td><td>GCP_CH₄ 2019 (global total: 15 Tg CH₄ yr⁻¹)</td><td></td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td>Oslson climatology</td></tr><tr><td>GCP</td><td>TM5-SURF1_EC</td><td>WETCHIMP ensemble mean;</td><td>GCP_CH₄ 2019 (global total: 15 Tg CH₄ yr⁻¹)</td><td></td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td>Oslson climatology</td></tr><tr><td>GCP</td><td>TM5-SURF2_EC</td><td>GCP_CH₄_2019</td><td>GCP_CH₄ 2019 (global total: 15 Tg CH₄ yr⁻¹)</td><td></td><td>Sanderson/GCP</td><td>Ridgwell/GCP</td><td>Lambert/GCP</td><td>Oslson climatology</td></tr><tr><td>GCP</td><td>CTE-GOSAT_FMI</td><td>GCP_CH₄_2019</td><td>Etiope (2015)</td><td>GCP_CH₄_2019 (= GFEDv4.1s)</td><td>GCP_CH₄_2019</td><td>GCP_CH₄_2019</td><td>GCP_CH₄_2019</td><td>GCP_CH₄_2019</td></tr><tr><td>GCP</td><td>CTE-SURF_FMI</td><td>GCP_CH₄_2019</td><td>Etiope (2015)</td><td>GCP_CH₄_2019 (= GFEDv4.1s)</td><td>GCP_CH₄_2019</td><td>GCP_CH₄_2019</td><td>GCP_CH₄_2019</td><td>GCP_CH₄_2019</td></tr><tr><td colspan="9">NAME-SURF_MetOffice</td></tr><tr><td>N₂O VERIFY CIF</td><td>FLEXPART-NILU CHIMERE</td><td></td><td></td><td>GFEDv4.1s</td><td>O-CN</td><td>PlankTOM-v10</td><td></td><td></td></tr></tbody></table>
**Note on former version.** A former version of this article was published on 28 May 2021 and is available at https://doi.org/10.5194/essd-13-2307-2021.

**Author contributions.** AMRP designed this research and led the discussions with all co-authors, wrote the initial draft of the paper, and edited all the following versions; CQ made the figures; PPe, MJM and VB processed the original data submitted to the VERIFY portal; MJM, PPe and PB designed and are managing the VERIFY web portal; BM provided the new gap-filled yearly UNFCCC NGHGI member state uncertainty calculations and checked the language and content of the final reviewed version; GPP provided Fig. B1 in Appendix B and checked the language and content of the final version; AB provided Figs. 5 and 14; DBr provided Fig. 7; AT provided the data for Figs. 8 and 9 in Sect. 3.1.5; AMRP made Figs. 8 and 9; AT, PR, RL and CQ provided input to the review answers; PIP, OT, RLT, CQ, GE, MK, LHI, PR, RL, DBa and WW provided in-depth advice and commented/edited the initial versions of the manuscript; all remaining co-authors provided data and commented on specific parts of the text related to their datasets.

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of *Earth System Science Data*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Acknowledgements.** We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: https://doi.org/10.18160/P7E9-EKEA, Integrated Non-CO₂ Observing System, 2018a, and https://doi.org/10.18160/B3Q6-JKA0, Integrated Non-CO₂ Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH₄: https://doi.org/10.25925/20221001, Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala

thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55).

**Financial support.** This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810).

Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO₂, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).

**Review statement.** This paper was edited by Nellie Elguindi and reviewed by two anonymous referees.

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