CarbonTracker Europe-CH₄ (CTE-CH₄; ) is a modular atmospheric inverse modelling system based on the ensemble Kalman filter (EnKF) . It minimizes the cost function J, 1J=(x-xb)P-1(x-xb)+(y-H(x))R-1(y-H(x)), where x is the state vector, xb is the prior state vector, P is the state covariance matrix, y is the observation of atmospheric concentrations (see Sect. ), H is the observation operator, and R is the observation covariance matrix. In this study, the state vector x included the flux multiplication factors for anthropogenic and wetland fluxes (see Sect. ). Fluxes were optimized at 1°×1° (latitude × longitude) resolution for land areas in northern Eurasia, 2°×3° grid for other land areas, and region-wise over the ocean (Fig. ). Note that we do not optimize natural ocean fluxes but do optimize anthropogenic emissions over the oceans, such as shipping and flight tracks that were included in the prior fluxes (EDGAR v8.0, Sect. ). The prior covariance P was a block diagonal matrix, assuming that 1°×1° optimization regions were uncorrelated with 2°×3° optimization regions, land and ocean regions were uncorrelated, and wetland fluxes were uncorrelated with anthropogenic fluxes. The prior uncertainty (diagonal values) was defined as the ratio to prior fluxes (Sect. ): 80 % over land and 20 % over the ocean. Off-diagonals were defined based on distances between the grids and regions with spatial correlation lengths of 100 km for 1°×1° optimization regions, 300 km over 2°×3° optimization regions and 900 km over the ocean. Localization schemes as in were applied. Regarding the EnKF setups, we used an ensemble size of 500 members and an optimization window of 7 d with a lag of 5 following .

For the observation operator H, the atmospheric transport model TM5 was used. TM5 was run at 1°×1° over Europe and 6°×4° globally, following e.g. and . The resolution is coarser than the flux optimization resolution outside Europe. We acknowledge that using different resolutions can be questionable because the atmospheric states may not be resolved in detail enough. However, this could be justified as the study mostly focus on large-scale fluxes. TM5 was constrained by 3 h European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 meteorology . Atmospheric chemistry included OH, Cl, and O(¹D). The OH concentrations were the same as in , which is based on distribution but scaled globally by 0.92. For reactions with Cl and O(¹D), the reaction rates pre-calculated from the ECHAM/MESSy1 model were used. The initial CH₄ concentration fields were taken from .

For anthropogenic emissions, prior estimates were taken from the Emissions Database for Global Atmospheric Research (EDGAR) v8.0 . For fluxes from wetlands and dry mineral soils (hereafter wetlands), the estimates from the LPX-Bern v1.4 process-based ecosystem model were used, with 2019 values replicated from 2018. Other sources include biomass burning and microbial (termites) and ocean emissions, and the estimates from the Global Fire Emissions Database (GFED) v4.1s , the Vegetation Integrative SImulator for Trace gases (VISIT; ), and were used, respectively. Among those, the fluxes from anthropogenic, wetlands, and biomass burning were monthly and inter-annually varying. The emissions from termites were annual estimates, and ocean fluxes were climatological.

In this study, we present the results from four inversions that differed in the observations assimilated:

InvSURF – surface CH₄ data only,

For InvGLT and InvGTOT, the observational uncertainty and rejection threshold were set to be 30 and 60 ppb, respectively, and for InvGLT_land they were set as 50 and 100 ppb, respectively. All inversions were run for 2015–2019, but 2015 was considered as a spin-up and not included in the analysis. The analysis on regional fluxes was done based on 30° latitudinal bands. Throughout the following sections, the prior and posterior mole fractions refer to those derived using prior and posterior fluxes, respectively.

In this section, we analyse the differences between posterior estimates from the surface-based inversion (InvSURF) and JAXA/GOSAT retrievals for total and partial columns, i.e. 5ΔXCH4=XCH4InvSURFpost-XCH4GOSATretΔpXCH4_LT=pXCH4_LTInvSURFpost-pXCH4_LTGOSATret. In the comparison of total column, latitudinal biases were found with positive ΔXCH₄ values in the extratropics and negative values in the tropics, especially in the SH tropics (Fig. b). This feature was systematic in time, such that similar biases were found regardless of years and seasons, although the absolute value of the biases varied. In the comparison of lower-tropospheric partial columns, such latitudinal biases were less prominent (Fig. c), especially over land, indicating better agreement over NH extratropics (Fig. a). This indicates the potential role of upper atmosphere in the total column biases, where posterior estimates and retrievals had difficulty agreeing.

Large biases were observed in regions such as Greenland, western South America, southernmost South Africa, eastern China, and northern Russia in both comparisons (Fig. b and c). Because of the challenges in retrieving data over ice-covered land, we assume that biases in Greenland were mostly associated with retrieval errors. Biases in other regions could be due to unresolved fluxes by surface observations, i.e. the inversion error in estimating fluxes, retrieval errors due to cloud cover, and difficulties in retrieving surface pressure in regions with highly elevated surface. The horizontal resolutions of the transport model also contribute to the biases in highly elevated areas. The additional simulation with TM5 showed that increasing resolution from 4°×6° (latitude × longitude) to 2°×3° decreased biases in mountain regions in Africa and the Tibetan Plateau, although the biases in regions such as the Andes mountains in South America and the edges of the Tibetan Plateau still remain (Fig. ). We also acknowledge that the vertical interpolation and averaging kernel (AK) contribute to the biases in highly elevated areas, especially for XCH₄ (Fig. ) (see also Sect. ).

Europe showed the smallest ΔpXCH₄_LT, which is encouraging considering that InvSURF probably had constrained the emission in Europe the best compared to other regions globally, and modelled tropospheric CH₄ from InvSURF is well in line with ground-based observations (Sect. ). Both the transport model and optimization resolutions were the smallest and number of observations were relatively large in Europe (Sect. ). There was a notable shift between better agreement in Europe and a worse agreement in Russia and Africa, attributed to the CTE-CH₄ setup: TM5 was run with highest spatial resolution over Europe and an extended area , with a coarser resolution elsewhere. This resulted to creating an artefact of a border between the zoom grid and the global grid that caused the shift. An additional simulation with TM5 with global 2°×3° (latitude × longitude) resolution and without zoom eliminated these boundaries (Fig. ).

Both lower-tropospheric partial and total column comparisons showed seasonal and land–sea biases (Fig. ). The global averages of ΔXCH₄ and ΔpXCH₄_LT were smaller during NH autumn–winter than spring–summer. The amplitude of the seasonal biases was larger in the total column, especially over the oceans. The comparison of the land and sea biases showed that the biases in the total column were larger over land than ocean, whereas an opposite behaviour was observed for the lower-tropospheric partial columns. In other words, the ΔXCH₄ and ΔpXCH₄_LT were closer over the ocean than land on average, with absolute median monthly differences of 5 ppb over the ocean and 23 ppb over land, indicating the possible influence of land fluxes to upper atmosphere. Overall, the average bias during 2016–2019 was smallest in ΔpXCH₄_LT over the ocean.

Comparison of posterior atmospheric CH₄ to the surface ground-based observations showed the smallest overall bias, root mean squared error (RMSE), and strongest correlation for InvSURF (Table ) as expected, since these observations were assimilated in the inversion. Among the GOSAT inversions, those using the lower-tropospheric partial column data (InvGLT and InvGLT_land) showed the best agreement to the surface ground-based observations, while the total column inversion (InvGTOT) showed large negative biases following the prior (Table ). RMSE and correlation within the GOSAT inversions were not significantly different. The latitudinal gradient was best captured by InvSURF, InvGLT, and InvGLT_land compared to the surface stations (Fig. a). The inversions mostly underestimated the surface observations with the underestimation being the smallest in the Southern Hemisphere (SH) for InvGLT and InvGLT_land and in the Northern Hemisphere (NH) for InvSURF. InvGTOT showed better agreement than InvSURF in the SH but considerable underestimation in the NH (Fig. a), which was the reason for strong negative bias in the overall agreement (Table ).

In the mid-latitudes and high northern latitudes (above 30° N) the seasonal cycle amplitude (SCA) was generally overestimated by model estimates in the prior inversion, which was worsened by inversion (SCA of posterior estimates were larger than prior) (Fig. ). In addition, seasonal minima in the NH occurred 1 to 2 months later than observations, although the results in InvSURF were slightly better than in the prior and GOSAT inversions (Fig. ). This indicates either possible errors in seasonal cycles of posterior emission or atmospheric chemical sinks.

Comparison to ATom aircraft data also showed that on average, InvSURF, InvGLT, and InvGLT_land resulted in the best statistics (Table ), and the latitudinal gradient agreed better with the observations compared to InvGTOT at the altitude bands 0–2000 and 2000–4000 m above sea level (ma.s.l.) (Figs. b, and ). In these altitude zones, the mean bias was significantly larger in InvGTOT, which was in line with the results compared to surface ground-based stations (Table ). This height corresponds approximately to the height where most of the surface ground-based stations were situated and from which JAXA/GOSAT XCH₄_LT data were calculated. Between 4000–8000 ma.s.l., the latitudinal gradients from InvGTOT were better captured, whereas the other inversions overestimated these gradients (Fig. ). Considering that the tropopause height is around 9000 ma.s.l. or above, these results indicate that the transport model has problems in representing upper-tropospheric concentrations. This is consistent with the finding that all model estimates worsened at high altitudes (>8000 ma.s.l.). All model estimates, both prior and posterior, failed to capture low mole fractions observed in high-latitude (>50° N/S) regions (Fig. ). Such low CH₄ mole fractions were observed especially in the winter of 2017 and the spring of 2018 when the tropopause height was lower and the ATom aircraft operated in the stratosphere (Fig. ). This is when the disagreement was strongest, possibly indicating the improper modelling of vertical profiles in polar vortex conditions.

The latitudinal gradient was generally weaker in TCCON tropospheric partial and total columns (Fig. ) compared to the surface observations, indicating the smaller influence of surface fluxes. Therefore, the evaluation against the TCCON data provides limited information about how well the inversions constrained the surface fluxes but focuses more on the model performance regarding long-range transport, tropopause mixing and atmospheric chemistry, which are important to take into account when assimilating total column satellite data. In the northern high latitudes, the extracted TCCON partial columns successfully separated the stratospheric component, showing no significant low biases under polar vortex conditions (Fig. ).

Compared to the surface data comparison, the TCCON comparison showed better overall agreement and improved representation of the latitudinal gradient in InvGTOT compared to other inversions for both total and tropospheric partial columns (Table , Fig. c and d). In the NH, all model estimates exhibited overestimation, driven by larger latitudinal gradients, with InvSURF, InvGLT, and InvGLT_land performing worse than InvGTOT. Considering also that InvGTOT showed the best agreement with the comparison to Atom data between 4000–8000 ma.s.l. on latitudinal gradients, this indicates the importance of the upper troposphere in the estimation of XCH₄. This finding also denotes that model biases in the estimation of XCH₄ are not solely caused by errors in resolving stratospheric concentrations; the errors in the upper troposphere also contribute to total column biases.

The 2016–2019 average global total and regional (30° latitudinal band) emissions are presented in Table . The posterior global total emissions were similar in all inversions (544–552±20–26 Tg CH₄ yr⁻¹) and showed increases from the prior with reduction in uncertainty (523±30 Tg CH₄ yr⁻¹). Most of the increase from the prior was attributed to anthropogenic emissions (c.a. 57 %–96 %). The sectorial emissions showed that anthropogenic emissions were higher and wetland emissions lower in InvGLT and InvGLT_land compared to InvSURF and InvGTOT. The anthropogenic emissions in the SH in InvGLT and InvGLT_land were higher compared to InvSURF but comparable in magnitude to InvSURF in the NH. InvGTOT also showed higher anthropogenic emissions in the SH compared to InvSURF, especially in the tropics (30° S–EQ), but lower anthropogenic emissions in 30–60° N.

Between the tropics and SH (90° S–30° N), the posterior anthropogenic emissions in the GOSAT inversions deviated more from the prior compared to InvSURF (Table , Fig. ). This difference was primarily associated with emissions over South America, Africa, and India (Fig. ). The monthly variations of anthropogenic emissions deviated largely from the prior in the GOSAT inversions (Fig. ). All GOSAT inversions showed an emission peak in November in the region from the Equator to 30° N, which was 3 months later than prior and InvSURF. In 30° S–EQ, all GOSAT inversions showed a clear seasonal cycle with emissions peaking during April–November. In contrast, the prior had no clear seasonal cycle, and InvSURF showed a small seasonal cycle with a peak in July. In 90–30° S, InvGLT and InvGLT_land showed two emission peaks, one around April and another in October. InvGTOT did not show the April peak, but it aligned with InvGLT and InvGLT_land in presenting an emission reduction in August and peak in October. InvSURF showed a less distinct seasonal cycle with emissions constantly increasing from the prior in all months, except January. In the tropics, there are only a few surface stations (Fig. ), which are often far from emission sources, measuring background mixing air. As a result, the JAXA/GOSAT data likely contained more valuable information to constrain the fluxes in these regions compared to the sparse surface data.

In 30–60° N, posterior anthropogenic emissions in InvGTOT were about 35 Tg CH₄ yr⁻¹ lower than those in the other inversions (Table ), representing about 6 % of the global total. These differences were not associated specifically with a specific region, but InvGTOT showed mostly negative emission increments indicating that posterior emissions were lower than the prior in this region (Fig. ). Unlike the tropics, there are a relatively large number of surface stations in Europe and the best optimization setup in terms of model spatial resolution, and thus, we suspect that the results from InvGTOT in Europe may have been too low. The comparison to ground-based observations in Europe also showed strong underestimation in InvGTOT (-44 ppb on average compared to stations within the 34–73° N, 12° W–37° E domain). For other regions, considering that the regions such as USA and China are large CH₄ emitters , we also suspect that the results in InvGTOT were underestimated, possibly due to the ability of the transport model representation of XCH₄ (see also Sect. ).

In 60–90° N, posterior wetland emissions in the GOSAT inversions stayed close to the prior compared to InvSURF (Table ). While InvSURF showed an increase in summer wetland emissions, such change was not found in the GOSAT inversions. Therefore, the seasonal cycle amplitude of wetland emissions was smaller in the GOSAT inversions (Fig. ). It is known that the GOSAT data have sampling limitation during winter due to polar nights and very low solar zenith angles. However, JAXA/GOSAT data were available above 60° N during summer (Fig. ). Further, as we found from the comparison of JAXA/GOSAT retrievals to InvSURF (see Sect. ), posterior mole fractions in InvSURF were higher than the JAXA/GOSAT retrievals, indicating that emissions from the GOSAT inversions would likely be lower than those from InvSURF. Therefore, we argue that the lower emissions from the GOSAT inversions were not due to limited number of data but rather reflected the agreement between the inversion and the prior. However, the inversion setups, such as the observation and transport model biases and uncertainties, rejection threshold and prior emission uncertainty, may need to be investigated further to better constrain the fluxes using the satellite data.

Despite a rather arbitrary choice of prior observational uncertainty (diagonals of R in Eq. 1; see Sect.  and ), the posterior emission uncertainties were generally lower in the GOSAT inversions compared to InvSURF, and lowest in InvGLT. These differences were most pronounced in the NH tropics (EQ–30° N) (Table ). The differences between InvSURF and GOSAT inversions were probably driven by the number of available observations, since GOSAT had much more data in the tropics. This could also explain in part the latitudinal bias found in Sect. 3.1. Within the GOSAT inversions, the number of assimilated data was higher in InvGTOT than InvGLT (Fig. ), even though the observational uncertainty and rejection thresholds were the same in both inversions. This indicates that the lower uncertainty in InvGLT was not simply due to number of assimilated observations but also related to sensitivity of the data to surface fluxes. Due to the nature of the retrieved quantity, total columns have less sensitivity to surface fluxes than ground-based data or lower-tropospheric partial column data; i.e. the total column data would have less power to constrain surface fluxes. Therefore, the larger number of assimilated observations in InvGTOT did not necessary lead to higher uncertainty reduction rates than in InvGLT. However, it is possible that the prior observational uncertainty prescribed in InvGLT may have been underestimated, considering that retrieval errors are probably higher for lower-tropospheric partial columns compared to total columns. Consequently, the posterior uncertainty of the fluxes may have been also underestimated. On the other hand, the transport model error may be lower for the lower-tropospheric partial column data, considering that the transport model performs better in representing tropospheric concentrations than stratospheric, so the total observational uncertainty could have been reasonable. The global total flux uncertainty of InvGLT_land was slightly higher compared to InvGLT. The posterior uncertainty was higher in the region with the largest uncertainty (EQ–30° N). Since the flux horizontal and seasonal distributions in InvGLT_land did not change significantly from InvGLT overall, we could argue that the effect of ocean data as a constraint was minor.

The spatial distribution of uncertainty reduction rates is shown in Fig. . This figure indicates that the GOSAT inversions constrained fluxes mostly in tropical regions and northeast China, while InvSURF showed strongest reductions in the USA, Canada, and Europe. InvSURF also showed a reduction hotspot areas in northeast China, but the signal was much weaker compared to the GOSAT inversions. The uncertainty reduction rates over land in InvGTOT were weak in general, with fewer reduction in hotspots compared to InvGLT and InvGLT_land, indicating that the use of the lower-tropospheric partial column data was more effective in constraining fluxes compared to total column data. The inversions with lower-tropospheric partial column data showed stronger uncertainty reduction rates in Africa and India, which were not as prominent in other inversions. The weaker uncertainty reduction rates from the GOSAT inversions in North America and Europe were probably due to (1) larger uncertainty in the observations, where many surface observations in these regions had less than 30 ppb observation uncertainty (Table ), and (2) the lack of observations in high latitudes over winter.

The uncertainty reduction rates over the ocean were relatively large compared to those over land (Fig. ). This is mainly due to the assigned prior uncertainty, which was significantly smaller over the ocean; the emissions were low (Fig. ), and prior uncertainty was set to 20 % over the ocean (see Sect. ). This has led to prior uncertainty of ∼10-22 mol m⁻² s⁻¹ over the ocean, which is approximately 1012 smaller than those over land. The uncertainty reduction rates are sensitive to small changes when the prior uncertainties are small, and therefore, they became larger over the ocean, although the absolute changes in the uncertainties were small.

In this study, we did not apply vertical interpolation for simplicity or averaging kernels (AKs) as JAXA/GOSAT v2.0 did not provide the information. However, we acknowledge that these are important to take into account when possible. With test simulations with TM5, we found that the latitudinal biases improved in ΔXCH₄ when compared to JAXA/GOSAT v3.0 data with vertical interpolation and AKs applied (Fig. ). In the NH temperate regions, the biases in ΔXCH₄ turned from positive to negative and became similar to the biases in the tropics. Therefore, using v3.0 would possibly increase the CH₄ flux estimates from the NH temperate regions in InvGTOT, aligning better with the inversions using surface and lower-tropospheric partial column data. There are still positive biases in the high latitudes, although they are smaller than these compared to v2.0 data without interpolation and AK.

For the lower-tropospheric partial column, ΔpXCH₄_LT, the overall latitudinal biases did not change significantly by applying vertical interpolation and AK (Fig. ). However, the large positive biases in highly elevated regions (e.g. Andean region in South America and the Tibetan Plateau in China and surroundings) and the northeastern part of China, where CH₄ emissions are large, turned to large negative biases. Nevertheless, large biases, both positive and negative, mean that the observations are likely to be rejected during assimilation and would not affect flux estimates. The ΔpXCH₄_LT over the ocean showed less latitudinal bias – the negative biases in the SH tropics turned positive. Overall, the robustness of modelling the lower-tropospheric partial column could be considered an advantage over the total column, having potential to provide more robust estimates regardless of differences in how transport models represent long-range transport, tropopause mixing, and atmospheric chemistry.

Latitudinal differences between posterior from the surface inversion and satellite total column retrievals have been seen in earlier studies using TM5 and other global Eulerian atmospheric chemistry transport models . Such discrepancies were reported regardless of the satellites' retrieval products, prior fluxes, years, or seasons. Part of the misrepresentation of CH₄ mole fractions in the upper troposphere could be due to convection schemes in the transport models , but the exact effect on the representation of total column values is to be examined. In high northern latitudes, the stratospheric profile is one of the challenges, especially in polar vortex conditions . However, polar vortex occurs only occasionally in spring, and our comparison to GOSAT, TROPOMI and TCCON XCH₄ values in this and previous studies showed biases even during the periods without polar vortex conditions . As shown in this study, latitudinal biases occur already in the upper troposphere (Fig. ), which confirms the findings from , who argued that the role of the stratosphere in the estimation of XCH₄ was minor.

In addition, we found that using higher horizontal resolution in TM5 improves agreement in the lower-tropospheric partial and total columns (Fig. ), indicating the importance of the transport model resolution. The latitudinal biases indeed seem to be slightly lower using higher spatial resolution , although the exact effect is to be examined. This study was limited in its exploration of the impact of the horizontal resolution, but increasing model resolution in the vertical dimension could also improve the representation of upper atmospheric CH₄.

The discrepancies in the latitudinal gradient could also be caused by the choice of chemistry schemes in the transport models. The distribution of OH, the largest sink of CH₄ , plays an important role in regulating ambient methane levels. The OH concentration fields by used in this study and several others have relatively uniform inter-hemispheric distributions, possibly underestimating the OH concentration in the NH and overestimating them in the SH . This leads to higher atmospheric CH₄ in the NH and lower atmospheric CH₄ in the SH, as shown by this study and previous studies . showed that the differences in the inter-hemispheric distribution of OH could lead to about 25–50 Tg CH₄ yr⁻¹ differences in the inter-hemispheric distribution of CH₄ emissions, where the inversion based on led to lower emissions in the NH and higher emissions in the SH. This outcome is in line with the conclusion that the emission distributions in InvGTOT, which estimated the lowest CH₄ emissions in the NH, could be unreliable. The vertical OH profiles used in this study that have distinct peaks at around 500–600 hPa may also partially explain why ATom profiles and GOSAT total columns were not accurately reproduced.

In CH₄ inversions using the GOSAT data, it has not been common to correct land–sea biases in the retrievals or exclude data over the ocean. CH₄ fluxes over the ocean are minor compared to those over land . Consequently, the effect of land–sea biases in the JAXA/GOSAT retrieval data is expected to be small in estimation of CH₄ fluxes. This study showed that the emission estimates and posterior mole fractions from InvGLT and InvGLT_land were very similar, despite the different systematic and seasonal biases over land and sea compared to InvSURF (Fig. ), confirming that the effect of ocean data as constraints has a minimum influence on the outcome of inversions. Therefore, to significantly decrease the number of data in the inversions and increase the computational efficiency.

The global total estimates from InvSURF were slightly lower than from the inversions using JAXA/GOSAT_LT data and on the lower edge of the range of the top-down (TD) estimates from the latest Global Methane Budget (553–586 Tg CH₄ yr⁻¹, 2010–2019 average; ). The breakdown of anthropogenic and wetlands sources showed that anthropogenic emissions from this study were slightly higher compared to (350–391 Tg CH₄ yr⁻¹ vs 145–214 Tg CH₄ yr⁻¹, 2010–2019 TD averages). These differences occur probably because of smaller wetland prior emissions (120 Tg CH₄ yr⁻¹), which are on the lower boundary of bottom-up estimates from (119–203 Tg CH₄ yr⁻¹, 2010–2019 bottom-up averages). This indicates that the prior uncertainty may need to be revised, and for example, spatially and seasonally varying uncertainty ratios could provide better freedom in the inversion. It is worth pointing out that we did not use a separate prior for freshwater emissions, and, thus, the freshwater emissions could be wrongly attributed to anthropogenic emissions if there is a spatial overlap with the anthropogenic emissions.

The total emissions in the eastern part of North America agree well with previous studies . Higher emissions in the northeastern part of the United States compared to EDGAR (Fig. ) point to underestimation of emissions from oil and gas. It should be noted that although we used the newer version of EDGAR (v8.0) in this study, the underestimation still seems to remain. The seasonal variability of anthropogenic CH₄ emissions in southern North America (including the contiguous United States and Mexico) from the GOSAT inversions (Fig. A10a) showed opposite patterns compared to InvSURF. The seasonal cycle in this region is likely to be associated with natural gas consumption and agriculture . In contrast to results from , who argue that the seasonal pattern of natural gas consumption has strong interannual variations and is spatially inhomogeneous, our results from the GOSAT inversions were similar to those from , which showed larger emissions during autumn and winter compared to summer.

In China, InvSURF and InvGTOT showed strong negative emission increments (i.e. posterior emissions were lower then prior) around Beijing (Fig. ), which is consistent with studies such as by and , who assimilated both surface and the GOSAT XCH₄ data. This seems to be a common feature found in other studies despite different GOSAT retrieval products, prior, transport, and inverse models being used and references therein. On the other hand, InvGLT and InvGLT_land showed positive emission increments in eastern and southern China. This is closer to the findings from , who assimilated the TROPOMI XCH₄ data. However, these results again contradict with who assimilated both GOSAT and TROPOMI XCH₄ data and argued that an inversion using only TROPOMI XCH₄ may be unreliable over China because the TROPOMI XCH₄ is strongly affected by cloud cover.

In central Africa and South Sudan wetland regions, our GOSAT inversions showed a large increase in total emissions (Fig. ), which were primarily associated with wetland emissions. This is in line with findings from , who used the GOSAT XCH₄, and , who used the TROPOMI XCH₄ in their inversions, although our posterior estimates were considerably lower. For instance, we found monthly maxima below 1 Tg CH₄ per month around South Sudan, compared to estimates of 7 Tg CH₄ per month and estimates of seasonal cycle amplitude of about 2–3 Tg CH₄ per month. Nevertheless, the seasonal cycle in this region from the GOSAT inversions (Fig. A10b) showed significantly lower emissions in June–July and later maxima in August–September compared to prior and InvSURF. This corresponds slightly better to dry and wet seasons and is in line with and , although we could not reproduce high emissions in late months.

The magnitude of high-northern-latitude (NHL) wetlands remained uncertain. Previous studies showed that in some cases, NHL emissions from the inversion based on the GOSAT data were lower compared to those based on surface data , while others showed the opposite . Our analysis showed that the wetland emissions for 60–90° N were lower in the GOSAT inversions regardless of the assimilated data type (total or lower-tropospheric partial column) compared to InvSURF. This is in line with our previous studies assimilating TROPOMI XCH₄ data and using other wetland priors, such as JSBACH-HIMMELI, where emissions were relatively larger among process-based models . assumed that the lower NHL wetland emissions in the TROPOMI inversions were due to latitudinal biases associated with model–retrieval differences . However, as shown in this study, the inversion using lower-tropospheric partial column data also resulted in lower NHL wetland emissions compared to InvSURF, indicating that there may be fundamental discrepancies between satellite and surface inversions. The differences were partly due to spatial coverages and temporal distributions of the observations and uncertainties associated with transport models, observations, prior emissions and possible biases in the satellite data, but further study is needed to find the exact cause of these discrepancies and to obtain more robust the emission estimates.