Mean global annual methane emissions in R4 inversion is 573.04 Tg yr –1, compared to 568.63 Tg yr –1 for R2 inversion (Table 1). The difference of about 4.4 Tg yr –1 is less than 1% of the mean annual emissions. Compared with other inversion results using GEOS-Chem runs at the R4 grid, we found that our results (561.10 Tg yr-1 for R4 and 559.91 Tg yr –1 for R2) are about 3% higher than the 2010–15 mean methane emission of 546 Tg yr –1 estimated by Maasakkers et al. (2019). Lu et al. (2021) also used GEOS-Chem simulations at the R4 grid to conduct GOSAT-only, in-situ-only, and joint GOSAT and in-situ inversions; our results are close to their joint inversion result (551 Tg yr –1) for 2010–17 but much higher than their GOSAT-only inversion. Zhang et al. (2021) expanded the study period from 2017 to 2018 and concluded that the 9-year annual total emissions were 512 Tg yr –1 when only GOSAT retrievals were assimilated in inversion. Sensitivity tests (not included) show that the large differences are mainly due to different model OH concentrations. Zhao et al. (2020) studied the influence of the production and loss processes of OH on CH4 lifetime and the global methane budget on decadal scales and found that interannual variation of OH has a significant impact on the top-down inversion of the methane budget, especially for tropical regions. However, Yin et al. (2021) compared six inversion results optimized by the inversion system PYVAR-LMDz based on the LMDz-INCA (1.9° × 3.75°) CTM (OH fields are from a full chemistry simulation by model LMDZ-INCA and the TransCom model intercomparison experiment). They suggested that the XCH4 accelerated growth could be mostly induced by increased emissions. Our decadal mean emissions are close to the upper bound (510–570 Tg yr –1) of the 8-year mean values for their six ensemble results. Janardanan et al. (2020) used GOSAT and surface measurements to optimize methane a priori estimates in the NIES-TM-FLEXPART-VAR (NTFVAR) inverse modeling system. They estimated the global annual mean methane emissions to be 573.4 Tg yr –1 from 2011 to 2017. Chandra et al. (2021) assimilated surface measurements from NOAA over three decades from 1988 to 2016 and reported global emissions over 2007–16 (renewed growth of atmospheric methane) of 543±16 Tg yr –1.Based on an ensemble of 22 top-down methane budgets from 2008 to 2017, the Global Carbon Project (GCP) reports that the decadal mean emissions range from 550 to 594 Tg yr –1, with a mean value of 576 Tg yr –1. Among the ensemble members, the 11 GOSAT-only or GOSAT and in-situ joint inversion results range from 564.1 to 594.1 Tg yr –1 with mean emissions of 579 Tg yr –1 (Saunois et al., 2020). Our results, using two different model grids, are comparable to these 11 GOSAT-related inversions. Several inversion results, including 9 surface CH4 and 10 satellite XCH4 inversions reported by Saunois et al. (2020), three decadal inversion results (1988–2016) using surface measurements from 19 sites given by Chandra et al. (2021) as well as global total emissions (Bousquet et al., 2006) were summarized in latest IPCC AR6 report (Canadell et al., 2021). Global total emissions in two inversions using surface CH4 measurements show similar trends from 2000 to 2017 but large discrepancies in 2015. While continued methane growth occurred in 2015 with anomalies close to 25 Tg yr –1 [relative to 2010–16 as given by Chandra et al. (2021)], nine ensemble mean emissions from Saunois et al. (2020) show a plateau after the substantial increase in 2014. These two inversions both show declining trends after 2015, when the ensemble mean result from 10 satellite inversions differs greatly with ongoing increases. Two trends in our inversions are similar to the ensemble mean trend from satellite inversions with sustained growth from 2010 to 2017. The discrepancy between the surface inverted trends and satellite assimilated trends after 2015 still needs further investigation.
Institution CTM Gridded (lon×lat) Period Observation used Global total
References IAP GEOS-Chem 4° × 5° 2010–19 GOSAT 573.04 this study 2° × 2.5° 568.63 University of Harvard GEOS-Chem 4° × 5° 2010–15 GOSAT 546±2 (Maasakkers et al., 2019) University of Harvard GEOS-Chem 4° × 5° 2010–17 GOSAT 515 (Lu et al., 2021) In-situ 504 GOSAT & in-situ 551 University of Harvard GEOS-Chem 4° × 5° 2010–18 GOSAT 512 (Zhang et al., 2021) JAMSTEC MIROC4-ACTM 2.8125° × 2.8125° 2007–16 In-situ 543±16 (Chandra et al., 2021) LSCE/CEA LMDz-INCA 3.75°×1.875° 2010–17 GOSAT 510–570 (Yin et al., 2021) NIES NIES-TM v08.1i 2.5° 2011–17 GOSAT & in-situ 573.4 (Janardanan et al., 2020) LSCE/IPSL 22 inversions
2008–17 In-situ or GOSAT
(Saunois et al., 2020) 11 inversions
Table 1. Global annual total emissions during the 2010s (Tg yr–1).
Figure 3a presents interannual variations of the global emissions from 2010 to 2019. Long-term increases can be found in both inversions, with the growth rate being 4.95 Tg yr –2 in R4 and 3.10 Tg yr –2 in the R2 model. The growth rate shows temporal fluctuations, with the largest increase in 2013 (25.51 Tg yr –2 in R4 and 13.00 Tg yr –2 in R2). Generally, most increases are derived from enhancements over 2012–17, during which time the annual total emissions for both the R2 and R4 inversions increased by more than 35 Tg (49.25 Tg for R4 and 36.09 Tg for R2). Yin et al. (2021) also found the lowest annual total emission in 2012 and the highest in 2017 in the PYVAR-LMDz (1.9° × 3.75°) inversion system. Its 8-year increasing trend is about 4.1 Tg yr –2, accounting for nearly 1% of the annual total emissions. On average, the first 8-year increase trends in our study are 7.04 Tg yr –2 and 5.15 Tg yr –2 in the R4 and R2, respectively, corresponding to annual increases of nearly 1.24% and 0.99%. Compared to R4 inversion, both the increasing trend and the percentage of total emissions in the R2 experiment are more consistent with the results of Yin et al. (2021).
Figure 3. Annual mean variations of global total methane emissions (a) in R4 (blue) and R2 (orange) versions of the GEOS-Chem model and their monthly variations (b) from 2010 to 2019.
Large discrepancies in annual total emissions between R4 and R2 can be observed in 2019. While a posteriori emissions in R2 continued to decrease after a peak in 2017, the emissions in R4 rebounded after decreasing in 2018. From the monthly comparison shown in Fig. 3b, we found that the difference in 2019 arose mainly from the maximum emissions during summer. In addition to the large emissions gap in 2019, we found that emissions in summer (July in the NH and December in the SH) caused most of the difference in other years.
The latitudinal breakdown of emissions from 22 ensemble inversion results concluded by Saunois et al. (2020) reveals the dominance of tropical emissions at 368 Tg yr –1 [337–399], representing 64% of the global total over 2008–17. A total of 32% of the emissions are from the mid-latitudes (186 Tg yr –1 [166–204]), and 4% are from high latitudes (above 60°N). In this study, the dominant emissions over the tropics are 364.71 Tg yr –1 and 355.31 Tg yr –1 in R4 and R2, representing 63.64% and 62.48% of their global totals over 2010–19, respectively. Emissions from mid-latitudes account for 34.22% (196.12 Tg yr –1) and 35.17% (200.02 Tg yr –1). Those from high-latitude regions are 12.22 Tg yr –1 (2.13%) and 13.31 Tg yr –1 (2.34%) in R4 and R2, respectively. The emission distribution proportions in various latitudinal zonal are similar between R2 and R4 and are basically consistent with those given by Saunois et al. (2020). For the emission variation over various latitudinal zones, our results reveal global emission enhancements over 2015–19, compared to the first half of the 2010s, are derived mostly from tropical regions, representing 77.76% (21.57 Tg yr –1) and 71.57% (15.85 Tg yr –1) of their totals in R4 (27.74 Tg yr –1) and R2 (22.11 Tg yr –1). Compared to the R4 results, emission increases are more gradual in the R2 grid, with larger contributions coming from the mid-latitudes (9.28%) and high-latitudes (19.14%).
Considering the 1.16% larger proportion of tropical emissions to global totals, as well as the corresponding 6.19% greater contribution to the 5-year global increase in R4, we suggest explanations that may include a high-latitude bias in GEOS-Chem due to an imprecise description of convection across the tropopause (Bisht et al., 2021) and the inaccurate estimation of the vertical exchange between troposphere and stratosphere (Strahan and Polansky, 2006). Especially in R4, there is less methane in the troposphere and more methane in the lower stratosphere at high latitudes (shown in Fig. A3), which produces a latitudinal XCH4 bias with large positive XCH4 anomalies at high latitudes and small negative anomalies in the tropics (shown in Fig. A2). Bisht et al. (2021) attributed this bias to stronger quasi-horizontal mixing across the tropopause from the tropics to the mid-high latitudes in the model with a low resolution by comparing MIROC4-ACTM simulated CH4 vertical profiles in the upper troposphere and lower stratosphere with the CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) aircraft observations. Stanevich et al. (2020) compared GEOS-Chem simulated XCH4 in R4 and R2 with GOSAT, TCCON, and ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations and found that the R2 model produced a better simulation of CH4, with smaller biases and a higher correlation to the independent data. They explained that the major reason for latitude-dependent errors is the excessive mixing in the upper troposphere and lower stratosphere at coarser resolutions. The larger model biases at R4 grid thus impact the distribution of a posteriori emissions and their long-term trends.
Detailed zonal mean a posteriori emissions variation by the R4 (a) and R2 (b) from 2010 to 2019 are presented in Fig. 4. We found the latitudinal distributions of the maximum values differ greatly in mid-latitude regions between R4 and R2. Compared with the single maximum around 35°N in R4, several additional high zonal mean values were obtained in R2, with the maximum near 30°N being the most apparent. Additionally, emissions around 25°N in R2 increased significantly in the 2010s, which was unclear in the R4 results. These results suggest that inversions using coarse models have difficulty reproducing hotspot emissions that are widely distributed over the mid-latitudes.
Decadal annual mean CH4 emissions (shown in Fig. A5) are aggregated into the widely used 11 TransCom-3 land regions (Gurney et al., 2002), which are shown in Figure 5a. Among the eleven regions, the largest annual emissions from Eurasia Temperate are 123.28 Tg yr –1 and 135.70 Tg yr –1 in R4 and R2, accounting for 21.51% and 23.85% of their global totals, respectively. Annual emissions in R4 are underestimated by about 12.42 Tg yr –1 compared with those in R2, accounting for 9.15% of the regional totals. In the tropics, where wetlands are widely distributed, underestimations in annual emissions by R4 are about 5.85 Tg yr –1 in Tropical South America (58.97 Tg yr –1 for R4 and 64.81 Tg yr –1 for R2) and 8.70 Tg yr –1 in Tropical Asia (46.23 Tg yr –1 for R4 and 54.93 Tg yr –1 for R2), accounting for 9.02% and 15.84% of their annual totals. For North Africa, emissions in R4 are 3.46 Tg yr –1 higher than those in R2 (50.80 Tg yr –1 for R4 and 47.34 Tg yr –1). For the high-latitude regions of Eurasia Boreal, North American Boreal, and Europe, the optimized emissions from R4 are smaller than emissions simulated by the R2 model. The differences between R4 and R2 are 2.19, 2.72, and 6.32 Tg yr –1, accounting for 13.75%, 25.68%, and 15.67% of the totals in these regions, respectively.
Figure 5. Annual total methane emissions in the eleven TransCom-3 land regions from 2010 to 2019. Decadal annual mean optimized emissions obtained using the R4 (blue) and R2 (orange) versions of the GEOS-Chem model (a), and the differences in methane emissions between the second and the first half of the 2010s in R4 and R2 (b).
In terms of differences in regional annual total emissions between the first half and the second half of the 2010s (shown in Fig. 5b), five-year annual total methane emissions in Eurasia Boreal, North Africa, and Tropical South America have shown significant growth (> 4 Tg yr –1) compared to those over 2010–14. While the increase from Eurasia Boreal in the R4 inversion is smaller than that in R2, emission increases over North Africa and Tropical South America are overestimated in the R4. For emission growth inside the half-decade over the 2010s, we found increased emissions in North Africa (1.79 Tg yr –2 for R4 and 1.29 Tg yr –2 for R2, shown in Fig. 6f) and South American Temperate (1.08 Tg yr –2 for R4 and 1.21 Tg yr –2 for R2, shown in Fig. 6k) contribute the most (71.69% for R4 and 59.67% for R2) to global emissions growth (4.01 Tg yr –2 for R4 and 4.19 Tg yr –2 for R2) before 2015. From six ensemble mean results over 2010–18, using the inversion system PYVAR-LMDz, Yin et al. (2021) also concluded that methane emissions from Tropical Africa (a large part of North Africa and a small part of South Africa in this study) and Eastern Brazil (basically located in South American Temperate) show an upward trend since 2012. However, several inversion results summarized by Canadell et al. (2021) show increases in the first half of the 2010s mainly from Eurasia Temperate (including West Asia, East Asia, and South Asia) and North America Temperate. In our study, the main contributions to emissions growth from 2010 to 2014 occurred in North Africa (including Northern Africa and part of Central Africa) and South American Temperate (South part of Tropical America and Temperate South America), while only a slight emission increase could be found from Central Africa and Temperate America with no trend in South Africa and large inter-annual variation in Tropical America. During the second half of the 2010s, accelerated methane growth in the atmosphere was mainly due to emissions from the Eurasia Boreal (1.46 Tg yr –2 for R4 and 1.63 Tg yr –2 for R2, shown in Fig. 6a) and Tropical South America (1.72 Tg yr –2 for R4 and 1.43 Tg yr –2 for R2, shown in Fig. 6g). The interannual variation in Eurasia Boreal (Russia) from Yin et al. (2021) is coincident with the increased emissions which occurred in 2014 and peaked in 2016. Chandra et al. (2021) also reported a similar increasing trend over Eurasia Boreal (North Asia), with emissions reaching a maximum in 2016. Our study additionally shows that in 2019, emissions from the Eurasia Boreal continued growing after a decline in 2017 and were nearly twice the emissions in 2010. In Europe (Fig. 6c), there are decadal declining trends, with the average rates of decrease of –0.58 Tg yr –2 and –0.59 Tg yr –2 in R4 and R2, respectively, accounting for about –1.7% of their totals. This result is consistent with an ongoing consensus (Saunois et al., 2020; Canadell et al., 2021; Chandra et al., 2021; Stavert et al., 2022). For Australia (Fig. 6i), annual emissions in both R2 and R4 show decreasing trends. However, these results incur large uncertainties as the relative difference in the 5-year decreases reached more than half of the total emissions.
Figure 6. Regional methane emission trends in the eleven TransCom-3 land regions (a–k) in R4 (blue) and R2 (orange) inversions and their monthly variations (b) from 2010 to 2019.
Over the second half of the 2010s, methane variations in the South America Temperate and South Africa differ greatly between R4 and R2. The result in R2 shows no obvious decreasing trend in South America Temperate and no substantial increase in South Africa, which is similar to the results from Yin et al. (2021) and Stavert et al. (2022). Therefore, emission growth in South Africa in R4 may be overestimated, and emission trends in South America Temperate are insufficiently estimated over 2015–19.
The half-decadal variation in Fig. 5b shows large discrepancies for the Eurasia Temperate. The mean annual emission increase compared to those from the first half of the 2010s in R4 (5.41 Tg yr –1) is nearly twice that in R2 (2.80 Tg yr –1), with large discrepancies over 2015–19 (Fig. 6d). While emissions in R4 show remarkable growth after fluctuating between 2012 and 2015, those in the R2 inversion show no consistent growth with a continuous fluctuation in the following years. There is also an ongoing debate about emissions from Eurasia Temperate among studies, which mainly focus on China, though all of them agree that coal emission has been overestimated by the widely used EDGAR (Maasakkers et al., 2019; Miller et al., 2019; Lu et al., 2021; Yin et al., 2021; Zhang et al., 2021; Stavert et al., 2022). Miller et al. (2019), Maasakkers et al. (2019), and Chandra et al. (2021) attributed atmospheric methane growth from 2010 to 2015 to increased emissions, partly from China, related to fossil fuel exploration. Stavert et al. (2022) and Yin et al. (2021) suggested a substantial increase in Chinese anthropogenic methane emissions from fossil fuels, agriculture, and waste from 2010 to 2017. However, Lu et al. (2021) compared inversion results using various observations, and found that anthropogenic emission trends in China for GOSAT-only (–0.6 Tg yr –2) and for GOSAT and in situ joint inversion (–0.4 Tg yr –2) over 2010–2017. Zhang et al. (2021) concluded that anthropogenic emission trends in China peaked midway within the 2010–18 record. One possible reason for the discrepancy in their inversion results may be the large uncertainties involved in the distribution of a priori estimates. While some studies suggest a decline in Chinese coal mining emissions since 2012 (Sheng et al., 2019, 2021; Gao et al., 2020, 2021), the trend reported by Lin et al. (2021) using national-level activity data from the National Bureau of Statistics of China and localized emission factors showed a slight increasing trend of 0.5 Tg yr –2 for the period 2015–19. Additionally, the tropospheric transport bias involved in the coarser model may be another important reason (Stanevich et al., 2020). Stanevich et al. (2021) found that resolution-dependent model errors in the stratosphere can be traced back to biases in the uplift of CH4 over the source regions in eastern China and North America. Regarding observational errors in GOSAT retrievals, Huang et al. (2020) found aerosols with a high single-scattering albedo and low asymmetry parameters (such as water-soluble aerosols, highly loaded in Northern China) induce large biases in the retrieval. Besides, there are very few retrievals over Southern China during the summer/monsoon season because of cloud cover (Chandra et al., 2017). Both the quality and the coverage of GOSAT XCH4 retrievals may affect the convergence of posterior emissions in inversion.
To further study the systematic discrepancies in regional emissions, we used a box plot to show the difference between R4 and R2 on a finer monthly scale in Fig. 7. Despite similar long-term trends that can be found in most regions, there are large discrepancies between R4 and R2 on monthly timescales. Interquartile Range (IR = Q3 – Q1) relative to their monthly emissions is larger than 30% in Eurasia, North American Boreal, and Australia, with the largest values in Australia at 105%. In other regions, IR values range from 10% to 20% of their monthly mean emissions, indicating that the horizontal resolution of CTMs can profoundly impact regional emission variation on monthly timescales. Therefore, monthly variations of regional methane emissions have large uncertainties and should be interpreted with caution.
|Institution||CTM||Gridded (lon×lat)||Period||Observation used||Global total |
|IAP||GEOS-Chem||4° × 5°||2010–19||GOSAT||573.04||this study|
|2° × 2.5°||568.63|
|University of Harvard||GEOS-Chem||4° × 5°||2010–15||GOSAT||546±2||(Maasakkers et al., 2019)|
|University of Harvard||GEOS-Chem||4° × 5°||2010–17||GOSAT||515||(Lu et al., 2021)|
|GOSAT & in-situ||551|
|University of Harvard||GEOS-Chem||4° × 5°||2010–18||GOSAT||512||(Zhang et al., 2021)|
|JAMSTEC||MIROC4-ACTM||2.8125° × 2.8125°||2007–16||In-situ||543±16||(Chandra et al., 2021)|
|LSCE/CEA||LMDz-INCA||3.75°×1.875°||2010–17||GOSAT||510–570||(Yin et al., 2021)|
|NIES||NIES-TM v08.1i||2.5°||2011–17||GOSAT & in-situ||573.4||(Janardanan et al., 2020)|
|LSCE/IPSL||22 inversions |
|2008–17||In-situ or GOSAT |
|(Saunois et al., 2020)|
|11 inversions |