An integrated analysis of contemporary methane emissions and concentration trends over China using in situ, satellite observations, and model simulations

China, being one of the major emitters of greenhouse gases, has taken strong actions to tackle climate change, e.g., to achieve carbon neutrality by 2060. It also becomes important to better understand the changes in the atmospheric mixing ratio and emissions of CH4, the second most important human-influenced greenhouse gas, in China. Here we analyze the 15 sources contributing to the atmospheric CH4 mixing ratio and their trends in China over 2007–2018 using the GEOS-Chem model simulations driven by two commonly used global anthropogenic emission inventories: the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) and the Community Emissions Data System (CEDS). The model results are interpreted with an ensemble of surface, aircraft, and satellite observations of CH4 mixing ratios over China and the Pacific region. The EDGAR and CEDS estimates show considerable differences reflecting large uncertainties in estimates of 20 Chinese CH4 emissions. Chinese CH4 emission estimates based on EDGAR and natural sources increase from 46.7 Tg per annum (Tg a-1) in 1980 to 69.8 Tg a-1 in 2012 with an increase rate of 0.7 Tg a-2, and estimates with CEDS increase from 32.9 Tg a-1 in 1980 and 76.7 Tg a-1 in 2014 (a much stronger trend of 1.3 Tg a-2 over the period). Both surface, aircraft, and satellite measurements indicate CH4 increase rates of 7.0–8.4 ppbv a-1 over China in the recent decade. We find that the model simulation using the CEDS inventory and interannually varying OH levels can best reproduce these observed CH4 25 mixing ratios and trends over China. Model results over China are sensitive to the global OH level, with a 10% increase in the global tropospheric volume-weighted mean OH concentration presenting a similar effect to that of a 47 Tg a-1 decrease in global CH4 emissions. We further apply a tagged tracer simulation to quantify the source contributions from different emission sectors and regions. We find that domestic CH4 emissions account for 11.4% of the mean surface mixing ratio and drive 68.3% of the surface trend (mainly via the energy sector) in China over 2007–2018. We emphasize that intensive CH4 30 measurements covering eastern China will help better assess the driving factors of CH4 mixing ratios and support the emission mitigation in China. https://doi.org/10.5194/acp-2021-464 Preprint. Discussion started: 19 August 2021 c © Author(s) 2021. CC BY 4.0 License.


Introduction
Atmospheric methane (CH4) is the second most important anthropogenic greenhouse gas contributing more than a quarter of 35 the human-induced radiative imbalance since 1750 (IPCC, 2013). It also plays an important role in atmospheric chemistry as an essential precursor for tropospheric ozone and stratospheric water vapor (Turner et al., 2019). Global mean atmospheric CH4 surface concentrations increased from about 1650 ppbv in the mid 1980s to about 1770 ppbv in the late 1990s, then stabilized around this level in the early 2000s, and started increasing again since 2007(Dlugokencky et al., 2009Nisbet et al., 2019). The regrowth of atmospheric CH4 concentrations has drawn worldwide attention and led to many different or even 40 contradictory explanations (Maasakkers et al., 2019;Turner et al., 2019;Zhao et al., 2019;Yin et al., 2020;Zhang et al., 2021). Difficulties in the attribution of the trends are mainly associated with large uncertainties in changes in the CH4 emissions from various sources as well as the chemical loss via oxidation by hydroxyl radical (OH) (Turner et al., 2019). A better understanding and quantification of the interannual variability of CH4 emissions and the drivers of the concentration growth in the recent decade is important to support its mitigation. 45 CH4 has both important anthropogenic and natural sources. It can be emitted from human activities including coal mining, oil and gas exploitation, livestock, rice cultivation, waste deposit, and wastewater treatment. It also has a large natural source from wetlands, with small sources from forest fires, termites, and geological seeps. Global bottom-up estimates of CH4 emissions based on statistics of source activities or process-based models have reported a wide range of total CH4 emissions 50 of 542-852 Tg a -1 in the 2000s (Kirschke et al., 2013). Atmospheric top-down analyses constrained by surface, satellite, and aircraft observations of CH4 concentrations tend to suggest lower total CH4 emissions of 526-569 Tg a -1 in the period (Kirschke et al., 2013) and find even greater uncertainties in the relative contributions from different CH4 emission sectors (Kirschke et al., 2013;Saunois et al., 2016;Saunois et al., 2020). Over 90% of atmospheric CH4 is lost via oxidation by OH decreased due to the mitigation policy in recent years (Miller et al., 2019;Sheng et al., 2019). Atmospheric inversion 65 analyses are typically applied at global scales due to very limited in situ CH4 measurements over this region in 2000s. The increases of spatiotemporal observations (from satellite or aircraft) and the development of atmospheric transport models would be helpful in constraining methane sources over China, but different dataset and methods could provide discrepant information (Thompson et al., 2015;Miller et al., 2019). China has pledged to peak the carbon dioxide emissions by 2030 and to reach carbon neutrality by 2060 for tackling climate change. As CH4 being the second most important anthropogenic 70 greenhouse gas, it also becomes crucial to quantify its emissions and concentration trends in China.
In this study, we aim to better understand the recent trends in CH4 emissions and concentrations in China using the GEOS-

Surface and aircraft measurements
We use the surface CH4 concentration measurements from the Global Monitoring Division (GMD) of the Earth System Research Laboratory (ESRL) at the National Oceanic and Atmospheric Administration (NOAA). The CH4 concentrations are 85 measured by gas chromatography with flame ionization detection (Dlugokencky, 2005). The measurement database (https://www.esrl.noaa.gov/gmd/dv/data/, last access: 3 March 2021) includes 95 sites globally providing monthly averages of mixing ratios (ppbv). The database has been widely used in assessing regional and global CH4 concentrations and budgets (Bergamaschi et al., 2013;Fraser et al., 2013;Cressot et al., 2014;Turner et al., 2016;Miller et al., 2019).

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Here we focus on four sites located in China, as summarized in Table 1 (Wofsy et al., 2011). ATom 100 consists of four campaigns from July 2016 to May 2018 (Wofsy et al., 2018). Figure 1 shows the flight tracks from two campaigns. Both HIPPO and ATom datasets provide the merged 10-second data products for all flights (Wofsy et al., 2017;Wofsy et al., 2018), which cover the four seasons temporally and the regions over the Pacific Ocean and North America spatially. Both campaigns conduct continuous profiling between ~0.15 km and 8.5 km altitude, with many profiles extending to nearly 14 km. Here we sample the model results at the hourly resolution along flight tracks as shown in Fig. 1 and average 105 them in 2º latitude bins for the comparison.

GOSAT satellite observations
The TANSO-FTS instrument onboard the Greenhouse Gases Observing Satellite (GOSAT) launched in early 2009 measures the backscattered solar radiation from a sun-synchronous orbit at around 13:00 local time (Butz et al., 2011;Kuze et al., 2016). The observations have a pixel resolution of around 10 km diameter and are separated by about 250 km along the 110 observing track with a global coverage every 3 days (Parker et al., 2015). GOSAT retrieves column-averaged dry-air CO2 and CH4 mixing ratios from the shortwave infrared (SWIR) spectrum with near-unit sensitivity down to the surface (Butz et al., 2011). We use the University of Leicester version 7.2 GOSAT XCH4 proxy retrieval over China from January 2010 to December 2017. The glint data over the oceans are not used in this study due to the sparse data coverage. The CH4 product has been validated by Parker et al. (2015) against the Total Carbon Column Observing Network (TCCON) and MACC-II 115 model XCH4 data and suggested a precision of 0.7%.
To compare with the GEOS-Chem model results as described below, the GOSAT CH4 observations and satellite averaging kernels are averaged over the 2°×2.5° or 4°×5° model grid. We use the satellite observations which pass the criteria that the grid has more than 12 months of valid observations which have passed their quality control. The simulated vertical profiles 120 (VMR mod ) are applied with the satellite averaging kernels (AK) and a priori estimates (VMR apr ) using Equ. (1) following Parker et al. (2020). (1) where AKi is the retrieval averaging kernel and hi is the pressure weight for the vertical level i. This provides column mean CH4 mixing ratios (XCH $ %&' ) with the vertical sensitivity of satellite retrievals accounted for.

The GEOS-Chem model description and simulation design
We use the GEOS-Chem global chemical transport model v11-02 release candidate (http://geos-chem.org, last access: 3 March 2021) driven by MERRA-2 meteorological fields from the NASA Global Modeling and Assimilation Office (GMAO). The MERRA-2 dataset has a native horizontal resolution of 0.5° latitude×0.625° longitude, and is degraded to 130 4°×5° or 2°×2.5° resolutions for input to GEOS-Chem. We use the CH4 simulation that calculates the CH4 sinks using prescribed global distributions of OH concentrations or loss frequencies. The model has been applied in a number of studies to understand the global and regional CH4 emissions and concentrations (Wecht et al., 2014;Turner et al., 2015;Maasakkers et al., 2019;Lu et al., 2021;Zhang et al., 2021). presented in Section 3. The EDGAR CH4 emissions do not account for seasonal variations. Here we have applied seasonal scalars to CH4 emissions from manure management based on a temperature dependence described by Maasakkers et al. 140 (2016) and to those from rice cultivation following Zhang et al. (2016) in the EDGAR inventory. The CEDS inventory as used in this study provides gridded emission estimates with monthly variations.  (Ridgwell et al., 1999).
We have conducted a series of model simulations over 1980-2018 as summarized in Table 1 to investigate the impacts of OH concentrations and model resolution. For all the datasets of emissions and sinks as described above, the closest available 160 year will be used for simulation years beyond their available time ranges. Evaluations of these model results with the NOAA surface measurements at the four Chinese sites indicate that the simulation with CEDS and interannually varying OH at 2°×2.5° resolution (GCC in Table 1) relatively better captures the measured concentrations and trends since 2007, as will be discussed in Section 3.2.

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We further apply a tagged CH4 tracer simulation to quantify the sources contributing to CH4 concentrations and trends in China over 2007-2018. We implement 100 tracers that tag CH4 emissions from different source types (agriculture, energy, industry, transportation, wastewater, residents, shipping, biomass burning, wetlands, seeps and termites) and different regions (China, India, Europe, South America, North America, Africa, Oceania, etc., as shown in Fig. 2). Global soil uptake is also tagged as a sink of CH4. We run the tagged CH4 simulation using the model settings of GCC (i.e., CEDS and 170 interannually varying OH) for the period of 1980-2018. The results allow us to quantify the detailed source contributions to CH4 concentrations and trends over China.  Saunois et al. (2016;, and are thus compared with corresponding estimates in this study. The anthropogenic emission source categories are different in the EDGAR and CEDS inventories, and we organize all sources into five main categories (agriculture and waste, biomass burning, fossil fuels, wetlands, and other sources) following Saunois et al. (2020), 180 as also summarized in Table S2.  (2008( ) in 190 GCE, and 65% (2000( ) and 67% (2008 in GCC, which are slightly larger than 60% and 62% in the top-down estimates of Saunois et al. (2020). The global CH4 chemical losses simulated in GCE and GCC are also consistent with the top-down estimates for both periods, while the sink of soil uptake might be underestimated in the model. Table 2   The largest differences between GCE and GCC, as also discussed in Fig. 4, come from the sectors of fossil fuels and 215 agriculture. Agriculture sources in GCE account for 54.7% of the total CH4 emissions in 1980 and gradually decrease to 37.0% in 2018, which mainly result from decreases in emissions from rice cultivation with some offset due to increases in the livestock emission. The contributions of agricultural sources in GCC are much smaller with values of 36.3% in 1980 and 21.5% in 2018. The energy or fossil fuels sector becomes the largest contributor of Chinese CH4 emissions in recent years in GCC, accounting for 52.2% of the total emissions in 2018, and largely drives the larger positive trend in GCC than GCE.

CH4 emissions and sinks over the globe and China
The comparisons above indicate large uncertainties in the Chinese CH4 emission estimates, as to some extent covered by the EDGAR and CEDS anthropogenic emission inventories. The magnitude and temporal variations of methane budgets over the past decades are known to have large uncertainties (Kirschke et al., 2013;Turner et al., 2019;Saunois et al., 2020).
Relative uncertainties are about 20-35% for anthropogenic emissions such as fuel exploitation, agriculture and waste, about 225 50% for biomass burning and wetlands, and reach 100% or greater for other natural sources (Saunois et al., 2020).
Uncertainties in the methane sinks are about 10-20% by proxy methods such as using methyl chloroform, and are 20-40% by atmospheric chemistry models (Saunois et al., 2016). More detailed regional methane datasets can help improve assessing the global budget (Xu and Tian, 2012;Valentini et al., 2014;Saunois et al., 2016). We will further discuss the uncertainties in CH4 emissions in the last section. 230

Observed and simulated methane concentrations and trends in China
Based on the emissions described above, we have conducted a series of model simulations as summarized in Table 1 and evaluated the model results with surface CH4 measurements at the four Chinese sites. We find that when using the interannually fixed OH (global tropospheric volume-weighted mean of 10.6×10 5 molecules cm -3 as shown in Fig. S1 concentrations would, respectively, increase by 2.0%-3.4% or decrease 1.9%-3.2% at the four Chinese sites (Fig. S2).
Increasing OH levels by 10% would lead negative trends in CH4 concentrations at all four sites over 2010-2018 (Fig. S2). 240 Such effects are also found in the simulation with global CH4 emissions decreasing 50 Tg a -1 over the same period (Run 7 in Table 1 and Fig. S2).
The uses of interannually varying OH (Fig. S1) in model simulations (Run3 and Run4 in Table 1) overall correct the high biases in simulated CH4 trends in simulations with fixed OH (Run1 and Run2) at the Chinese sites. We find that changing 245 model horizontal resolution from 4°×5° to 2°×2.5° does not significantly affect the simulated surface CH4 trends. Hereafter, we will focus our analyses on the model simulations at 2°×2.5° resolution and with interannually varying OH (i.e., GCE and GCC in Table 1).  Table S1. GCE model results underestimate the aircraft measurements with mean negative biases of 27.5-31.1 ppbv at the three altitude layers for HIPPO, and even larger negative biases of 61.5-73.7 ppbv for ATom. By contrast, GCC model results are in general too high with biases of 18.4-22.8 ppbv for HIPPO, and -1.7-9.4 ppbv for ATom. The biases in GCC are overall smaller than those in GCE.
The changes in the model bias for the comparisons with HIPPO and ATom measurements reflect their simulated trends in the CH4 concentration. Since both HIPPO (2009HIPPO ( -2011 and ATom (2016-2018) provide measurements over the Pacific 290 (black box in Fig. 1), we calculate the differences between HIPPO and ATom measurements as the observed CH4 concentration trends over this region. Figure 10  Summarizing the comparisons of model results with all available measurements over China and the Pacific, we find that the surface, aircraft, and satellite CH4 measurements have indicated rather consistent increase rates of CH4 concentrations over China with values ranging 7.0-8.4 ppbv a -1 in recent years. As CH4 has a lifetime of about 9.14 years, such increases reflect 305 changes in not only domestic emissions but also global emissions. The GCE and GCC model simulations with the interannually varying OH levels both capture the main features of the observed CH4 concentrations and trends over China, and the GCC results show much smaller model biases than GCE. We will thus use the GCC model simulation to quantify the domestic and global sources contributing to the CH4 concentrations and trends over China.

Source attribution of CH4 concentrations and trends in 2007-2018 310
Here we apply the GCC model configuration (i.e., the CEDS inventory and interannually varying OH) in the tagged CH4 simulation. The GCC model results can generally reproduce the spatial distribution of GOSAT observed CH4 levels and trends as shown in Fig. S3, with mean biases of 27.4 ppbv (observed 1805 ppbv vs. simulated 1833 ppbv) in the global CH4 concentration and -0.8 ppbv a -1 (observed 7.08 ppbv a -1 vs. simulated 6.26 ppbv a -1 ) in the trend. As described in the Section 2.3, our tagged CH4 simulation includes 100 region-and sector-specific CH4 tracers. The tagged CH4 simulation is 315 conducted over 1980-2018, and we analyze the results for 2007-2018. Figure 11 shows contributions of CH4 emissions from different source regions and different sectors on the mean surface concentrations and trends in China during this time period, and the values are also summarized in Table 3 for concentrations and Table 4 for trends. As for the concentrations, we find that the largest contributor of Chinese CH4 concentrations averaged over 2007-2018 is the wetland emission in South America, accounting for 11.2% due to the large emission magnitude. Together with other sources, emissions in South 320 America contribute 20.6% of the surface CH4 levels over China, followed by the sources from Europe (17.0%), Africa (16.6%), North America (13.9%), and Rest Asia (12.6%). The Chinese domestic emissions account for 11.4% of the CH4 concentrations. The emission contributions to the concentrations are generally proportional to their emission magnitudes because of the CH4 lifetime of about 9.14 years, and seasonal variations in the percentage contributions are small as can be seen in Fig. 11 (the top left panel). 325 Figure 11 and Table 4 also show the source contributions to the 2007-2018 trends in surface CH4 concentrations over China.
Based on the emission inventory in GCC, the simulated mean trend in the surface CH4 concentration is 8.32 ppbv a -1 over the land of China. The domestic energy sector is identified as the largest driver of the trend in China contributing an increase rate of 4.32 ppbv a -1 . Accounting for the trends driven by emissions from agriculture and wastewater sectors, domestic 330 contributions can reach 5.68 ppbv a -1 (68% of 8.32 ppbv a -1 ). The remaining trends of 2.64 ppbv a -1 are then contributed by emission changes outside China. We find that the anthropogenic sources (mainly from energy, agriculture and wastewater sectors) in Africa and other Asian regions (India and Rest Asia) contribute, respectively, trends of 3.29 and 2.40 ppbv a -1 over China, highlighting the strong CH4 emission increases in these regions such as large emission increases mainly from livestock sources over South Asia and tropical Africa in 2010-2018 . On the contrary, Europe is the only 335 region where CH4 emissions from nearly all sectors have been decreasing (Jackson et al., 2020), which lead to a negative trend of -3.56 ppbv a -1 over China. Not only near the surface, we find similar results for the CH4 concentrations throughout the troposphere over China with slightly smaller growth rates in the upper troposphere (Fig. S4).
Our results indicate that trends in China are dominated by energy emissions from coal, oil and gas, with significant 340 contributions from wastewater and agriculture sectors. This is consistent with the top-down emission inversion results by Miller et al., (2019)  The analyses above demonstrate strong foreign source contributions to the CH4 concentrations as well as CH4 trends over China. We further find large spatial heterogeneity in the domestic vs. foreign contributions. Figure 12  We apply a tagged CH4 tracer simulation that implements region-and sector-specific tracers to diagnose and to understand their emission contributions. Using the model simulation with CEDS and interannually varying OH, we find strong 370 influences from foreign sources on both CH4 concentrations and recent increases over China as CH4 as a relatively long lifetime of about 9.14 years. For the mean surface CH4 concentration over China (1873China ( .0 ppbv over 2007China ( -2018, domestic CH4 emissions account for 11.4%, and contributions from the sources outside China reaching 88.6%, including 20.6% from South America, 17.0% from Europe, 16.6% from Africa, 13.9% from North America, and 12.6% from the Rest Asia. For the mean CH4 concentration trend over China (8.32 ppbv a -1 over 2007-2018), the largest driver is estimated to be the domestic 375 energy source contributing 4.32 ppbv a -1 , and other important domestic source contributions include emissions from wastewater (1.00 ppbv a -1 ) and agriculture (0.53 ppbv a -1 ); natural sources such as wetland emissions have insignificant trend contributions. Emission changes in foreign sources are also significant. The increase rate of 3.20 ppbv a -1 in the Chinese surface CH4 concentration can be attribute to sources in Africa, 2.20 ppbv a -1 to other Asian countries (India and It shall be noted that our source attribution results can be biased by the use of CEDS and the uncertainty in the interannual variations of OH levels. The Chinese anthropogenic CH4 emissions in the CEDS inventory are higher and increase more rapidly than EDGAR v4.3.2 in the recent decade. The two emission inventories significantly differ in the sectors of fossil fuels and agriculture. CEDS estimates higher CH4 emissions from fossil fuels while lower emissions from agriculture 385 compared with EDGAR v4.3.2. A number of top-down emission inversion studies using surface and satellite observations have found that the EDGAR v4.3.2 (Maasakkers et al., 2019;Miller et al., 2019) andprevious EDGAR versions (Alexe et al., 2015;Thompson et al., 2015;Turner et al., 2015;Pandey et al., 2016) overestimated the CH4 emissions from coal production in China, likely due to the CH4 emission factors for coal mining are too high in the region . A recent bottom-up estimate suggested that Chinese coal mining CH4 emissions have been decreasing since 2012 driven by the 390 China's coal mine regulation (Sheng et al., 2019), but the interannual trend in Chinese coal emissions still has large uncertainties among studies (Miller et al., 2019;Sheng et al., 2019;Lu et al., 2021).
We also find that the interannual variability of OH concentrations can strongly affect the simulated CH4 concentration trends.
Using interannually fixed OH concentrations, the model would overestimate the observed CH4 growth since 2007 in China 395 with both the EDGAR and CEDS anthropogenic emissions. The influence of a 10% increase in the global volume-weighted mean OH concentration (from 10.9×10 5 molecule cm -3 to 12.0×10 5 molecule cm -3 ) on the simulated Chinese CH4 concentrations is equivalent to that of a 47 Tg a -1 decrease in global CH4 emissions. The use of interannual variability of OH provided by Zhao et al. (2019) improve the model simulated Chinese CH4 concentrations and trends. However, large discrepancies exist in the different model OH simulations that would lead to a large wide range (>±30 ppbv) of simulated 400 CH4 concentrations (Zhao et al., 2019). Despite these uncertainties, our study emphasizes the importance of emission changes in both domestic and foreign, anthropogenic and natural sources on the Chinese CH4 concentration trends. Future work with more intensive CH4 measurements covering the eastern China will help better assess the driving factors of Chinese CH4 concentrations and recent growth.   Peng et al., (2016). OH chemical loss estimates in 2000s are from Kirschke et al., (2013).  (Table S2) Table S2 including emissions from agriculture and waste (AW), fossil fuels (FF), wetlands (WL), biomass burning (BB), and others (OT), and sinks due to soil uptake (SU) and chemical loss (CL). The bar charts show bottom-up (darkcolored bars) and top-down (light-colored bars) estimates in previous studies as summarized by Saunois et al. (2020). The global CH4 710 sources and sinks in the GCE (black circles) and GCC (black triangles) model simulations are also shown. Table S1 summarizes the values presented in the figure.