Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH4) concentrations. India has the world's largest ruminant population and produces ~ 20% of the world's rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India's CH4 emissions for the period 2010-2015 using a combination of satellite, surface and aircraft data. We apply a high-resolution atmospheric transport model to simulate data from these platforms to infer fluxes at sub-national scales and to quantify changes in rice emissions. We find that average emissions over this period are 22.0 (19.6-24.3) Tg yr-1, which is consistent with the emissions reported by India to the United Framework Convention on Climate Change. Annual emissions have not changed significantly (0.2 ± 0.7 Tg yr-1) between 2010 and 2015, suggesting that major CH4 sources did not change appreciably. These findings are in contrast to another major economy, China, which has shown significant growth in recent years due to increasing fossil fuel emissions. However, the trend in a global emission inventory has been overestimated for China due to incorrect rate of fossil fuel growth. Here, we find growth has been overestimated in India but likely due to ruminant and waste sectors.India's methane emissions have been quantified using atmospheric measurements to provide an independent comparison with reported emissions. Here Ganesan et al. find that derived methane emissions are consistent with India's reports and no significant trend has been observed between 2010-2015.
Abstract. The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO2) and methane (CH4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH4 and CO2 using zoomed versions (abbreviated as “ZAs”) of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of ∼0.66∘ in longitude and ∼0.51∘ in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH4 and CO2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50∘ in longitude and 1.27∘ in latitude (abbreviated as “STs”), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH4 annual gradients between stations in SEA, with reduction of RMSE by 16–20 % compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO2 suggests that the CO2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH4 and CO2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions.
Abstract. Atmospheric methane (CH4) is a potent climate change agent responsible for a fraction of global warming. The present study investigated the spatial and temporal variability of atmospheric column-averaged (X) CH4 (XCH4) concentrations using Greenhouse gases Observing SATellite (GOSAT) and TROPOspheric Monitoring Instrument onboard the Sentienl-5 Precursor (S5P/TROPOMI) data from 2009 to 2022 over the South Asia region. During the study period, the long-term trends in XCH4 increased from 1700 ppb to 1950 ppb with an annual growth rate of 8.76 ppb year-1. Among all natural and anthropogenic sources of CH4, the rate of increase in XCH4 was higher over the Mundra thermal power station and Mundra ultra mega power plant at about 9.62 ppb year-1, followed by the coal site at about 8.76 ppb year-1 (Korba). With a growth rate of 8.61 ppb year-1, the Sundarbans natural wetland competes with coal sites, producing over 30 MT, indicating an equivalent anthropogenic source. For the 15 Indian Agroclimatic zones, significant high emissions of CH4 were observed over the Middle Gangetic Plains (MGP), Trans Gangetic Plains (TGP), Upper Gangetic Plains (UGP), East Coast Plains & Hills (ECPH), Lower Gangetic Plains (LGP) and East Gangetic Plains (EGP). Further, the bottom-up anthropogenic CH4 emissions data are mapped against the XCH4 concentrations and found high correlation in the Indo Gangetic Plains (IGP) region, indicating the hotspots of anthropogenic CH4. The present study highlighted the impact of natural and anthropogenic sources of XCH4 and quantified the spatio-temporal changes in XCH4 at each study site over the Indian region.
Abstract. Atmospheric methane (CH4) is a potent climate change agent responsible for a fraction of global warming. The present study investigated the spatial and temporal variability of atmospheric column-averaged (X) CH4 (XCH4) concentrations using Greenhouse gases Observing SATellite (GOSAT) and TROPOspheric Monitoring Instrument onboard the Sentienl-5 Precursor (S5P/TROPOMI) data from 2009 to 2022 over the South Asia region. During the study period, the long-term trends in XCH4 increased from 1700 ppb to 1950 ppb with an annual growth rate of 8.76 ppb year-1. Among all natural and anthropogenic sources of CH4, the rate of increase in XCH4 was higher over the Mundra thermal power station and Mundra ultra mega power plant at about 9.62 ppb year-1, followed by the coal site at about 8.76 ppb year-1 (Korba). With a growth rate of 8.61 ppb year-1, the Sundarbans natural wetland competes with coal sites, producing over 30 MT, indicating an equivalent anthropogenic source. For the 15 Indian Agroclimatic zones, significant high emissions of CH4 were observed over the Middle Gangetic Plains (MGP), Trans Gangetic Plains (TGP), Upper Gangetic Plains (UGP), East Coast Plains & Hills (ECPH), Lower Gangetic Plains (LGP) and East Gangetic Plains (EGP). Further, the bottom-up anthropogenic CH4 emissions data are mapped against the XCH4 concentrations and found high correlation in the Indo Gangetic Plains (IGP) region, indicating the hotspots of anthropogenic CH4. The present study highlighted the impact of natural and anthropogenic sources of XCH4 and quantified the spatio-temporal changes in XCH4 at each study site over the Indian region.
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