Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India

Abstract

Abstract. Atmospheric methane (CH4) contributes to global warming and climate change. Multiple factors control its atmospheric growth rate, posing challenges for climate change mitigation in regions with limited observations, like India. In this study, we examine the potential of dry air column methane mixing ratio (XCH4) observations from the TROPOspheric Monitoring Instrument (TROPOMI) in conjunction with the high-resolution Weather Research and Forecasting model with Greenhouse Gas module (WRF-GHG) to improve the annual CH4 budget of India. In addition to an inversion framework, we present a spatiotemporal assessment of bottom-up Indian methane emissions and their influence on XCH4, supplying the context needed for regional emission optimization. Our analysis demonstrates the potential of WRF-GHG to represent the atmospheric XCH4 and CH4 distributions, including seasonal patterns, albeit with non-negligible uncertainties when compared with satellite and ground-based observations for 2018 and 2019. We find that the WRF-GHG simulations tend to overestimate XCH4 while underestimating near-surface CH4 concentrations at the Thumba site. Our inversion analyses report annual CH4 emissions ranging from 21.9 to 24.9 Tg with an uncertainty of 3.3 Tg (anthropogenic sources), implying an overestimation of 13 % to 24 % by the EDGAR global inventory. Also, our estimates are approximately 19 % higher than those in the India Fourth Biennial Update Report (19.6 Tg) and close to the latest Global Methane Budget 2000–2020. Overall, this study demonstrates the usefulness of TROPOMI observations for assessing Indian CH4 emissions and shows a way to improve our understanding of how regional processes can modulate atmospheric CH4 mixing ratios. We highlight the need for expanded observational coverage and an improved carbon assimilation system over India to refine the methane budget in support of global climate goals.