Assessment of the impact of observations at Nainital (India) and Comilla (Bangladesh) on the CH4 flux inversion

Abstract

Abstract Methane emission in South Asia is poorly understood due to a lack of observations, despite being a major contributor to methane emissions globally. We present the first results of atmospheric CH4 inversions using air samples collected weekly at Nainital, India (NTL), and Comilla, Bangladesh (CLA), in addition to surface background flask measurements by NOAA, CSIRO and AGAGE using the MIROC4-ACTM. Our simulations span from 2000 to 2020 (considering the fixed “edge” effect), but the main analysis period is 2013–2020, when both the NTL and CLA datasets are available. An additional flux uncertainty reduction of up to 40% was obtained (mainly in the northern part of the Indian subcontinent), which enhanced our confidence in flux estimation and reaffirmed the significance of observations at the NTL and CLA sites. Our estimated regional flux was 64.0 ± 4.7 Tg-CH4 yr−1 in South Asia for the period 2013–2020. We considered two combinations of a priori fluxes that represented different approaches for CH4 emission from rice fields and wetlands. By the inversion, the difference in emissions between these combinations was notably reduced due to the adjustment of the CH4 emission from the agriculture, oil and gas, and waste sectors. At the same time, the discrepancy in wetland emissions, approximately 8 Tg-CH4 yr−1, remained unchanged. In addition to adjusting the annual totals, the inclusion of NTL/CLA observations in the inversion analysis modified the seasonal cycle of total fluxes, possibly due to the agricultural sector. While the a priori fluxes consisted of a single peak in August, the a posteriori values indicated double peaks in May and September. These peaks are highly likely associated with field preparation for summer crops and emissions from rice fields during the heading stage (panicle formation). The newly incorporated sites primarily exhibit sensitivity to the Indo-Gangetic Plain subregion, while coverage in southern India remains limited. Expanding the observation network is necessary, with careful analysis of potential locations using back-trajectory methods for footprint evaluation.

Keywords

• Methane
• Inverse modeling
• South Asia
• Indo-Gangetic plain