Interannual variability on methane emissions in monsoon Asia derived from GOSAT and surface observations

F Wang, S Maksyutov, R Janardanan… - Environmental …, 2021 - iopscience.iop.org
F Wang, S Maksyutov, R Janardanan, A Tsuruta, A Ito, I Morino, Y Yoshida, Y Tohjima…
Environmental Research Letters, 2021iopscience.iop.org
In Asia, much effort is put into reducing methane (CH 4) emissions due to the region's
contribution to the recent rapid global atmospheric CH 4 concentration growth. Accurate
quantification of Asia's CH 4 budgets is critical for conducting global stocktake and achieving
the long-term temperature goal of the Paris Agreement. In this study, we present top-down
estimates of CH 4 emissions from 2009 to 2018 deduced from atmospheric observations
from surface network and GOSAT satellite with the high-resolution global inverse model …
Abstract
In Asia, much effort is put into reducing methane (CH 4) emissions due to the region's contribution to the recent rapid global atmospheric CH 4 concentration growth. Accurate quantification of Asia's CH 4 budgets is critical for conducting global stocktake and achieving the long-term temperature goal of the Paris Agreement. In this study, we present top-down estimates of CH 4 emissions from 2009 to 2018 deduced from atmospheric observations from surface network and GOSAT satellite with the high-resolution global inverse model NIES-TM-FLEXPART-VAR. The optimized average CH 4 budgets are 63.40±10.52 Tg y− 1 from East Asia (EA), 45.20±6.22 Tg y− 1 from Southeast Asia (SEA), and 64.35±9.28 Tg y− 1 from South Asia (SA) within the 10 years. We analyzed two 5 years CH 4 emission budgets for three subregions and 13 top-emitting countries with an emission budget larger than 1 Tg y− 1, and interannual variabilities for these subregions. Statistically significant increasing trends in emissions are found in EA with a lower emission growth rate during 2014–2018 compared to that during 2009–2013, while trends in SEA are not significant. In contrast to the prior emission, the posterior emission shows a significant decreasing trend in SA. The flux decrease is associated with the transition from strong La Ninña (2010–2011) to strong El Ninño (2015–2016) events, which modulate the surface air temperature and rainfall patterns. The interannual variability in CH 4 flux anomalies was larger in SA compared to EA and SEA. The Southern Oscillation Index correlates strongly with interannual CH 4 flux anomalies for SA. Our findings suggest that the interannual variability in the total CH 4 flux is dominated by climate variability in SA. The contribution of climate variability driving interannual variability in natural and anthropogenic CH 4 emissions should be further quantified, especially for tropical countries. Accounting for climate variability may be necessary to improve anthropogenic emission inventories.
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