Quantifying global and regional methane budget by inverse modeling

Methane is the second most important greenhouse gas after the carbon dioxide, but it is 25 times more effective in contributing to the radiative forcing than CO2. Since 2009, global dry air column-averaged mole fractions are measured from space by the Thermal And Near Infrared Sensor for carbon Observations-Fourier Transform Spectrometer (TANSO-FTS), on-board the GOSAT satellite. TANSO-FTS is a nadir looking Fourier transform spectrometer, observes sunlight reflected from the earth's surface and light emitted from the atmosphere and the surface. Inverse modeling using chemical transport model and measurements from space has been widely used to quantify emissions of atmospheric trace gases. In this study, we used NIES (National Institute for Environmental Studies) retrievals in the short wave infrared radiation (SWIR) covering five years of observations, from January 2010 to December 2014, in order to estimate global methane fluxes by PYVAR-LMDZ model. The PYVAR-LMDZ model is based on the Bayesian theory which combine observations and model, to estimate sources and sinks of atmospheric compounds. The system iteratively minimizes a cost function, and provides the best linear unbiased estimate of fluxes, which are averaged at eight-day periods in each grid cell of the model (3.75°x2.3°). Results show a decreasing of methane global emissions in 2011, this decreasing is more significant between 30°N and 60°N, whereas an increasing is seen from 2012 to 2014. We will present the results of global and regional methane budget, and its inter-annual variability.