CH4 variability over India derived from the GOSAT/TANSO-FTS TIR observations and simulated by MIROC4-ACTM model

We examine CH₄ variability over different regions of India and the surrounding oceanic regions using thermal infrared (TIR) band observations by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) and simulated by the updated MIROC4.0-based Atmospheric Chemistry Tracer Model (ACTM) for the period 2009-2014. GOSAT TIR provides data coverage and density captures detailed features of CH₄ distributions at height levels from the top of the boundary layer to the upper troposphere even in the cloudy conditions, which is very common for the region with a monsoon climate. Analysis of transport and emission contributions to CH4 variabilities suggests that the CH₄ seasonal cycle over India is controlled by the heterogeneous distribution of surface emissions under the influence of the monsoonal divergent wind circulations. Distinct seasonal variations of CH₄ are observed over northern and southern regions of India corresponding to the southwestern monsoon (July–September) and early autumn (October–December) seasons. In summer, three principal circulations pumping CH₄ upward over South Asia: the lateral (the cross-equatorial circulation) and transverse (flows between the arid regions of North Africa and the Near East and South Asia) monsoons, and the Walker Circulation extends across the Pacific Ocean. GOSAT TIR derives CH₄ profiles due to retrieval of signal from 22 vertical levels. In general, the mean ACTM (no averaging kernel incorporated)-GOSAT misfit is within 50 ppb, excepting the level of 150 hPa and upward, where the GOSAT TIR sensitivity becomes too low. Due to the use of MIROC4.0 AGCM performance of ACTM in the upper troposphere and lower stratosphere has been improved. The GOSAT-ACTM misfit above the level of 150 hPa is likely to arise from a priori model for TIR retrievals. Convolution of the modeled profiles with retrieval a priori and averaging kernels reduces the misfit to below uncertainty. However, the weight of the a priori profiles becomes too large with such smoothing. Overall, the ACTM simulations of CH₄ in the Indian regions compare favorably with the GOSAT TIR samplings, in terms of seasonality and regional variability. However, the GOSAT-ACTM inconsistencies indicate opportunities for further flux optimization and emission uncertainty reduction by inverse modeling methods.