A52C-02
Constraining methane emissions from the Indo-Gangetic Plains and South Asia using combined surface and satellite data
Friday, 18 December 2015: 10:35
3006 (Moscone West)
Anita Ganesan1, Mark F Lunt1, Matthew L Rigby1, Abhijit Chatterjee2, Hartmut Boesch3, Robert Parker3, Ronald G Prinn4, Marcel Vale van der Schoot5, Paul B Krummel6, Yogesh Kumar Tiwari7, Hitoshi Mukai8, Toshinobu Machida9, Yukio Terao9, Shohei Nomura8 and Prabir Kumar Patra10, (1)University of Bristol, Bristol, United Kingdom, (2)Bose Institute, Kolkata, India, (3)University of Leicester, Leicester, United Kingdom, (4)MIT, Cambridge, MA, United States, (5)CSIRO Marine and Atmospheric Research, Aspendale, Australia, (6)CSIRO, Aspendale, VIC, Australia, (7)Indian Institute of Tropical Meteorology, Pune, India, Pune, Maharashtra, India, (8)National Institute for Environmental Studies, Tsukuba, Japan, (9)NIES National Institute of Environmental Studies, Ibaraki, Japan, (10)JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan
Abstract:
We present an analysis of the regional methane (CH4) budget from South Asia, using new measurements and new modelling techniques. South Asia contains some of the largest anthropogenic CH4 sources in the world, mainly from rice agriculture and ruminants. However, emissions from this region have been highly uncertain largely due to insufficient constraints from atmospheric measurements. Compared to parts of the developed world, which have well-developed monitoring networks, South Asia is very under-sampled, particularly given its importance to the global CH4 budget. Over the past few years, data have been collected from a variety of surface sites around the region, ranging from in situ to flask-based sampling. We have used these data, in conjunction with column methane data from the GOSAT satellite, to quantify emissions at a regional scale. Using the Met Office’s Lagrangian NAME model, we calculated sensitivities to surface fluxes at 12 km resolution, allowing us to simulate the high-resolution impacts of emissions on concentrations. In addition, we used a newly developed hierarchical Bayesian inverse estimation scheme to estimate regional fluxes over the period of 2012-2014 in addition to ancillary “hyper-parameters” that characterize uncertainties in the system. Through this novel approach, we have characterized the effect of “aggregation” errors, model uncertainties as well as the effects of correlated errors when using regional measurement networks. We have also assessed the effects of biases on the GOSAT CH4 retrievals, which has been made possible for the first time for this region through the expanded surface measurements. In this talk, we will discuss a) regional CH4 fluxes from South Asia, with a particular focus on the densely populated Indo-Gangetic Plains b) derived model uncertainties, including the effects of correlated errors c) the impacts of combining surface and satellite data for emissions estimation in regions where poor satellite validation exists and d) the challenges in estimating emissions for regions of the world with a sparse measurement network.
