Abstract
Abstract. At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH4) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH4 retrievals can be compared during the period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH4, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH4. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH4 emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH4 emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH4 fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH4 retrievals available from the Total Carbon Column Observing Network (TCCON).
Highlights
Atmospheric methane (CH4) is the second-most important anthropogenic greenhouse gas (GHG) – after carbon dioxide (CO2) – in terms of net radiative forcing (RF)
Bayesian inverse modelling (Tarantola, 2004) of CH4 emissions operates under a well-defined mathematical framework that combines a priori information on CH4 emissions, atmospheric observations, and an atmospheric chemistry and transport model (CTM), to yield a statistical best estimate of CH4 emissions and concentrations over the time period of interest
Given the limited lifetime of satellite instruments, inverse modelling comparison studies using different satellite retrievals are of great importance for understanding the difference between products
Summary
Atmospheric methane (CH4) is the second-most important anthropogenic greenhouse gas (GHG) – after carbon dioxide (CO2) – in terms of net radiative forcing (RF). Given the limited lifetime of satellite instruments (the communication link to ENVISAT was lost in April 2012, while the GOSAT mission plans extend only until 2014), inverse modelling comparison studies using different satellite retrievals are of great importance for understanding the difference between products. Such analyses are a crucial step when using satellite data to analyse IAV and trends. Our approach differs significantly from previous studies in that we examine an extended time period, use a different inversion set-up, and employ several distinct (optimized) bias correction strategies for the SCIAMACHY and GOSAT retrievals.
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