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Detection and quantification of local methane sources using novel high-resolution satellite data (part of the SENSE Centre for Doctoral Training)

Project Description

Anthropogenic greenhouse gases are increasing the Earth’s temperature. Among these greenhouse gases, methane (CH4) is second in importance after carbon dioxide (CO2). While CH4 is eventually oxidised to CO2 on a timescale of 9 years, during its lifetime its greenhouse warming effect per molecule is much larger. Atmospheric CH4 levels are rising, following a brief period of stalled growth during the 1990s and early 2000s, which came as a surprise (e.g. McNorton et al. 2016). The resumed growth seems consistent with continuing increases in anthropogenic emissions but clear explanations for these changes have not yet emerged. There is a wide range of anthropogenic CH4 source processes, including emissions from agriculture, wetlands, rice paddies and fossil fuel production – e.g. from coal mining or fracking. Understanding the magnitudes and locations of CH4 sources is essential for informing policy action to limit emissions of this short-lived greenhouse gas, which is also linked to degradation in local air quality through ozone production.

Atmospheric CH4 concentrations have been measured in situ at a global network of sites since the 1980s, coordinated primarily by the U.S. National Oceanic and Atmospheric Administration (NOAA). More recently, satellite estimates of atmospheric CH4 have become available, but spatial coverage of such data were still relatively coarse. This situation has been revolutionised with the ESA TROPOMI instrument on the Sentinel 5P satellite which has been recording observations of atmospheric CH4 since October 2017.

This project aims to exploit the high spatial and time resolution (daily coverage, Fig. 1) of the TROPOMI satellite, which is entirely unprecedented. Together with the use of models which represent the chemical and physical transport processes undergone by atmospheric CH4, this new data opens up the possibility to resolve CH4 sources in much greater detail than before, relating them to specific processes like biogenic or anthropogenic activity. So far, TROPOMI data have been little exploited either for identifying anthropogenic emissions ‘hotspots’ or to obtain a better understanding of changes in various source processes and thus to gain a more complete understanding of the global CH4 cycle.

Figure 1. An example of mean atmospheric column methane mixing ratio over northern Africa from TROPOMI. From Hu et al. (2018).
To take full advantage of these new methane observations it is necessary that the modelled atmospheric transport is also represented in high resolution. This project will develop the relatively coarse atmospheric transport model TOMCAT (Chipperfield et al. 2006) to build a version which allows the insertion – or ‘nesting’ – of a limited high resolution grid over specific regions of interest. Since the TOMCAT model works in both a ‘forwards’ and ‘inverse’ mode, the new nested high resolution model will then be applied to identify and quantify CH4 sources for specific regions of interest.

The initial work will involve using comparisons between the current version of the TOMCAT model and the TROPOMI data in order to draw large-scale conclusions about recent changes in the global CH4 cycle, before the student moves on to develop the nested grid within the TOMCAT model. Once this has been completed, a focus on a particular location of interest will allow for a high-resolution case study of a particularly poorly understood methane source region through use of the inverse version of TOMCAT (Wilson et al., 2014). As part of the project the student will spend time at the UK Met Office to gain familiarity with a greater variety of atmospheric modelling techniques and to take advantage of the expertise there, along with experience of working within the organisation.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,500 for 2019/20), tax-free stipend at the UK research council rate (£15,009 for 2019/20), and a research training and support grant to support national and international conference travel. View Website


Chipperfield, M. P. (2006), New version of the TOMCAT/SLIMCAT off-line chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. R. Meteorol. Soc., 132, 1179–1203.

Hu, H., et al., Toward global mapping of methane with TROPOMI: First results and intersatellite comparison to GOSAT. Geophysical Research Letters, 45, 3682–3689,, 2018.

McNorton, J., M.P. Chipperfield, M. Gloor, C. Wilson, et al., Role of OH variability in the stalling of the global atmospheric CH4 growth rate from 1999 to 2006, Atmos. Chem. Phys., 16, 7943-7956,, 2016.

Wilson, C., Chipperfield, M. P., Gloor, M., and Chevallier, F.: Development of a variational flux inversion system (INVICAT v1.0) using the TOMCAT chemical transport model, Geosci. Model Dev., 7, 2485–2500,, 2014.

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