Advanced computing architecture to support the estimation and reporting of UK GHG emissions

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Greenhouse gas (GHG) emissions can be inferred from measurements of their atmospheric concentration using computationally demanding Bayesian "inverse" methods. This information is being used by research groups at the University of Bristol (UoB) and the University of Edinburgh (UoE) to a) quantify the magnitude and uncertainty of emissions from the UK and other countries, and b) determine the drivers of natural atmospheric GHG variability. This work is underpins several major projects including: a) the Department for Energy and Climate Change (DECC) monitoring network, responsible for reporting UK GHG emissions to the United Nations Framework Convention on Climate Change, b) the £3m NERC-funded Greenhouse gAs Uk and Global Emissions (GAUGE) consortium (Palmer is PI, Rigby is co-I), c) the NASA and DECC-funded Advanced Global Atmospheric Gases Experiment (Rigby is member), and d) the National Centre for Earth Observation. This work involves two stages. Firstly, chemical transport models (CTMs; e.g. the UK Met Office NAME model) are run on multi-node clusters, before their output is compared to observations for emissions verification using (usually) single-node data analysis systems. The statistical techniques for the latter involve the use of CPU- and memory-intensive linear algebra algorithms on extremely large arrays, which are already pushing the limits of our existing infrastructure. Activities within the DECC network and GAUGE now pose further challenges: 1) to fully exploit a rapidly growing quantity of heterogeneous measurement data (many millions of data points); 2) to use these data to infer emissions at higher resolution than ever before (e.g. making use of NAME model output at a horizontal resolution of 1.5 km over the UK). The proposed assets will help to strengthen our ability to carry out this second stage of this work.