Atmospheric measurements and modelling to support the Montreal Protocol and international climate agreements

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In a series of four papers published in Nature between 2018 and 2021 [e.g., 1,2], our team used atmospheric data to present evidence of a major violation of the Montreal Protocol, the universally ratified treaty designed to protect the stratospheric ozone layer; emissions of the potent ozone depleting substance, CFC-11, had increased since 2012, despite the global ban in 2010. Intense media coverage followed, and enforcement activities were initiated in China, where a substantial fraction of the new emissions were found to originate. Subsequently, emissions dropped substantially, with a CO2-equivalent magnitude equal to that of the whole of London. Here, we will develop global and regional atmospheric modelling tools to provide an "early warning system" for the detection of similar anomalous trends in the emissions of a wide range of greenhouse gases and ozone depleting substances covered by the Montreal Protocol and other international climate treaties.The Atmospheric Chemistry Research Group (ACRG) is a key member of the international Advanced Global Atmospheric Gases Experiment (AGAGE). AGAGE measures over 50 greenhouse gases and ozone depleting substances covered by the Montreal and Kyoto Protocols. To infer emissions from the AGAGE data, models of atmospheric chemistry and transport are required. These inferred emissions are reported to the UK government, and are vital to international decision making on climate. In this project, you will:a) create new computationally efficient open-source modelling tools to infer global greenhouse gas emissions using observations from remote monitoring sites from AGAGE and other international networks, allowing us to understand how global emissions are changingb) devise an operational regional emissions estimation framework using the Met Office NAME model and Bayesian methods to "pinpoint" major sources near measurement stations. c) develop a cloud-based system for the efficient and open sharing of data, model output and code, thus ensuring transparent and rapid access to results by interested parties[1] Montzka, S. A. et al., Nature, 557(7705), 413-417, doi:10.1038/s41586-018-0106-2, 2018. [2] Rigby, M. et al., Nature, 569(7757), 546-550, doi:10.1038/s41586-019-1193-4, 2019.