HUGS: a Hub for Uk Greenhouse gas data Science

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Atmospheric observations of greenhouse gas (GHG) concentrations can be used to estimate emissions when combined with models of atmospheric transport and an understanding of the emission sources surrounding the observations. These top-down methods are complementary to the bottom-up, accounting-based, approaches that are currently used to create national GHG inventories. To improve the transparency and accuracy of these inventories and better evaluate progress on emissions reduction policies, scientists and policy makers have been advocating for the integration of top-down methods into the emissions reporting process. The United Nations Framework Convention on Climate Change (UNFCCC) recently acknowledged the important role that emissions quantified through atmospheric observations could have in supporting inventory evaluation (UNFCCC, COP 23, SBSTA/2017/L.21). The UK GHG science community is leading the world in this regard, with a dedicated national monitoring network, a range of regional networks and regular over-passes by various satellites. Currently, the UK is one of only three countries on Earth to include top-down estimates in its National Inventory Report to the UNFCCC. The process of inferring emissions from GHG observations is extremely data intensive. In order to understand the observed variability in GHG concentrations, scientists must combine data from diverse networks in different environments and using different instrumentation, understand the distribution of potential sources and land use types in the vicinity of the sensor and be able to accurately model the atmospheric processes that transport GHGs from sources to the measurement site. Therefore, to date, analysis of GHG data is largely carried out on a case-by-case basis for individual research papers.Here, we propose that new developments in cloud computing are required to help GHG scientists overcome some of the major obstacles for the integration of GHG networks and the production of operational, higher resolution GHG flux estimates. We will create the cloud-based framework for a UK GHG data science "hub". This hub will allow users (GHG scientists and, eventually, the public) to:- Improve the flow of information to and from GHG data providers, because cloud services are not behind institutional firewalls - Operationalise the processing of datasets into common formats, which can then be made globally accessible to users (subject to any required usage restrictions) - Automatically trigger operations on new data, such as the running of chemical transport models, which are essential for the interpretation of GHG data - Analyse data, model output and ancillary information (maps of land use, emissions inventories, etc.) on the cloud, without the need for individual users to download datasets and run models (requiring technical expertise)- Visualise data, models and other relevant information on a web-based platformOur team is world leading in the measurement and analysis of GHGs, cloud computing and spatial mapping. This project will rely heavily on a cloud platform (built as part of the EPSRC-funded BioSimSpace project) and GHG analysis codebase that has already been developed by team members. These tools are built on top of standard tools such as Jupyter notebooks, distributed object stores, and serverless functions. It is this expertise and these open tools that will allow us to develop the framework for our data science hub that will be extensible by GHG researchers at the end of this project.We envisage that such a hub could be at the centre of the UK's large and growing GHG science community, allowing scientists to upload, analyse and visualise their data on a single platform, enhancing data integration and sharing between groups. Ultimately, this platform could be extended to allow the public to interact with GHG data, letting them learn whether the UK's emissions reductions efforts are reflected in atmospheric observations.