Accelerating the computation of air quality projections over India using novel computing

Nitrate aerosol is likely to be an important contributor to future air pollution events and will become more important in time as emissions of sulphur dioxide decline due to air quality concerns. However, due to the complexity of representing it in climate models, it iis not included in the majority of the current generation of Earth System models. Case studies of haze events during the COVID-19 lockdowns also showed the importance of secondary chemical reactions to air pollution episodes. A comprehensive assessment of such events requires the representation of complex chemistry, and has not been feasible to date due to computational constraints. Recent work on UKCA has implemented a suitable scheme for air quality applications, but this is significantly larger and more complex than the current scheme used within the Earth System model. To enable such simulations, new computational techniques which can exploit highly parallel compute devices such as GPUs are required.

Specifically, this project addresses two research questions. The first is what advanced computational science techniques can be applied to the UKCA science code to make the model portable to multiple computer architectures and, critically, performant on those architectures? This then provides the methodology to address the second question: what is the effect of aerosol reductions on projections of near-future Indian air quality, and how do these projections differ in simulations with nitrate aerosol coupled with this improved chemistry, compared to projections with the standard scheme?

The newly developed functionality of the UKCA box model will be used to test and implement the computationally expensive routines on GPUs. These are highly parallel computational devices which have higher computational performance per watt than traditional computer processors. They require novel and parallel, programming methods to exploit. Moreover, the proliferation of processor architectures means different methods and coding patterns are required for each. By following the concept of the separation of concerns, which has been successfully pioneered by the Met Office in developing the new atmospheric model, Gung Ho/ LFRic, the code can be made both portable to different architectures and performant on them.

The increase in computational power will enable the use of greater complexity in the representation of aerosols and chemistry in multi-decadal simulations. The LFRic model developed by the Met office can already run with UKCA and further development is planned in the next two years, thus the model will be sufficiently mature to deploy the new computational capability and enable a previously unfeasible calculation. The ability to examine the effect of nitrate aerosol, and the complex chemistry required to capture secondary chemical reactions, on future air quality is at the forefront of the field. The application to an Indian air quality case study will be novel, and, as large emission changes are anticipated in this region, will inform important policy questions regarding emissions and their health impacts.