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

硝酸盐气溶胶很可能是未来空气污染事件的一个重要因素，随着二氧化硫排放量因空气质量问题而下降，硝酸盐气溶胶将变得更加重要。然而，由于在气候模型中表示它的复杂性，它没有被包括在大多数当前一代的地球系统模型中。COVID-19封城期间烟霾事件的案例研究也显示了空气污染事件的二次化学反应的重要性。对这些事件的全面评估需要复杂化学的表示，并且由于计算限制，迄今为止还不可行。UKCA最近的工作实施了一个合适的空气质量应用计划，但这是显着更大，更复杂的比目前的计划中使用的地球系统模型。为了实现这样的模拟，需要可以利用高度并行的计算设备（例如GPU）的新计算技术。具体而言，该项目解决了两个研究问题。首先是什么先进的计算科学技术可以应用到UKCA科学代码，使模型可移植到多种计算机架构，关键是，在这些架构上的性能？然后，这提供了解决第二个问题的方法：什么是预测不久的将来印度的空气质量的气溶胶减少的影响，以及如何做这些预测不同的模拟与硝酸盐气溶胶再加上这种改进的化学，与标准计划的预测相比？UKCA盒模型的新开发功能将用于测试和实现GPU上计算成本高的例程。这些是高度并行的计算设备，其具有比传统计算机处理器更高的每瓦计算性能。它们需要新颖的并行编程方法来开发。此外，处理器架构的激增意味着每种架构都需要不同的方法和编码模式。通过遵循关注点分离的概念，这已经成功地由气象局在开发新的大气模型Gung Ho/ LFRic中开创，代码可以移植到不同的架构上并在它们上执行。计算能力的提高将使在多年模拟中使用更复杂的气溶胶和化学表示。英国气象局开发的LFRic模型已经可以与UKCA一起运行，并计划在未来两年内进一步开发，因此该模型将足够成熟，可以部署新的计算能力，并实现以前不可行的计算。研究硝酸盐气溶胶的影响以及捕获二次化学反应所需的复杂化学物质对未来空气质量的影响的能力处于该领域的最前沿。应用到印度的空气质量案例研究将是新颖的，而且，由于预计该地区的排放量将发生巨大变化，将为有关排放及其健康影响的重要政策问题提供信息。

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.