Untangling Natural Aerosol Processes in Polar Regions by Implementing Novel Machine Learning Techniques

Accurate climate models are essential to improve our understanding of the ever pressing issue of anthropogenic climate change. Aerosol-cloud interactions continue to be one of the largest sources of uncertainty in climate modelling (Myhre et al., 2013), limiting the understanding of climate change and ability to improve climate predictions. In order to improve aerosol representation in climate models it is essential to understand how natural aerosols might buffer the sensitivity of clouds to anthropogenic emissions. Present-day natural aerosol observations in pristine regions, like the poles, can be used to constrain pre-industrial aerosol parameterisations in GCMs (General Circulation Models) and ESMs (Earth System Models). However, there remains a lack of knowledge of the physical processes that control aerosol sources and sinks during transport.
A step forward has been the use of a Lagrangian approach, in which trajectories are used to track the passage of air masses back from an aerosol measuring station. Meteorological variables and potential sources can be collocated to these trajectories and then analysed in conjunction with the aerosol measurements to identify and understand natural aerosol sources. Progress is hindered in this area due to the vast number of variables to consider, as well as interactions between them; the picture is therefore too complex for traditional statistical methods. In order to be able to untangle and interpret these processes this project will explore novel machine learning techniques, focusing on Convolutional Neural Networks and Long-Short Term Memory architecture recurrent neural networks.
Polar Regions will be the area of focus, not only due to the pristine environment, but also because of the high climatic impact of these regions. The Arctic is very sensitive to perturbations in the radiative budget associated with complex climate feedbacks (Pithan and Mauritsen, 2014). The Antarctic peninsula is also one of the most rapidly warming areas on the planet, leading to changes in the local cryosphere (Turner et al., 2005). Climate models continue to under predict the level of cloud in this region, limiting our ability to provide accurate predictions for surface melt and therefore feedbacks in the climate system. In order to improve model representation of clouds in this region, there needs to be a greater understanding of natural aerosol sources (King et al., 2015).
Climate models under predict aerosol levels in both the Arctic and Antarctic, particularly the larger climate-active aerosols. The lack of aerosols during the winter period in comparison to observations is indicative of a missing source in climate models (Frey et al., 2015). Aerosols at the poles mostly consist of sea salt particles. However, the peak in sea salt aerosol concentration is during the winter period, coinciding with the season of largest sea ice extent. There has recently been direct evidence to suggest that snow blowing above sea-ice and sublimating is a source of these aerosols (Frey et al., 2019). A parametrisation of blowing snow has not been implemented into a GCM or ESM before, so crucial polar climate feedbacks could be neglected in current models.
The novel framework developed as part of this project, utilising neural networks in a Lagrangian framework, will improve understanding of the relative influence of sources of natural aerosols at the poles. This analysis will identify if blowing snow is a significant aerosol source in the region and the main influences on aerosol production through the blowing snow mechanism. Once this is explored, a parameterisation of blowing snow (from the British Antarctic Survey) will be implemented into an ESM (the Met Office's UKESM) for the first time to investigate the resultant feedbacks in the climate system. This research will further the understanding of natural aerosol processes and improve climate predictions.