Exploiting machine-learning to provide dynamical, microphysical, radiative and electrifying insight from observations of deep convective cloud

The equilibrium climate sensitivity (i.e. the warming from a doubling of CO2) is a fundamental metric for assessing the risks arising from CO2 emissions. Yet the plausible values of climate sensitivity have remained stubbornly uncertain for 40 years, with cloud feedbacks a particularly uncertain component (Sherwood et al., 2020).

Tropical high cloud (e.g. anvils), produced by deep convection, is an important cloud type when it comes to feedbacks. The IPCC Assessment Report 6 recently assessed there to be a negative feedback from tropical high cloud amount (Forster et al., 2021). This, however, came with low confidence that arises, in part, from the lack of understanding of the response of microphysics and turbulence to warming. Cloud ice microphysics is particularly poorly parametrised.

In July-August 2022, the DCMEX campaign successfully collected a vast set of observations of developing convective clouds over the Magdalena Mountains, New Mexico. The FAAM BAe-146 aircraft measured cloud microphysics and dynamics within the clouds whilst Doppler radars and automated cameras monitored the development of the clouds from nearby. Aerosol measurements, including of Ice Nucleating Particles (INP), were collected on the aircraft and at Langmuir Laboratory on the summit of the mountain range. This extensive dataset can now be analysed in combination with satellite data and modelling with the recently developed Met Office Unified Model CASIM microphysics scheme. Altogether, the data will support the reduction of climate sensitivity uncertainty by improving the representation of microphysical processes in global climate models.

Detailed observations of aerosol and imagery of cloud ice particles have been obtained which contain a great complexity of information. The characteristics of aerosol, and the size and shape of ice particles, as well as their position within the cloud, modify the microphysical processes and cloud radiative effect in different ways (Voigtländer et al., 2018; Gasparini et al., 2019; Diedenhoven et al, 2020). The complexity of the aerosol and ice processes, and the cloud response, justifies using novel analysis techniques, such as machine learning, to gain insight.

The PhD candidate will build upon ongoing DCMEX research by exploring a range of analysis techniques. An initial focus will be on what can be learned from both supervised and unsupervised machine learning techniques, which are fast becoming key tools in the study of clouds and climate (Gagne et al., 2017; Beucler et al., 2021; Kashinath et al, 2021;,Gettelman et al., 2021). Initially, the work will focus on understanding the ice particle formation processes to support the development of the UM-CASIM model. The research will then expand to relate the dynamical-microphysical processes to impacts on radiation, and also electrification of storms. By extending the analysis to consider electrification, we build understanding of a globally measured variable, lightning, which is a key feature of the deep convective storms of interest. This is an opportunity not to be missed given the detailed satellite (GOES GLM) and ground-based lightning detection networks in operation over the DCMEX study region.

The following research questions will be addressed:
1) Can in-cloud ice images be categorised and related to their formation dynamics? And what role do INPs play in this?
2) Is machine-learning able to constrain UM-CASIM model parameters and processes using observations?
3) How is the radiation at the deep convective anvil affected by cloud dynamics and microphysics?
4) What is the relationship between lightning activity and cloud microphysics and dynamics?
5) Can lightning be used as an indicator of cloud processes that result in variations of anvil radiative properties?