Supervisors: Hua Lu (BAS), Dr Amanda Maycock (Leeds), Dr Andrew Orr (BAS), Andrew Fleming (BAS), Dr Anna Hogg (Leeds)
Background: West Antarctica ice shelves (WAIS, Fig. 1) have been melting at an alarming rate in recent years. The associated retreat / thinning of the floating WAIS leads accelerated global sea level rise because it allows glaciers to flow into the ocean faster. Although the primary driver of recent WAIS retreat / thinning is ocean-driven basal melting, atmospheric processes also play a crucial role in either stabilizing or further weakening the ice shelves. In particular, atmospheric rivers (ARs) – long and narrow filament-shaped structures that carry abundant moisture from the subtropics – can impact WAIS in multiple ways. For example, while AR-induced extreme snowfall acts to stabilise the ice shelves, AR-induced high-temperature events (e.g., associated with dense clouds that trap longwave radiation) can cause prolonged surface melting1. If the associated meltwater flows downwards into crevices and enlarges the fractures in the ice, possible disintegration of the ice shelf can occur. ARs are often associated with intense extratropical cyclones termed “atmospheric bombs” and blocking events, which drive warm, moist air towards Antarctica. As these air masses flow over coastal mountain ranges, they generate warm, dry föhn winds that can raise leeside temperatures by more than 10°K. Föhn winds have enhanced surface melting of several Antarctic Peninsula ice shelves in recent years, triggering their subsequent collapse2. However, so far AR detection algorithms over West Antarctica have been limited to atmospheric reanalysis data sets and use rigid thresholds of very high water vapour transport1;3. A more detailed classification of ARs is crucial to gain a comprehensive understanding of their influence on WAIS stability.
Figure 1. The study area: Coastal West Antarctica and its floating ice shelves (grey).
Objective: This project will address this key knowledge gap by classifying ARs in terms of their intensity, duration, and circulation characteristics and undertake a process-based investigation of their impacts on the stability of WAIS using a combination of Earth Observation satellite data (Fig. 2), advanced data science, and atmospheric model hindcasts.
Figure 2. Earth Observation satellite data to be used for classification of ARs (cyan), detection of surface melting (red), and case studies (black).
What the student will do: they will first apply machine learning algorithms (e.g. TempestExtremes or ClimateNet) to multi-channel satellite brightness temperatures, i.e. Special Sensor Microwave/Imager (SSM/I) available at the National Snow and Ice Data Center to detect and classify ARs over West Antarctica. They will then analyse the Quick and Advanced scatterometer (QuikSCAT & ASCAT) data to identify WAIS surface melting associated with each AR classification. To better understand the dynamics, evolution and seasonality of AR-WAIS connection, case studies will be explored using high spatial-resolution synthetic aperture radar products from Sentinel-1 and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to quantify the impact of ARs on WAIS. Results will also be compared with existing high-resolution model hindcasts and ground-based observations, generated by the EU-funded PolarRES and NERC-funded SURFEIT projects.
Training: This project offers a cross-disciplinary training opportunity that utilises satellite observations, machine learning, climate model simulations, and in-situ observations to address a real-world problem of great interest to the polar climate research community. The multi-disciplinary and diverse supervisory team provides an exceptional learning and networking opportunity through their close connections with a wide range of academic, research and industrial/policy partners. The student will be hosted at the British Antarctic Survey, a world leading polar research centre focused on Earth System Science. The student will interact with a cohort of ~100 PhD students at BAS and SENSE CDT. The student will have the opportunity to be integrated into the BAS AI Lab and possible placement opportunities at the UK Space Agency and the Met Office. The SENSE CDT also provides comprehensive personal and professional development training, supplemented by summer schools, workshops, and BAS seminars.