Scientific Background
Extreme precipitation events have significant impacts on human society as shown by the tragic derailment near Stonehaven in August 2020. The proximate cause of the derailment was debris from an overflowing drain (RAIB, 2022) with a train forced to return due to a landslide blocking the track. After a damaging weather and climate event science and society would like to know to what extent, if at all, human influences have changed the risk of such an event. One of the expected impacts of human driven climate change is an increase in extreme precipitation, of about 7%/K local warming as total atmospheric water vapour increases at this rate(Allen & Ingram, 2002). Mean precipitation is expected to increase at a smaller rate of only 2-3%/K local warming due to the balance between heating from condensation and loss to space. However, both local and global scale atmospheric circulation changes could considerably modify both the extreme and mean increases. Atmospheric processes that lead to extreme precipitation are generally small scale and rare. In many climate models these processes cannot be directly resolved and so are approximately represented. Recent developments in high resolution modelling mean it is now possible to study individual extreme events( Pall et al 2017) though for this event defining the relevant extreme requires a good understanding of the relevant weather conditions that lead to land slips over parts of Scotland.
Research Questions
- What are the relevant weather and precipitation thresholds for landslips in eastern Scotland?
- How much has climate change modified the risk of these thresholds and other extreme precipitation thresholds being exceeded ?
- Have the characteristics of convective events changed due to anthropogenic climate change?
- If so, what are the mechanisms for this change?
Methodology
The planned approach is for the student to use remote sensed data and radar rainfall data to establish a landslide database from which precipitation and weather thresholds that generate landslip swill be estimated. The student would then run two ensembles of global scale models nudged to observations to drive high-resolution models over eastern Scotland. The ensembles differ in that one runs with current boundary conditions while another runs with boundary conditions estimated for a world without human influences. Differences between the ensembles allow estimation of the changing probability of breaching relevant extreme precipitation thresholds. Student would the explore how much the change in extreme precipitation are due to local warming, circulation changes or changes in precipitation efficiency (Pall et al 2017).The student would set up a computational system focussed on Scotland using the Met Office’s high-resolution weather model (UKV).This would also allow rapid studies of how much damaging extreme precipitation events are impacted by climate change.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
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