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SCENARIO DTP - Decadal Variability of Tropical Cyclones: Natural Variability or Anthropogenic Influence?

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  • Full or part time
    Prof Pier Luigi Vidale
    Dr K Hodges
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Tropical cyclones (TCs) are one of the most damaging storms on the planet. Understand the main environmental drivers governing their variability is key to providing trustworthy and actionable information to society. Global TC activity is known to undergo significant decadal variability both in terms of numbers and preferred paths, affecting landfall and damage (e.g. U$380bn in 2011, but >U$100bn in most years since 1990). This variability is thought to be modulated by a mixture of dynamic and thermodynamic environmental conditions and the convolution of natural variability with anthropogenic influence makes attribution a scientific challenge. Yet, existing observational records are too short and inhomogeneous to enable a full understanding of TC – climate system interactions, hence limiting our ability to assess related climate risks.

The scientific questions are:
1) can state-of-the art high-resolution GCMs (capable to resolve TCs and providing a samplebsize far larger than observations) simulate the decadal variability of TCs, based on credible process chains?
2) is decadal variability caused by internal climate variability alone, or is there an anthropogenic influence, particularly via radiative forcing (aerosol and GHGs)?

In this PhD output from new high resolution (25km mesh size or less) multi-decadal (10s to 100s of years, multi-ensemble member and multi-model) simulations with state of the art global coupled climate models will be used as foundations, to robustly answer those open questions. These models credibly simulate the structure and intensities of TCs, as well as their paths
(“tracks”) and interannual variability. This project will entail the use of existing (Horizon-2020 PRIMAVERA, HighResMIP) as well as novel simulations (performed by the student) to isolate the individual mechanisms controlling TC, now extending to decadal variability. Analysis will make use of sophisticated tools (e.g. the University of Reading “TRACK”) and develop new diagnostics,
in collaboration with Prof Vidale’s projects, to unpick the importance of natural variability versus anthropogenic influence in controlling the variability of TCs

The student will gain skills in the analysis of hazardous weather systems in high-resolution simulations and how this can be translated into the assessment of risk. A placement and training activities with (re-)insurance members of Risk Prediction Initiative (Bermuda) will be available at one of the partner offices in London. The student will learn how TC-related risk is managed within the
(re-)insurance industry, by inserting decadal variability “conditioning” into a catastrophe model and how the research in this PhD can have an impact in the insurance/reinsurance industry.

The project will be co-supervised by Dr Kevin Hodges (Department of Meteorology, Reading) and Dr Sielke Dierer (AXIS Capital, Zürich)

The full project description is available at http://www.met.reading.ac.uk/nercdtp/home/available/desc/entry2017/SC201721.pdf
A video is also available at https://youtu.be/RyZlhLY8fUw

To apply, please refer to the SCENARIO website at http://www.met.reading.ac.uk/nercdtp/home/available/

Funding Notes

The project is part of the SCENARIO Doctoral Training Partnership and is potentially fully-funded, subject to selection based on candidate excellence. Funding is available for UK or EU students. Funding is not available for international students. The project has CASE funding.

To apply, please refer to the SCENARIO website at http://www.met.reading.ac.uk/nercdtp/home/available/

References

See project description for full references.

How good is research at University of Reading in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 75.68

Research output data provided by the Research Excellence Framework (REF)

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