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RE-Cyclone: Tropical cyclone predictability for improved decision making

  • Full or part time
  • Application Deadline
    Friday, January 10, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Severe tropical cyclones (TCs) are a major threat to societies and cause significant loss all over the tropical and sub-tropical regions. For example, China is affected by on average seven tropical cyclones (with Typhoon strength) making landfall each year, resulting in annual damages of about US$5.6 billion. Severe damages are especially documented on the local scale, impacting on local property damages of up to 50% and reductions in the local economy by about 20% for the respective year. Total net economic losses are estimated to be in the range of US$28 billion for the recent climate period (1992-2010, Elliott et al., 2015). One fundamental question is the potential predictability of these rare severe events in different time scales. Latest research showed significant and partly usable skill of TC frequencies on the seasonal time scale (Vitart and Stockdale, 2001; on specific regional scales: Vecchi et al., 2014); on landfall predictions (Camp et al., 2015), but varying between ocean basins. Specific large-scale factors (e.g. like ENSO) are related to TC occurrence variability and do also show seasonal prediction skill.

The project will address this topic by investigating the following research questions:
• What meteorological variables or metrics (e.g. ENSO, West Pacific High) give predictability to estimates of loss in the upcoming season?
• When should these measures have attention paid to them? This will develop the growing understanding of the conditions in which predictability is possible.
• Where (i.e. geographically) is prediction possible?
• Why is the metric valid? i.e. how is explained in terms of physical processes? This underpinning understanding is essential to give comfort to decision makers, and it gives the basis for insights into another key consideration i.e. what is the uncertainty in the predictions?

These will feed into practical considerations:
• In what years would additional information about the season ahead have made most difference to reinsurance decisions?
• Should (re)insurers align their annual renewals to immediately prior to a season (e.g. Oct-Mar) such that the information available to them is maximized?

Funding Notes

CENTA studentships are for 3.5 years and are funded by the Natural Environment Research Council (NERC). In addition to the full payment of their tuition fees, successful candidates will receive the following financial support.
• Annual stipend, set at £15,009 for 2019/20
• Research training support grant (RTSG) of £8,000

References

Befort, D.J., Wild, S., Knight, J.R., Lockwood, J.F., Thornton, H.E., Hermanson, L., Bett, P.E., Weisheimer, A., Leckebusch, G.C., 2018. Seasonal Forecast Skill for Extra-tropical Cyclones and Windstorms. Quart J R. Meteorol Soc 145, 92–104.
Camp, J., Roberts, M., MacLachlan, C., Wallace, E., Hermanson, L., Brookshaw, A., Arribas, A. and Scaife, A.A. (2015) Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Quarterly Journal of the Royal Meteorological Society, 141, 2206–2219. https://doi.org/10.1002/qj.2516.
Donat, M.G., Leckebusch, G.C., Wild, S., Ulbrich, U., 2011. Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations. Nat Hazards Earth Syst Sci 11, 1351–1370.
Hillier, J.K., 2017. The Perils in Brief, in: Natural Catastrophe Risk Management and Modelling: A Practitioner’s Guide. Wiley-Blackwell, Oxford, UK, p. pp 536.
Lavers, D.A., Allan, R.P., Wood, E.F., Villarini, G., Brayshaw, D.J., Wade, A.J., 2011. Winter floods in Britain are connected to atmospheric rivers. Geophys Res Lett 38, L23803. https://doi.org/10.1029/2011GL049783
Renggli, D., Leckebusch, G.C., Ulbrich, U., Gliexner, S.N., 2011. The Skill of Seasonal Ensemble Prediction Systems to Forecast Wintertime Windstorm Frequency over the North Atlantic and Europe. Mon. Weather Rev. 139, 3052–3068.
Scaife, A.A., Arribas, A., Blockley, E., 2014. Skillful long-range predictions of European and North American winters. Geophys Res Lett 41, 2514–2519.
Scaife, AA, et al., 2019: Does increased atmospheric resolution improve seasonal climate predictions? Atm Sci Let, DOI: 10.1002/asl.922
Vecchi, G.A., Delworth, T., Gudgel, R., Kapnick, S., Rosati, A., Wittenberg, A.T., Zeng, F., Anderson, W., Balaji, V., Dixon, K., Jia, L., Kim, H., Krishnamurthy, L., Msadek, R., Stern, W.F., Underwood, S.D., Villarini, G., Yang, X. and Zhang, S. (2014) On the seasonal forecasting of regional tropical cyclone activity. Journal of Climate, 27, 7994–8016. https://doi.org/10.1175/JCLI-D-14-00158.1.
Vitart, F. and Stockdale, T.N. (2001) Seasonal forecasting of tropical storms using coupled GCM integrations. Monthly Weather Review, 129, 2521–2537.
Walz, M.A., Donat, M.G., Leckebusch, G.C., 2018. Large-Scale Drivers and Seasonal Predictability of Extreme Wind Speeds Over the North Atlantic and Europe. J. Geophys. Res. Atmospheres 123, 11518–11535. https://doi.org/10.1029/2017JD027958

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