Modelling windstorm losses in a climate model

Extratropical cyclones cause vast amounts of damage across Europe throughout the winter seasons. The damage from these cyclones mainly comes from the associated severe winds. The most intense cyclones have gusts of over 200 kilometres per hour, resulting in substantial damage to property and forestry, for example, the Great Storm of 1987 uprooted approximately 15 million trees in one night. The average loss from these storms is over $2 billion per year (Schwierz et al. 2010) and is second only to Atlantic Hurricanes globally in terms of insured losses from natural hazards. However, the most severe cyclones such as Lothar (26/12/1999) and Kyrill (18/1/2007) can cause losses in excess of $10 billion (Munich Re, 2016). One property of extratropical cyclones is that they have a tendency to cluster (to arrive in groups – see example in Figure 1), and in such cases these impacts can be greatly increased. For example Windstorm Lothar was followed just one day later by Windstorm Martin and the two storms combined caused losses of over $15 billion. The large-scale atmospheric dynamics associated with clustering events have been discussed in a previous blog post and also in the scientific literature (Pinto et al., 2014; Priestley et al. 2017).

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Figure 1. Composite visible satellite image from 11 February 2014 of 4 extratropical cyclones over the North Atlantic (circled) (NASA).

A large part of my PhD has involved investigating exactly how important the clustering of cyclones is on losses across Europe during the winter. In order to do this, I have used 918 years of high resolution coupled climate model data from HiGEM (Shaffrey et al., 2017) which provides a huge amount of winter seasons and cyclone events for analysis.

In order to understand how clustering affects losses, I first of all need to know how much loss/damage is associated with each individual cyclone. This is done using a measure called the Storm Severity Index (SSI – Leckebusch et al., 2008), which is a proxy for losses that is based on the 10-metre wind field of the cyclone events. The SSI is a good proxy for windstorm loss. Firstly, it scales the wind speed in any particular location by the 98th percentile of the wind speed climatology in that location. This scaling ensures that only the most severe winds at any one point are considered, as different locations have different perspectives on what would be classed as ‘damaging’. This exceedance above the 98th percentile is then raised to the power of 3 due to damage from wind being a highly non-linear function. Finally, we apply a population density weighting to our calculations. This weighting is required because a hypothetical gust of 40 m/s across London will cause considerably more damage than the same gust across far northern Scandinavia, and the population density is a good approximation for the density of insured property. An example of the SSI that has been calculated for Windstorm Lothar is shown in Figure 2.

 

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Figure 2. (a) Wind footprint of Windstorm Lothar (25-27/12/1999) – 10 metre wind speed in coloured contours (m/s). Black line is the track of Lothar with points every 6 hours (black dots). (b) The SSI field of Windstorm Lothar. All data from ERA-Interim.

 

From Figure 2b you can see how most of the damage from Windstorm Lothar was concentrated across central/northern France and also across southern Germany. This is because the winds here were most extreme relative to what is the climatology. Even though the winds are highest across the North Atlantic Ocean, the lack of insured property, and a much high climatological winter mean wind speed, means that we do not observe losses/damage from Windstorm Lothar in these locations.

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Figure 3. The average SSI for 918 years of HiGEM data.

 

I can apply the SSI to all of the individual cyclone events in HiGEM and therefore can construct a climatology of where windstorm losses occur. Figure 3 shows the average loss across all 918 years of HiGEM. You can see that the losses are concentrated in a band from southern UK towards Poland in an easterly direction. This mainly covers the countries of Great Britain, Belgium, The Netherlands, France, Germany, and Denmark.

This blog post introduces my methodology of calculating and investigating the losses associated with the winter season extratropical cyclones. Work in Priestley et al. (2018) uses this methodology to investigate the role of clustering on winter windstorm losses.

This work has been funded by the SCENARIO NERC DTP and also co-sponsored by Aon Benfield.

 

Email: m.d.k.priestley@pgr.reading.ac.uk

 

References

Leckebusch, G. C., Renggli, D., and Ulbrich, U. 2008. Development and application of an objective storm severity measure for the Northeast Atlantic region. Meteorologische Zeitschrift. https://doi.org/10.1127/0941-2948/2008/0323.

Munich Re. 2016. Loss events in Europe 1980 – 2015. 10 costliest winter storms ordered by overall losses. https://www.munichre.com/touch/naturalhazards/en/natcatservice/significant-natural-catastrophes/index.html

Pinto, J. G., Gómara, I., Masato, G., Dacre, H. F., Woollings, T., and Caballero, R. 2014. Large-scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2014JD022305.

Priestley, M. D. K., Dacre, H. F., Shaffrey, L. C., Hodges, K. I., and Pinto, J. G. 2018. The role of European windstorm clustering for extreme seasonal losses as determined from a high resolution climate model, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2018-165, in review.

Priestley, M. D. K., Pinto, J. G., Dacre, H. F., and Shaffrey, L. C. 2017. Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe. Geophysical Research Letters. https://doi.org/10.1002/2016GL071277.

Schwierz, C., Köllner-Heck, P., Zenklusen Mutter, E. et al. 2010. Modelling European winter wind storm losses in current and future climate. Climatic Change. https://doi.org/10.1007/s10584-009-9712-1.

Shaffrey, L. C., Hodson, D., Robson, J., Stevens, D., Hawkins, E., Polo, I., Stevens, I., Sutton, R. T., Lister, G., Iwi, A., et al. 2017. Decadal predictions with the HiGEM high resolution global coupled climate model: description and basic evaluation, Climate Dynamics, https://doi.org/10.1007/s00382-016-3075-x.

Industrial Sponsored Doctorates

Email: a.halford@pgr.reading.ac.uk

When it comes to doctoral funding, the current method means project funds can come from a variety of sources, such as research councils, charities, industry partners or a mixture of these. In this blog post I will talk about my experience of being jointly funded by a research council and industrial partner.

To start with, I am not actually a PhD student like most people in the Meteorology department here at the University of Reading, but an EngD student. An EngD is a more industrial focused PhD, based on collaboration between industry and academia. There is a taught element to an EngD in the first year, during which a range of modules are covered, on everything from business analysis to sustainability. Additionally, a portion of time is dedicated to work for the industrial sponsor during the course of the project. An EngD still has the same end goal of a PhD, of an intellectual contribution to knowledge.

EngDs were started by the Engineering and Physical Sciences Research Council (EPSRC) back in 1992 and after initial success, the program was expanded in 2009. Out of this expansion came the Technologies for Sustainable Built Environments (TSBE) Centre at the University of Reading. The TSBE Centre has produced 40 EngDs over 8 years, covering a wide variety of disciplines, from modelling energy usage in the home to the effect of different roofing materials on bats. Each student is based within multiple academic departments and the industrial partner organisation with the aim of answering real world research questions.

My project is in collaboration with the BT Group and looks at weather impacts on the UK telecommunications network. I have found that being in an industrial sponsored project is of great benefit. It has been useful to get experience of how industry works, as it can be very different to the academic life in which most doctoral students find themselves. There have also been a lot of opportunities for training in specialist subjects including industrial project management and help to get chartership from professional bodies for those who want it. Being linked with an industrial partner can also offer strong networking and knowledge transfer opportunities, as was the case when I attended a recent interdisciplinary conference of the newly formed Tommy Flowers Institute. This institute has been formed by BT, along with other partner organisations, to further support collaboration between industry and academia.

It can be a challenge at times to balance the approaches of academia and industry. They do not always pull you in the same direction but this is often the same with any lengthy piece of work produced under the guidance of different advisors from different disciplines. The strength with the EngD partnership comes from the different perspectives offered from those different fields to ultimately solve the problem in question.

For me working on a heavily applied problem in the setting of a real organisation has been of greater benefit to me than working on a purely theoretical problem would have been. I have enjoyed seeing my preliminary output being tested within the organisation and look forward to being able to test a more advanced version in the final stages of my project.

Alan Halford is funded by the EPSRC and BT and supported by the TSBE centre.