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.

Baroclinic and Barotropic Annular Modes of Variability

Email: l.boljka@pgr.reading.ac.uk

Modes of variability are climatological features that have global effects on regional climate and weather. They are identified through spatial structures and the timeseries associated with them (so-called EOF/PC analysis, which finds the largest variability of a given atmospheric field). Examples of modes of variability include El Niño Southern Oscillation, Madden-Julian Oscillation, North Atlantic Oscillation, Annular modes, etc. The latter are named after the “annulus” (a region bounded by two concentric circles) as they occur in the Earth’s midlatitudes (a band of atmosphere bounded by the polar and tropical regions, Fig. 1), and are the most important modes of midlatitude variability, generally representing 20-30% of the variability in a field.

Southern_Hemi_Antarctica
Figure 1: Southern Hemisphere midlatitudes (red concentric circles) as annulus, region where annular modes have the largest impacts. Source.

We know two types of annular modes: baroclinic (based on eddy kinetic energy, a proxy for eddy activity and an indicator of storm-track intensity) and barotropic (based on zonal mean zonal wind, representing the north-south shifts of the jet stream) (Fig. 2). The latter are usually referred to as Southern (SAM or Antarctic Oscillation) or Northern (NAM or Arctic Oscillation) Annular Mode (depending on the hemisphere), have generally quasi-barotropic (uniform) vertical structure, and impact the temperature variations, sea-ice distribution, and storm paths in both hemispheres with timescales of about 10 days. The former are referred to as BAM (baroclinic annular mode) and exhibit strong vertical structure associated with strong vertical wind shear (baroclinicity), and their impacts are yet to be determined (e.g. Thompson and Barnes 2014, Marshall et al. 2017). These two modes of variability are linked to the key processes of the midlatitude tropospheric dynamics that are involved in the growth (baroclinic processes) and decay (barotropic processes) of midlatitude storms. The growth stage of the midlatitude storms is conventionally associated with increase in eddy kinetic energy (EKE) and the decay stage with decrease in EKE.

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Figure 2: Barotropic annular mode (right), based on zonal wind (contours), associated with eddy momentum flux (shading); Baroclinic annular mode (left), based on eddy kinetic energy (contours), associated with eddy heat flux (shading). Source: Thompson and Woodworth (2014).

However, recent observational studies (e.g. Thompson and Woodworth 2014) have suggested decoupling of baroclinic and barotropic components of atmospheric variability in the Southern Hemisphere (i.e. no correlation between the BAM and SAM) and a simpler formulation of the EKE budget that only depends on eddy heat fluxes and BAM (Thompson et al. 2017). Using cross-spectrum analysis, we empirically test the validity of the suggested relationship between EKE and heat flux at different timescales (Boljka et al. 2018). Two different relationships are identified in Fig. 3: 1) a regime where EKE and eddy heat flux relationship holds well (periods longer than 10 days; intermediate timescale); and 2) a regime where this relationship breaks down (periods shorter than 10 days; synoptic timescale). For the relationship to hold (by construction), the imaginary part of the cross-spectrum must follow the angular frequency line and the real part must be constant. This is only true at the intermediate timescales. Hence, the suggested decoupling of baroclinic and barotropic components found in Thompson and Woodworth (2014) only works at intermediate timescales. This is consistent with our theoretical model (Boljka and Shepherd 2018), which predicts decoupling under synoptic temporal and spatial averaging. At synoptic timescales, processes such as barotropic momentum fluxes (closely related to the latitudinal shifts in the jet stream) contribute to the variability in EKE. This is consistent with the dynamics of storms that occur on timescales shorter than 10 days (e.g. Simmons and Hoskins 1978). This is further discussed in Boljka et al. (2018).

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Figure 3: Imaginary (black solid line) and Real (grey solid line) parts of cross-spectrum between EKE and eddy heat flux. Black dashed line shows the angular frequency (if the tested relationship holds, the imaginary part of cross-spectrum follows this line), the red line distinguishes between the two frequency regimes discussed in text. Source: Boljka et al. (2018).

References

Boljka, L., and T. G. Shepherd, 2018: A multiscale asymptotic theory of extratropical wave, mean-flow interaction. J. Atmos. Sci., in press.

Boljka, L., T. G. Shepherd, and M. Blackburn, 2018: On the coupling between barotropic and baroclinic modes of extratropical atmospheric variability. J. Atmos. Sci., in review.

Marshall, G. J., D. W. J. Thompson, and M. R. van den Broeke, 2017: The signature of Southern Hemisphere atmospheric circulation patterns in Antarctic precipitation. Geophys. Res. Lett., 44, 11,580–11,589.

Simmons, A. J., and B. J. Hoskins, 1978: The life cycles of some nonlinear baroclinic waves. J. Atmos. Sci., 35, 414–432.

Thompson, D. W. J., and E. A. Barnes, 2014: Periodic variability in the large-scale Southern Hemisphere atmospheric circulation. Science, 343, 641–645.

Thompson, D. W. J., B. R. Crow, and E. A. Barnes, 2017: Intraseasonal periodicity in the Southern Hemisphere circulation on regional spatial scales. J. Atmos. Sci., 74, 865–877.

Thompson, D. W. J., and J. D. Woodworth, 2014: Barotropic and baroclinic annular variability in the Southern Hemisphere. J. Atmos. Sci., 71, 1480–1493.

Sting Jet: the poisonous (and windy) tail of some of the most intense UK storms

Email: a.volonte@pgr.reading.ac.uk

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Figure 1: Windstorm Tini (12 Feb 2014) passes over the British Isles bringing extreme winds. A Sting Jet has been identified in the storm. Image courtesy of NASA Earth Observatory

It was the morning of 16th October when South East England got battered by the Great Storm of 1987. Extreme winds occurred, with gusts of 70 knots or more recorded continually for three or four consecutive hours and maximum gusts up to 100 knots. The damage was huge across the country with 15 million trees blown down and 18 fatalities.

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Figure 2: Surface wind gusts in the Great Storm of 1987. Image courtesy of UK Met Office.

The forecast issued on the evening of 15th October failed to identify the incoming hazard but forecasters were not to blame as the strongest winds were actually due to a phenomenon that had yet to be discovered at the time: the Sting Jet. A new topic of weather-related research had started: what was the cause of the exceptionally strong winds in the Great Storm?

It was in Reading at the beginning of 21st century that scientists came up with the first formal description of those winds, using observations and model simulations. Following the intuitions of Norwegian forecasters they used the term Sting Jet, the ‘sting at the end of the tail’. Using some imagination we can see the resemblance of the bent-back cloud head with a scorpion’s tail: strong winds coming out from its tip and descending towards the surface can then be seen as the poisonous sting at the end of the tail.

Conceptual+model+of+storm+development
Figure 3: Conceptual model of a sting-jet extratropical cyclone, from Clark et al, 2005. As the cloud head bends back and the cold front moves ahead we can see the Sting Jet exiting from the cloud tip and descending into the opening frontal fracture.  WJ: Warm conveyor belt. CJ: Cold conveyor belt. SJ: Sting jet.

In the last decade sting-jet research progressed steadily with observational, modelling and climatological studies confirming that the strong winds can occur relatively often, that they form in intense extratropical cyclones with a particular shape and are caused by an additional airstream that is neither related to the Cold nor to the Warm Conveyor Belt. The key questions are currently focused on the dynamics of Sting Jets: how do they form and accelerate?

Works recently published (and others about to come out, stay tuned!) claim that although the Sting Jet occurs in an area in which fairly strong winds would already be expected given the morphology of the storm, a further mechanism of acceleration is needed to take into account its full strength. In fact, it is the onset of mesoscale instabilities and the occurrence of evaporative cooling on the airstream that enhances its descent and acceleration, generating a focused intense jet (see references for more details). It is thus necessary a synergy between the general dynamics of the storm and the local processes in the cloud head in order to produce what we call the Sting Jet .

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Figure 4: Sting Jet (green) and Cold Conveyor Belt (blue) in the simulations of Windstorm Tini. The animation shows how the onset of the strongest winds is related to the descent of the Sting Jet. For further details on this animation and on the analysis of Windstorm Tini see here.

References:

http://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/case-studies/great-storm

Browning, K. A. (2004), The sting at the end of the tail: Damaging winds associated with extratropical cyclones. Q.J.R. Meteorol. Soc., 130: 375–399. doi:10.1256/qj.02.143

Clark, P. A., K. A. Browning, and C. Wang (2005), The sting at the end of the tail: Model diagnostics of fine-scale three-dimensional structure of the cloud head. Q.J.R. Meteorol. Soc., 131: 2263–2292. doi:10.1256/qj.04.36

Martínez-Alvarado, O., L.H. Baker, S.L. Gray, J. Methven, and R.S. Plant (2014), Distinguishing the Cold Conveyor Belt and Sting Jet Airstreams in an Intense Extratropical Cyclone. Mon. Wea. Rev., 142, 2571–2595, doi: 10.1175/MWR-D-13-00348.1.

Hart, N.G., S.L. Gray, and P.A. Clark, 0: Sting-jet windstorms over the North Atlantic: Climatology and contribution to extreme wind risk. J. Climate, 0, doi: 10.1175/JCLI-D-16-0791.1.

Volonté, A., P.A. Clark, S.L. Gray. The role of Mesoscale Instabilities in the Sting-Jet dynamics in Windstorm Tini. Poster presented at European Geosciences Union – General Assembly 2017, Dynamical Meteorology (General session)

Understanding the dynamics of cyclone clustering

Priestley, M. D. K., J. G. Pinto, H. F. Dacre, and L. C. Shaffrey (2016), Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe, Geophys. Res. Lett., 43, doi:10.1002/2016GL071277.

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

Extratropical cyclones are the number one natural hazard that affects western Europe (Della-Marta, 2010). These cyclones can cause widespread socio-economic damage through extreme wind gusts that can damage property, and also through intense precipitation, which may result in prolonged flood events. For example the intensely stormy winter of 2013/2014 saw 456mm of rain fall in under 90 days across the UK; this broke records nationwide as 175% of the seasonal average fell (Kendon & McCarthy, 2015). One particular storm in this season was cyclone Tini (figure 1), this was a very deep cyclone (minimum pressure – 952 hPa) which brought peak gusts of over 100 mph to the UK. These gusts caused widespread structural damage that resulted in 20,000 homes losing power. These extremes can be considerably worse when multiple extratropical cyclones affect one specific geographical region in a very short space of time. This is known as cyclone clustering. Some of the most damaging clustering events can result in huge insured losses, for example the storms in the winter of 1999/2000 resulted in €16 billion of losses (Swiss Re, 2016); this being more than 10 times the annual average.

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Figure 1. A Meteosat visible satellite image at 12 UTC on February 12th 2014 showing cyclone Tini over the UK. Image credit to NEODAAS/University of Dundee.

Up until recently cyclone clustering had been given little attention in terms of scientific research, despite it being a widely accepted phenomenon in the scientific community. With these events being such high risk events it is important to understand the atmospheric dynamics that are associated with these events; and this is exactly what we have been doing recently. In our new study we attempt to characterise cyclone clustering in several different locations and associate each different set of clusters with a different dynamical setup in the upper troposphere. The different locations we focus on are defined by three areas, one encompassing the UK and centred at 55°N. Our other two areas are 10° to the north and south of this (centred at 65°N and 45°N.) The previous study of Pinto et al. (2014) examined several winter seasons and found links between the upper-level jet, Rossby wave breaking (RWB) and the occurrence of clustering. RWB is the meridional overturning of air in the upper troposphere. It is identified using the potential temperature (θ) field on the dynamical tropopause, with a reversal of the normal equator-pole θ gradient representing RWB. This identification method is explained in full in Masato et al. (2013) and also illustrated in figure 2. We have greatly expanded on this analysis to look at all winter clustering events from 1979/1980 to 2014/2015 and their connection with these dynamical features.

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Figure 2. Evolution of Rossby waves on the tropopause. RWB occurs when these waves overturn by a significant amount. H: High potential temperature; L: Low potential temperature (Priestley et al., 2017).

We find that when we get clustering it is accompanied with a much stronger jet at 250 hPa than in the climatology, with average speeds peaking at over 50 ms-1 (figures 3a-c). In all cases there is also a much greater presence of RWB in regions not seen from the climatology (Figure 3d). In figure 3a there is more RWB to the south of the jet, in figure 3b there is an increased presence on both the northern and southern flanks, and finally in figure 3c there is much more RWB to the north. The presence of this anomalous RWB transfers momentum into the jet, which acts to strengthen and extend it toward western Europe.

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Figure 3. The dynamical setup for clustering occurring at (a) 65°N; (b) 55°N; and (c) 45°N. The climatology is shown in (d). Coloured shading is the average potential temperature on the tropopause, black contours are the average 250 hPa wind speeds and black crosses are where RWB is occurring.

The location of the RWB controls the jet tilt; more RWB to the south of the jet acts to angle it more northwards (figure 3a), there is a southward deflection when there is more RWB to the north of the jet (figure 3c). The presence of RWB on both sides extends it along a more central axis (figure 3b). Therefore the occurrence of RWB in a particular location and the resultant angle of the jet acts to direct cyclones to various parts of western Europe in quick succession.

In our recently published study we go into much more detail regarding the variability associated with these dynamics and also how the jet and RWB interact in time. This can be found at http://dx.doi.org/10.1002/2016GL071277.

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

References

Della-Marta, P. M., Liniger, M. A., Appenzeller, C., Bresch, D. N., Köllner-Heck, P., & Muccione, V. (2010). Improved estimates of the European winter windstorm climate and the risk of reinsurance loss using climate model data. Journal of Applied Meteorolo

Kendon, M., & McCarthy, M. (2015). The UK’s wet and stormy winter of 2013/2014. Weather, 70(2), 40-47.

Masato, G., Hoskins, B. J., & Woollings, T. (2013). Wave-breaking characteristics of Northern Hemisphere winter blocking: A two-dimensional approach. Journal of Climate, 26(13), 4535-4549.

Pinto, J. G., Gómara, I., Masato, G., Dacre, H. F., Woollings, T., & Caballero, R. (2014). Large‐scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. Journal of Geophysical Research: Atmospheres, 119(24).

Priestley, M. D. K., J. G. Pinto, H. F. Dacre, and L. C. Shaffrey (2017). The role of cyclone clustering during the stormy winter of 2013/2014. Manuscript in preparation.

Swiss Re. (2016). Winter storm clusters in Europe, Swiss Re publishing, Zurich, 16 pp., http://www.swissre.com/library/winter_storm_clusters_in_europe.html. Accessed 24/11/16.