Windstorms and Insured Loss in the UK RMetS Scottish Centre Postgraduate Student Evening 12 January 2007 Richard Hewston University of East Anglia Supervisors: Dr Stephen Dorling Dr David Viner (University of East Anglia) Funding: Worshipful Company of Insurers

Outline 1. Weather and economic loss 2. An example of a destructive European windstorm – Windstorm Erwin (Jan 2005) 3. Assessing and Modelling Risk 4. A windstorm model for the UK 5. Climate Change and Future losses

Outline 1. Weather and economic loss

Worldwide Natural Catastrophes Increasing economic and insured loss Increasing economic and insured loss Since 1970, of the most expensive 40 insured losses, 33 were weather related with 29 windstorm related. Since 1970, of the most expensive 40 insured losses, 33 were weather related with 29 windstorm related. Successive record annual insured losses in 2004 and 2005 Successive record annual insured losses in 2004 and 2005 Source: Munich Re (2006)

Worldwide Natural Catastrophes Increasing economic and insured loss Increasing economic and insured loss Since 1970, of the most expensive 40 insured losses, 33 were weather related with 29 windstorm related. Since 1970, of the most expensive 40 insured losses, 33 were weather related with 29 windstorm related. Successive record annual insured losses in 2004 and 2005 Successive record annual insured losses in 2004 and 2005 Source: Munich Re (2006) 

Worldwide Natural Catastrophes Increasing economic and insured loss Increasing economic and insured loss Since 1970, of the most expensive 40 insured losses, 33 were weather related with 29 windstorm related. Since 1970, of the most expensive 40 insured losses, 33 were weather related with 29 windstorm related. Successive record annual insured losses in 2004 and 2005 Successive record annual insured losses in 2004 and 2005 The trend exhibited is influenced by economic and demographic shifts and well as natural factors. The trend exhibited is influenced by economic and demographic shifts and well as natural factors. Source: Munich Re (2006)

Weather Related Insured Loss in the UK ~70% of all losses associated with storms ~70% of all losses associated with storms ~30% of all losses associated with Subsidence ~30% of all losses associated with Subsidence

Weather Related Insured Loss in the UK Domestic claims make up 70-85% of total losses Domestic claims make up 70-85% of total losses Business Interruption accounts for ~12% of commercial claims (~3% of total weather related insured loss) Business Interruption accounts for ~12% of commercial claims (~3% of total weather related insured loss)

Weather Related Insured Loss in the UK ~50% of domestic insured loss is related to windstorms ~50% of domestic insured loss is related to windstorms

Weather Related Insured Loss in the UK 4 th quarter Wettest Autumn for more than 200 years

Weather Related Insured Loss in the UK 4 th quarter Wettest Autumn for more than 200 years 1 st quarter 2005 due largely to windstorm Erwin

Outline 1. Weather and economic loss 2. An example of a destructive European windstorm – Windstorm Erwin (Jan 2005)

Windstorm Erwin Struck Northern Europe 7-9 th January 2005 Struck Northern Europe 7-9 th January 2005 Torrential rain and gale force winds Torrential rain and gale force winds One of the most severe storms to hit Northern Europe in the last 15 years One of the most severe storms to hit Northern Europe in the last 15 years Source: Met Office (2005)

Windstorm Erwin Evidence of a “Sting Jet” Evidence of a “Sting Jet” Propagates strong winds from above to the ground Propagates strong winds from above to the ground Distinct from the usual strong wind region associated with the warm conveyor belt and main cold front Distinct from the usual strong wind region associated with the warm conveyor belt and main cold front Most damage is associated with this feature Most damage is associated with this feature

Windstorm Erwin Wind at altitude of 9km for 1800GMT on 7th January, 2005 Source: Danish Met Institute

Windstorm Erwin Insured losses Insured losses UK ~£250m UK ~£250m Denmark ~£400m Denmark ~£400m Sweden ~£230m Sweden ~£230m Sweden recorded windspeeds of 33m/s inland, resulting in unprecedented damage to the forest industry (£ bn) Sweden recorded windspeeds of 33m/s inland, resulting in unprecedented damage to the forest industry (£ bn) Industry wide, the figure for total insured losses relating to Windstorm Erwin stands at £1.1bn Industry wide, the figure for total insured losses relating to Windstorm Erwin stands at £1.1bn Wind at altitude of 9km for 1800GMT on 7th January, 2005 Source: Danish Met Institute

Outline 1. Weather and economic loss 2. An example of a destructive European windstorm – Windstorm Erwin (Jan 2005) 3. Assessing and Modelling Risk

Assessing Risk Crichton, D. (1999). The Risk Triangle, Natural Disaster Management. Journal, Ingleton. London, Tudor Rose: Risk Triangle Source: Crichton (1999) Exposure – position, orientation, regional terrain, topography Vulnerability - shape, constructional details and state of maintenance (preparedness) Hazard – weather event

Assessing Risk Crichton, D. (1999). The Risk Triangle, Natural Disaster Management. Journal, Ingleton. London, Tudor Rose: Windstorm damage is the result of wind loads exceeding the resistance of the structure, affecting parts of the building such as roofs, envelopes and openings. Windstorm damage is the result of wind loads exceeding the resistance of the structure, affecting parts of the building such as roofs, envelopes and openings. 79% of all damage since 1970 is related to roofs 79% of all damage since 1970 is related to roofs Important factor is Vulnerability Important factor is Vulnerability Different regions have different building standards Different regions have different building standards Risk Triangle Source: Crichton (1999) Exposure – position, orientation, regional terrain, topography Vulnerability - shape, constructional details and state of maintenance (preparedness) Hazard – weather event

Outline 1. Weather and economic loss 2. An example of a destructive European windstorm – Windstorm Erwin (Jan 2005) 3. Assessing and Modelling Risk 4. A windstorm model for the UK

Modelling Windstorm Loss Following Klawa and Ulbrich (2003) Following Klawa and Ulbrich (2003) Windstorm loss modelling using Windstorm loss modelling using wind speeds from a network of UK Met Office observing stations wind speeds from a network of UK Met Office observing stations various socioeconomic data sets which help to quantify vulnerability various socioeconomic data sets which help to quantify vulnerability Claims data from insurers for verification Claims data from insurers for verification Klawa, M and U. Ulbirch (2003), A Model for the Estimation of Storm Losses and the Identification of Severe Winter Storms in Germany, Natural Hazards and Earth Systems Sciences, Vol. 3, pp

Modelling Windstorm Loss Observed hourly wind information from UK Met Office Observed hourly wind information from UK Met Office The 98th percentile value of the daily maximum mean and gust speeds used, incorporating “wind climate” The 98th percentile value of the daily maximum mean and gust speeds used, incorporating “wind climate” 98 th percentile Maximum Gust Speeds Dorland, K., J. Palutikof and R. Tol (2000). Modelling Storm Damage in the Netherlands and the UK., in Weather Impacts on Natural, Social and Economic Systems in the Netherlands. Institute for Environmental Studies, Vrije Universiteit, Amsterdam: Hanson, C., T. Holt and J. Palutikof (2004). An Integrated Assessment of the potential for Change in Storm Activity over Europe: Implications for Insurance and Forestry in the UK. Norwich, Tyndall Centre: 98. Wind Data

Modelling Windstorm Loss Maximum windspeeds, not mean windspeeds, closely related to damage (eg. Dorland et al (2000), Hanson et al (2004)). Maximum windspeeds, not mean windspeeds, closely related to damage (eg. Dorland et al (2000), Hanson et al (2004)). Dorland, K., J. Palutikof and R. Tol (2000). Modelling Storm Damage in the Netherlands and the UK., in Weather Impacts on Natural, Social and Economic Systems in the Netherlands. Institute for Environmental Studies, Vrije Universiteit, Amsterdam: Hanson, C., T. Holt and J. Palutikof (2004). An Integrated Assessment of the potential for Change in Storm Activity over Europe: Implications for Insurance and Forestry in the UK. Norwich, Tyndall Centre: 98. Wind Data R values for R values for Windspeed v Insured Loss Relationship R Value Mean Windspeed Max Windspeed Exponential Squared Cubic Based on sixty four 3-month periods

Modelling Windstorm Loss Socio-economic Data Census data from 1981,1991 and 2001 Census data from 1981,1991 and 2001 Experian data  “Wealth indicators” Experian data  “Wealth indicators” Household Density (2000)

Modelling Windstorm Loss Claims Data Ecclesiastical Insurance Group Ecclesiastical Insurance Group Norwich Union (via loss adjustors Cunningham Lindsey) Norwich Union (via loss adjustors Cunningham Lindsey) Claims data for domestic properties suffering losses associated from windstorm Claims data for domestic properties suffering losses associated from windstorm

Modelling Windstorm Loss Claims Data Ecclesiastical Insurance Group Ecclesiastical Insurance Group Norwich Union (via loss adjustors Cunningham Lindsey) Norwich Union (via loss adjustors Cunningham Lindsey) Claims data for domestic properties suffering losses associated from windstorm Claims data for domestic properties suffering losses associated from windstorm Claims Associated with Windstorm Erwin (Jan 2005)

Modelling Windstorm Loss Windstorm Erwin Max Gust Speeds (normalised to 98 th percentile value)

Modelling Windstorm Loss Windstorm Erwin Predicted insured loss

Modelling Windstorm Loss Windstorm Erwin Predicted insured lossActual insured loss

Outline 1. Weather and economic loss 2. An example of a destructive European windstorm – Windstorm Erwin (Jan 2005) 3. Assessing and Modelling Risk 4. A windstorm model for the UK 5. Climate Change and Future losses

Climate Change and Future Losses Crichton, D. (1999). The Risk Triangle, Natural Disaster Management. Journal, Ingleton. London, Tudor Rose: Dlugolecki, A. (2004). A Changing Climate for Insurance - A summary report for Chief Executives and Policymakers, Association of British Insurers. Risk Triangle Source: Crichton (1999) Climate change Climate change  Change in Hazard  Change in Hazard

Climate Change and Future Losses Crichton, D. (1999). The Risk Triangle, Natural Disaster Management. Journal, Ingleton. London, Tudor Rose: Dlugolecki, A. (2004). A Changing Climate for Insurance - A summary report for Chief Executives and Policymakers, Association of British Insurers. Risk Triangle Source: Crichton (1999) Climate change Climate change  Change in Hazard  Change in Hazard ABI believe we are currently seeing an annual increase in losses of 2- 4% due to climate change (Dlugolecki 2004) Specific Risks of Climate Change: Shortening times between events Shifting spatial distribution Changing variability of loss More hybrid events with multiple consequences (eg. ENSO) Abrupt or nonlinear changes in loss

Climate Change and Future Losses Threshold Exceedance (eg. Dam failure)  Threshold Point

Climate Change and Future Losses Threshold Exceedance (eg. New Orleans dykes breaking during Katrina) Damage functions NOT a linear relationship to weather hazard (eg. Wind Damage is related to cube of wind speed)  Threshold Point Non-Linear Increase

Climate Change and Future Losses Knippertz et al (2000) Knippertz et al (2000) Increase of mean and extreme windspeeds in Northern Europe and Eastern North Atlantic Increase of mean and extreme windspeeds in Northern Europe and Eastern North Atlantic

Climate Change and Future Losses Knippertz et al (2000) Knippertz et al (2000) Increase of mean and extreme windspeeds in Northern Europe and Eastern North Atlantic Increase of mean and extreme windspeeds in Northern Europe and Eastern North Atlantic Leckebusch and Ulbrich (2004) Leckebusch and Ulbrich (2004) Reduction in track density of extratropical cyclones over Europe by 6.9% Reduction in track density of extratropical cyclones over Europe by 6.9% Increase in track density of intense (95 th percentile) extratropical cyclone below 60N (up to 20% increase) Increase in track density of intense (95 th percentile) extratropical cyclone below 60N (up to 20% increase)

Climate Change and Future Losses Knippertz et al (2000) Knippertz et al (2000) Increase of mean and extreme windspeeds in Northern Europe and Eastern North Atlantic Increase of mean and extreme windspeeds in Northern Europe and Eastern North Atlantic Leckebusch and Ulbrich (2004) Leckebusch and Ulbrich (2004) Reduction in track density of extratropical cyclones over Europe by 6.9% Reduction in track density of extratropical cyclones over Europe by 6.9% Increase in track density of intense (95 th percentile) extratropical cyclone below 60N (up to 20% increase) Increase in track density of intense (95 th percentile) extratropical cyclone below 60N (up to 20% increase) Leckebusch et al (2006) Leckebusch et al (2006) 4 GCMs, 5 RCMs  Increasingly frequent intense storms 4 GCMs, 5 RCMs  Increasingly frequent intense storms

Climate Change and Future Losses Using RCM output in the loss model to simulate future losses Using RCM output in the loss model to simulate future losses

Climate Change and Future Losses Using RCM output in the loss model to simulate future losses Using RCM output in the loss model to simulate future losses Providing REgional Climates for Impacts Studies (PRECIS) Providing REgional Climates for Impacts Studies (PRECIS) driven by ECMWF 40 Year Re-analysis (ERA-40) data driven by ECMWF 40 Year Re-analysis (ERA-40) data hourly data for 25km grid hourly data for 25km grid

Climate Change and Future Losses Using RCM output in the loss model to simulate future losses Using RCM output in the loss model to simulate future losses Providing REgional Climates for Impacts Studies (PRECIS) Providing REgional Climates for Impacts Studies (PRECIS) driven by ECMWF 40 Year Re-analysis (ERA-40) data driven by ECMWF 40 Year Re-analysis (ERA-40) data hourly data for 25km grid hourly data for 25km grid Run PRECIS with boundary conditions from Run PRECIS with boundary conditions from HadAM3P ( ) HadAM3P ( ) ECHAM4 ( ) ECHAM4 ( ) Run PRECIS under different IPCC Special Report on Emissions Scenarios (SRES) Run PRECIS under different IPCC Special Report on Emissions Scenarios (SRES)

Thank You Richard Hewston University of East Anglia