Modelling Short Term Impacts of Climate Change Dr. Claire Souch

Cat Models Used in Re/Insurance Industry for 20+ years Probabilistic distribution of extreme events –> impact on physical assets -> economic and insured losses of the damage Pricing the cat component of premiums Accumulation and portfolio management Solvency calculations Internal Capital Model Rating Agency Capital Model Enterprise-wide Risk Management Emerging Risk Scanning Timeframe of concern 1 - 10 years

Output of Cat Models Describes the probability that various levels of loss will be exceeded Return period - annual probability of a loss being exceeded 1/250 = 0.4% = probability of losses exceeding $634bn in any given year is 0.4% Average Annual Loss (AAL) Expected loss per year, averaged over many years Return Period 2010 AEP 2011 AEP 25 145,742,224,004 150,469,597,283 50 249,720,510,504 258,018,076,190 100 364,549,856,003 376,458,553,934 250 613,981,697,858 634,895,307,835 500 807,635,079,164 834,609,543,707 1,000 984,178,046,528 1,015,205,319,660   AAL 29,140,651,630.99 30,094,441,096.02 SD 87,253,772,713.26 90,106,664,826.95 COV 2.99

Anatomy of Cat Model Event Set Hazard Vulnerability Financial Module Exposure Define Stochastic Events Quantify Financial Loss Calculate Hazard for each event Calculate Damage Event Set Hazard Vulnerability Financial Module

Model Data Inputs Historical data, e.g. Weather charts – back to the late 1800s Windspeed data – back to the 1960s Re-analysis data e.g. European Center Medium Range Weather Forecasting (ECMWF) ERA40 and ERA- Interim “best guess” snapshot of the world’s weather at six-hourly intervals since 1957 Numerical models - mathematical models of the atmosphere Global Climate Models (GCMs) - simulate global weather patterns Numerical weather prediction (NWP) models – forecasting and simulating smaller scale patterns Statistical modelling Based on storm parameters gathered from historical data Surface roughness, Land Use/Land Cover, Topography Insurance claims data and post-event building damage surveys

We care about all forms of “climate change” Seasonal Multi-decadal Long-Term ENSO AMO Global Warming Natural cycles & trends + Anthropogenic influences

The Case of Hurricanes Hurricane activity has gone through cycles in the past Clearest trend is for Category 3+ hurricanes:

The Case of Hurricanes Correlation with Atlantic Multi-decadal oscillation Causes cycles in sea-surface temperatures AMO correlation to Atlantic hurricane activity, 1949-2013. (Source: AIR)

The Case of Hurricanes ENSO also has an impact on hurricane activity El Nino increases wind shear, La Nina decreases wind shear 2015 = strong El Nino: slightly below long term average (4 hurricanes) despite above average SSTs

Impact of Climate Change on Extreme Events …?? IPCC SREX 2012 Reproduced from S. Seneviratne, SCOR Foundation Seminar on Climate Risks, 2015

Are we observing systematic changes: or are we simply experiencing natural variability? Detection and Attribution Studies “Detection of a change is the process of demonstrating that climate has changed in some defined statistical sense, without providing a reason for that change. Attribution of causes of the change is the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence” Fischer & Knutti 2014 (Nature): 18% of the moderate daily precipitation extremes over land are attributable to the observed temperature increase since pre-industrial times

U.K. Rainfall Trends Over Time Interdecadal variability demonstrates periods where the moving average exhibits both cycles of reduced (‘flood- poor’) and elevated (‘flood-rich’) periods During ‘flood-rich’ periods, the natural rainfall volatility increases U.K. rainfall variability (1911-2015) demonstrating a greater prevalence of extreme rainfall in recent years. (Source: U.K. Metoffice) Source: Willis, 2015

December 2015 Floods Climate change made the UK’s record December rainfall 50% more likely Natural variability had a similar (or greater) effect Results within days from climateprediction.net

Cat Models and Climate Change Scenarios Catastrophe models can be used to estimate future climate change impacts Adjust frequency assumptions – increase number of events Adjust severity assumptions – increase windspeeds Alter geographical distribution of event tracks Adjust input parameters e.g. increased sea level or rainfall intensity Climate conditioned model version vs baseline (historical average) version Cat model users largely unable to do this – requires the developer to provide these options/re-parameterisations Opportunity for academic partnerships to provide these options/re- parameterisations Large uncertainty in the science and knowledge of feedback loops Need a focus on 5-10 year risk horizon and impact on extreme events

European Windstorm Variability Cusack S. (2013): A 101 year record of windstorms in the Netherlands. Climatic Change 116, 693-704

Impact on Expected Losses for Europe Modelled Europe-wide (AAL) for different historical periods (normalized relative to the 38-year record 1972–2009) Source: RMS

Sea Level Rise Already Increasing US Hurricane losses Sandy caused $20-25 billion insured losses Study by RMS on impact of sea-level change on Superstorm Sandy using Hurricane model non-linear increase in loss potential with increasing sea-level rise +30% increase storm surge damage from Sandy +20 cm sea level rise Manhattan NYC since 1900 RMS: Catastrophe Modelling and Climate Change, Lloyd’s 2014

More Intense Hurricanes -> Increasing Economic Damage As SSTs increase, so does hurricane intensity (windspeeds) Increase of 8 m/s per degree C Getting stronger at a rate of 1 m/s per decade = 5% increase in loss in ten years (independent of any change in exposure) Professor James B. Elsner, President of Climatek Catastrophe Modelling and Climate Change, Lloyd’s 2014

Conclusions Catastrophe models start from physical first principles to simulate full range of possible extreme events Generate a probabilistic or deterministic view of the risk Model impacts of catastrophe events on physical assets – damage Translate into economic or insured losses Can alter input parameters and assumptions to simulate future climate scenarios in the medium term – 10 years or so Need more scientific research and understanding of mechanisms that we can incorporate into model assumptions