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Serial clustering of US hurricane landfalls

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Presentation on theme: "Serial clustering of US hurricane landfalls"— Presentation transcript:

1 Serial clustering of US hurricane landfalls
Iakovos Barmpadimos, Markus Gut, Jacky Andrich and Kirsten Mitchell-Wallace SCOR Global P&C 13th EMS Annual Meeting & 11th ECAM September Reading, UK 1

2 Motivation and Aim Hurricane is one of the most important perils to the insurance industry, historicaly causing insured annual average losses of 11 billion USD. For an insurance company it is important to have a good estimate of the average value and the variability of losses. In the last decade, both years without hurricane landfalls (2006, 2009, 2010) and with multiple hurricane landfalls (e.g. 2004, 2005) have occurred in the US. Aim of this study is to investigate serial clustering of US hurricane landfalls. Losses are a PCS estimate.

3 Data and method The North Atlantic Oscillation (NAO) influences hurricane frequency (Jagger and Elsner, 2012) and hurricane track (Elsner et al. 2000). The May-June average of NAO index has been used as a predictor of hurricane frequency on a seasonal basis (Jagger and Elsner, 2012). Klotzbach and Gray (2013) use May sea-level pressure (SLP) in the central North Atlantic in their June forecast. Data source: NOAA Station-based NAO (Azores-Iceland) Data source: University of East Anglia

4 Data and method Yearly landfall data were separated into 2 groups: positive NAO (55 years) and negative NAO (57 years). Four regions were examined separately: all-US, FL, Gulf States and East Coast. Count data can be categorized in three categories, depending on the relationship between the mean and the variance. Underdispersed: variance < mean Equidispersed: variance = mean Overdispersed (clustered): variance > mean The statistical significance of clustering was tested using the variance test (Fisher, 1925):

5 Results and discussion
The table shows the variance-over-mean ratio. Green boxes indicate values different from one at the 95 % level of confidence There is significant clustering for seasons where May-June NAO was negative. US FL East Coast Gulf States all data 1.2 1.1 1.0 positive NAO 0.84 0.90 0.65 negative NAO 1.5 1.4 1.3

6 Results and discussion
In what way is NAO related to serial clustering of hurricane landfalls? We use model-based spatial clustering of hurricane tracks (Kossin et al. 2010) to confine the problem in a geographical sense. Separate spatial clusters are defined, again for positive and negative NAO years. In order to have more data (and reduce possible bias), tropical storms have also been included in the spatial clustering analysis. Tropical Storm has 1-minute sustained winds of km/h. Above that it’s a hurricane.

7 Results and discussion
Clustering output for hurricanes and tropical storms. Years with negative May-June NAO in the period. 1 2 LRM O6 NEG 3 4

8 Results and discussion
Clustering output for hurricanes and tropical storms. Years with positive May-June NAO in the period. 1 2 LRM O6 POS 3 4

9 Results and discussion
Landfall serial clustering statistics Basin serial clustering statistics Serial clustering of landfalls can be explained to a great extent by serial clustering in the number of basin tropical storms (as opposed to changes in the track). Significant clustering is only observed for hurricanes in and around the Gulf of Mexico and Caribbean regions. cluster 1 cluster 2 cluster 3 cluster 4 positive NAO 0.6 1.0 0.8 - negative NAO 1.7 1.3 1.2 cluster 1 cluster 2 cluster 3 cluster 4 positive NAO 0.9 1.1 1 0.7 negative NAO 1.7 1.0 1.3

10 Results and discussion
Hurricane activity in the vicinity of the Caribbean is influenced by atmospheric and oceanic conditions in the Atlantic and in the Pacific. In general, tropical storm activity depends heavily on wind shear: low values of wind shear favor the development of tropical storms. Wind shear can be quantified by the 200mb-850mb zonal wind difference. Anomalous 200 mb westerlies in the Tropics imply stronger wind shear. SLP conditions in the North Atlantic affect 200 mb zonal winds in the Caribbean during the hurricane season: The 200 mb zonal winds in the Eastern Tropical Pacific influence 200 mb winds in the Caribbean too (Gray, 1984). These winds are partly associated with El Nino, a well-known driver of hurricane activity. Correlation between May SLP in the Central Atlantic (blue square) and August-October 200 mb zonal wind speed. Legend is flipped. Courtesy: Klotzbach and Gray, 2013. Low SLP (negative NAO) imply easterly anomalies in the 200 mb zonal wind over the Caribbean. This promotes development of tropical storms.

11 Results and discussion
We investigate the influence of the 200 mb Equatorial Pacific (165W-110W) zonal wind (NOAA, data since 1979) on the number of Cluster 1 tropical storms for positive and negative NAO years. The dependence of basin hurricanes in Cluster 1 «turns off» for positive NAO years. The additional variance in the number of hurricanes we saw for negative NAO years could be explained by additional variability introduced by the 200 mb zonal wind in the Eastern Tropical Pacific. positive NAO negative NAO Pacific, 165W-110W R2 = 0 R2 = 31% Positive slope significant at the 95% level.

12 Conclusions and outlook
Significant hurricane clustering is observed during the hurricane season when NAO is negative in the preceding May-June. We hypothesize that clustering is the result of upper-level zonal winds in the Eastern Tropical Pacific interacting in a non-linear way with upper-level zonal winds in the vicinity of the Caribbean. Instead of looking at tropical storm numbers, investigate directly the response of Caribbean/Gulf of Mexico wind shear to NAO and Eastern Tropical Pacific 200 mb zonal wind. Investigate the relationship between the number tropical storms and local wind shear. Is it a non-linear relationship? Formulate a physical mechanism that would explain a possible non-linearity. Replace the «positive/negative NAO years» with an index based only on SLP in the Central Atlantic (SLP in Iceland is probably not relevant). Consider the influence of other variables (e.g. AMO, AMM…). We have used NAO because it has been traditionaly used in seasonal tropical storm forecasts. Hypothesis: for positive NAO conditions, wind shear is high and reaches a threshold beyond which no more hurricanes are formed, independent of what Pacific winds do.

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