12ACWE Liu Darft3

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The 12th Americas Conference on Wind Engineering (12ACWE)Seattle, Washington, USA, June 16-20, 2013

Projection of Future US Design Wind Speeds due to Changes inHurricane Activity: Storm Frequency and Sea Surface

Temperature

Fangqian Liu a, Weichiang Pang b

aGlenn Department of Civil Engineering, Clemson University, SC, USAbGlenn Department of Civil Engineering, Clemson University, SC, USA

ABSTRACT: This paper investigates the effects of climate change on tropical cyclone activitiesin the Atlantic basin and potential change in design wind speeds along the U.S. coastal region.Projections of tropical cyclone activities were made up to year 2100 under three speculated futureclimate conditions: I . low greenhouse gas emission, II. high greenhouse gas emission and III. in-

creased annual storm frequency with high greenhouse gas emission. Three synthetic hurricanedatabases each consists of 49,970 simulation years (526 realizations for years 2006 to 2100) were produced for the three future climate scenarios. Characteristic hurricane parameters (e.g. landfallrate, central pressure etc.) for mileposts along the U.S. coastline were examined. Compared tocurrent hurricane activities, appreciable decreases in central pressure are observed for scenarios II and III , while the hurricane intensity remains at around the same level for scenario I . The project-ed future surface wind speeds were evaluated for selected locations and were compared to the de-sign wind speeds in the current structural design code (ASCE 7-10) for mean recurrence intervals(MRIs) of 10, 25, 50, 100, 300, 700 and 1700 years. The most noticeable increases in surface 3-sgust wind speeds were found to be between 10m/s to 15m/s for locations in Florida and alongEastern coast of the United States.

KEYWORDS: hurricane, climate change, stochastic, long-term simulation, design wind speed,MRI

1 INTRODUCTION

Hurricane is among the most dangerous and costliest natural hazards that affect the coastal envi-ronment of the United States (U.S.). The average normalized annual hurricane loss from 1900 to2005 was estimated at $10 billion U.S. dollars (2005 dollars) (Pielke et al. 2008 1). Continued

population growth along the coastal areas with more high value properties being exposed to hur-ricane risk compounds with climate change will likely result in future hurricane damage greatlyexceeds the current estimated annual loss of $10 billion dollars.

In 2007, the United Nations Intergovernmental Panel on Climate Change (IPCC) issued itsfourth assessment report (AR4) on scientific information and global issues concerning climatechange (Metz et al. 2007 2). The AR4 report is a synthesis of the assessments of three workingGroups (WGs). WG1 focuses on the physical science basis behind climate change, which in-cludes summaries of observed changes in climate and their effects on natural and human systems,assessment on causes of the observed changes (Metz et al. 2007 2).

The AR4 report includes the summaries of over thousands of scientific studies around theworld, making it the most detailed report on the latest climate change situation. According to the


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AR4 report, the average sea surface temperature (SST) has risen by approximately 1C from1850 to 2006 and the projected global average surface warming will continue to increase by asmuch as 6.4C by the end of the century.

According to a study conducted by the Climate Change Science Program (CCSP), a programestablished by the U.S. government, there is a strong statistical connection between the rise in seasurface temperature (SST) in hurricane formation regions and increased hurricane activities ob-served over the past 50 years (CCSP 2008 3). Figure 4 shows the number of storms recorded inHURDAT since 1851. It can be seen in Figure 4 that the number of storms spawned in the Atlan-tic Ocean per year follows the pattern of multi-decadal oscillation cycles (Goldenberg et al. 20014). However, the amplitude of the oscillation appears to increase following each subsequent oscil-lation cycle. It can be observed that the annual storm frequency exhibits an increasing trend overthe entire period with records and the most notable upswing in storm activities can be observedsince 1995. The CCSP study projects the increase of hurricane surface wind speed at 1%- 8% forevery 1C increase in SST. Multiple studies cited increases in means of tropical cyclone peakwind speeds in the Atlantic basin (Field et al. 2012 5, Knutson et al. 2010 6). The extent to whichclimate change contributes to change in hurricane risk along the coastal environment is not wellunderstood at this point. This paper presents the preliminary results of an on-going study con-ducted by the authors to quantify the impacts of hurricane climate change on future wind speeds.

1.1 Relevant Previous Studies

A study conduct by Jagger and Elsner (2005) 7 examined the relationships between change in ex-treme wind returning and change in climate conditions including El NioSouthern Oscillation,the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, and global temperature.The distributions of extreme near-coastal hurricane winds were determined for three general di-visions of U.S. coastline (Gulf coast, Florida and East coast) with the evaluation of the effectfrom the aforementioned climate variables using statistical tools. The study estimated the 100-yrreturn level wind speed for the entire coast to be 81(5) m/s.

Projections of future hurricane activities were performed by Wang et al. (2012) 8 and Nishiji-

ma et al. (2012) 9

. Their approaches directly considered the relationships between tropical cy-clones and certain basic climatic data (e.g. sea surface temperature) derived from climate projec-tion models to simulate future tropical cyclone activities. In Wang et al. (2012) 8, stochastichurricane simulation techniques were used to generate 10,000 years of hurricane events under

both the current and possible future climate conditions based on the scenarios from Representa-tive Concentration Pathways (RCPs) (Van Vuuren et al., 2011 10). The 50-year maximum windspeed distribution at selected coastal locations and the joint distribution of maximum wind speedand storm size for the Northeast US coastline were compared between the current and future cli-mate conditions. In their case study at New York City, an increase between 25 m/s to 35 m/s On50-year maximum hurricane wind speed cumulative distribution function (CDF) was found underthe future climate scenario considered herein. In Nishijima et al (2012) 9, in addition to the windspeed distribution from the stochastic typhoon events generated for current and future climate

conditions, the wind resistance of residential buildings was coupled with the simulation results toquantify the change in risk level from current climate to future condition in Japan.Another approach to quantify the relationship between climate and tropical cyclone activity is

to down-scale tropical cyclone activity from re-analysis or climate model datasets (Emanuel2011 11 ; Emanuel et al. 2008 12). Different from the simulation techniques used in Wang et al.(2012) 8, the approach by Emanual et al. (2008) 12 does not rely heavily on historical tropical cy-clone data. The main advantage of the downscaling approach is that it enables long-term projec-tion of tropical cyclone activities with limited tropical storm records. On the other hand, kinemat-


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ic and thermo-dynamic quantities from climate models or from re-analysis data are required forthe downscaling approach. Using the downscaling method, hurricanes were simulated for the 20 th century climate (for a period of 1981 2000) and they were compared to the simulations for aspeculated future warming climate period of 20812100. In this study, the author used these syn-thetic hurricanes to damage a portfolio of insured property according to an aggregate wind-damage function. It was found that hurricanes from three of the four climate models produced in-creasing damage with the global warming signal emerging on time scales of 40, 113, and 170year respectively.

1.2 Sea Surface Temperature

The concept of power dissipation index (PDI) was introduced to describe the relationship be-tween storm frequency, wind speed and duration (Emanuel 2005 13). The PDI index is considereda better indicator of hurricane threat than frequency or intensity alone. By analyzing the PDI val-ues of historical hurricanes, Camargo et al. (2007) 14 showed that the PDI value is directly corre-lated to the Atlantic sea surface temperature. This suggests that the recent increase in global av-erage temperature will likely result in more intense storms with increased wind speeds. Aseparate study conducted by the Climate Change Science Program (CCSP) independently con-firmed that there is a strong statistical connection between the rise in sea surface temperature inthe hurricane formation regions and the increased hurricane activity observed over the past 50years (CCSP 2008 3). A study by Holland and Webster (2007) 15 also confirmed that the overalltrends and changes in sea surface temperatures, tropical cyclone activities and frequency of majorhurricanes are influenced by greenhouse warming.

1.3 Annual Storm Frequency

Based on the records in the Hurricane Database (HURDAT) maintained by the United States Na-tional Hurricane Center (NHC), there has been an increase in the number of storms (both tropicalstorms and hurricanes) observed over the past decades. Figure 1 shows the number of storms

recorded in HURDAT since 1851. The mutidecadal oscillation is a well-documented phenome-

Figure 1: Number of storms per year since 1851.

1900 1950 2000 20500

5

10

15

20

25

30

Year

N u m

b e r o

f S t o r m s p e r

Y e a r

HURDATMoving average over 20yr Linear regression of meanLinear regression of s tandard deviation


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non (e.g. Landsea et al. 1999 16; Goldenberg et al. 2001 4), and it can be clearly seen in Figure 1that the number of storms spawned in the Atlantic Ocean per year follows the mutidecadal oscil-lation cycle. However, the amplitude of the oscillation (i.e. the number of storms) appears to in-crease following each subsequent oscillation cycle.

1.4 Future Climate ScenariosHuman activities result in emissions of four long-lived greenhouse gases (GHGs): CO 2, methane(CH 4), nitrousoxide (N 2O) and halocarbons. Changes in the atmospheric concentrations of GHGsare among the reasons that alter the energy balance of the climate system and are drivers of cli-mate change. It has been shown that the global atmospheric concentrations of CO 2, CH 4 and N 2Ohave increased dramatically because of human activities since 1750 (Nakicenovic et al. 2000 17).The levels of these GHGs are anticipated to continue to grow over the next few decades evenwith the current climate change mitigation policies and related sustainable development practices(IPCC 2007 18).

The Representative Concentration Pathways (RCPs) is the latest set of GHGs emission sce-narios developed to facilitate future assessment of climate change prepared by IPCC since 2007(Van Vuuren et al. 2011 9). This new set of emission scenarios is intended to replace and extendthe scenarios used in earlier IPCC assessments. These new RCPs have been shown to provide agood basis for exploring the range of future climate scenarios (Van Vuuren et al. 2011 9). TheRCPs are directly named according to the projected radiative forcing for the year of 2100. Radia-tive forcing is used to quantify warming of the earth, expressed in terms of the difference be-tween radiant energy received on the surface of the earth and that radiated back to space.

There are four RCPs projections, which include one climate change mitigation scenario lead-ing to a very low forcing level (RCP2.6), two medium stabilization scenarios (RCP4.5/RCP6)and one high emission scenarios (RCP8.5). The scenarios are sufficiently separated (by about 2W/m 2) in terms of the radiative forcing pathways to provide distinguishable future climate sce-narios. Table 1 lists the hypothetical considerations for each RCP scenario and the corresponding

projected range of temperature change for each scenario. In this study, the low emission RCP 2.6and high emission RCP 8.5 scenarios were used to simulate future hurricane activities.

Table 1. RCP Projections (Van Vuuren et al. 2011 9, Rogelj et al. 2012 19)Scenario component RCP 2.6 RCP 4.5 RCP 6 RCP 8.5

Greenhouse gas emissions Very low Medium-low mit-igationMedium baseline;high mitigation High baseline

Agricultural areaMedium forcropland and pas-ture

Very low for bothcropland and pas-ture

Medium forcropland but verylow for pasture

Medium for bothcropland and pas-ture

Air pollution Medium-Low Medium Medium Medium-highTemperaturechange (C at2090-2099 relativeto 1980-1999

Median 1.5 2.4 2.9 4.6


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2 STOCHASTIC HURRICANE SIMULATION PROCEDURE

2.1 Hurricane Parameters

The time history of a storm is defined by seven parameters. These seven parameters are: (1) lati-tude and (2) longitude of storm eye, (3) storm forward speed, (4) heading angle, (5) central pres-sure, (6) storm size expressed as radius to maximum wind ( Rmax) and (7) pressure profile parame-ter (also known as Holland B parameter). The following sections briefly discuss the models usedto simulate these parameters.

2.2 Hurricane Simulation Procedure

The stochastic hurricane simulation model proposed by Vickery et al. (2000) 20 was employed inthis study to simulate hurricanes. The outline of the simulation framework is shown in Figure 2.The stochastic hurricane model is consisted of several modules, which included hurricane for-mation (genesis) model, tracking model, intensity (central pressure) model, central pressure fill-

Tracking Model

Annual Hurricane FrequencyAnd Storm Genesis

StormStatistics

Storm InsideLand Boundary?

Relative Intensity Model

Central Pressure

No

Sea SurfaceTemperature

Yes

Decay Model

Storm ParametersR max , Pressure Pro-

file Parameter B

Generate New Storm

1 0 1 87 5 1 90 0 1 92 5 1 95 0 1 97 5 2 00 0 2 01 1

Time T e m p e r a t u r e

Figure 2: Hurricane simulation framework.

1900 1950 2000 20500

10

20

30

F r e q u e n c y


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Figure 3: 5 o by 5 o grids and initial locations of hurricanes in Atlantic Basin.

ing rate (decay) model and wind field model. Each module in the hurricane simulation frame-work was represented by a series of statistical models calibrated using historical hurricane data(HURDAT). The Marko Chain Monte Carlo simulation technique was applied to simulate thespatial and temporal evolutions of the storm states from their generation to final dissipation.

2.2.1 Genesis ModelThe simulation domain was defined by latitudes from 10N and 60N and longitudes from 0 to100W (Figure 3). For modeling purpose, the simulation domain was sub-divided into 5 o by 5 o grids and a set of statistical models (e.g. tracking model, intensity model and etc.) were devel-oped for each grid. In the baseline model (i.e. without consider climate change), for each simula-tion year, the number of storms in that particular year was randomly generated using a negative

binomial distribution with a mean of 8.4 storms pear year and a standard deviation of 3.56 storms per year. The mean value was based on the average number of storms from 1851 to 2012. Notethat Figure 1 shows that the annual storm frequency appears to increase in recent years. This

phenomenon was not considered in the baseline model. In the climate change model, which will be discussed in later sections, three annual storm frequency projection models were developedand used to account for the increasing trend of storm frequency shown in Figure 1.

2.2.2 Tracking ModelEach hurricane simulation began with a random sampling of the initial location (latitude and lon-gitude) of the storm eye from the actual initial locations of historical events recorded in HUR-DAT. The subsequent positions of the storm eye were updated every 6 hours using the trackingmodel developed by Vickery et al. (2000) 20. The tracking model describes the movement of thestorm in terms of its forward speed ( c) and heading angle ( ):

1 2 3 4 5ln ln i i cc a a a a c a (1)

1 2 3 4 5 6 1i i ib b b b c b b (2)

where a1-a 5 are the grid specific coefficients for the storm forward speed regression model; b1-b5 are the grid specific coefficients for the heading angle regression model; and are the lati-tude and longitude of storm eye, respectively; ic forward speed at time step i; i headingangle at time step i. The grid specific coefficients a and b were determined via least-square fitting


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of Eqns. (1) and (2) using historical observations (hurricane data from the year of 1851 to2012). c and are the error terms which quantify the modeling errors (differences) between theregression models and the actual observations for forward speed and heading angle, respectively.

2.2.3 Central Pressure ModelIn the simulation framework, when the storm eye was on the ocean, the storm central pressurewas converted into a transformed quantity, termed relative intensity which is a function of the seasurface temperature (SST). The expression for relative intensity is given by Vickery et al. (2000) 20:

11 1 2 1 3 2 4 5ln( ) ln( ) ln( ) ln( ) ( )

i ii o i i i s s s I I c c I c I c I c T c T T (3)

where c o-c4 are the grid specific intensity coefficients; i I relative intensity at step i; i sT

seasurface temperature (SST) at location i; I random error term.Once a storm made landfall, the central pressure deficit decay model (or filling rate model)was used to quantify the reduction of hurricane intensity. The filling rate model describes the de-cay of a storm (or rise in central pressure) as a function of time after landfall. A storm wasdeemed dissipated when its central pressure was at or above the standard atmospheric pressure(1013 mbar).The simulation process of a storm is ended when it was dissipated or exited thesimulation domain.

2.2.4 Gradient Wind SpeedThe following asymmetric wind field model (Georgiou 1985 21) was utilized to compute the gra-dient wind speeds at top of the boundary layer:

2 max max1 1sin sin exp2 4

B B R R B p

V c fr c fr r r

(4)where, V gradient wind speed; f Coriolis parameter; air density; the angle (clock-wise positive) from the translational direction to the location of interest; r distance fromstorm center to location of interest; c translational wind speed; max R location parameter,taken as the radius-to-maximum wind speed. The radius-to-maximum wind speed ( Rmax) was de-termined using an empirical model (Vickery et al. 2000 20):

2maxln 2.636 0.00005086 0.0394899 R R p (5)

where, is the latitude of the storm center and R is the modeling error of the radius-to-maximum wind model. The following regression model developed by Vickery et al. (2000) 20 was utilized to simulate parameter B (also known as the Holland B parameter):

max1.38 0.00184 0.00309 B p R (6)

The gradient wind field of a storm can be computed using Eqns. (4) to (6). Conversion factorswere used to convert wind speed from boundary layer height (around 500-1000m) to surface lev-el (10 m).


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3 RESULTS AND DISCUSSIONS

Projections of hurricane activities were made up to year 2100 under three speculated future cli-mate scenarios:

I. low greenhouse gas emission (RCP 2.5) II. high greenhouse gas emission and (RCP 8.5) III. increased annual storm frequency + high greenhouse gas emission(RCP 8.5).

Three synthetic hurricane databases each consists of 49,970 simulation years (526 realizationsfor years 2006 to 2100) were produced for each of these three climate scenarios. In addition tothe three future climate scenarios, a baseline hurricane database was also generated based on thecurrent hurricane climate.

Note that for the baseline model, the current monthly average SSTs data were used in the cen-tral pressure model (see Section 2.2.3). For the RCP 2.6 and RCP 8.5 scenarios, the latest SST

projections from the Coupled Model Intercomparison Project (CMIP5) experiments resourceswere used. The SST data were downloaded from the CMIP5 website ( http://cmip-

pcmdi.llnl.gov/cmip5/ ).Based on the analysis of historical storm statistics, three storm frequency projection models

were developed in this research (Figure 4). The benchmark model 1 had a constant annual stormfrequency which was taken as the average of the historical annual storm frequencies from 1851to 2012. The second annual storm projection model was an extrapolation model based on themoving average mean (MAM) of historical annual storm frequencies. A moving average window

of 20 years was used. Projection model 3 was a decadal oscillation model (OSM) with an in-creasing moving average storm frequency. More details on the formulation of the storm frequen-cy models can be found in (Liu and Pang 2012 22). For the climate condition III considered in thisstudy, the decadal oscillation model was used.

3.1 Storm Occurrence Rate and Central Pressure

The simulated hurricane parameters (central pressures and etc.) were examined for each of thethree future climate conditions. 62 evenly spaced mileposts along the Gulf coast and easterncoast of the U.S. (Figure 5) were used to summarize the statistics of the simulated hurricanes.The annual occurrence rate of hurricanes for a particular milepost was computed by dividing thetotal number of storms observed within 250 km from that milepost by the total simulation years.

1900 1950 2000 20500

5

10

15

20

25

30

Year

N u m

b e r o f

S t o r m s p e r

Y e a r

HURDATConstantMAMOSM

Figure 4: Annual storm frequency models.

Historical Trend Projection

Baseline

Mean


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Figure 6 shows the comparisons for the annual occurrence rates and central pressures at the 62mileposts for the baseline model and the three climate change scenarios.

From the annual occurrence plot (Figure 6a), it can be observed that the annual storm rates donot vary with the change in SSTs (Scenarios I and II ). However, the annual occurrence rate in-creases dramatically for scenario III . This is directly attributed to the use of decadal oscillationmodel (OSM) with more storms spawned in the Atlantic Ocean. With more storms spawned inthe ocean, there are more chances for storms to make landfall or approach the mileposts. For thecentral pressure statistics (Figure 6b), central pressures decrease slightly for low emission scenar-io I (RCP 2.6). This finding suggests that under the low greenhouse gas emission scenario I , thefuture hurricane hazard (in the next 90 years) would remain more or less the same as the currenthurricane hazard as both the annual storm occurrence rates and central pressures are not sensitiveto the levels of change in SSTs for the RCP 2.6 scenario. For the high emission scenarios II and

III (with RCP 8.5), noticeable decreases in central pressures can be observed for milepost 0-500(along the Texas coastline) and 800-1800 (Gulf coast to Florida). The largest drop in central

pressure is found to be about 15mb occurs at milepost 1400, which is at the tip of Florida Penin-

sula. There are no significant differences on the central pressure statistics between the two high

emission scenarios II and III .

3.2 Surface wind speed

In hurricane loss assessment, surface wind speed distribution plays an important role. The surfacewind speed (at 10 m height) at a specified location can be determined using Eqns. (4) to (6)and conversion factors that adjust the wind speed to appropriate height and duration. The mean

100 W 90 W 80 W 70 W20 N

30 N

40 N

50 N

400 300

200 100

31003000

290028002700

26002500

24002300

22002100

20001900

18001700

1600

1500

14001300

120011001000

900800 700600500

Mileposts-100Mileposts-50

Figure 5: Locations of mileposts.


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recurrence interval (MRI) of a given wind speed, V , for a particular site can be determined usingthe following equation:

1

ii

Y MRI v V

P v V n (7)

where i P v V is the probability that V vi in any one hurricane; is the mean annual occur-rence rate of hurricanes; n is the total number of candidate hurricanes those having peak windspeed iv larger than V and Y is the number of simulation years (Pei et al. 2013

23). The 3-s gustwind speeds versus MRI for two selected locations (Miami, FL and Charleston, SC) are comput-

Figure 6: Comparisons of (a) annual storm occurrence rates, and (b) central pressures for differ-ent climate change scenarios

0 500 1000 1500 2000 2500 3000

0.5

1

1.5

2

Mileposts

Annual Occurrence Rate

0 500 1000 1500 2000 2500 3000920

940

960

980

1000

1020

Mileposts

Central Pressure (mbar)

HURDATLow EmissionHigh EmissionHigh Emission+Increasing FrequencyHURDATLow EmissionHigh EmissionHigh Emission+Increasing Frequency

(a)

(b)


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ed and plotted in Figure 7. The benchmarking values from current design code (ASCE 7-10)were extracted from the Applied Technology Council website(http://www.atcouncil.org/windspeed ) for MRIs 10, 25, 50, 100, 300, 700 and 1700 years.

From the plots it can be seen that the design wind speeds of low emission level (RCP 2.6) isvery close to that of the benchmarking level (i.e. the current design wind speeds shown as boxesin Figure 7), which indicates future hurricane wind would remain approximately the same under

scenario I . While for scenarios II and III , noticeable increase in design wind speeds can be expected. For Miami (MIA), under scenarios II and III , the design wind speeds can be expected toincrease by approximately 10 m/s for MRIs between 25 to 1700 years and the 10-year MRI windspeed can be expected to increase by as much as 15 m/s. In general, the wind speeds for the highemission scenario plus OSM frequency model (i.e. scenario III ) are approximately 2 m/s higherthan that of high emission scenario with a constant annual storm frequency (scenario II ). The ef-fects of high emission scenarios II and III (RCP 8.5) on the change in design wind speeds forCharleston, SC (CHS) are not as significant as that observed for Miami, FL. The increase in windspeed from the high emission scenario with a constant annual storm frequency (scenario II ) is

Figure 7: 3-s gust surface wind speeds vs. MRI for selected locations.

101

102

103

0

20

40

60

80

100

3 - s e c

G u s

t ( m

/ s )

Mean Recurrence Interval (yr)

MIA 25.82,-80.28

Low EmissionHigh EmissionHigh Emission+Increasing Frequancy

ATC design wind

101

102

1030

20

40

60

80

100

3 - s e c

G u s

t ( m / s )

Mean Recurrence Interval (yr)

CHS 32.9,-80.03

Low EmissionHigh EmissionHigh Emission+Increasing Frequancy

ATC design wind


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about 5 m/s for all MRIs. For the scenario with increased annual storm frequency (scenario III ),the increase in wind speeds at Charleston can reach as high as 10 m/s.

4 CONCLUSION

In this study, stochastic hurricane simulation models were developed to estimate future hurricanewind speeds along the U.S. coast. Two climate related changes were considered in this study,namely change in sea surface temperature and annual storm frequency. A baseline scenario andthree climate change scenarios were investigated. The three future climate scenarios consideredare I . low greenhouse gas emission, II. high greenhouse gas emission and III. increased annualstorm frequency with high greenhouse gas emission. It is found that for the two high emissionscenarios II and III , dramatic increases in surface wind speeds for all MRIs can be observed. Themagnitude of the increase varies from location to location in different segment of the US coast-line. From the two example locations considered in this study (Miami FL and Charleston SC), itwas observed that the increase in future wind speeds can be as high as 10 m/s and 15 m/s. Sincethe wind pressure exerted on a building envelope is directly proportional to the square of windspeed, the observed levels of wind speed changes might bring significant increase in future hurri-cane risk.

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22 F. Liu, and W. Pang, Influence of climate change on the future hurricane wind hazards along the US EasternCoast and Gulf of Mexico, ATC-SEI Advanced in Hurricane Engineering Conference, Miami, FL, 2012.

23 B. Pei, W. Pang, F.Y. Testik, N. Ravichandran and F. Liu, Mapping joint hurricane wind and surge hazardsfor Charlestion, South Carolina, Nat. Hazards Rev., 2013, submitted.

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