Changes of storm surge and typhoon intensities under the future global warming conditions Storm Surge Congress 2010 Il-Ju Moon & S. M. Oh Jeju (Cheju) National University, Korea Tropical Cyclone (TC) and Climate Change Hurricanes Katrina Typhoon ShanShan New Orleans, 2005. 8. 30 Kyushu, Japan, 2006. 9 After recent catastrophic attacks of strong hurricanes and typhoons over the world, whether the characteristics of tropical cyclones have changed or will change in a warming climate became a very interesting research subject Agreement Hurricane Intensity Category 5 Category 4 Category 3 Present Future Knutson et al. (1998, Science) No. of TC Occurrence Knutson et al. (2010, Nature) Knutson et al. (1998) Bender et al. Central pressure (hPa) (2010, Science) Although there are still a lot of arguments in this issue, now some agreements are made. One of the agreements is that future greenhouse warming will cause the globally averaged intensity of tropical cyclones to shift towards stronger storms. In the future, we will experience more frequent strong TC than present. Impacts on Storm Surge Change in the surge height of 50 year return Change of simulated extreme water period extreme water level event levels at Immingham [m] future present future- present Lowe and Gregory (2005) Some results based on statistical and dynamical climate projection experiments suggested that future warm conditions will increase extreme wind speeds and this will cause an increase in surge height extreme of the same proportion (Lowe and Gregory, 2005; Woth et al., 2006; Sterl et al., 2009) Motivation and Purpose Curiosity... If the historical worst storm surge events happen again in the future global warming conditions Questions... 1. How much will the storm intensity increase? 2. How much will the extreme storm surge height increase? This allows us to estimate a possible extreme of storm surge height in the future in a certain region Procedure of the present study 1. Selecting the historical worst typhoons - Based on the highest storm surge events - Targeted region: the Korean peninsula 2. Constructing future climate conditions - Using results of IPCC global climate models - Extracting global warming mode using Cyclostationary EOF 3. Simulating selected typhoons - under both the present and future conditions - Using WRF model 4. Simulating storm surges - Using storm surge model based on POM - Compare surge heights between present and future - Analyzing causes of the changed storm surge Selected Two Typhoons 1. Typhoon Maemi (2003) Korea Records during typhoon’s landfall - Central pressure= 950hPa - Maximum wind speed = 60 m/s - Surge height = 2.16 m at Masan (the highest record in Korean history TyphoonTyphoon MaemiMaemi (2003)(2003) ▶ Strong winds and storm surge brought considerable damage to Korea (Total property damage : about 5 billions) Busan Collapse of Cargo Crane (900ton) in Busan TyphoonTyphoon MaemiMaemi (2003)(2003) ▶ Typhoon Maemi became one of the deadliest typhoons to hit South Korea. ▶ Serious storm surge killed 117 people Masan Storm Surge Selected Typhoons 2. Typhoon Rusa (2002) Record during typhoon’s landfall - Central pressure= 960hPa - Maximum wind speed = 55 m/s - Surge height = 81 cm at Seoguypo TyphoonTyphoon RusaRusa (2002)(2002) Storm surge + Flooding Nakdong River Kimhae ▶ 870mm precipitation during a day : serious flooding ▶ The most expensive typhoon in Korean history ( Total property damage : about 6 billions) ▶ All the highest storm surges along the Korean coasts are recorded by Typhoon Maemi and Rusa Flow Charts Selecting typhoons Constructing future climate conditions CSEOF Analysis of IPCC Global warming mode Predictor variables climate model results Target variable: skin temp. regressed on skin temp. Construction of future environments -. Using six IPCC global climate model results based on global warming scenario (A1B) • Mpi_echam5 : MPI: Max Planck Institute for Meteorology • Bccr_bcm2_0 : Bjerknes Centre for Climate Research • Cccma_cgcm3_1 : Canadian Centre for Climate Modeling and Analysis • Ncar_pcm1_run2 : National Center for Atmospheric Research • Mri_cgcm2_3_2a_run1 : MRI: Meteorological Research Institute, Japan • Ukmo_hadgem1 : United Kingdom Met Office -. Extracting global warming components from climate projection results: Cyclostationaly EOF (CSEOF) analysis • Targeted variable : Surface (skin) temperature • Predictor variables : Air pressure, Air temperature, Relative humidity, Geopotential height, Wind at 16 levels EOF vs. CSEOF = ⅹ EOF Analysis CSEOF Analysis = ⅹ In EOF analysis, physical response is uniform (stationary) in time, while in CSEOF analysis, physical response characteristics is periodically time independent with a given nested period CSEOF analysis [Kim and North, 1997] Under the assumption of cyclostationarity, extracting temporally evolving spatial patterns by modal decomposition 1-Dimensional T (t) = ∑ Bn (t)Pn (t) Bn(t): physical process (e.g. El Niňo, seasonal cycle) Pn(t): Principal Component (PC) time series (amplitude); same length as T(t) B(t)=B(t+d); physics is periodic, d: nested period 2-Dimensional T (r,t) = ∑ Bn (r,t)Pn (t) Bn (r,t)=Bn (r,t+d); covariance statistics (Eigen vector) is periodic Results of CSEOF Analysis Temporally evolving spatial patterns of skin temp. 1st mode 3 2 3 Skin Temperature 1 2 1 (Surface land air temp.+SST) 0 -1 -2 First mode -3 Variance (Eigen value) = 89% 4 5 6 PC time series 7 8 9 Seasonal Mode (100yr) • Northern and southern hemisphere 10 11 12 shows opposite sign • PC-time series shows always positive signs. This represents a seasonal mode • Seasonal amplitude is decreasing • Temperature differences between winter and summer become smaller Nested period =12 months Results of CSEOF Analysis 1 2 3 3 2 Skin Temperature 1 0 -1 -2 Second mode -3 Variance = 6.5% 4 5 6 3 2 1 0 7 8 9 -1 -2 Global Warming Mode -3 10 11 12 • Globally all are positive signs. • PC-time series is changing from negative to positive signs • Arctic and land areas show the largest increase. • In winter time, the increase is dominant Skin Temp. variations due to global warming for 100 years (Based on the CSEOF second mode) ~7oC increase ~4oC increase (℃ ) ( ) Sea level pressure variations due to global warming for 100 years All predictor variables are regressed on the 2nd mode PC time series of the skin temperature. This allows us to produce the variation of all atmospheric variables in the future conditions, which is consistent with the variation of future skin temperature 1000 hPa Geopotential Height Variations for 100 years (GPM) 1000 hPa Air Temperature Variations for 100 years 1000 hPa Relative Humidity Variations for 100 years (%) 1000 hPa Wind Variations for 100 years Producing future background conditions Atmosphere 10 under global warming scenario (A1B) for 30 16 levels (hPa) typhoon simulations 50 70 100 -. Target variable: Skin Temperature 150 -. Predictor variables: Air Pressure 200 Air Temperature 250 300 Relative Humidity 400 Geopotential Height 500 Wind 600 700 Future variations for all variables at 16 levels 850 + 925 Past conditions (NCEP/NCAR reanalysis data) 1000 Surface Future background conditions Flow Charts Selecting typhoons Constructing future climate conditions CSEOF Analysis of IPCC Global warming mode Predictor variables climate model results Target variable: skin temp. regressed on skin temp. Simulating typhoons under Simulating typhoon under the the past conditions Comparison global warming conditions Model for typhoon simulation WRF (Weather Research and Forecasting model) WRF 3.1 model configuration Horizontal Spacing 10 Km Dimension 100 x 100 x 17 Time Step 30s Initial Data NCEP/NCAR reanalysis FNL 1〬 x 1〬 data Bogussing WRF 3.1 Bogussing scheme PBL scheme YSU Microphysics WSM 3 class 1. 2003. 09. 12. 00UTC ~ 09. 13. 00UTC (24hours) Run time 2. 2002. 08. 30. 12UTC ~ 08. 31. 18UTC (30hours) Experimental Designs Variables Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 SST P GW GW GW GW GW SLP PPGW PPGW T PPGW GW PP RH PPGW P GW P GH PPGW PPGW W PPGW PPGW SST : Sea Surface Temperature P : Present Condition SLP : Sea Level Pressure GW : Global Warming Condition T : Air Temperature RH : Relative Humidity -. Control Exp is conducted under present conditions for all variables GH : Geopotential Height -. In Exp 1, future conditions are applied only for SST W : Wind -. In Exp 2., future conditions are applied for all variables -. In other Exp., future conditions are applied for some variables Results of Typhoon simulations Typhoon Maemi (2003) Control Exp. 1 (hPa) (hPa) Surface wind and pressure simulation results for Under the future SST conditions. Typhoon is typhoon Maemi under 2003 real conditions more intensified Comparison of typhoon intensity between present and future simulation for typhoon Meami (2003) Central Pressure Landfall Control period Exp. 1 18hPa If we use a future SST condition, the central pressure is decreased about 18 hPa during the landfall period. This is a huge increase of typhoon intensity considering that this future SST forcing is applied only for 12 hours in this experiment Experimental Designs Variables Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 SST P GW GW GW GW GW SLP PPGW PPGW T PPGW GW PP RH PPGW P GW P GH PPGW PPGW W PPGW PPGW SST : Sea Surface Temperature P : Present Condition SLP : Sea Level Pressure GW : Global Warming Condition T : Air Temperature RH : Relative Humidity -. Control Exp is conducted under present real condition for all variables GH : Geopotential Height -. In Exp 1, only for SST, future conditions are applied W : Wind -. In Exp 2., future conditions are applied for all variables Results of Typhoon simulations Typhoon Maemi (2003) Control Exp. 2 (hPa) (hPa) Under 2003 real conditions Under the future conditions applied for all variables. TC intensity is not much changed Comparison of typhoon intensity between present and future simulation for typhoon Meami (2003) Central Pressure Landfall Exp. 2 period Control 4hPa Exp. 1 18hPa If we use future conditions for all variables, the central pressure is decreased only 4 hPa during the landfall period. This is a small increase of typhoon intensity compared to the Exp.