Changes of storm surge and typhoon intensities under the future global warming conditions Storm Surge Congress 2010

Transcription

1 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

2 Tropical Cyclone (TC) and Climate Change Hurricanes Katrina New Orleans, Typhoon ShanShan Kyushu, Japan, 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

3 Agreement Hurricane Intensity No. of TC Occurrence Category 5 Future Category 4 Category 3 Present Knutson et al. (1998, Science) Knutson et al. (2010, Nature) Knutson et al. (1998) Central pressure (hpa) Bender et al. (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.

4 Impacts on Storm Surge Change in the surge height of 50 year return period extreme water level event Change of simulated extreme water 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)

5 Curiosity... Motivation and Purpose If the historical worst storm surge events happen again in the future global warming conditions Questions 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

6 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

7 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

8 Typhoon Maemi (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

9 Typhoon Maemi became one of the deadliest typhoons to hit South Korea. Typhoon Maemi (2003) Serious storm surge killed 117 people Masan Storm Surge

10 2. Typhoon Rusa (2002) Selected Typhoons Record during typhoon s landfall - Central pressure= 960hPa - Maximum wind speed = 55 m/s - Surge height = 81 cm at Seoguypo

11 Typhoon Rusa (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

12 Flow Charts Selecting typhoons Constructing future climate conditions CSEOF Analysis of IPCC climate model results Global warming mode Target variable: skin temp. Predictor variables regressed on skin temp.

13 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

14 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

15 CSEOF analysis [Kim and North, 1997] Under the assumption of cyclostationarity, extracting temporally evolving spatial patterns by modal decomposition 1-Dimensional T ( t) = B ( t) P ( t) n n B n (t): physical process (e.g. El Niňo, seasonal cycle) P n (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) = B ( r, t) P ( t) n n B n (r,t)=b n (r,t+d); covariance statistics (Eigen vector) is periodic

16 Results of CSEOF Analysis Temporally evolving spatial patterns of skin temp. 1 st mode Skin Temperature (Surface land air temp.+sst) First mode Variance (Eigen value) = 89% PC time series Seasonal Mode Nested period =12 months (100yr) Northern and southern hemisphere 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

17 Results of CSEOF Analysis Skin Temperature Second mode Variance = 6.5% Global Warming Mode 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

18 Skin Temp. variations due to global warming for 100 years (Based on the CSEOF second mode) ~7 o C increase ~4 o C increase ( ) ( )

19 Sea level pressure variations due to global warming for 100 years All predictor variables are regressed on the 2 nd 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

20 1000 hpa Geopotential Height Variations for 100 years (GPM)

21 1000 hpa Air Temperature Variations for 100 years

22 1000 hpa Relative Humidity Variations for 100 years (%)

23 1000 hpa Wind Variations for 100 years

24 Producing future background conditions under global warming scenario (A1B) for typhoon simulations -. Target variable: Skin Temperature -. Predictor variables: Air Pressure Air Temperature Relative Humidity Geopotential Height Wind Future variations for all variables at 16 levels + Past conditions (NCEP/NCAR reanalysis data) Future background conditions Atmosphere 16 levels (hpa) Surface

25 Flow Charts Selecting typhoons Constructing future climate conditions CSEOF Analysis of IPCC climate model results Global warming mode Target variable: skin temp. Predictor variables regressed on skin temp. Simulating typhoons under the past conditions Comparison Simulating typhoon under the global warming conditions

26 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 1 x 1 data Bogussing WRF 3.1 Bogussing scheme PBL scheme YSU Microphysics WSM 3 class Run time UTC ~ UTC (24hours) UTC ~ UTC (30hours)

27 Experimental Designs Variables Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 SST P GW GW GW GW GW SLP P P GW P P GW T P P GW GW P P RH P P GW P GW P GH P P GW P P GW W P P GW P P GW SST : Sea Surface Temperature SLP : Sea Level Pressure T : Air Temperature RH : Relative Humidity GH : Geopotential Height W : Wind P : Present Condition GW : Global Warming Condition -. Control Exp is conducted under present conditions for all variables -. In Exp 1, future conditions are applied only for SST -. In Exp 2., future conditions are applied for all variables -. In other Exp., future conditions are applied for some variables

28 Results of Typhoon simulations Typhoon Maemi (2003) Control Exp. 1 (hpa) (hpa) Surface wind and pressure simulation results for typhoon Maemi under 2003 real conditions Under the future SST conditions. Typhoon is more intensified

29 Comparison of typhoon intensity between present and future simulation for typhoon Meami (2003) Central Pressure Landfall period Control 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

30 Experimental Designs Variables Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 SST P GW GW GW GW GW SLP P P GW P P GW T P P GW GW P P RH P P GW P GW P GH P P GW P P GW W P P GW P P GW SST : Sea Surface Temperature SLP : Sea Level Pressure T : Air Temperature RH : Relative Humidity GH : Geopotential Height W : Wind P : Present Condition GW : Global Warming Condition -. Control Exp is conducted under present real condition for all variables -. In Exp 1, only for SST, future conditions are applied -. In Exp 2., future conditions are applied for all variables

31 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

32 Comparison of typhoon intensity between present and future simulation for typhoon Meami (2003) Central Pressure Landfall period Exp. 2 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. 1. The reason why this happens?

33 Comparison of typhoon intensity between present and future simulation for typhoon Rusa (2002) Central Pressure 5hPa Landfall Period Control Exp. 2 13hPa Exp. 1 Conducting same experiments for typhoon Rusa in 2002, in Exp. 1, the central pressure is decreased about 13 hpa during the landfall period, while in Exp. 2, the pressure is decreased about 5 hpa.

34 Flow Charts Selecting typhoons Constructing future climate conditions CSEOF Analysis of IPCC climate model results Global warming mode Target variable: skin temp. Predictor variables regressed on skin temp. Simulating typhoons under the past conditions Comparison Simulating typhoon under the global warming conditions Remaking wind fields based on real track and intensity under the past conditions Future TC intensity projection Remaking wind fields only considering the intensity change under the GW conditions

35 Results of Typhoon Maemi (2003) simulations for control experiment We cannot simulate exact track and intensity for a specific typhoon due to limitation of present typhoon model although our simulations for typhoon Maemi and Rusa are very consistent with observations. To verify the performance of our storm surge model in the control experiment, we also need to reproduce real exact typhoon track and intensity So, in the control. wind and pressure fields are reproduced using best track data and Holland (1981) model. In the experiments for future conditions, we reproduced them only considering intensity change based on Exp. 1 & 2 results

36 Typhoon intensities adjusted to the real best track data Maemi (2003) Rusa (2002) Central Pressure Central Pressure Max. Wind Speed Max. Wind Speed

37 Wind and pressure fields of Typhoon Maemi (2003) adjusted to the real best track Control Exp. 1 Exp. 2 Control Exp. 1 (hpa)

38 Control Exp. 1 Typhoon Rusa (2002) (hpa) Exp. 2 (hpa) (hpa)

39 Flow Charts Selecting typhoons Constructing future climate conditions CSEOF Analysis of IPCC climate model results Global warming mode Target variable: skin temp. Predictor variables regressed on skin temp. Simulating typhoons under the past conditions Comparison Simulating typhoon under the global warming conditions Remaking wind fields based on real track and intensity under the past conditions Future intensity projection Remaking wind fields only considering the intensity change under the GW conditions Simulating storm surge under the past conditions Comparison Simulating storm surge under the global warming conditions

40 Storm Surge Model Real-time tide and storm surge prediction model Pohai Based on Princeton Ocean Model (POM) Latitude [ o N] Yangtz River China Taiwan Strait Yellow Sea East China Sea Korea T/K Strait Japan Tokara Strait Grid resolution: 1/12º by 1/12º for both longitude and latitude Extending from 23ºN to 42ºN and from 117ºE to 132ºE Real-time tides are expressed by using 8 tidal constituents (M 2, S 2, K 1, O 1, K 2, N 2, P 1 and Q 1 ). 24 Taiwan Longitude [ o E]

41 Verification of Tides Model-predicted amplitudes and phases for major tidal components are in a good agreement with observations Observation Points M 2 K 1 Amplitude Phase Amplitude Phase S 2 O 1 Amplitude Phase Amplitude Phase

42 Verifications of Sea Level Height (Tides + Storm Surges) during Typhoon Maemi (2003) event TongYoung Masan Busan Yeosu Model results and observations are generally in good agreements

43 Sea Level Height (Storm surge + Tides) Simulations for Typhoon Maemi (2003) Control Exp. 1 Exp. 2 (m) (m)

44 Comparison of storm surge between present and future conditions for typhoon Maemi (2003) Exp. 1: 45 cm Exp. 2: 10 cm Exp. 1: 67 cm Exp. 2: 16 cm Busan Masan Exp. 1: 58 cm Exp. 2: 14 cm Exp. 1: 41 cm Exp. 2: 2 cm Tongyoung Yeosu

45 Control Exp. 1 Typhoon Rusa (2002) (m) (m) Exp. 2 Exp. 2 (m)

46 Comparison of storm surge between present and future conditions for typhoon Rusa (2002) Exp. 1: 58 cm Exp. 2: 23 cm Exp. 1: 39 cm Exp. 2: 20 cm Mokpo Yeosu Exp. 1: 24 cm Exp. 2: 16 cm Exp. 1: 17 cm Exp. 2: 9 cm Tongyoung Jeju

47 Maximum increase of storm surge extreme under future climate conditions based on two typhoon s results Unit=[cm] Unit=[cm] 5 EXP. 1 Ave=37cm 3 EXP. 2 Ave=11cm

48 What caused the difference between EXP 1 and EXP 2? Additional Experiments Variables Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 SST P GW GW GW GW GW SLP P P GW P P GW T P P GW GW P P RH P P GW P GW P GH P P GW P P GW W P P GW P P GW P : Present Condition GW : Global Warming Condition

49 Results SST and air temperature are major factors to affect typhoon intensity. For all cases, SST increase contributes to a great increase of typhoon intensity However, when we include future temperature conditions, the typhoon s intensity is not much increased Variables Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 SST P GW GW GW GW GW SLP P P GW P P GW T P P GW GW P P RH P P GW P GW P GH P P GW P P GW W P P GW P P GW EXP 3 EXP 2 Future air temperature distributions seem to provide an unfavorable condition to typhoon s development

50 SST increase during 100 yr ~4 o C -. SST is increased about 4 /100yr near the Korean peninsula (E122º - 132º, N28º - 36º) -. Air temperature at top troposphere is more increased than surface temperature (~ 7 o C increase) -. Based on Emanual (2005), maximum typhoon intensity can be expressed by ( ) Change of air temperature during 100 yr according to altitude Temperature Anomaly ( ) ~7 o C ~4 o C Vmax : Maximum wind speed Thot : Temperature of bottom troposphere Tcold : Temperature of top troposphere E : Evaporative potential of the sea surface -. Here, temperature difference between upper and lower level is important -. Large temperature increase at top troposphere leads to reduction of vertical temp.. gradient and reduction of maximum typhoon intensity

51 Temperature anomaly between present and future for other IPCC climate models Temperature Anomaly ( ) Temperature Anomaly ( ) Air temperature at top troposphere is more increased than surface temperature, which is definitely unfavorable condition for TC development

52 Conclusions -. This study investigates the intensity change of typhoon and storm surge under the global warming conditions, which are projected by IPCC climate models based on A1B scenario. -. The CSEOF is used to produce future warming conditions based on 21 st century prediction results. For all variables, the 2 nd mode shows global warming signals. Using skin temperature as a target variable, future atmospheric conditions are reproduced for all layers -. From the numerical experiments for typhoon Maemi (0314) and Rusa (0215), we found that, when the future warmed SST is considered, the intensities are greatly increased. The central pressures were dropped about 18hPa for Maemi and 13hPa for Rusa. This results in a increase of storm surge, maximum 67cm in Masan and 58cm in Mokpo, respectively. -. However, considering future changes for all atmospheric variables, the magnitude of typhoon intensification is much reduced. -. This is mainly because air temperature near the top of troposphere is more increased than the surface temperature

53 Thank you!

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55 Empirical Orthogonal Functions (EOF) vs. CycloStationary EOF (CSEOF) When a independent physical system (or process) fluctuates due to an external stochastic forcing, the responses of two physical variables might be different from each other because of the different physical response characteristics of the variables. Then, the resulting evolution histories will be different between two

56 CSEOF Analysis Flow chart

57 Holland (1981) Model -. Holland model made wind and pressure fields using RSMC typhoon best track P(r) : the pressure at radius r W(r) : the gradient wind at radius r