Remote Sensing of Typhoons Impact and Disaster Management

- A New Ecological Index for SST Cooling Response to Typhoon

唐丹玲 DanLing Tang 唐丹玲

South Sea Institute of Oceanology Chinese Academy of Sciences合

http://lingzis.51.net/ [email protected] Marine Silk Road

Guangzhou Climate Changes /Natural Hazards

沙尘暴 台风玛姬 印度洋海啸2004

California Noctiluca ？

2013 青岛浒苔 夜光藻赤潮 山东日照 阿拉伯海藻华

Distribution of typhoon (hurricane) 台风玛姬

?

1 Typhoon impacts on marine ecosystem

A New Ecological Index for SST Cooling 2 Response to Typhoon

3 Typhoon Disaster Management Remote Sensing Observations Marine Environment and ecology

中科院南海海洋研究所 唐丹玲

Typhoons impact 1 on Marine Ecosystems

DanLing TANG

B. Post-typhoon Chl-a B1. 2005

a HNI

HNI Damrey, Sep 2005 b

SCS

A3. 22:36, Sep 25 China B2. 2002-2004

HNI SCS HNI

A4. 11:26, Sep 26 China

HNI Modis Chl-a (mg m-3)

SCS

0.4 0.4 0.8 1.2 1.6 2.0 2.8 3.0

2.4

0 0

QuikScat Wind Vectors (m/s) Zheng and Tang, MEPS 2007 0 10 20 30 Offshore and nearshore chlorophyll increases induced by typhoon and typhoon rain.

Offer shore

Near shore

Guangming Zheng and Danling Tang, 2007, Marine Ecology Progress Series, 333：61-74，2007 (SCI) TMI-AMSRE A B C DanLing TANG SST A B C (mg m-3) TRMM Rainfall (mm) Chl-a TMI-AMSRE SST ( C) Chl-a (mg m-3) TRMM Rainfall (mm) 25 26 27 28 29 30 0 0.4 0.8 1.2 1.6 2.03.0 2.4 0 60 120 180 240 25 26 30027 28 29 30 0 0.4 0.8 1.2 1.6 2.0 2.4 3.0 0 60 120 180 240 300 A1. Sep 21 B1. Sep 20-21 Modis C1. Sep 21 A7. Sep 27 C7. Sep 27

b A2. Sep 22 C2. Sep 22 A8. Sep 28 B2. Sep 28 Modis C8. Sep 28 a T1 A3. Sep 23 C3. Sep 23 T4 A9. Sep 29 B3. Sep 29 Modis C9. Sep 29 a

A4. Sep 24 C4. Sep 24 No

data. A10. Sep 30 B4. Sep 30 Modis C10. Sep 30 a A5. Sep 25 C5. Sep 25

b T2 T3 A11. Oct 1 B5. Oct 1 SeaWiFS C11. Oct 1 A6. Sep 26 C6. Sep 26 a a b http://lingzis.51.net/ DanLing TANG Sea level variation & sea surface cooling 0 A. 3-d pre-typhoon B. during typhoon 4 C

30

100

SST Center 29

SLA Typhoon 50 SLA

0 28

C)

˚

-50 ( 27

(cm) -100 26 -150 SST T1 T2

-200 25

C. 1-d post-typhoon

D. 4-d post-typhoon 30

100

29

Center

Center Typhoon 50 Typhoon SLA

0 28

-50 SLA

27 Sea level (SLA) anomalies level Sea

-100 (SST) temperature surface Sea SST 26 -150 SST T3 T4 -200 25 16 17 18 19 20 21 22 16 17 18 19 20 21 22 Latitude (ºN) Zheng and Tang, MEPS 2007 http://lingzis.51.net/ DanLing TANG

19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12

0.9

0.6

Typhoon wind

Sampling box (Pa) QuikScat

0.3 Wind Stress Wind 0.0

60

30 Multi-sensor (cm)

SLA 0

-30 sea level variation -60

30

SST decrease

28 TMI-AMSRE

(ºC)

SST

26

24

Typhoon

Modis )

3 passage

- a

- 0.6 SeaWiFS Chl-a peak

Chl

(mg m (mg

0.3

0.0 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12

September October (2005) Zheng and Tang, MEPS 2007 • intensities /Wind Speed? • translation speeds

Zhao H, DanLingTang, Wang Y. 2008 MEPS Tang DanLing 唐丹玲 Strong, fast-moving (4.4m Weak, slow moving (2.87 m s-1) s-1) KT Wind

SST

Chl a

MEPS 2008 Eddy-feature phytoplankton bloom induced

by in the , m S-1

TWI 30+ 19 Jun China 25 a 20

20°N 15 10

b 5 15°N 115°E 125°E 0 NB

(Chen & Tang, 2010,IJRS) Sea surface Sea-surface HeightMODIS Sea- MODIS Chl-a Ekman pumping QuikScat wind currents Anomaly (SHA) surface Concentration velocity vector B1.15 Jun Prior Linfa C1. Jun 1998 E1.15 Jun A1.7 D1.10 F1.15 Jun Temperature - 13 Jun - 17 Jun

China

-

2009

Luzon

Luzon Luzon Luzon Luzon Luzon B2.18 & 19 Jun E2.26 Jun F2.24 Jun After After or during Linfa D2.18 C2.23 Jun A2.19 Jun - 25 Jun China

Luzon

Luzon Luzon Luzo Luzon Luzon 26.0 27.6 28.4 29.2 30.0 26.8 1.00 0.00 0.20 0.40 0.60 0.80 15 20 25 30+ 10 180 270 450 360 - - - 10 12 14 n 0 5 80 20 40 60 0 0 0 35 25 15 15 25 35 90 -

0 0 0 5

cm s-1 cm ℃ mg m –3 10-6 m s-1 m s-1

-3 -250 -200 -150 -100 -1

SST ( ) rainfall (mm) depth (m)-50 speed (m s ) ℃ - -

Chl-0.2 a (mg0.4 m0.6 ) 0.8 - - 100 100 250 150 200 100 - 26 27 28 29 30 20 40 60 80 0.2 0.4 0.6 0.8 14-Jun 20 40 60 80 26 27 28 29 30 12 16 20 50 0 0 0 0 14-Jun 14-Jun 14-Jun 12 16 20 0 8 4 0 0

15-Jun 0 4 8 15-Jun

15-Jun

Jun 14- 16-Jun 14 16-Jun 16-Jun 17-Jun 16-Jun 17-Jun 18-Jun 17-Jun 16 B. Ekman B. Ekman layer depth C. TRMM rainfall D. GHRSST A. QuikScat A. QuikScat wind speed 18-Jun E. Chl 18-Jun 18-Jun

Jun 17- 19-Jun 19-Jun 20-Jun 19-Jun 18 20-Jun 20-Jun 21-Jun 20-Jun

June 21-Jun 21-Jun

Jun 20- 22-Jun 20 22-Jun

22-Jun - 23-Jun 22-Jun a a concentration 23-Jun 24-Jun 23-Jun 22 24-Jun 24-Jun

24-Jun Jun 23- 25-Jun 25-Jun 26-Jun 25-Jun 24 26-Jun 26-Jun 26-Jun

27-Jun 27-Jun 27-Jun Jun 26- 28-Jun 26 28-Jun 28-Jun 29-Jun 28-Jun

29-Jun 30-Jun 29-Jun 28 30-Jun 30-Jun 30-Jun Jun 29-

1-Jul 1-Jul 1-Jul 30 2-Jul 2-Jul 2-Jul 2-Jul

3-Jul

3-Jul Jul 3-Jul

2- 2 4-Jul 4-Jul 4-Jul 4-Jul

5-Jul 5-Jul 5-Jul

4 6-Jul 6-Jul Jul 6-Jul 6-Jul

5-

July 7-Jul 7-Jul 7-Jul

6 8-Jul 8-Jul 8-Jul 8-Jul 9-Jul 9-Jul Jul 10-Jul 9-Jul 8-

8 10-Jul 10-Jul 11-Jul 10-Jul 11-Jul 12-Jul 11-Jul 10 12-Jul 12-Jul 12-Jul Jul 11-

12

1. Cyclone Linfa wind Yongqiang CHEN, DANLING TANG, 2012, A 2. Lingering & looping 5. phytoplankton bloom Eddy-feature 3. Vertical pumping phytoplankton bloom induced by tropical

4. Upwelling & entrainment B cyclone in the South China Sea,

Chl-a mg m-3 International Journal of Remote Sensing. Vol. 33, No. 23, 10 December 2012, 7444–7457.（SCI）

50cm s-1 C Chen, Tang , 2011, IJRS 6. Sea surface currents (little white arrows) Chlorophyll on surface and subsurface

23 After typhoon 2 22 1.9 21 1.8 1.7 20 1.6 19 1.5 1.4 18 a 1.3 surface 1.2 17 111 112 113 114 115 116 117 118 119 120 1.1 1 23 0.9 0.8 22 0.7

21 0.6 0.5 20 0.4 0.3 19 0.2 18 50b m 0.1 0 17 -3 Ye and Tang, 2013, JMS 111 112 113 114 115 116 117 118 119 120 mg.m 25

23

21

Other area 19

17 3 1 15 109 112 115 118 121 124

25

23

21

19 Other years 17 4 2 15 109 112 115 118 121 124

chl a (mg m- HaiJun. Ye, Yi. Sui,3) Danling. Tang, Y. D. Afanasyev，2013, A Subsurface Chlorophyll a Bloom Induced by Typhoon in the South China Sea. Journal of Marine Systems （SCI）.

Dissolved oxygen (DO) ? typhoon / tropical cyclone

20

the super-typhoon Nanmadol between 22 and 30 August 2011,

21 Horizontal distribution

Fig.3 Horizontal distribution

The Chl-a (mg m3) and SST (C) maps are 8-day composites retrieved from MODIS data

the SLA (cm) maps are weekly composites retrieved from altimeter data of severalFig.6 satellites. Daily averaged Ekman pumping velocity (m s1) China

28 Aug 2011. Pacific ocean

South China Sea

Ekman Pumping Velocity Pumping Velocity Ekman (m/s)

Fig.7 Sea surface current (m s1), the passage of the typhoon in .

a b

1 week before week 1 before

c d

Sea Surface Current (m/s) Current Surface Sea

1 week (c) week 1 after 2 weeks (d) after weeks 2

Fig.8 Typhoon

Entrainment

DO Kuroshio Vertical Increase mixing Nutrient + increase Upwelling Chl-a Temperature increase + decrease

Fig.9 1. Surface water 2. TD uplifting eddy- diluted by heavy rain, driven high pCO2,sw with low pCO2,sw

Freshwater Typhoon Strong from Mekong wind River Pre-existing Enhanced cold eddy Ekman cold eddy Dilution: low transport SSS Eddy-driven Enhanced eddy-driven uplifting uplifting Sink CO Source Wind-driven 2 uplifting

change to a source temporarily (1) Increase in fish abundance during two typhoons in the South China Sea

Yu & Tang, 2013, ASR ②species number

New records

Yu & Tang, 2013, ASR

• Fish

Primary production food phytoplankton nutrient fall down discharges deep water SST oceanic environment upwelling ……. • wind

Typhoon 1. one of the most frequent and serious natural hazards ,causing enormous economic losses.

2. significant negative impacts on coastal environments 3. positive impacts on marine ecosystems

A New Ecological Index for SST Cooling Response to Typhoons

SST -- indicator or ecological factor of marine ecosystem environment conditions typhoons propagate over the ocean surface,  affect both the atmosphere and the ocean:

 cause great changes in the upper ocean:

Atmosphere dynamic processes

thermodynamic processes Ocean TyphoonBackgrounds Indices from different aspects  Saffir-Simpson Hurricane Intensity Scale

 Maximum Potential Intensity (MPI) [Emanuel, 1987] Using thermodynamic MPI model, Henderson-Sellers et al. (1998) predicted more intense tropical cyclones as the tropical SST warm.

 Hurricane Destruction Potential (HDP) [Gary et al., 1992] Taking into account the number of hurricane days and intensity, reflected its potential energy for destruction of wind and storm-surge. Backgrounds

 Accumulate Cyclone Energy (ACE) Modified from HDP, [Bell et al., 2000]; And positively correlated with ENSO indices. [Camargo et al., 2005]  Power Dissipation Index (PDI) [Emanuel, 2005] Based on the total dissipation of power, described the potential destructiveness of hurricanes.  storm activity index (SAI) [Wu et al., 2008] A function of the intensity, lifetime and annual frequency of tropical cyclones; considering SST, showed where tropical cyclones were likely to be generated.  ocean coupling/cooling potential intensity (OC_PI) index [Lin et al., 2013] To predict tropical cyclones maximum intensity and describe the effect of ocean cooling.  How much do typhoons impact on marine environments by affecting SST?

A

 A index to describe the effects of SST B cooling in response to a typhoon that can combine its physical and biological effects TCT new (SST Coolingindex response——TCT to Typhoon) and TCT d TCTd m 4 TCT Sii   T 10 (1) i1 nm (2) 1 4 TCTd S i   T i 10 d ji11

ΔTi (°C ) magnitude of SST cooling 2 Si (km ) area of SST cooling corresponding to ΔTi for a duration of 24 hrs. d (days) duration of △SST <-2oC m, i number of SST drops above 2 degrees in intervals of 1 degree (see text) , i=2, 3, … n, j the entire time (in days) of the process of SST cooling, j=1, 2, 3, …

o o ∆Ti : when -3< ∆SST ≤ -2 C, ∆T1= 2, -4 < ∆SST ≤ -3 C, ∆T2 = 3, -5< ∆SST o ≤ -4 C, ∆T3= 4, and so on. SST coolingData response and methods to typhoon Chanchu

(C1). SST before typhoon Chanchu, color scale: SST (°C), gray solid line is the 28°C isotherm.

(C2~C9). Changes of ΔSST during and after typhoon Chanchu passing, color scale: ΔSST (°C), gray solid line is the -2°C isotherm. TCT depended essentially on: the extent of SST cooling; cold pool area; and the duration of the cold wake.

TCTd was determined by the duration of SST cooling.

Time series of SST cooling area variation accompanied with typhoon Ke moving Results and discussions Variation of sea surface Chl-a during and after typhoon Chanchu B: Variation of sea surface Chl-a during and after typhoon Chanchu

o o o 23 N 23 N 23 N b : May 3~10, 2006 b : May 19~26, 2006 b : May5 13~Jun 12, 2006 1 2 3 4 o o o 3 21 N 21 N 21 N May 17 ○ 2 1.5 19oN o o

19 N 19 N 1 )

-1 L 0.6 o o o 17 N 17 N May 16 17 N ○ 0.4

0.3 (μg Chl-a o o o 15 N 15 N 15 N 0.2 ○ May 14 May 15 ○ o o ○o 0.1 13 N 13 N May 1313 N

0.05 o o o 11 N 11 N 11 N

o o o o o 112 E 114 E 116 E 118 E 120 E 112oE 114oE 116oE 118oE 120oE 112oE 114oE 116oE 118oE 120oE Study area and TCs Statistics of history typhoons swept over SCS in summer (2007~2014)

Calculation Typhoon parameters Environment impact parameters parameters SST Year TCs Duration Min Pres. Max Wind Ave Speed TCT TCT Cat. d m n cooling d

(hrs) (hPa) (knots) (m/s) OC 104km2oC 104km2oC HAGIBIS (201407) 84 996 45 5.81 TS 8 2 8 3.75 10 7.22 2014 RAMMASUN (201409) 180 935 135 6.86 4 12 3 12 4.95 40 19.94 KALMAEGI (201415) 126 960 75 7.36 1 9 2 9 3 7 2.72 BEBINCA (201305) 84 990 40 4.39 TS 7 2 7 3.6 23 8.31 RUMBIA (201306) 96 985 65 7.75 1 6 1 6 2.55 1 0.24 JEBI (201309) 84 985 60 5.17 TS 7 2 7 3.45 4 2.87 2013 UTOR (201311) 138 925 130 5.5 4 9 3 9 4.05 16 8.23 USAGI (201319) 156 910 140 4.33 5 9 2 9 3 7 1.69 WUTIP (201321) 90 965 90 5.08 2 12 2 12 3.75 15 6.29 TALIM (201205) 84 985 50 4.39 TS 13 2 13 3.9 34 13.03 VICENTE (201208) 78 950 115 4.47 4 11 3 11 4.5 38 18.69 2012 KAI-TAK (201213) 126 970 70 6.08 1 7 2 7 3.15 6 2.01 TEMBIN (201214) 270 950 115 4.08 4 4 3 4 3.9 8 4.52 GAEMI (201220) 120 990 55 3.58 TS 7 2 7 3.3 7 3.96 1800 11 A 1600 May 10 Jun Jul 9

°C) 1400 2 Aug 8

1200 Sep km 4 Oct 7 1000 6 800

TCT(10 5 TCsnumber 600 4

400 3

200 2

0 1 Year 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Typhoon disaster management • Typhoon Disaster Loss valuation System based on satellite data and Multi- Models Wind

bloom

nutrient

Wind

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Remote Sensing Marine Ecology

Wind

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Nov. 2016, PORSEC2012, India Welcome to PORSEC2016 4 – 11 Nov 2016 , Fortaleza, Ceará, Brazil

• Guangzhou, China, 25-27 Aug 2015

South China Sea Institute of Oceanology Chinese Academy of Sciences合

http://lingzis.51.net/ [email protected]