Seasonal Forecasting ‐ Three(3) topics on 2011 rainfall ‐

Shinjiro KANAEDepartment of Civil EngineeringTokyo Institute of TechnologyTokyo Institute of Technology

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First of All, This Figure shows Flooding under Global Warming

(Median of 11 GCMs under the extreme future scenario, RCP 8.5difference between 2071‐2100 and 1971‐2000)difference between 2071‐2100 and 1971‐2000)

IncreaseIntensified

DecreaseIntensified

Seasonal‐scale rainfall prediction is very important!!

Shinjiro Kanae*1, Yukiko Imada*1, Masahide Kimoto*2

*1 Tokyo Institute of Technology*2 Atomosphere and Ocean Research Institute

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3

Tool GCM Tool – GCM --Global‐scale numerical Climate ModelAtmosphere‐Ocean Coupled Model

B d diti I iti l ditiBoundary condition, Initial condition 

Global Climateex.) wind direction: m/s

rainfall : mm/dayrainfall : mm/daySST (sea surface temp.)

Background

We utilized a seasonal prediction system: We utilized a seasonal prediction system: atmosphere and ocean coupled GCM, MIROCatmosphere and ocean coupled GCM, MIROC

1979‐2011 hindcast

ENSO predictionENSO prediction

hi d f AEnsemble meanEnsemble members

hindcast from Aug. Observation

Predictable largePredictable large‐‐scale SSTscale SST

ProbremProbrem I ffi i t ti lI ffi i t ti l

2011 Oct. rainfall      [mm/day]

ProbremProbrem…… Insufficient  spatial Insufficient  spatial resolutionresolution SST anomaly correlation of 

3‐month forecast from Aug.

Need for downscaling with Need for downscaling with new predictor instead of new predictor instead of 

GCM i it tiGCM i it ti

Difficulty to predict RainfallDifficulty to predict Rainfall

GCM precipitationGCM precipitation

5GCM prediction Satellite Obs.

Rainfall anomaly correlation of 3‐month forecast from Aug.

Concept

Concepts/Ideas

T i l SST Utilize the advantage of GCM High predictability in High predictability in 

largelarge‐‐scale tropical SSTscale tropical SST

Tropical SST as predictor

Utilize statistical relationships (SST and Rainfall) supported by each physical mechanism.

SVD analysis※Singular Value DecompositionSingular Value Decomposition: 

derive dominant patterns beginning at the largest covariance g g g

between two variables

INPUTINPUT

Rainfall

INPUTINPUT

･･･

･･･

Predicted SST by GCM  Rainfall

D li iD li iOUTPUTOUTPUT

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Downscaling using Downscaling using statistical relationship statistical relationship derived by derived by SVD analysisSVD analysis

SVD analysis

Singular Value Decomposition (SVD) analysis 

,( )×1st  mode

Local rainfall historical data

Large scale SST historical data,( )=

( )

( )×+

,( )×+2nd  mode Spatial Time

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･･･

Spatial pattern

Time variation

Relationship between local IndoChina rainfall and tropical SST (Aug Sep Oct)

Observed statistical relationship (SST and Rainfall)

1st  mode (15.2%)

Relationship between local IndoChina rainfall and tropical SST (Aug‐Sep‐Oct)

Ocean reanalysis HOcean reanalysis dataset by Ishii and Kimoto (2009)

COR=0.743HENSOEl Nino/Southern Oscillation

Large‐scale SST

l

2nd  mode (12.4%)

SVDSVDRegional Rainfall

St ti bCOR=0.764New‐type ENSO

Station base APHRODITE dataset

・・

・・

・・Nth  mode SST

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・ ・ ・Prcp

[K] [mm/day]

Seasonal Rainfall Forecast by SVD (+GCM)STEP1

SVD2SVD analysis for observed/past datasets

STEP1

Sea surface temperature

SVD1

STEP2

Regional rainfall (12.9%)

Estimate the temporal coefficient for prediction

STEP2

← 2n

← Nth m

o

？

STEP3 Predicted SST by GCM

← 1st m

od

d mode

ode

Prediction of rainfall

STEP3 Predicted SST by GCM de

9Predictand

Prediction for 2011 AugAug‐‐OctOct Thailand flood year

Observation GCM 1‐3 month prediction

Predictor: 1st–3rd month GCM hindcast of SST started from Aug 2011Aug 2011

10Satellite observation

(GSMaP)SVD (New Method)1‐3 month prediction

GCM original output1‐3 month prediction

Prediction for 2011 MayMay‐‐Jul Jul Thailand flood year

Observation GCM 1‐3 month prediction

Predictor: 1st–3rd month GCM hindcast of SST started from May 2011May 2011

11Satellite observation

(GSMaP)SVD (New Method)1‐3 month prediction

GCM original output1‐3 month prediction

Discussion – Is 2011 prediction an easy case or not? –

Observed SST anomaly 2011 Aug‐OctSST pattern of SVD #7

SVD7 dominant yearsSVD7 dominant years

Probably, there is a year/season easy for prediction.There is also a year/season difficult for prediction.y / p

Such information on the reliability of prediction is12

Such information on the reliability of prediction is required, and examined in near future.

Summary 1Summary 1• Current status of seasonal rainfall forecast byCurrent status of seasonal rainfall forecast by an atmos‐ocean coupled model + SVD.

• Some results are good some result are notSome results are good, some result are not good. Probably there is a year/season easy for predictionProbably, there is a year/season easy for prediction, and there is also a year/season difficult for prediction.

• Seasonal prediction is still a big challengeSeasonal prediction is still a big challenge. But, there is a hope. 13

2 The number of tropical cyclones2. The number of tropical cyclones during La Nina years 

by a Stochastic Typhoon Model (STM)

Shinjiro KANAE, Keisuke KUSUHARA, Yoshihiko Iseri

The Impact of Typhoon in Thailand （2011）

4: HAIMA   8: NOCK‐TEN 17: NESAT 18: HAITANG 19: NALGAE

There are 5 typhoons that attacked or reached near by Thailand.

6/21 ‐ 6/25  7/26 ‐ 7/31  9/24 ‐ 9/30  9/25 ‐ 9/27  9/27 ‐ 10/05 

985 hPa 984 hPa 950 hPa 996 hPa 935 hPa40 knots 50 knots 80 knots 35 knots  95 knots

The number of typhoons to Thailand  ‐ Average is 1.5‐ only 3 years (1964 1971 1972)only 3 years (1964, 1971, 1972)→ more than 5 landfalls‐ 1964, 1971 are La Nina years

The impact of La Nina ?1951          1961           1971          1981          1991          2001          2010  

96 , 9 a e a a yea s

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Stochastic Typhoon Model  (STM)

End condition :l l i

Based on the observed typhoon track data of JMA

End condition :P >1020hPa, go into no observed area

cyclolysis Current conditions are calculated from previous conditions and the variation from the previous plocation, like a recurrence formula.

Si(x, y) x : longitude ; y : latitudei : time series

ΔS(x, y)i : time series

for each 6 hoursS : The value of typhoon

components P V and θSi‐1(x, y)1°

y

components P, V and θΔs : the variation at the grid

cyclogenesis1°

x

yFigure5. Modeled translation of typhoon. 

Time step : 6 hours 16

Simulation for on10,000 years, for example

2011/2/1517

b dValidation

STMObserved

For 60 years (1951 – 2010)   For 60 years out of 10000 years

Typhoon tracksTyphoon tracks are similar to observed tracksCentral pressure pis reproduced nicely. 

Validation of Central Pressure

Model outputs agree well with observed data.

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STM Result (= number of typhoon passing per year)

La Nina Normal

Obser ation (2011)Observation (2011)

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Number of Typhoons during La Nina yearsLa NinaNormal

6

5

4

3

2

1

0

La NinaNormal The annual number of typhoon 

0

which attacks Thailand ‐ La Nina  2.13N l 1 42‐ Normal  1.42

5 typhoons in Thailand, 2011

200         1         2        3         4        5~

5 typhoons in Thailand, 2011→ rare event (4%)    

Summary 2Summary 2• Application of Stochastic Typhoon Model for LaApplication of Stochastic Typhoon Model for La Nina years. (= 2.1/year, La Nina year)

• The number of 2011’s typhoons to Thailand (=5) is large in a statistical sense.

• We need further investigation “why 5 in 2011?”

• We are trying further improvements in the y g pmodeling.

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3. Event Attribution of 2011 Rainfall2011 Rainfall

http://www.boston.com/bigpicture/2010/08/severe_flooding_in_pakistan.html

Shinjiro KANAE, Kouhei HAMAGUCHI, Yukiko IMADA, and many colleagues

INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION23

Objective : Quantify climate change contribution to extreme heavy rainfall in 2011

C h b bili f f i f llCompare the probability of occurrence of rainfallUnder WithoutUnder

climate changeWithout

climate change v.s.

24 INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION

EVENT ATTRIBUTIONEVENT ATTRIBUTION

Attribution of Climate‐related Events(ACE project)(ACE project) 

England  American JAPAN Group GroupGroup

[Schiermeier,2011]

Dataset from the Japanese GroupDataset from the Japanese Group

Tool25 INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION

ToolAtmospheric General Circulation Model  

Boundary condition, Initial condition Sea Surface Temperature, Sea ice ,etc.

Atmospheric Variablespex.) wind direction: m/s

rainfall : mm/dayrainfall : mm/day

SIMULATION DESIGN26 INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION

SIMULATION DESIGNNAME 62yr 2011 Non‐CC 2011

Data source: Shiogama et al., 2013

1950‐2011(62 years)

2011 (1 year)

2011 (1 year)Period

Climate change

l b l

Climate change

l b l

Climate change

l b lClimate

Natural Variability Natural Variability Natural Variability

10 sets 100 sets 100 setsDatasetb 10 sets 100 sets 100 setsnumber

62 years period periodmm/day 1950 ・・・ 2011No.1 ‐0.2 ・・・ 0.8No.2 2.7 ・・・ ‐1.6

mm/day 2011No.1 ‐1.1No.2 1.0

mm/day 2011No.1 0.5No.2 ‐0.1as

etmbe

r

620  100  100 ・・・No.10 ‐1.7 ・・・ 2.6

・・・No.100 2.7

・・・No.10 1.3Da

tanu

m samples samples samples

Generate histograms27 INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION

Generate histograms62yr 2011 Non‐CC 2011

Climate change Climate change Climate change

Natural Variability Natural Variability Natural Variability

620 100 100

ce 

ty(%

) 620 samples

100 samples

100 samples

curren

obabilit

Differences in histogram appears !

Occ

pro appears !

RAINFALL (e.g.  mm/day)

2011 VS Non CC 201128 INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION

2011  VS Non‐CC 20112011 Non‐CC 20112011 Non CC 2011

Climate change Climate change

Natural Variabilityfor 1 year

Natural Variabilityfor 1 year

??????

Method –concept of FAR

INTRODUCTION METHOD RESULT SUMMARY&DISCUSSION29

Method –concept of FAR

FAR* of the specific factor is defined as (RA‐RN)/RAFAR of the specific factor is defined as (RA RN)/RA* Fraction of Attributable Risk

e.g.) occurrence rate  of cancerOccurrence rateOccurrence rate Occurrence rate NON-SMOKER :SMOKER :

RN=15%RA=75% 5%5%

CANCER is attributable to Smoking by 80 %. 

Result of Event Attributionf 2011 Th i R i f ll

Simulation name Ensemble number

2011 (ALL) A h i d N l ff i 2011 100

for 2011 Thai Rainfall2011 (ALL) Anthropogenic and Natural effects in 2011 100

NonCC 2011 (NAT) Natural effects (anthropogenic effect is removed) in 2011 100

62yr (LONG) Simulations from 1950 to 2011 under realistic condition 10

March/April April to October0.40.45

0.40.45

0.20.250.30.35

0.20.250.3

0.35

obability

10 grids

00.050.10.15

2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.50

0.050.1

0.15

3 3 5 4 4 5 5 5 5 6 6 5 次の級mm/day/month

Pro 10 grids

So far there is no apparent signal due to climate change

ALL NAT LONG

3 3.5 4 4.5 5 5.5 6 6.5 次の級

ALL NAT LONG

mm/day/month

So far, there is no apparent signal due to climate change. But, it is also sure that we need more investigation. 

Final Concluding Remarksg• Seasonal‐scale prediction of rainfall and Event Attribution of seasonal rainfall are bothEvent‐Attribution of seasonal rainfall are both big challenges. 

• But, there is hope. Progress is here.31

• Under global warming, flooding is projected to i ifi tl i i A i P ifisignificantly increase in Asia‐Pacific. Seasonal prediction is becoming very important!

• In the next step of research, these rainfall predictions should be used forthese rainfall predictions should be used for hydrological/flood predictions like Prof. Oki’s.

Thank you for your attention !!Thank you for your attention !!

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