seasonal forecasting - the 2nd asia-pacific water summitfirst of all, this figure shows...
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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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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
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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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