INTERANNUAL AND INTERDECADAL VARIABILITY OF THAILAND SUMMER MONSOON: DIAGNOSTIC AND FORECAST

INTERANNUAL AND INTERDECADAL VARIABILITY OF THAILAND SUMMER MONSOON:

DIAGNOSTIC AND FORECAST

NKRINTRA SINGHRATTNACIVIL, ENVIRONMENTAL AND ARCHITECTURAL

ENGINEERING DEPARTMENTUNIVERSITY OF COLORADO AT BOULDER

2003

MOTIVATION

THAILAND BACKGROUND• Location between 5-20

N latitudes and 97-106 E longitudes

• Population ~ 61.2 million• Major occupation:

agriculture (50%-60% of national economy)

• Agriculture depends on precipitation and irrigation that is dependent on precipitation to store in reservoirs as well

• “Precipitation” is crucial

MOTIVATION

SEASON OF RAINFALL• 80%-90% of annual

precipitation occurs during monsoon season (May-Oct)

• Runoff is stored in reservoirs for use until the next year’s monsoon

• Variability over inter-annual and decadal time scales– Need to understand

this variability• All of these serve as

“motivation” of this research

Total Annual Rainfall

600.0

800.0

1000.0

1200.0

1400.0

1600.0

1800.0

1950 1960 1970 1980 1990 2000

Year

Rain

fall (

mm

)

OUTLINE

1. THAILAND HYDROCLIMATOLOGY2. TRENDS3. INTERANNUAL/INTERDECADAL

VARIABILITY- RELATIONSHIP TO ENSO- PHYSICAL MECHANISM

4. PREDICTORS OF THAILAND RAINFALL

5. FORECASTING THAILAND MONSOON RAINFALL

6. CONCLUSIONS AND FUTURE WORK

DATA DETAILS

• http://hydro.iis.u-tokyo.ac.jp/GAME-T

• Thailand Meteorological Dept.

• Six rainfall stations (r ~ 0.51)

• Five temperature stations (r ~ 0.50)

• Atmospheric circulation variables such as SLPs, SSTs and vector winds: NCEP/NCAR Re-analysis (www.cdc.noaa.gov)

Hydro climatology

TrendsInterannual

/InterdecadalPredictors of

Thailand Rainfall

Forecast Thailand Monsoon Rainfall

Conclusions /Future Work

DATA DETAILS

• Correlation maps (CMAP and SATs) ensure their consistency

• Thus, average rainfall ~ “rainfall index”

average temperature ~ “temperature index”

Hydro climatology

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MECHANISM OF CLIMATOLOGY

• Spring (MAM) temperatures set up land-ocean gradient driving the summer monsoon

• Summer monsoon (rainy season): Aug-Oct (ASO)

• Little peak in May: Due to Northward movement of ITCZ

• Enhanced MAM temperatures Enhanced ASO rainfall Decreasing monsoon seasonal (ASO) temperatures

Hydro climatology

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MECHANISM OF CLIMATOLOGY

• ITCZ northward movement:- Cover Thailand in May- Move to China in June- Southward move to cover Thailand again in August

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AM

SON

TRENDS• Decreasing MAM

temperature over decadal (-0.4 C)

• Decreasing ASO rainfall (-180 mm)

• Tend to cool land and atmosphere less Increasing ASO temperature

• Trends after 1980: Increasing MAM temperature Increasing ASO rainfall (IPCC 2001 report)

• Trends are part of global warming trends (IPCC 2001)

Hydro climatology

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KEY QUESTION

“What drives the interannual and interdecadal variability of Thailand

summer monsoon?”

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Schematic view of sea surface temperature and tropical rainfall in the the equatorial Pacific Ocean during normal, El Niño, and La Niña conditions

..

Global Impacts of ENSO

FIRST INVESTIGATION• 21-yr moving window correlation with SOI index: Strong

significant correlation only post-1980• Spectral Coherence with SOI index

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FIRST INVESTIGATION

• Correlation maps (pre- and post-1980)

SS

TS

LPHydro

climatologyTrends

Interannual /Interdecadal

Predictors of Thailand Rainfall



Pre-1980 Post-1980

FURTHER INVESTIGATION

• To understand nonlinear relationship: Composite maps (pre- and post-1980) of high and low rainfall years (3 highest and lowest years)

Hig

hLo

w

Pre-1980 Post-1980

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INSPIRED QUESTIONS

“Why ENSO related post-1980 only?”“Is there change in ENSO after 1980?”

Hydro climatology

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CONVECTION

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Pre-1980 Post-1980

corr

ela

tion

com

posi

te

El Nino-La Nina Pre-1980 El Nino-La Nina Post-1980

ENSO INVESTIGATIONS

• Composite maps of SSTs:

• Strong and eastward anomalies during post-1980

Pre-1980

Post-1980

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HYPOTHESIS

“East Pacific centered ENSO reduces convections in Western Pacific regions (Thailand) while dateline centered ENSO decreases convections in Indian subcontinent”

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Pre-1980

Post-1980

COMPARISON WITH INDIAN MONSOON

• To show changes in regional impacts of ENSO• 21-yr moving window correlation: Indian monsoon lose

its correlation with ENSO around post-1980• Thailand monsoon picks up correlation at the same time

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CASE STUDIES

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1997 2002

SS

TC

MA

P

SUMMARY (up to this point)

• Strong relationship with ENSO during post-1980

• Indian monsoon shows weakening relationship with ENSO at the same time

• Eastern Pacific centered ENSO (post-1980) contain descending branch over Western Pacific (included Thailand)

• Dateline Pacific centered ENSO (pre-1980) contain descending branch over Indian subcontinent

Hydro climatology

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REQUIREMENTS FOR GOOD PREDICTORS

• Good relation with monsoon rainfall (post-1980)

• Reasonable lead-time to forecast before monsoon season

Hydro climatology

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CORRELATED WITH STANDARD INDICES

• Significant correlations show 1-2 seasons lead-time

Hydro climatology

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CORRELATION MAPS WITH LARGE-SCALE VARIABLES

MAM AMJ

MJJ

Hydro climatology

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SATs


MAM AMJ

MJJ

Hydro climatology

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SLPs


MJJ

AMJMAM

SSTs

Hydro climatology

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TEMPORAL VARIABILITY OF PREDICTORS

• 21-yr moving window correlation with three seasons (MAM, AMJ, MJJ) of all predictors

• Indicate MJJ SLPs and MAM SSTs are the best predictors during post-1980

MAM

AMJ

MJJ

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TRADITIONAL MODEL: LINEAR REGRESSION

• Y = a * SLP + b * SST + e• e = residual: normal distribution with mean =

0, variance = 2

• Variable assumed normally distributed• Relationship among variables assumed linear

relation• Drawbacks:

– unable to capture non-Gaussian/nonlinear features– High order fits require large amounts of data– Not portable across data sets

Hydro climatology

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Modified K-nn

0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14

x

y

NONPARAMETRIC MODEL: MODIFIED K-NN

• Y = (SLPs, SSTs) + e = local regression (residual: e are

saved)• Capture any arbitrary: Linear or

nonlinear• To forecast at any given “x*”, the

mean forecast “y*” obtained by local regression (first step)

• To generate ensemble forecasts: Resample residual (e) of neighbors “K” to “x*” by weighted assumption:– More weight to nearest

neighbor, less weight to farther neighbor

• Add residual to mean forecast “y*”• Be able to generate unseen values

in historical data

y*

x*

Resample “e” of neighbors

E1E2

E3

E4

Hydro climatology

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NONPARAMETRIC MODEL: MODIFIED K-NN

• Number of neighbors for resampling residuals: “K” = (n-1) (n = # of variables)

• Weighted function: W = 1/j (1/j) (j = 1 to “K”)

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MODEL EVALUATION

• Models are verified by cross validation• Data at given year is dropped out of the

model• Model generates ensemble forecasts of

the dropped year• Do for all years• Forecast will be evaluated by 3 criteria

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CRITERIA OF MODEL EVALUATION• Correlation (r) between ensemble median and observed values:

Rx,y = cov(x,y) ,cov(x,y) = (1/n)(xi-x)(yi-y) , higher;better

x * y

• Likelihood (LLH): Evaluates skill in capturing the PDF N

[ Pf ]1/N

t=1

LLH = 0 ~ no skill N

[ Pc ] 1/N 1 – 3 ~ better to capture PDF

t=1

• Rank probability skill score (RPSS): Evaluates skill in capturing categorical probability

k i j

RPS = 1 [ ( Pn - dn)2] , RPSS = 1 – RPS (forecast)

i=1 n=1 n=1 RPS (standard)

k –1- < RPSS < +1 ; bad skill to perfect skill

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MODEL SKILL

better skill: Higher r; LLH > 1; RPSS ~ +1

ALL YEARS WET YEARS DRY YEARS

R = 0.65

llh = 2.09

RPSS = 0.79

llh = 2.85 llh = 1.90

RPSS = 0.98 RPSS = 0.22

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SCATTER PLOTS

Better forecasting in post-1980

ALL YEARS PRE-1980 POST-1980

R = 0.21 R = 0.19

R = 0.65

Hydro climatology

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PDFs

• PDF obtain exceedence probability for extreme events (wet: >700 mm and dry: <400 mm) show good skill (especially for wet scenarios)

Year Cl imatol ogy K-nn1983 10.0% 89.0%1988 10.0% 82.9%1995 10.0% 25.1%

WET YEARSYear Cl imatol ogy K-nn1984 90.0% 84.1%1987 90.0% 100.0%1994 90.0% 39.5%

DRY YEARS

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CATEGORICAL PROBABILITY FORECAST

• When cannot obtain large-scale variables

• Use only forecasted categorical probability of ENSO

• Quick and simple technique

Hydro climatology

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PROBABILITY RELATIONSHIP

• Conditional probability theorem: P(BA) = P(AB)

P(A)

• Total probability theorem: P(B(A1A2A3) = P(BA1)*P(A1)+P(BA2)*P(A2)+P(BA3)*P(A3)

Hydro climatology

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CATEGORICAL PROBABILITY MODEL: ALGORITHM

• Monsoon rainfall and standard index (SOI) are divided into 3 categorizes: Low, neutral, high (by 33rd and 66th percentile)

• Estimate conditional probabilities

• Forecasted categorical of SOI (P(SL),P(SN),P(SH)) are issued Estimate total categorical probability of monsoon rainfall (P(RL),P(RN),P(RH))

• To generate ensemble forecasts: Bootstrap historical data by total categorical probability

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CASE STUDY

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Rl Rn Rh

Sl 0.57 0.29 0.14

Sn 0.37 0.25 0.38

Sh 0.00 0.57 0.43P(Sl) P(Sn) P(Sh)

0.70 0.15 0.15

Conditional Probability

Forecasted Probability of SOI

P(Rl) P(Rn) P(Rh)

0.45 0.33 0.22

Total Categorical Probability of Rainfall

PDFs

• Exceedence of extreme events (wet: >700 mm and dry: <400 mm) show considerate skill

Hydro climatology

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Year Cl imatol ogy ModelWet 1982 10.0% 14.2%

1997 10.0% 14.8%Dr y 1982 90.0% 85.6%

1997 90.0% 77.5%

El Nino Year sYear Cl imatol ogy Model

Wet 1988 10.0% 15.1%2000 10.0% 16.9%

Dr y 1988 90.0% 89.0%2000 90.0% 91.0%

La Nina Year s

CONCLUSIONS

• Decreasing trend in MAM SATs and monsoon rainfall during 1950-2001 – slight increase during post 1980s

• Thailand Monsoon rainfall shows strong relationship with ENSO in post-1980 while Indian monsoon lose its ENSO association in the same period

• ENSO related Anomalies over eastern equatorial Pacific Walker circulation subsidence largely in the pacific region impacting south east Asia. Vice-versa for the Indian subcontinent.

• Shifts in enso patterns post 1980• Pre-monsoon SSTs and SLPs the tropical indian and pacific

regions are identified as predictors of Thailand monsoon rainfall

• Results from both statistical models show good skill at 1-4 months lead time

• Significant implications to water (resource) management and planning

Hydro climatology

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FUTURE WORK

• Forecast improvement: Forecasts in this research is based on data during post-1980 only

- Need to test this on data from other regions- Need to test this on earlier periods (e.g., pre

1950) on longer data sets• Streamflow forecasting : Obtain good quality

streamflow data repeat analyses results directly impact reservoir operations and management

• Causes for ENSO shifts: Area of active research (modeling and observational analysis)

• Statistical-physical forecasting models: Combined statistical and physical watershed models can potentially improve the forecasts

• Decision support system: to evaluate various decision options in light of the forecasts

Hydro climatology

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Thailand Rainfall



ACKNOWLEDGEMENT

• My sponsor: Public Works Department• Balaji Rajagopalan and all members of

committee• Somkeit Apipattanavis• Katrina Grantz• Krishna Kumar

THANK YOU

Documents

INTERANNUAL AND INTERDECADAL VARIABILITY OF THAILAND SUMMER MONSOON: DIAGNOSTIC AND FORECAST