Contacts: Werapol Bejranonda and Manfred Koch [email protected]@uni-kassel.de,...
1
Contacts: Werapol Bejranonda and Manfred Koch [email protected] , [email protected] ; www.uni-kassel.de/fb14/geohydraulik Using teleconnections from the Pacific and Indian oceans for short- range climate prediction in the eastern seaboard of Thailand Werapol Bejranonda and Manfred Koch Department of Geohydraulics and Engineering Hydrology, University of Kassel, Germany Abstract ID : 875 Because of the peculiar location of Thailand with a long peninsula between the Pacific and the Indian Ocean, Thailand’s local climate and, consequently, its surface water resources are strongly influenced by monsoon seasons which, in turn, depend themselves upon the thermal states of the oceans. As water gathered by the reservoirs during the monsoon season provide 80% of the annual rainfall, small changes of this seasonal weather pattern can have detrimental effects on the water resources availability. Such a water crisis happened in 2005 in the industrial eastern seaboard of the country, and led to a huge drop in the industrial production there. It follows that proper water resources planning, in order to forestall such vagaries of seasonal precipitation changes, as they have happened in the past - and will occur more so in the near future due to potential climate change - is indispensable. A tool to achieve this goal is short-term climate prediction of the upcoming local weather pattern in a region. I. Introduction 1) Analysis of teleconnections of various Pacific and Indian ocean indices with climate across Thailand 2) Use of the teleconnections to develop a method for local, short-term climate prediction in the eastern seaboard of Thailand of up to 12 months 3) Evaluation of the performance of enhanced climate prediction in conjunction with the teleconnections III. Data and Methods II. Objectives 1) 1971-2006 local climate series of monthly maximum (Tmax) and minimum (Tmin) temperatures from 4 stations, precipitation (PCP) from 24 stations and 13 Pacific and Indian ocean indices 2) Cross-correlation analysis to examine teleconnections between ocean states and the local climate 3) ARIMA and ARIMA-EX (with external regressors) models to forecast the local climate variables on the seasonal- or longer horizons 4) Use of the teleconnections to further improve multi-linear regression (MLR) climate downscaling to predict the local climate from the GCMs ECHO- G, BCCR, ECHAM5, GISS,PCM and Hi-Res GCMs 5) Comparison of the classical downscaling methods, SDSM and LARS-WG, with the enhanced short-term climate predictions up to 12 months ahead IV. Time-series analysis Temperature and rainfall time series from 121 meteorological stations from 5 regions across Thailand and of the Pacific / Indian Ocean state indices sea surface temperature (SST), recorded between 1971-2006 are examined with cross- correlation methods (Fig.2). Results show that El-Niño 1+2 SST anomaly index of the Pacific Ocean correlates the strongest with the Thai local climate, with maximal correlation lag-times of -3 months (Fig.2). Cross-correlation demonstrates that the local climate in the eastern seaboard is optimally correlated to ocean indices with lag-times -1 to -6 months that can be used for climate forecasting (Fig.3). ARIMA model NS coef. (calib.)= 0.36 0.36 NS coef. (verf. 12 months)= 0.25 0.25 ARIMA-EX model use NINO 4 (with 6 month lag) NS coef. (calib.)= 0.50 0.50 NS coef. (verf. 12 months)= 0.46 0.46 Temperature (°C) Time (year) VI. Conclusions IV.(b) Seasonal regressions Teleconnective relationship between time series of ocean state variable x t and of the regional climate variable y t , set up as a time-lagged linear regression model where x t+τ is the time-lagged by τ months series of the ocean state predictor variable x t . Selection of ocean indices carried out for different periodic separations, i.e. by dividing the 12 months of a year into periods of 1, 2, 3 and 4 seasons, respectively in the seasonal regression approach. Quality of the seasonal MLR-regression models measured by the R 2 of the linear regression, with the optimal lag of each season selected in the MLR climate prediction model (Fig.4). Fig.1: Eastern seaboard of Thailand and locations of ocean indices in Pacific and Indian oceans -0.67 0.17 0.64 0.34 -0.04 0.42 0.34 -0.04 0.50 0.56 0.62 0.50 0.80 0.62 0.38 0.83 0.94 0.78 0.82 0.92 0.83 0.83 0.93 0.95 0.85 0.85 0.92 0.91 0.41 0.48 0.50 0.44 0.56 0.53 0.50 0.57 0.54 0.65 0.52 0.49 0.67 0.68 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 LARS-W G SD SM ECHO -G H iRes SSTs G CM s G CM s+HiRes ECH O -G + SST G CM s+SSTs G CM s+H iRes+SSTs H iRes+SSTs AR ARIM A ARIM Aex-G CM ARIM Aex-SST conventional M LR multi-domain M LR multi-domain M LR+teleconnection Autoregressive N ash–Sutcliffe m odel efficiency coefficient Tm ax Tmin PCP V. Climate predictions without and with teleconnections (a) ARIMA- and ARIMA-EX models Autoregressive time series models ARMA (ARIMA) and ARMA-EX (with exogenous variables) allow the description of a stochastic time series by taking into account memory effects in the series. Use of SST-teleconnection regression improves the forecast of the local climate variables significantly, as indicated by the Nash–Sutcliffe (NS) coefficients, for example, increasing the performance to predict 12 month- ahead rainfall from originally NS=0.25 to NS= 0.46 (Fig.5). 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 Indian-SETIO Indian-SW IO Indian-W TIO Pacific-EP Pacific-Nino1.2 Pacific-Nino3 Pacific-Nino3.4 Pacific-Nino4 Pacific-N O I Pacific-PD O Pacific-PN A Pacific-SO I Pacific-WP nn ne cc ee ss IV.(a) Cross-correlation Prediction model based on multiple linear regression (MLR) on GCM- predictors. Use of additional teleconnections in the MLR-model enhances the short-term predictive power by about 13% on average (Fig.6), with up to 54% for the pre-monsoon season. Comparisons of ARIMA- and MLR- models on observed climate show that the MLR-model (NS=0.67) is slightly better than the ARIMA model (NS=0.52) to predict 12-month-ahead precipitation (Fig.7) . Fig.2: Correlation power between local precipitation in 5 regions and ocean indices Fig.3: Cross-correlations of four El Niño SST indices with 1971-2005 local monthly min., max. temperature and rainfall Fig.4: Linear regressions of 1971-2005 min. temperature at station 48459 on El Niño 1+2 SST for lags 0.-1,-2 and -3 months by separating the data into annual and seasonal subsets (with R 2 on top). Fig.8: Prediction power of the various models for 1986 12-month-ahead forecasts of Tmax, Tmin and PCP, calibrated for 1971- 1985 time period Lag = 0 Lag = -1 months Lag = -2months Lag = -3 months Niño 1+2 Niño 1+2 Niño 1+2 Niño 1+2 Tmin (◦C) Fig.5: Comparisons of pure ARIMA- and ARIMA-EX (including external ocean indices) models to predict 1986-1999 monthly rainfall at station 48459, using 1971-1985 as calibration period Fig.7: Time-series of 12-month-ahead prediction of rainfall using MLR- and AR- models and calibrated for 1971-1985 period ARIMA (0,0,2)(1,0,2) [12] Optimal : S1 Optimal : S2 Model verifications exhibit that ordinary downscaling models, i.e., SDSM and LARS-WG are not acceptable for short-term forecasting (NS < 0) (Fig.8) . ARIMA and MLR models have NS>0.5 with the following ranking for best short-term climate prediction: Tmax MLR: Hi-Res and ARIMAex with GCM Tmin MLR: Hi-Res+SSTs and ARIMAex with GCM PCP MLR: Hi-Res+SSTs and ARIMAex with SSTs To improve the prediction power of MLR (transfer model) downscaling, regression equations are set up as a dynamic regression model using the ocean indices as predictors to forecast the regional climate series. Purpose: Analysis of time series of ocean state indices and regional climate by various statistical techniques to provide potential teleconnector- relationships between the two within the eastern seaboard. Climate predictability by means of the ocean-indices found in this section can then be used for short- term seasonal climate predictions in various regions across Thailand. (b) MLR-downscaled GCM predictions Fig.6: Verification of MLR with and without tele- connections to predict rainfall for 1971-1999 using 1971-1985 as calibration period
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Transcript of Contacts: Werapol Bejranonda and Manfred Koch [email protected]@uni-kassel.de,...
Contacts: Werapol Bejranonda and Manfred [email protected], [email protected]; www.uni-kassel.de/fb14/geohydraulik
Using teleconnections from the Pacific and Indian oceans for short- range climate prediction in the eastern seaboard of Thailand
Werapol Bejranonda and Manfred Koch
Department of Geohydraulics and Engineering Hydrology, University of Kassel, Germany
Abstract ID : 875
Because of the peculiar location of Thailand with a long peninsula between the Pacific and the Indian Ocean, Thailand’s local climate and, consequently, its surface water resources are strongly influenced by monsoon seasons which, in turn, depend themselves upon the thermal states of the oceans. As water gathered by the reservoirs during the monsoon season provide 80% of the annual rainfall, small changes of this seasonal weather pattern can have detrimental effects on the water resources availability. Such a water crisis happened in 2005 in the industrial eastern seaboard of the country, and led to a huge drop in the industrial production there. It follows that proper water resources planning, in order to forestall such vagaries of seasonal precipitation changes, as they have happened in the past - and will occur more so in the near future due to potential climate change - is indispensable. A tool to achieve this goal is short-term climate prediction of the upcoming local weather pattern in a region.
I. Introduction
1) Analysis of teleconnections of various Pacific and Indian ocean indices with climate across Thailand
2) Use of the teleconnections to develop a method for local, short-term climate prediction in the eastern seaboard of Thailand of up to 12 months
3) Evaluation of the performance of enhanced climate prediction in conjunction with the teleconnections
III. Data and Methods
II. Objectives
1) 1971-2006 local climate series of monthly maximum (Tmax) and minimum (Tmin) temperatures from 4 stations, precipitation (PCP) from 24 stations and 13 Pacific and Indian ocean indices
2) Cross-correlation analysis to examine teleconnections between ocean states and the local climate
3) ARIMA and ARIMA-EX (with external regressors) models to forecast the local climate variables on the seasonal- or longer horizons
4) Use of the teleconnections to further improve multi-linear regression (MLR) climate downscaling to predict the local climate from the GCMs ECHO-G, BCCR, ECHAM5, GISS,PCM and Hi-Res GCMs
5) Comparison of the classical downscaling methods, SDSM and LARS-WG, with the enhanced short-term climate predictions up to 12 months ahead
IV. Time-series analysis
Temperature and rainfall time series from 121 meteorological stations from 5 regions across Thailand and of the Pacific / Indian Ocean state indices sea surface temperature (SST), recorded between 1971-2006 are examined with cross-correlation methods (Fig.2). Results show that El-Niño 1+2 SST anomaly index of the Pacific Ocean correlates the strongest with the Thai local climate, with maximal correlation lag-times of -3 months (Fig.2). Cross-correlation demonstrates that the local climate in the eastern seaboard is optimally correlated to ocean indices with lag-times -1 to -6 months that can be used for climate forecasting (Fig.3).
ARIMA modelNS coef. (calib.)= 0.360.36NS coef. (verf. 12 months)= 0.250.25
ARIMA-EX model use NINO 4(with 6 month lag)NS coef. (calib.)= 0.500.50NS coef. (verf. 12 months)= 0.460.46
Tem
pera
ture
(°C)
Time (year)
VI. Conclusions
IV.(b) Seasonal regressions Teleconnective relationship between time series of ocean state variable xt and of the regional climate variable yt, set up as a time-lagged linear regression model
where xt+τ is the time-lagged by τ months series of the ocean state predictor variable xt.
Selection of ocean indices carried out for different periodic separations, i.e. by dividing the 12 months of a year into periods of 1, 2, 3 and 4 seasons, respectively in the seasonal regression approach.
Quality of the seasonal MLR-regression models measured by the R2 of the linear regression, with the optimal lag of each season selected in the MLR climate prediction model (Fig.4).
Fig.1: Eastern seaboard of Thailand and locations of ocean indices in Pacific and Indian oceans
-0.67
0.17
0.64
0.34
-0.04
0.42
0.34
-0.04
0.500.56
0.62
0.50
0.80
0.62
0.38
0.83
0.94
0.780.82
0.92
0.83 0.83
0.93 0.95
0.85 0.85
0.92 0.91
0.41
0.48 0.500.44
0.560.53
0.50
0.570.54
0.65
0.520.49
0.67 0.68
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
LARS-
WG
SDSM
ECH
O-G
HiR
es
SSTs
GC
Ms
GC
Ms+
HiR
es
ECH
O-G
+ S
ST
GC
Ms+
SSTs
GC
Ms+
HiR
es+
SSTs
HiR
es+
SSTs AR
AR
IMA
AR
IMA
ex-G
CM
AR
IMA
ex-S
ST
conventional MLR multi-domainMLR
multi-domainMLR+teleconnection
Autoregressive
Nas
h–S
utcl
iffe
mod
el e
ffici
ency
coe
ffici
ent
Tmax
Tmin
PCP
V. Climate predictions without and with teleconnections
(a) ARIMA- and ARIMA-EX models Autoregressive time series models ARMA (ARIMA) and ARMA-EX (with exogenous variables) allow the description of a stochastic time series by taking into account memory effects in the series.
Use of SST-teleconnection regression improves the forecast of the local climate variables significantly, as indicated by the Nash–Sutcliffe (NS) coefficients, for example, increasing the performance to predict 12 month-ahead rainfall from originally NS=0.25 to NS= 0.46 (Fig.5).
0.000.100.200.300.400.500.600.700.80
Indian-SETIO
Indian-SWIO
Indian-WTIO
Pacific-EP
Pacific-Nino1.2
Pacific-Nino3
Pacific-Nino3.4 Pacific-Nino4
Pacific-NOI
Pacific-PDO
Pacific-PNA
Pacific-SOI
Pacific-WP
nnnecceess
IV.(a) Cross-correlation
Prediction model based on multiple linear regression (MLR) on GCM- predictors. Use of additional teleconnections in the MLR-model enhances the short-term predictive power by about 13% on average (Fig.6), with up to 54% for the pre-monsoon season. Comparisons of ARIMA- and MLR- models on observed climate show that the MLR-model (NS=0.67) is slightly better than the ARIMA model (NS=0.52) to predict 12-month-ahead precipitation (Fig.7) .
Fig.2: Correlation power between local precipitation in 5 regions and ocean indices
Fig.3: Cross-correlations of four El Niño SST indices with 1971-2005 local monthly min., max. temperature and rainfall
Fig.4: Linear regressions of 1971-2005 min. temperature at station 48459 on El Niño 1+2 SST for lags 0.-1,-2 and -3 months by separating the data into annual and seasonal subsets (with R2 on top).
Fig.8: Prediction power of the various models for 1986 12-month-ahead forecasts of Tmax, Tmin and PCP, calibrated for 1971-1985 time period
Lag = 0 Lag = -1 months Lag = -2months Lag = -3 months
Niño 1+2 Niño 1+2 Niño 1+2 Niño 1+2
Tmin
(◦C)
Fig.5: Comparisons of pure ARIMA- and ARIMA-EX (including external ocean indices) models to predict 1986-1999 monthly rainfall at station 48459, using 1971-1985 as calibration period
Fig.7: Time-series of 12-month-ahead prediction of rainfall using MLR- and AR- models and calibrated for 1971-1985 period
ARIMA (0,0,2)(1,0,2)[12]ARIMA (0,0,2)(1,0,2)[12]
Optimal : S1Optimal : S2
Model verifications exhibit that ordinary downscaling models, i.e., SDSM and LARS-WG are not acceptable for short-term forecasting (NS < 0) (Fig.8) .
ARIMA and MLR models have NS>0.5 with the following ranking for best short-term climate prediction:
Tmax MLR: Hi-Res and
ARIMAex with GCM
Tmin MLR: Hi-Res+SSTs and
ARIMAex with GCM
PCP MLR: Hi-Res+SSTs and
ARIMAex with SSTs
To improve the prediction power of MLR (transfer model) downscaling, regression equations are set up as a dynamic regression model using the ocean indices as predictors to forecast the regional climate series.
Purpose: Analysis of time series of ocean state indices and regional climate by various statistical techniques to provide potential teleconnector-relationships between the two within the eastern seaboard.
Climate predictability by means of the ocean-indices found in this section can then be used for short- term seasonal climate predictions in various regions across Thailand.
(b) MLR-downscaled GCM predictions
Fig.6: Verification of MLR with and without tele-connections to predict rainfall for 1971-1999 using 1971-1985 as calibration period
