Contacts : Werapol Bejranonda and Manfred Koch bwerapol@uni-kassel.de , kochm@uni-kassel.de ;

1. 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 I. Introduction 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+τis 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). 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. Tmin (◦C) Optimal : S2 Optimal : S3,S4 Optimal : S1 *a=annual,s1=dry season [Oct-Dec],s2= pre-monsoon [Jan-Mar],s3= monsoon1 [Apr-Jun],s4= monsoon2 [Jul-Sep] Eastern Seaboard 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). • 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. Fig.1: Eastern seaboard of Thailand and locations of ocean indices in Pacific and Indian oceans II. Objectives V. Climate predictions without and with teleconnections (a) ARIMA- and ARIMA-EX models Pacific • Analysis of teleconnections of various Pacific and Indian ocean indices with climate across Thailand • 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 • Evaluation of the performance of enhanced climate prediction in conjunction with the teleconnections Indian • 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). III. Data and Methods • 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 • Cross-correlation analysis to examine teleconnections between ocean states and the local climate • ARIMA and ARIMA-EX (with external regressors) models to forecast the local climate variables on the seasonal- or longer horizons • 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 • Comparison of the classical downscaling methods, SDSM and LARS-WG, with the enhanced short-term climate predictions up to 12 months ahead ARIMA model NS coef.(calib.)= 0.36 NS coef.(verf. 12 months)= 0.25 ARIMA (0,0,2)(1,0,2)[12] Temperature (°C) ARIMA-EX model use NINO 4 (with 6 month lag) NS coef.(calib.)= 0.50 NS coef.(verf. 12 months)= 0.46 Time (year) 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 IV. Time-series analysis (b) MLR-downscaled GCM predictions 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. • 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) . IV.(a) Cross-correlation • 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). Fig.6: Verification of MLR with and without tele-connections to predict rainfall for 1971-1999 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 Niño 1+2 Niño 3 VI. Conclusions • Model verifications exhibit that ordinary downscaling models, i.e., SDSM and LARS-WG are not acceptable for short-term forecasting (NS < 0) (Fig.8) . Niño 3.4 • 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 Niño 4 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 • 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. Contacts: Werapol Bejranonda and Manfred Koch bwerapol@uni-kassel.de, kochm@uni-kassel.de; www.uni-kassel.de/fb14/geohydraulik 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