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.S. Climate Prediction Center (CPC) analysis ( Reynolds and Smith 1994 ). The upper temperature climatology is from the National Oceanographic Data Center’s World Ocean Database (WOD98; NODC 1999 ). It is clear that the model reproduces the basic structures of the observations qualitatively. Compared to the observations ( Fig. 1b ), the model mean SST ( Fig. 1a ) shows a cold bias in the equatorial and northern ocean, which is larger than 1°C to the north of 5°N and around 2°C near the northwestern coast
.S. Climate Prediction Center (CPC) analysis ( Reynolds and Smith 1994 ). The upper temperature climatology is from the National Oceanographic Data Center’s World Ocean Database (WOD98; NODC 1999 ). It is clear that the model reproduces the basic structures of the observations qualitatively. Compared to the observations ( Fig. 1b ), the model mean SST ( Fig. 1a ) shows a cold bias in the equatorial and northern ocean, which is larger than 1°C to the north of 5°N and around 2°C near the northwestern coast
square regression analysis. The analysis of the variance accounted for by each possible combination of five random moorings is used to determine the five locations that best reproduce the original error covariance matrix. The results consistently identify equatorial sites as the ones that carry most of the error information. This is not surprising considering that the linear model relies on equatorial dynamics to propagate large-scale features. The five equatorial locations that best reproduce the
square regression analysis. The analysis of the variance accounted for by each possible combination of five random moorings is used to determine the five locations that best reproduce the original error covariance matrix. The results consistently identify equatorial sites as the ones that carry most of the error information. This is not surprising considering that the linear model relies on equatorial dynamics to propagate large-scale features. The five equatorial locations that best reproduce the
proxy for the tropical convective activity. In addition, in order to isolate intraseasonal convective events that are organized at large scales from the background red-noise variability at these time scales (say 20–90 days in a broad sense), we use the local mode analysis (LMA) approach ( Goulet and Duvel 2000 ; Duvel et al. 2004 ). A multivariate LMA is developed here in order to extract SST and surface wind perturbations related specifically to large-scale organized convective perturbations
proxy for the tropical convective activity. In addition, in order to isolate intraseasonal convective events that are organized at large scales from the background red-noise variability at these time scales (say 20–90 days in a broad sense), we use the local mode analysis (LMA) approach ( Goulet and Duvel 2000 ; Duvel et al. 2004 ). A multivariate LMA is developed here in order to extract SST and surface wind perturbations related specifically to large-scale organized convective perturbations
fluctuate quite significantly there within the ensemble. Overall, it seems that the SST signals near the Sumatra coast are more strongly influenced by fluctuations independent of the prescribed external forcing. This is also shown in the signal-to-noise ratios for the variance analysis of the SST anomalies to be discussed in the next section. In spite of some of these shortcomings and uncertainty, it is clear that the hindcast captures many observed features. Figures 3 and 4 compare the first modes
fluctuate quite significantly there within the ensemble. Overall, it seems that the SST signals near the Sumatra coast are more strongly influenced by fluctuations independent of the prescribed external forcing. This is also shown in the signal-to-noise ratios for the variance analysis of the SST anomalies to be discussed in the next section. In spite of some of these shortcomings and uncertainty, it is clear that the hindcast captures many observed features. Figures 3 and 4 compare the first modes
1. Introduction For short-term global climate prediction, the sea surface temperature (SST) anomalies associated with the El Niño–Southern Oscillation (ENSO) phenomenon are recognized as the most dominant forcing factor (e.g., Wallace et al. 1998 ; Trenberth et al. 1998 ; Lau and Nath 2000 ; Su et al. 2001 ; Annamalai and Liu 2005 ). Of special interest here is the role of El Niño on the Pacific–North American (PNA) pattern defined by Wallace and Gutzler (1981) . From regression analysis
1. Introduction For short-term global climate prediction, the sea surface temperature (SST) anomalies associated with the El Niño–Southern Oscillation (ENSO) phenomenon are recognized as the most dominant forcing factor (e.g., Wallace et al. 1998 ; Trenberth et al. 1998 ; Lau and Nath 2000 ; Su et al. 2001 ; Annamalai and Liu 2005 ). Of special interest here is the role of El Niño on the Pacific–North American (PNA) pattern defined by Wallace and Gutzler (1981) . From regression analysis
