Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors

Haiying Chen; Guohong Fang; Baoshu Yin; Yonggang Wang; Zexun Wei

doi:10.1007/s00343-010-9197-9

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Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors

Updated：2023-05-16

- Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors
- Journal of Oceanology and Limnology Vol. 28, Issue 1, Pages: 144-152(2010)
- Affiliations：
  
  1. Key Laboratory of Ocean Circulation and Wave Institute of Oceanology Chinese Academy of Sciences
  2. Key Laboratory of Marine Science and Numerical Modeling the First Institute of Oceanography SOA
- Author bio：
- Funds：
- DOI：10.1007/s00343-010-9197-9
  CLC：
- Online First：27 January 2010，
  
  Published：2010
- Accepted：
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Chen, H., Fang, G., Yin, B. et al. Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors., Chin. J. Ocean. Limnol. 28, 144–152 (2010). https://doi.org/10.1007/s00343-010-9197-9
DOI：

Chen, H., Fang, G., Yin, B. et al. Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors., Chin. J. Ocean. Limnol. 28, 144–152 (2010). https://doi.org/10.1007/s00343-010-9197-9 DOI：

摘要

Abstract

Based on an empirical orthogonal function (EOF) analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature (OISST) data in the South China Sea (SCS) after removing the climatological mean and trends of SST

over the period of January 1982 to October 2003

the corresponding TCF correlates best with the Dipole Mode Index (DMI)

Niño1+2

Niño3.4

Niño3

and Niño4 indices with time lags of 10

and 6 months

respectively. Thus

a statistical hindcasts in the prediction model are based on a canonical correlation analysis (CCA) model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1–12 month lead time after the canonical variants are calculated

using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1–12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1–12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years

in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models: one using both DMI and Niño indices and the other using only Niño indices without DMI

and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI

the forecast accuracy is lower in the coastal areas off the Philippines in the SCS

suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI

while using both of Niño indices and DMI

the highest accuracies occur when the forecast interval time is eight months

suggesting that the Niño indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years (1993/94 to October 2004) by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1–12 month lead time (the persisted prediction). Predictions based on the CCA model show a significant improvement over the persisted prediction

especially with an increased lead time (longer than 3 months). The forecast model performs steadily and the forecast accuracy

i.e.

the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas

when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts

the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1–12 month lead time are also examined.

关键词

Keywords

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