Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly

doi:10.3969/j.issn.1001-909X.2018.01.001

Abstract

Abstract: The tropical Indian Ocean Sea Surface Temperature Anomaly (SSTA) and the Indian Ocean Dipole (IOD) indices are predicted, using the multiple linear regression model, the Markov model and the neural network model respectively. 63 years' hindcast experiments are set up to compare the differences between linear and nonlinear statistical models in detail. And the results reveal that the statistical models are little different from the complicated dynamic model. Their skillful prediction (correlation coefficients above 0.5) could reach 3 months for DMI, about 5-6 months for EIO index and 8-9 months for WIO. Since the IOD event has a strong seasonal phase lock, the DMI can be predicted previously for 4 months in fall. When the synchronistic ENSO index is added as a predictor, the prediction skill, especially the IOD peak, will be improved. The complicated neural network and the simple regression model are proved to be with a similar prediction skill.

Key words: statistical prediction, IOD, neural network, ENSO

CLC Number:

P732.6

FANG Yue-wei, TANG You-min, LI Jun-de, LIU Ting. Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly[J]. Journal of Marine Sciences, 2018, 36(1): 1-15.

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