Improvement of ENSO prediction using a linear regression model with a southern Indian Ocean sea surface temperature predictor

Details Improvement of ENSO prediction using a linear regression model with a southern Indian Ocean sea surface temperature predictor Improvement of ENSO prediction using a linear regression model with a southern Indian Ocean sea surface temperature predictor

Sep 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005georl..3218702d&link_type=abstract

Geophysical Research Letters, Volume 32, Issue 18, CiteID L18702

Physics

7

Atmospheric Processes: Climate Change And Variability (1616, 1635, 3309, 4215, 4513), Oceanography: General: Climate And Interannual Variability (1616, 1635, 3305, 3309, 4513), Oceanography: Physical: Enso (4922), Geographic Location: Indian Ocean

Scientific paper

This study presents a detailed comparison between three ENSO precursors which can predict across the spring persistence barrier: the anomalous equatorial Pacific upper ocean heat content, the zonal equatorial wind stress anomaly in the far-western Pacific and SST anomalies in the South-East Indian Ocean (SEIO) during the late boreal winter. A new correlation analysis confirms that El Niño (La Niña) onsets are preceded by significant cold (warm) SST anomalies in the SEIO during the late boreal winter after the 1976-77 climate regime shift. Thus, the objective is to examine the respective potential of these three ENSO precursors to predict ENSO events across the boreal spring barrier during recent decades. Surprisingly, in this focus, cross-validated hindcasts of the linear regression models based on the lagged relationship between Niño3.4 SST and the predictors suggest that SEIO SST anomalies during the late boreal winter is the more robust ENSO predictor.

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