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The simulation of peak and delayed ENSO teleconnections

Spencer, H. and Slingo, J. M. (2003) The simulation of peak and delayed ENSO teleconnections. Journal of Climate, 16 (11). pp. 1757-1774. ISSN 0894-8755

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To link to this item DOI: 10.1175/1520-0442(2003)016<1757:TSOPAD>2.0.CO;2

Abstract/Summary

There is much evidence that El Niño and La Niña lead to significant atmospheric seasonal predictability across much of the globe. However, despite successful predictions of tropical Pacific SSTs, atmospheric seasonal forecasts have had limited success. This study investigates model errors in the Hadley Centre Atmospheric Model version 3 (HadAM3) by analyzing composites of similar El Niño and La Niña events at their peak in December–January–February (DJF) and through their decay in March–April–May (MAM). The large-scale, tropical ENSO teleconnections are modeled accurately by HadAM3 during DJF but the strongest extratropical teleconnection, that in the North Pacific during winter, is modeled inaccurately. The Aleutian low is frequently observed to shift eastward during El Niño but the modeled response always consists of a deepening of the low without a shift. This is traced to small errors in the sensitivity of precipitation to SST in the tropical Pacific, which does not display enough variability so that the precipitation is always too high over the warmest SSTs. This error is reduced when vertical resolution is increased from 19 to 30 levels but enhanced horizontal resolution does not improve the simulation further. In MAM, following the peak of an El Niño or La Niña, atmospheric anomalies are observed to decay rapidly. The modeled ENSO response in DJF persists into MAM, making the extratropical anomalies in MAM too strong. This inaccuracy is again likely to be due to the high modeled sensitivity of tropical Pacific precipitation to SST, which is not significantly improved with enhanced vertical or horizontal resolution in MAM.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:5197
Publisher:American Meteorological Society

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