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Statistical prediction of ENSO (Nino 3) using sub‐surface temperature data
Author(s) -
Drosdowsky Wasyl
Publication year - 2006
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2005gl024866
Subject(s) - predictability , empirical orthogonal functions , climatology , el niño southern oscillation , sea surface temperature , forecast skill , benchmark (surveying) , statistical model , surface air temperature , oscillation (cell signaling) , statistical analysis , environmental science , meteorology , geology , statistics , mathematics , geography , precipitation , geodesy , biology , genetics
A number of statistical schemes for predicting the evolution of the El Niño–Southern Oscillation (ENSO) have been developed in recent years. These tend to show some skill out to 9 to 12 months from late in the southern autumn, but only limited skill for a few months from late summer through the so‐called “predictability barrier”. More recently statistical models utilizing sub–surface temperature data have shown improvement of skill over persistence through this autumn period. Empirical Orthogonal Function analysis is used to extract the dominant signals in the sub‐surface variability. The resulting statistical model shows similar skill to that obtained using other simple indices of sub‐surface temperature, such as the warm water volume or average depth of the 20°C isotherm, or from coupled ocean – atmosphere models. These statistical models can therefore be used as a more stringent benchmark against which the complex coupled dynamical models are assessed.