
The Response of a Stochastically Forced ENSO Model to Observed Off-Equatorial Wind Stress Forcing
Author(s) -
Shayne McGregor,
Neil J. Holbrook,
Scott B. Power
Publication year - 2009
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/2008jcli2387.1
Subject(s) - wind stress , climatology , forcing (mathematics) , thermocline , baroclinity , anomaly (physics) , geology , sea surface temperature , atmospheric sciences , amplitude , atmospheric model , environmental science , oceanography , physics , condensed matter physics , quantum mechanics
This study investigates the response of a stochastically forced coupled atmosphere–ocean model of the equatorial Pacific to off-equatorial wind stress anomaly forcing. The intermediate-complexity coupled ENSO model comprises a linear, first baroclinic mode, ocean shallow water model with a steady-state, two–pressure level (250 and 750 mb) atmospheric component that has been linearized about a state of rest on the β plane. Estimates of observed equatorial region stochastic forcing are calculated from NCEP–NCAR reanalysis surface winds for the period 1950–2006 using singular value decomposition. The stochastic forcing is applied to the model both with and without off-equatorial region wind stress anomalies (i.e., poleward of 12.5° latitude). It is found that the multiyear changes in the equatorial Pacific thermocline depth “background state” induced by off-equatorial forcing can affect the amplitude of modeled sea surface temperature anomalies by up to 1°C. Moreover, off-equatorial wind stress anomalies increased the modeled amplitude of the two biggest El Niño events in the twentieth century (1982/83 and 1997/98) by more than 0.5°C, resulting in a more realistic modeled response. These equatorial changes driven by off-equatorial region wind stress anomalies are highly predictable to two years in advance and may be useful as a physical basis to enhance multiyear probabilistic predictions of ENSO indices.