Predictability of SST-Modulated Westerly Wind Bursts

DSpace/Manakin Repository

Predictability of SST-Modulated Westerly Wind Bursts

Citable link to this page


Title: Predictability of SST-Modulated Westerly Wind Bursts
Author: Gebbie, Geoffrey A; Tziperman, Eli

Note: Order does not necessarily reflect citation order of authors.

Citation: Gebbie, Geoffrey A., and Eli Tziperman. 2009. Predictability of SST-modulated westerly wind bursts. Journal of Climate 22(14): 3894-3909.
Access Status: Full text of the requested work is not available in DASH at this time (“dark deposit”). For more information on dark deposits, see our FAQ.
Full Text & Related Files:
Abstract: Westerly wind bursts (WWBs), a significant player in ENSO dynamics, are modeled using an observationally motivated statistical approach that relates the characteristics of WWBs to the large-scale sea surface temperature. Although the WWB wind stress at a given location may be a nonlinear function of SST, the characteristics of WWBs are well described as a linear function of SST. Over 50% of the interannual variance in the WWB likelihood, zonal location, duration, and fetch is explained by changes in SST. The model captures what is seen in a 17-yr record of satellite-derived winds: the eastward migration and increased occurrence of wind bursts as the western Pacific warm pool extends. The WWB model shows significant skill in predicting the interannual variability of the characteristics of WWBs, while the prediction skill of the WWB seasonal cycle is limited by the record length of available data. The novel formulation of the WWB model can be implemented in a stochastic or deterministic mode, where the deterministic mode predicts the ensemble-mean WWB characteristics. Therefore, the WWB model is especially appropriate for ensemble prediction experiments with existing ENSO models that are not capable of simulating realistic WWBs on their own. Should only the slowly varying component of WWBs be important for ENSO prediction, this WWB model allows a shortcut to directly compute the slowly varying ensemble-mean wind field without performing many realizations.
Published Version: doi:10.1175/2009JCLI2516.1
Other Sources:
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search