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Publication Date

2008-01-01

Availability

UM campus only

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Meteorology and Physical Oceanography (Marine)

Date of Defense

2008-11-17

First Committee Member

Chidong Zhang - Committee Chair

Second Committee Member

Ben Kirtman - Committee Member

Third Committee Member

Javier Zavala-Garay - Outside Committee Member

Abstract

A systematic procedure is designed to evaluate the role of stochastic forcing (SF) in El Nino ? Southern Oscillation (ENSO) exhibited by coupled ocean-atmosphere general circulation models (CGCMs). The procedure is applied on a 163-year run of a CGCM which has realistic representation of ENSO and SF. The realism of ENSO in the CGCM is determined by statistical comparison with a 29-year global reanalysis product. SF is extracted from both the CGCM and reanalysis as residual atmospheric variability uncoupled to the ocean. Further, the Madden-Julian Oscillation (MJO) and non-MJO components are isolated from SF. The CGCM stochastic components are compared to those from the reanalysis to validate their representation. A coupled ocean-atmosphere model of intermediate complexity is first forced with stochastic components from the reanalysis. The resulting ENSO is examined for realism to evaluate the strengths and weaknesses of the intermediate coupled model, which is then forced with the stochastic components from the CGCM. Results are diagnosed to investigate the role of SF. It is found that the SF can play an important role in ENSO in the CGCM, especially in its warm events. The role is similar to reanalysis SF in generating ENSO period and spring predictability barrier. However, unlike in case of the reanalysis, the seasonal dependence of ENSO variance in the CGCM does not seem to be originating from its SF. The contribution to statistics appears to be higher from the MJO component of SF compared to the non-MJO component. The intermediate model simulations also suggest that both in CGCM and nature, the SF operates on a weakly stable coupled system to produce ENSO variability.

Keywords

Regression; Regressive Model; Intermediate Coupled Model; SVD; Climate; Tropical; Pacific; Hilbert

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