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Master of Science (MS)
Meteorology and Physical Oceanography (Marine)
Date of Defense
First Committee Member
Second Committee Member
Third Committee Member
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.
Regression; Regressive Model; Intermediate Coupled Model; SVD; Climate; Tropical; Pacific; Hilbert
Kapur, Atul, "Role of Stochastic Forcing in ENSO Variability in a Coupled GCM" (2008). Open Access Theses. 168.