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  • Author or Editor: Terence J. O’Kane x
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Terence J. O’Kane, Didier P. Monselesan, and James S. Risbey

Abstract

The authors undertake a multiscale spectral reexamination of the variability of the Pacific–South American (PSA) pattern and the mechanisms by which this variability occurs. Time scales from synoptic to interannual are investigated, focusing on the means by which tropical variability is communicated to the midlatitudes and on in situ forcing within the midlatitude waveguides. Particular interest is paid to what fraction of the total variability associated with the PSA, occurring on interannual time scales, is attributable to tropical forcing relative to that occurring on synoptic and intraseasonal time scales via internal waveguide dynamics. In general, it is found that the eastward-propagating wave train pattern typically associated with the PSA manifests across time scales from synoptic to interannual, with the majority of the variability occurring on synoptic-to-intraseasonal time scales largely independent of tropical convection. It is found that the small fraction of the total variance with a tropical signal occurs via the zonal component of the thermal wind modulating both the subtropical and polar jets. The respective roles of the Hadley circulation and stationary Rossby wave sources are also examined. Further, a PSA-like mode is identified in terms of the slow components of higher-order modes of tropospheric geopotential height. This study reestablishes the multiscale nonlinear nature of the PSA modes arising largely as a manifestation of internal midlatitude waveguide dynamics and local disturbances.

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Carly R. Tozer, James S. Risbey, Terence J. O’Kane, Didier P. Monselesan, and Michael J. Pook

Abstract

We assess the large-scale atmospheric dynamics influencing rainfall extremes in Tasmania, located within the Southern Hemisphere storm track. We characterize wet and dry multiday rainfall extremes in western and eastern Tasmania, two distinct climate regimes, and construct atmospheric flow composites around these extreme events. We consider the onset and decay of the events and find a link between Rossby wave trains propagating in the polar jet waveguide and wet and dry extremes across Tasmania. Of note is that the wave trains exhibit varying behavior during the different extremes. In the onset phase of rainfall extremes in western Tasmania, there is a coherent wave train in the Indian Ocean, which becomes circumglobal in extent and quasi-stationary as the event establishes and persists. Wet and dry extremes in this region are influenced by opposite phases of this circumglobal wave train pattern. In eastern Tasmania, wet extremes relate to a propagating wave train, which is first established in the Indian Ocean sector and propagates eastward to the Pacific Ocean sector as the event progresses. During dry extremes in eastern Tasmania, the wave train is first established in the Pacific Ocean, as opposed to Indian Ocean, and persists in this sector for the entire event, with a structure indicative of the Pacific–South American pattern. The findings regarding different wave train forms and their relationship to rainfall extremes have implications for extreme event attribution in other regions around the globe.

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Paul A. Sandery, Terence J. O’Kane, Vassili Kitsios, and Pavel Sakov

Abstract

Data assimilation (DA) experiments are performed to assess impacts of observations in climate model state estimation through the cross-domain ocean–atmosphere forecast error covariances (cross covariances). Specifically, we explore strongly and weakly coupled DA variants using the Climate Analysis Forecast Ensemble (CAFE) system. This comprises 96 ensemble members of the Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 climate model assimilating observational data from the ocean, atmosphere, and sea ice realms with the ensemble Kalman filter (EnKF). Sequences of atmospheric synoptic time-scale coupled forecasts (7 days) are carried out with model consistent initialization. Unassimilated forward-independent observations are used to quantify forecast innovation error-growth rates. The results show benefit for the slow components of the atmosphere and ocean subsurface when strongly coupling ocean observations to the atmosphere. In the present system, projecting fast atmospheric observations onto the ocean subsurface through the cross covariances benefits the oceanic and atmospheric near-surface layers; however, this leads to deterioration in the ocean subsurface. Particular variants of coupled DA are able to constrain the ocean and atmosphere. The forecasts initialized with these variants have predictability at intraseasonal time scales. Errors associated with the dominant intraseasonal mode of variability, the Madden–Julian oscillation (MJO), are decomposed into normal mode functions. Consistent with recent studies showing large MJO events are concurrent with rapid error growth associated with nonlinear interactions, we find a clear relationship between the strength of a given MJO event and the related forecast innovations. Our results demonstrate consistent system behavior in relation to capturing real-world disturbances that affect climate predictability.

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