Structure of Oceanic and Atmospheric Low-Frequency Variability over the Tropical Pacific and Indian Oceans. Part I: COADS Observations

Sumant Nigam Department of Meteorology, University of Maryland, College Park, Maryland

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Horng-Syi Shen Department of Meteorology, University of Maryland, College Park, Maryland

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Abstract

The recurrent modes of combined oceanic and atmospheric low-frequency variability over the tropical Pacific and Indian oceans are calculated to provide quantitatively and structurally well-defined targets for simulation/prediction studies of ocean-atmosphere variability. Within the atmosphere, this study focuses on the structure of the large-scale near-surface flow variability.

The structure of combined variability of outgoing longwave radiation (OLR) and the Comprehensive Ocean-Atmosphere Data Set (COADS) SSTs and surface winds (1974–87) is analyzed using the covariance-based rotated principal component analysis technique during three nonoverlapping periods of the calendar year. The leading combined variability mode in each of these periods is found to be EJ Niño related, and the structural relationships among its four components quite revealing. For example, the surface divergence is found to be largely determined by the meridional winds, and while there is broad correspondence between the similarly signed OLR and surface divergence anomalies across the Pacific, pronounced oppositely signed anomalies are also present, notably over the Maritime Continent during the December–February (DJF) months.

The second leading mode of combined variability during the DJF months is robust, has nondescript equatorial SST or OLR anomalies, and represents coherent Jarge-scale expansion/contraction of the Pacific trade-wind system-north-south in case of the northeast trades and cast-west in case of the southeast trades.

In a related modeling study, the leading combined variability mode ~ on COADS observations was used to provide both the input (its OLR component) and the target (its surface wind components) for a simplified “Gill-type” model of the tropics (the atmospheric component of the Cane-Zebiak model) in order to ascertain the quality of its surface wind simulation.

Abstract

The recurrent modes of combined oceanic and atmospheric low-frequency variability over the tropical Pacific and Indian oceans are calculated to provide quantitatively and structurally well-defined targets for simulation/prediction studies of ocean-atmosphere variability. Within the atmosphere, this study focuses on the structure of the large-scale near-surface flow variability.

The structure of combined variability of outgoing longwave radiation (OLR) and the Comprehensive Ocean-Atmosphere Data Set (COADS) SSTs and surface winds (1974–87) is analyzed using the covariance-based rotated principal component analysis technique during three nonoverlapping periods of the calendar year. The leading combined variability mode in each of these periods is found to be EJ Niño related, and the structural relationships among its four components quite revealing. For example, the surface divergence is found to be largely determined by the meridional winds, and while there is broad correspondence between the similarly signed OLR and surface divergence anomalies across the Pacific, pronounced oppositely signed anomalies are also present, notably over the Maritime Continent during the December–February (DJF) months.

The second leading mode of combined variability during the DJF months is robust, has nondescript equatorial SST or OLR anomalies, and represents coherent Jarge-scale expansion/contraction of the Pacific trade-wind system-north-south in case of the northeast trades and cast-west in case of the southeast trades.

In a related modeling study, the leading combined variability mode ~ on COADS observations was used to provide both the input (its OLR component) and the target (its surface wind components) for a simplified “Gill-type” model of the tropics (the atmospheric component of the Cane-Zebiak model) in order to ascertain the quality of its surface wind simulation.

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