Characteristic Patterns of Variability of Sea Level Pressure in the Northern Hemisphere

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  • 1 Laboratory for Atmospheric Research, University of Illinois, Urbana 61801
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Abstract

Seasonal and annual mean sea level pressures for the Northern Hemisphere have been analyzed to determine the dominant modes of interannual and longer period variability using monthly sea level pressure analyses as revised by Trenberth and Paolino (1980). Empirical orthogonal function (EOF) analysis is used to reveal the modes which explain most of the variance for the period 1925–77. In winter, Kutzbach's (1970) EOF 1 for January remains the dominant mode and a closely related pattern dominates all seasons and the annual means. Although there are differences in detail in each season, the dominant mode is basically a high-latitude zonal-index-type pattern with departures in pressure at high latitudes corresponding to anomalies of opposite sign in low latitudes. EOF 1 is linked to the North Atlantic Oscillation, but the north–south fluctuations in mass also occur in the Pacific and, to a lesser extent, elsewhere. Time series associated with this pattern have highly significant spectral peaks at the quasi-biennial and 6-year periodicities. The latter is related to the Southern Oscillation.

Time series associated with higher order EOF's reveal significant low-frequency fluctuations. In spite of the significant non-randomness present, preliminary attempts at prediction using autoregressive techniques indicate only very limited skill to be possible.

Correlation of sea level pressures with those of Darwin are used to define Southern Oscillation patterns in the Northern Hemisphere. In all seasons and annually, a characteristic pattern is present across the United States. High pressures over the central United States are associated with low pressures in the Pacific and Atlantic, and vice versa. This pattern is often referred to as the North Pacific–North American teleconnection pattern, but it appears that its origins stem from well beyond the North Pacific and that a global perspective is needed before we can hope to fully understand interannual variability.

Abstract

Seasonal and annual mean sea level pressures for the Northern Hemisphere have been analyzed to determine the dominant modes of interannual and longer period variability using monthly sea level pressure analyses as revised by Trenberth and Paolino (1980). Empirical orthogonal function (EOF) analysis is used to reveal the modes which explain most of the variance for the period 1925–77. In winter, Kutzbach's (1970) EOF 1 for January remains the dominant mode and a closely related pattern dominates all seasons and the annual means. Although there are differences in detail in each season, the dominant mode is basically a high-latitude zonal-index-type pattern with departures in pressure at high latitudes corresponding to anomalies of opposite sign in low latitudes. EOF 1 is linked to the North Atlantic Oscillation, but the north–south fluctuations in mass also occur in the Pacific and, to a lesser extent, elsewhere. Time series associated with this pattern have highly significant spectral peaks at the quasi-biennial and 6-year periodicities. The latter is related to the Southern Oscillation.

Time series associated with higher order EOF's reveal significant low-frequency fluctuations. In spite of the significant non-randomness present, preliminary attempts at prediction using autoregressive techniques indicate only very limited skill to be possible.

Correlation of sea level pressures with those of Darwin are used to define Southern Oscillation patterns in the Northern Hemisphere. In all seasons and annually, a characteristic pattern is present across the United States. High pressures over the central United States are associated with low pressures in the Pacific and Atlantic, and vice versa. This pattern is often referred to as the North Pacific–North American teleconnection pattern, but it appears that its origins stem from well beyond the North Pacific and that a global perspective is needed before we can hope to fully understand interannual variability.

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