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Pao-Shin Chu

Abstract

The large-scale atmospheric circulation of the Brazil-Atlantic sector is studied in relation to extreme rainfall anomalies in two large regions of Northeast Brazil (Nordeste). Long-term rainfall series, aerological records of stations in South America, and ship observations over the tropical Atlantic form the data base for this study.

Departure patterns of meteorological elements over the Atlantic are investigated for composites of extremely dry and wet years in the southern and northern Nordeste. Southern Nordeste's peak rainy season is around November/December. The wet years in the southern Nordeste are marked by negative pressure departures over the South Atlantic, weak onshore southeast trades and anomalously cold waters along the south Brazil coast. These features appear to be related to Southern Hemispheric frontal systems. During the dry years, departure patterns are nearly reversed to those of the wet years.Northern Nordeste receives its maximum rainfall in March/April. Rainfall variations are modulated by the latitudinal displacement of the baric trough and confluence axis over the equatorial Atlantic and concomitant sea surface temperature anomalies. Case studies of recent extreme years indicate the possible existence of a local meridional circulation. The more northerly position of the convergence band over the Atlantic, the anomalously cold waters to the south of the equator and the subsidence in the southern portion of the thermally-induced meridional circulation cell over the Nordeste, characteristic of drought years, are all unfavorable for rainfall in Northeast Brazil.

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Pao-Shin Chu and Richard W. Katz

Abstract

A relative measure of actual, rather than potential, predictability of a meteorological variable on the basis of its past history alone is proposed. This measure is predicated on the existence of a parametric time series model to represent the meteorological variable. Among other things, it provides an explicit representation of forecasting capability in terms of the individual parameters of such time series models.

As an application, the extent to which the Southern Oscillation (S0), a major component of climate, can be predicted on a monthly as well as a seasonal time scale on the basis of its past history alone is determined. In particular, on a monthly time scale up to about 44% of the variation in SO can be predicted one month ahead (zero months lead time) and about 35% two months ahead (one month lead time), or on a seasonal time scale about 53% one season ahead (zero seasons lead time) and about 31% two masons ahead (one season lead time). In general, the degree of predictability naturally decays as the lead time increases with essentially no predictability on a monthly time scale beyond ten months (nine months lead time) or on a seasonal time scale beyond seasons (two seasons lead time).

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Pao-Shin Chu and Soon-Ung Park

Abstract

A case study is presented of the cold surge over East Asia during 9–13 December 1978; using nine vertical levels of Winter MONEX data. The surge event is manifested by a rapid meridional mass flow in the lower troposphere from the midlatitudes (∼30°N) to the equator within three days. During the period studied, the lower tropospheric circulation dramatically changed in extent and intensity. With the onset of the surge event, the main divergent maximum began shifting from the South China Sea to southeast China. At the same time, the upper tropospheric circulation correspondingly changed in a reversed order from that of the lower troposphere, and a direct vertical coupling between flows in the low and high troposphere was observed.

The time-averaged meridional mass circulation between 100 and 126°E reveals a two-cell structure; the southern cell is located between the northern South China Sea and the equator, and the northern cell between midlatitudes and the northern South China Sea. Analysis of sensible heat transport indicates that the southern cell is associated with warm air in the south and cold air in the north; thus it resembles a thermally-direct local meridional circulation. Moisture transport analysis shows that the moisture source is found in the southern branch of this cell, and the sink in the northern branch. Conversely, the northern cell is thermally indirect.

The time-mean zonal mass circulation between 32°N and 4°S is marked by two cells, linked by subsidence near the longitudes of the South China Sea. The eastern cell is accompanied by heat and moisture sources while the western cell is associated with heat and moisture sinks.

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Pao-Shin Chu and Richard W. Katz

Abstract

An index consisting of the difference of normalized sea level pressure departures between Tahiti and Darwin is used to represent the Southern Oscillation (SO) fluctuations. Using a time-domain approach, autoregressive-moving average (ARMA) progress are applied to model and predict this Southern Oscillation Index (SOI) on a monthly and seasonal basis. The ARMA process which is chosen to fit the monthly SOI expresses the index for the current month as a function of both the SOI one month and seven (or nine) mouths ago, as well as the current and previous month's random error. A purely automotive (AR) process is identified as representative of the seasonal SO fluctuations, with the SOI for the current season being derived from the index for the immediate past three seasons and a single random disturbance term for the current season. To allow for the phase locking of the SOI with the annual cycle, ARMA processes with seasonally varying coefficients are also considered.

As one example of how these models could be used, seasonal SO variations have been forecast. When SOI observations from 1935 through the summer of 1983 are employed, the seasonal model indicates forecast of positive SOI from fall 1983 through fall 1984. Forecasts based only on SOI observations from 1935 through spring 1982 show a low predictive skill for the SOI values from summer 1982 through winter 1984, whereas one-season-ahead forecasts starting with summer 1982 agree reasonably well with the actual SOI observations. These examples help illustrate the degree to which the future behavior of the SOI is predictable on the basis of its past history alone.

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