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Chester F. Ropelewski

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Chester F. Ropelewski

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Monthly estimates of Antarctic sea-ice area for the past decade were extracted from operational churts. Empirical orthogonal function analyses of these satellite-derived data revealed the existence of six distinct ice area sub-regions. Comparison of ice area time series for these sub-regions highlights the substantial differences among them. For example, total sea-ice extent typically reached a maximum in either August or September, while the Ron Sea often exhibited two relative maxima (July and October). The data show considerable year-to-year variability during this short period of record with the minimum sea-ice area varying by more than a factor of two and maximum sea-ice area varying by almost 20%. The large year-to-year variability precludes a reliable identification of longer term trends during the relatively short era of satellite observations.

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Chester F. Ropelewski

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Thomas M. Smith and Chester F. Ropelewski

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This paper is an extension of a study by C. Ropelewski and M. Halpert, which examines observed precipitation relationships with the Southern Oscillation. Here, the authors repeat their analysis using atmospheric general circulation model precipitation from the average of a 13-run ensemble. The GCM is the atmospheric component of the coupled model used for seasonal prediction at the National Centers for Environmental Prediction, except that in this study, the observed sea surface temperatures were specified for the ensemble runs. Results are compared and contrasted with the observed Southern Oscillation–related precipitation behavior. These comparisons show that the multiple ensemble simulations compare favorably to the observations for most areas in the Tropics and subtropics. However, outside of the deep Tropics, the model simulations show large shifts or biases in the location of the Southern Oscillation–related anomalies. In particular, anomalies shown by the observations to occur in the southeastern United States are shifted westward in the simulation.

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Chester F. Ropelewski and Evgeney S. Yarosh

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The mean annual cycle of the atmospheric and terrestrial water balance over the central United States is examined through an analysis of observational data over the 20-yr period 1973–92. The mean quantities from this study are expected to serve as a climatology for empirical investigations and a benchmark for numerical model-based water balance computations for the central United States. Monthly means and statistics of atmospheric water balance quantities were computed from twice daily radiosonde data. These data form a monthly“climatology” and 240 month time series of the major water budget components, including the vertically integrated vapor flux divergence, the rate of change of precipitable water, and precipitation minus evapotranspiration, PE. The mean annual cycle of evaporation given estimates of precipitation over the same area is also computed. Through comparison with observed river discharge, estimates are formed of the mean annual cycle of surface and subsurface storage and its interannual variability (as a residual). The mean observed and residual quantities of the historical water budget components are in general agreement with earlier studies based on shorter time series.

The 20-yr mean water budget shows a maximum of PE in March–April with a secondary maximum in November–December. In this analysis, mean evaporation exceeds mean precipitation during the June–September period with largest evaporation values in July and August. Thus, the heartland of the United States acts as mean net moisture source during the summer months. Individual monthly estimates of evaporation, given the gauge-estimated precipitation over the region, show negative evaporation estimates during some cold season months over the 1973–92 period. This suggests that gauge-measured precipitation is underestimated, at least during the cold months, in agreement with several rain gauge intercomparison studies.

The sonde-based budgets also confirm previous studies in showing that the rate of change of precipitable water is a small contributor to the atmospheric water budget through most of the mean annual cycle. However, the relative importance of this term increases during the transition seasons (late spring and early fall) when the magnitude of the vapor flux divergence term in the atmospheric water balance is also quite small.

The mean PE estimates computed from the vertically integrated atmospheric moisture flux were found to average 0.4 mm day−1 low in comparison to the observed total net river discharge. When the mean atmospheric PE is adjusted to the net discharge, the annual cycle of storage shows an amplitude of 14 cm yr−1, consistent with local measurements of soil moisture in Illinois () and also in agreement with earlier studies. The 20-yr time series shows multiyear variations in the storage term with magnitudes of near 45 cm, far in excess of the mean annual cycle. This low-frequency variability in storage is generally consistent with the accumulated precipitation anomaly, an independently estimated quantity, for most of the analysis period.

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Donald P. Wylie and Chester F. Ropelewski

A tethered sonde, the Boundary Layer Instrument System (BLIS), was designed for use from shipboard platforms in the GARP Atlantic Tropical Experiment (GATE). This system was able to monitor the thermal and kinematic properties of the boundary layer from approximately 100 m to the level of cloud base (800–1000 m). Five levels were simultaneously sampled for periods up to 24 h in length. More detailed vertical structure measurements were obtained by raising and lowering the tethered balloon. The mechanical details of the system and its accuracy in monitoring boundary layer changes and vertical motions are described.

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Chester F. Ropelewski, Alan Robock, and Michael Matson

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Michael S. Halpert and Chester F. Ropelewski

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Anthony G. Barnston and Chester F. Ropelewski

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Canonical correlation analysis (CCA) is explored as a multivariate linear statistical methodology with which to forecast fluctuations of the El Niño/Southern Oscillation (ENSO) in real time. CCA is capable of identifying critical sequence of predictor patterns that tend to evolve into subsequent patterns that can be used to form a forecast.

The CCA model is used to forecast the 3-month mean sea surface temperature (SST) in several regions of the tropical Pacific and Indian oceans for projection times of 0 to 4 seasons beyond the immediately forthcoming season. The predictor variables, representing the climate situation in the four consecutive 3-month periods ending at the time of the forecast, are 1) quasi-global seasonal mean sea level pressure (SLP) and 2) SST in the predictand regions themselves. Forecast skill is estimated using cross-validation, and persistence is used as the primary skill control measure.

Results indicate that a large region in the eastern equatorial Pacific (120°−170°W longitude) has the highest overall predictability, with excellent skill realized for winter forecasts made at the end of summer. CCA outperforms persistence in this region under most conditions, and does noticeably better with the SST included as a predictor in addition to the SLP.

It is demonstrated that better forecast performance at the longer lead times would be obtained if some significantly earlier (i.e., up to 4 years) predictor data were included, because the ability to predict the lower-frequency ENSO phase changes would increase. The good performance of the current system at shorter lead times appears to be based largely on the ability to predict ENSO evolution for events already in progress.

The forecasting of the eastern tropical Pacific SST using CCA is now done routinely on a monthly basis for a 0-, 1-, and 2-season lead at the Climate Analysis Center. Further refinements, and expected associated increases in skill, are planned for the coming several years.

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