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Stefan Hastenrath and Lawrence Greischar

Abstract

The upper-air circulation characteristics of the North Atlantic oscillation (NAO) are studied from the 1958–97 National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis, with regard to interannual variability and long-term trends. The low (high) phase of the NAO is defined by small (large) values of the Ponta Delgada, Azores, minus Akureyri, Iceland, surface pressure difference. For the fields of 200-mb topography, divergence, and divergent flow; 500-mb vertical motion; 1000-mb topography and total wind; and SST during January, differences are computed between ensembles of years of extremely low- minus high-NAO phase and are tested for statistical significance. In the low-NAO phase, topographies throughout the tropospheric column stand anomalously high (low) in the subpolar (subtropical) domains. Ascending motion and upper-tropospheric divergent outflow from the realm of the Icelandic low broadly southward, and convergent inflow and subsidence on the poleward side of the Azores high, are reduced. These vertical motion departures support the raised (lowered) 1000-mb topography in the subpolar (subtropical) domains. The weakened subtropical high entails slower trade winds that through reduced evaporation and wind stirring are conducive to warmer sea surface and slower midlatitude westerlies that through reduced Ekman transport lead to colder waters on the poleward side of the anticyclonic axis. Similarly, the weakened cyclonic circulation around the Icelandic low, through reduced Ekman transport, makes for a warmer sea surface. These SST departures are imparted to the overlying atmosphere. Long-term evolutions in the patterns of upper-tropospheric divergence and divergent flow, midtropospheric vertical motion, and SST accompany the trends in 1000-mb topography toward greater prevalence of the high-NAO phase.

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Lawrence Greischar and Stefan Hastenrath

Abstract

Based on circulation diagnostics in the tropical Atlantic sector and the equatorial Pacific, empirical methods have been developed to forecast anomalies in the March–June rainy season of northeast Brazil from observations to the end of the preceding January. Techniques include stepwise multiple regression, neural networking, and linear discriminant analysis. The methods were developed from the dependent training period 1921–57, and their performance was validated on the independent record 1958–89, prior to real-time application. Real-time forecasts have been regularly issued during the 1990s. These forecasts were in close agreement with the observed rainfall, except for the extreme El Niño year of 1998. A possible cause of this failure is seen in the lack of comparably extreme Pacific warm events within the training period used for the development of the empirical methods.

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Lawrence Greischar and Roland Stull

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Turbulent flux measurements from five flights of the National Center for Atmospheric Research Electra aircraft during the Tropical Oceans and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) are used to test convective transport theory (CTT) for a marine boundary layer. Flights during light to moderate winds and under the clearest sky conditions available were chosen. Fluxes of heat, moisture, and momentum were observed by the eddy-correlation method. Mean kinematic values for the observed sensible and latent heat fluxes and momentum flux were 0.0061 K m s−1, 0.0313 g kg−1 m s−1, and 0.0195 m2 s−2, respectively.

For the range of mixed-layer wind speeds (0.8–8.4 m s−1) studied here, the version of CTT that includes the mixed effects of buoyant and shear-driven transport give a better fit to the observations than either the COARE bulk algorithm or the pure free-convection version of CTT. This is to be expected because both of those latter parameterizations were designed for light winds (<5 m s−1 approximately).

The CTT empirical coefficients listed in exhibited slight sensitivity to the COARE light flux conditions, compared to their previous estimates during larger fluxes over land. For example, COARE heat fluxes were roughly 10 times smaller than previous land-based flux measurements used to calculate CTT coefficients, but the corresponding empirical mixed-layer transport coefficients were only 3% smaller. COARE momentum fluxes were also roughly 10 times smaller, but the CTT coefficients were about four times smaller. The greater variation in momentum coefficient may be due, in part, to insufficient flight-leg length used to compute momentum fluxes, to uncertainties in the effects of the ocean surface current and waves, or perhaps to roughness differences.

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Stefan Hastenrath and Lawrence Greischar

Abstract

This study expands our earlier climate prediction work for Brazil's Nordeste to develop methods of forecasting the March–June precipitation with differing lead times by exploring the potential of various data sources and options of information extraction. Observations include indices of Nordeste rainfall, an index of sea surface temperature (SST) in the equatorial Pacific, and the fields of meridional wind component and SST in the tropical Atlantic. Empirical orthogonal function (EOF) analysis was applied to construct indices of the meridional wind component and SST. These series formed the input to stepwise multiple regression models, an experimental neural network model, as well as to linear discriminant analysis. The dependent dataset 1921–57 (excluding 1943–47) was used for the method development, while the independent dataset 1958–89 was reserved for prediction.

Of primary interest is the prediction of March–June rainfall from information through January. A new SST dataset with improved quality control proved useful, especially with the EOF analysis confined to the more sensitive portion of the tropical Atlantic. The cardinal predictor is the preseason rainfall. Using indices of the Atlantic meridional wind and SST fields in conjunction with this allows one to predict half to three-fourths of the interannual rainfall variability in the independent dataset. Regarding predictions with greater lead times, about one-fourth of the variance of March–June precipitation can be forecast from the Atlantic SST field in December. Prediction from the February meridional wind and SST fields and preseason rainfall yields no improvement over the forecasts based on information through January. With similar skill, the April–June rainfall is predictable from the end of March. Experiments with neural networking revealed no advantage over regression. Linear discriminant analysis performed best in forecasting extremes.

The essential input information for Nordeste climate prediction consists of accumulated regional rainfall and quality-controlled databases of the Atlantic meridional wind and SST fields, as well as equatorial Pacific SST. For an operational prediction system three phases are found realistic: an early warning from the December SST field; the main forecast of March–June precipitation from rainfall, meridional wind, and SST information by the end of January; and a prediction for the April–June tail of the rainy season based on corresponding information through March. The remarkable recent communal effort in updating datasets was crucial for a real-time forecast of the 1992 Nordeste rainy season.

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Stefan Hastenrath, Lawrence Greischar, and Johan van Heerden

Abstract

This study develops methods for the extended-range forecasting of summer rainfall in the eastern part of southern Africa. The predictand is an index (TVR) of the December–January–February precipitation in the Transvaal. The predictors are based on empirical-diagnostic analyses and include the July–August–September values of Tahiti minus Darwin pressure difference as an index (SOI) of the Southern Oscillation; the preceding January–February–March value of the 50-mb zonal wind over Singapore (U50); an index of the October–November surface westerlies along the Indian Ocean equator (UEQ); and an index of November sea surface temperature in the southwestern Indian Ocean (UKT). These predictors serve as input to stepwise multiple regression, linear discriminant analysis, and neural networking. The training period is 1954–78, and the verification period 1979–93. Regression models, using as predictors U50, UEQ, and UKT, account for more than 30% of the variance in the independent dataset. The linear discriminant analysis does not perform well. Most powerful is a neural networking model having as input information through the end of September, namely U50 and SOI, and explaining 62% of the variance in the verification period. The predictors used here could, in principle, be compiled in quasi-real time, so that the method lends itself to operational application.

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Stefan Hastenrath, Lawrence Greischar, Esperanza Colón, and Alfredo Gil

Abstract

This study develops methods for the extended-range forecasting of the February–March minimum of water discharge of the Caroní River in eastern Venezuela, a watershed providing more than 70% of the hydroelectric power for the country. The predictors are the Tahiti minus Darwin pressure index and the Caroní discharge, both in the preceding July–August, and serve as input to stepwise multiple regression and neural networking. For regression the training period is 1950–79 and the verification period 1980–98. For neural networking the training period is 1950–75 plus 1976–85, and the verification period is 1986–98. The regression model captures more than a third of the variance of the February–March discharge and of the neural method more than half. The predictors are readily available, and application in real time is being initiated.

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