Search

A QCD-motivated potential with five parameters and a cutoff on high virtual momentum is used in a relativistic bound-state equation to examine the predictive power of valence quark potential models by computing fits to the seven well-established charmonium levels. The predictions of potential models are really ranges for new levels given by the ranges of parameters that give good fits to established levels, rather than the single best fit. The range of parameters that give good fits is presented in three-dimensional plots by isosurfaces of fits with fixed theoretical standard deviations in energy from the known levels. Next, for each value of a predicted splitting we show the minimum deviation found over all the fits that lead to that value of splitting. We also show correlations between predictions by finding for each pair of values for two predictions the minimum deviation among the fits that give those predicted values, and making contour plots. For the P11-Pc.g.3 splitting we find the predictions to cover a range of several MeV both positive and negative. A correlation of the spin-spin P11-Pc.g.3 splitting with the similar -c splitting prediction is presented, and values for -c have a range of order 20-50 MeV centered about 35 MeV. To range as high as the single experimental observation of 92 MeV for -c, such a poor fit to the seven levels is encountered that the model lacks any significant predictive power. Predictions for the D23 and D21 levels are also given. © 1993 The American Physical Society.

The impact of sampling variability on the correlation between all-India rainfall (AIR) and the El Niño-Southern Oscillation is investigated in a large ensemble of seasonal climate simulations made using the European Centre for Medium-Range Weather Forecasting Ensemble Prediction System at T319 (64 km). The analyzed runs consist of 51 ensemble members initialized each May 1 for the period 1980-2011 and integrated for 7 months. 10,000 pairs of 32-year timeseries of June-September (JJAS) mean AIR and NINO3 indices are created from this database by randomly drawing one of the 51 ensemble members for each year. The correlation between each pair of AIR and NINO3 series is then calculated, generating a distribution of AIR-NINO3 correlation values. The model is reinitialized with observations each May 1 and thus all members are drawn from the same background state by construction and any differences in correlation are attributable to sampling variability. The spread in the calculated correlation values and the differences between 32-year segments are sufficient to explain the observed variations in AIR-NINO3 correlation since the beginning of the 1900s, including the sharp decrease in correlation strength since the late 1970s. Sampling variability thus represents a strong null hypothesis for the observed changes and one that cannot be rejected at the 95 % level based on our simulations. [ABSTRACT FROM AUTHOR]