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Siegfried D. Schubert and Marie-Jeanne Munteanu

Abstract

The relationship between total ozone and tropopause pressure is analyzed using 4 years (1979–82) of Nimbus-7 total ozone data and NMC global analyses of tropopause on a 5° by 5° grid. The fields are separated into medium (synoptic) and large spatial scales via a spherical harmonic expansion. The global distribution of variability and correlation are presented for each season. The large-scale analysis is based primarily on data from 1979 due to pronounced temporal inhomogeneities in the tropical tropopause data.

The synoptic scales show strong correlations (>0.6) in the middle latitudes of both hemispheres with a rapid equatorward drop and a more gradual poleward decline: a similar dependence on latitude is found using tropopause values derived directly from station data. Within a season, the areas of highest correlation tend to be associated with the regions of maximum variance of the storm track regions. In contrast, the seasonal dependence is such that the summer hemispheres tend to have the most extensive regions of high correlation while the more energetic winter seasons have the smallest. A frequency analysis (limited to time scales longer than 3 days) of selected regions indicates that in middle latitudes synoptic-scale fluctuations of total ozone and tropopause pressure exhibit generally similar distributions in power and no significant phase differences: equatorward the coherence drops rapidly at all frequencies.

Nonseasonal fluctuations of the large-scale fields generally show weak correlations (<0.6) everywhere. A major exception is the springtime middle latitude South Pacific. The strongest correspondence between large-scale ozone and tropopause pressure fields involves long period (seasonal) fluctuations in high latitudes. Over Antarctica the coupling is strongest in middle and late spring in association with the spring warming while the decrease in total ozone in early spring shows no apparent relation to tropopause variations.

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Randal D. Koster, Thomas L. Bell, Rolf H. Reichle, Max J. Suarez, and Siegfried D. Schubert

Abstract

Model deficiencies limit a subseasonal or seasonal forecast system’s ability to produce accurate predictions. In this paper, an approach for transforming the output of a forecast system into a revised forecast is presented; it is designed to correct for some of the deficiencies in the system (particularly those associated with the spatial correlation structures of the forecasted fields) and thereby increase forecast skill. The approach, based on the joint consideration of the correlation structures present in the observational record and the inherent potential predictability of the model, is tested on a preexisting subseasonal forecast experiment. It is shown to produce modest but significant increases in the accuracy of forecasted precipitation and near-surface air temperature at monthly time scales.

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Xianan Jiang, Duane E. Waliser, Matthew C. Wheeler, Charles Jones, Myong-In Lee, and Siegfried D. Schubert

Abstract

Motivated by an attempt to augment dynamical models in predicting the Madden–Julian oscillation (MJO), and to provide a realistic benchmark to those models, the predictive skill of a multivariate lag-regression statistical model has been comprehensively explored in the present study. The predictors of the benchmark model are the projection time series of the leading pair of EOFs of the combined fields of equatorially averaged outgoing longwave radiation (OLR) and zonal winds at 850 and 200 hPa, derived using the approach of Wheeler and Hendon. These multivariate EOFs serve as an effective filter for the MJO without the need for bandpass filtering, making the statistical forecast scheme feasible for the real-time use. Another advantage of this empirical approach lies in the consideration of the seasonal dependence of the regression parameters, making it applicable for forecasts all year-round. The forecast model exhibits useful extended-range skill for a real-time MJO forecast. Predictions with a correlation skill of greater than 0.3 (0.5) between predicted and observed unfiltered (EOF filtered) fields still can be detected over some regions at a lead time of 15 days, especially for boreal winter forecasts. This predictive skill is increased significantly when there are strong MJO signals at the initial forecast time. The analysis also shows that predictive skill for the upper-tropospheric winds is relatively higher than for the low-level winds and convection signals. Finally, the capability of this empirical model in predicting the MJO is further demonstrated by a case study of a real-time “hindcast” during the 2003/04 winter. Predictive skill demonstrated in this study provides an estimate of the predictability of the MJO and a benchmark for the dynamical extended-range models.

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