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M. Latif, R. Kleeman, and C. Eckert


The dominant variability modes in the Tropics are investigated and contrasted with the anomalous situation observed during the last few years. The prime quantity analyzed is anomalous sea surface temperature (SST) in the region 30°S–60°N. Additionally, observed tropical surface wind stress fields were investigated. Further tropical atmospheric information was derived from a multidecadal run with an atmospheric general circulation model that was forced by the same SSTs. The tropical SST variability can be characterized by three modes: an interannual mode [the El Niño–Southern Oscillation (ENSO)], a decadal mode, and a trend or unresolved ultra-low-frequency variability.

The dominant mode of SST variability is the ENSO mode. It is strongest in the eastern equatorial Pacific, but influences also the SSTs in other regions through atmospheric teleconnections, such as the Indian and North Pacific Oceans. The ENSO mode was strong during the 1980s, but it existed with very weak amplitude and short period after 1991. The second most energetic mode is characterized by considerable decadal variability. This decadal mode is connected with SST anomalies of the same sign in all three tropical oceans. The tropical Pacific signature of the decadal mode resembles closely that observed during the last few years and can be characterized by a horseshoe pattern, with strongest SST anomalies in the western equatorial Pacific, extending to the northeast and southeast into the subtropics. It is distinct from the ENSO mode, since it is not connected with any significant SST anomalies in the eastern equatorial Pacific, which is the ENSO key region. However, the impact of the decadal mode on the tropical climate resembles in many respects that of ENSO. In particular, the decadal mode is strongly linked to decadal rainfall fluctuations over northeastern Australia in the observations. It is shown that the anomalous 1990s were dominated by the decadal mode.

Considerable SST variability can be attributed also to a linear trend or unresolved ultra-low-frequency variability. This trend that might be related to greenhouse warming is rather strong and positive in the Indian Ocean and western equatorial Pacific where it accounts for up to 30% of the total SST variability. Consistent with the increase of SST in the warm pool region, the trends over the tropical Pacific derived from both the observations and the model indicate a strengthening of the trade winds. This is inconsistent with the conditions observed during the 1990s. If the wind trends reflect greenhouse warming, it must be concluded that the anomalous 1990s are not caused by greenhouse warming.

Finally, hybrid coupled ocean–atmosphere model experiments were conducted in order to investigate the sensistivity of ENSO to the low-frequency changes induced by the decadal mode and the trend. The results indicate that ENSO is rather sensitive to these changes in the background conditions.

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Simon C. Scherrer, Christof Appenzeller, Pierre Eckert, and Daniel Cattani


The Ensemble Prediction System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) was used to analyze various aspects of the ensemble-spread forecast-skill relation. It was shown that synoptic-scale upper-air spread measures can be used as first estimators of local forecast skill, although the relation was weaker than expected. The synoptic-scale spread measures were calculated based on upper-air fields (Z500 and T850) over western Europe for the period June 1997 to December 2000. The spread–skill relations for the operational ECMWF EPS were tested using several different spread definitions including a neural network-based measure. It was shown that spreads based on upper-air root-mean-square (rms) measures showed a strong seasonal cycle unlike anomaly correlation (AC)-based measures. The deseasonalized spread–skill correlations for the upper-air fields were found to be useful even for longer lead times (168–240 h). Roughly 68%–83% of small or large spread was linked to the corresponding high or low skill. A comparison with a perfect model approach showed the potential for improving the ECMWF EPS spread–skill relations by up to 25–30 correlation percentage points for long lead times.

Local forecasts issued by operational forecasters for the Swiss Alpine region, as well as station precipitation forecasts for Geneva were used to test the limits of the synoptic-scale upper-air spread as an estimator of local surface skill. A weak relation was found for all upper-air spread measures used. Although the probabilistic EPS direct model precipitation forecast for Geneva exhibited a considerable bias, the spread–skill relation was recovered at least up to 144 h. A neural network downscaling technique was able to correct the precipitation forecast bias, but did not increase the synoptic-scale spread surface-skill relation.

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