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S. Pezzulli, D. B. Stephenson, and A. Hannachi

Abstract

Seasons are the complex nonlinear response of the physical climate system to regular annual solar forcing. There is no a priori reason why they should remain fixed/invariant from year to year, as is often assumed in climate studies when extracting the seasonal component. The widely used econometric variant of Census Method II Seasonal Adjustment Program (X-11), which allows for year-to-year variations in seasonal shape, is shown here to have some advantages for diagnosing climate variability. The X-11 procedure is applied to the monthly mean Niño-3.4 sea surface temperature (SST) index and global gridded NCEP–NCAR reanalyses of 2-m surface air temperature. The resulting seasonal component shows statistically significant interannual variations over many parts of the globe. By taking these variations in seasonality into account, it is shown that one can define less ambiguous ENSO indices. Furthermore, using the X-11 seasonal adjustment approach, it is shown that the three cold ENSO episodes after 1998 are due to an increase in amplitude of seasonality rather than being three distinct La Niña events. Globally, variations in the seasonal component represent a substantial fraction of the year-to-year variability in monthly mean temperatures. In addition, strong teleconnections can be discerned between the magnitude of seasonal variations across the globe. It might be possible to exploit such relationships to improve the skill of seasonal climate forecasts.

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C. A. S. Coelho, S. Pezzulli, M. Balmaseda, F. J. Doblas-Reyes, and D. B. Stephenson

Abstract

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecasts were obtained by linear regression between December and the preceding July Niño-3.4 index values over the period 1950–2001. Coupled model ensemble forecasts for the period 1987–99 were provided by ECMWF, as part of the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project. Empirical and raw coupled model ensemble forecasts alone have similar mean absolute error forecast skill score, compared to climatological forecasts, of around 50% over the period 1987–99. The combined forecast gives an increased skill score of 74% and provides a well-calibrated and reliable estimate of forecast uncertainty.

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D. B. Stephenson, K. Rupa Kumar, F. J. Doblas-Reyes, J-F. Royer, F. Chauvin, and S. Pezzulli

Abstract

The Indian summer monsoon rainfall is the net result of an ensemble of synoptic disturbances, many of which are extremely intense. Sporadic systems often bring extreme amounts of rain over only a few days, which can have sizable impacts on the estimated seasonal mean rainfall. The statistics of these outlier events are presented both for observed and model-simulated daily rainfall for the summers of 1986 to 1989. The extreme events cause the wet-day probability distribution of daily rainfall to be far from Gaussian, especially along the coastal regions of eastern and northwestern India. The gamma and Weibull distributions provide good fits to the wet-day rainfall distribution, whereas the lognormal distribution is too skewed. The impact of extreme events on estimates of space and time averages can be reduced by nonlinearly transforming the daily rainfall amounts. The square root transformation is shown to improve the predictability of ensemble forecasts of the mean Indian rainfall for June 1986–89.

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