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Timothy N. Stockdale

Abstract

Two different coupled atmosphere–ocean GCMs are used to forecast SST anomalies with lead times of up to one year. The initialization procedure does not balance the ocean and atmosphere components, nor is the coupled model flux corrected to maintain the correct mean state. Rather, the coupled model is allowed to evolve freely during the forecast. The inevitable climate drift is estimated across an ensemble of forecasts and subtracted to give the true forecast. Although the climate drift is often bigger than the interannual signal, the method works. This is true for a drift toward both warmer and colder SSTs, as exemplified by the two models.

The best way of establishing the mean bias correction from a small sample of prior forecasts is discussed. In some circumstances the sample median may be a more robust estimator than the sample mean. For the limited set of forecasts here, use of the median bias in the cross-correlated forecasts reduces forecast error, when compared to use of the mean bias.

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Frédéric Vitart and Timothy N. Stockdale

Abstract

The ECMWF Seasonal Forecasting System, based on ensembles of 200-day coupled GCM integrations, contains tropical disturbances that are referred to as model tropical storms in the present paper. Model tropical storms display a genesis location and a seasonal cycle generally consistent with observations, though the frequency of model tropical storms is significantly lower than observed, particularly over the North Atlantic and the eastern North Pacific. Several possible causes for the low number of model tropical storms are discussed.

The ECMWF Seasonal Forecasting System produces realistic forecasts of the interannual variability of tropical storm frequency over the North Atlantic and the western North Pacific, with strong linear correlations and low rms error obtained when comparing the forecasts to observations. The skill of the seasonal forecasting system in predicting the frequency of tropical storms is likely to be related to its skill in predicting sea surface temperatures. In particular, the model seems successful in predicting the occurrence and development of El Niño and La Niña events, and their impact on the large-scale circulation over the Atlantic. For the period 1991–99, a comparison with the statistical forecasts issued by the Colorado State Hurricane Forecast Team suggests that the ECMWF seasonal forecasting system produces a better June forecast of the total number of tropical storms over the North Atlantic. These results establish the feasibility of real-time forecasting of tropical storm statistics by dynamical methods.

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Timothy N. Stockdale, Magdalena A. Balmaseda, and Arthur Vidard

Abstract

Variations in tropical Atlantic SST are an important factor in seasonal forecasts in the region and beyond. An analysis is given of the capabilities of the latest generation of coupled GCM seasonal forecast systems to predict tropical Atlantic SST anomalies. Skill above that of persistence is demonstrated in both the northern tropical and equatorial Atlantic, but not farther south. The inability of the coupled models to correctly represent the mean seasonal cycle is a major problem in attempts to forecast equatorial SST anomalies in the boreal summer. Even when forced with observed SST, atmosphere models have significant failings in this area. The quality of ocean initial conditions for coupled model forecasts is also a cause for concern, and the adequacy of the near-equatorial ocean observing system is in doubt. A multimodel approach improves forecast skill only modestly, and large errors remain in the southern tropical Atlantic. There is still much scope for improving forecasts of tropical Atlantic SST.

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Geert Jan van Oldenborgh, Magdalena A. Balmaseda, Laura Ferranti, Timothy N. Stockdale, and David L. T. Anderson

Abstract

Since 1997, the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.

The main cause of seasonal predictability is El Niño and La Niña perturbing the average weather in many regions and seasons throughout the world. As a baseline to compare the dynamical models with, a set of simple statistical models (STAT) is constructed. These are based on persistence and a lagged regression with the first few EOFs of SST from 1901 to 1986 wherever the correlations are significant. The first EOF corresponds to ENSO, and the second corresponds to decadal ENSO. The temperature model uses one EOF, the sea level pressure (SLP) model uses five EOFs, and the precipitation model uses two EOFs but excludes persistence.

As the number of verification data points is very low (15), the simplest measure of skill is used: the correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties, we restrict ourselves to areas and seasons of known ENSO teleconnections.

The dynamical ECMWF models show better skill in 2-m temperature forecasts over sea and the tropical land areas than STAT, but the modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in southeast Asia, the equatorial Pacific, and the Americas in December–February. In March–May the skill is lower. Overall, S1 (S2) shows better skill than STAT at lead time of 2 months in 29 (32) out of 40 regions and seasons of known ENSO teleconnections.

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Jérôme Vialard, Frédéric Vitart, Magdalena A. Balmaseda, Timothy N. Stockdale, and David L. T. Anderson

Abstract

Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.

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Geert Jan van Oldenborgh, Magdalena A. Balmaseda, Laura Ferranti, Timothy N. Stockdale, and David L. T. Anderson

Abstract

The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.

Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.

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Michael J. McPhaden, Axel Timmermann, Matthew J. Widlansky, Magdalena A. Balmaseda, and Timothy N. Stockdale

Abstract

Forty years ago, Klaus Wyrtki of the University of Hawaii launched an “El Niño Watch” expedition to the eastern equatorial Pacific to document oceanographic changes that were expected to develop during the onset of an El Niño event in early 1975. He and his colleagues used a very simple atmospheric pressure index to predict the event and convinced the National Science Foundation and Office of Naval Research to support an expedition to the eastern Pacific on relatively short notice. An anomalous warming was detected during the first half of the expedition, but it quickly dissipated. Given the state of the art in El Niño research at the time, Wyrtki and colleagues could offer no explanation for why the initial warming failed to amplify, nor could they connect what they observed to what was happening in other parts of the basin prior to and during the expedition. With the benefit of hindsight, the authors provide a basin-scale context for what the expedition observed, elucidate the dynamical processes that gave rise to the abbreviated warming that was detected, and present retrospective forecasts of the event using modern coupled ocean–atmosphere dynamical model prediction systems. Reviewing this history highlights how early pioneers in El Niño research, despite the obstacles they faced, were able to make significant progress through bold initiatives that advanced the frontiers of our knowledge. It is also evident that, even though the scientific community today has a much deeper understanding of climate variability, more advanced observational capabilities, and sophisticated seasonal forecasting tools, skillful predictions of El Niño and its cold counterpart La Niña remain a major challenge.

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