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The North American Multimodel Ensemble prediction experiment is described, and forecast quality and methods for accessing digital and graphical data from the model are discussed.

Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.

In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.

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