A team of scientists has figured out a shortcut way to produce skillful seasonal climate forecasts with a fraction of the computing power normally needed. The technique involves searching within existing global climate models to learn what happened when the ocean, atmosphere and land conditions were similar to what they are today. These "model-analogs" to today end up producing a remarkably good forecast, the team found—and the finding could help researchers improve new climate models and forecasts of seasonal events such as El Niño.
