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Global seasonal rainfall forecasts using a coupled ocean-atmosphere model

机译：利用海洋-大气耦合模型对全球季节性降雨进行预报

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摘要

One conceptual model of weather is that of a series of events which are unconnected. That is, that the weather next week is essentially independent of the weather this week. However, although individual weather systems might be chaotic and unpredictable beyond a week or so, the statistics describing them may be perturbed in a deterministic and predictable way, particularly by the ocean. In the past, seasonal forecasts of atmospheric variables have largely been based on empirical relationships, which are weak in most areas of the world. More recently, atmosphere models forced by assumed or predicted ocean conditions have been used. Here a fully coupled global ocean-atmosphere general circulation model is used to make seasonal forecasts of the climate system with a lead time of up to 6 months. Such a model should be able to simulate the predictable perturbations of seasonal climate, but to extract these from the chaotic weather requires an ensemble of model integrations, and hence considerable computer resources. Reliable verification of probabilistic forecasts is difficult, but the results obtained so far, when compared to observations, are encouraging for the prospects for seasonal forecasting. Rainfall predictions for 1997 and the first half of 1998 show a marked increase in the spatial extent of statistically significant anomalies during the present El Nino, and include strong signals over Europe.
机译：天气的一个概念模型是一系列未关联的事件的模型。也就是说，下周的天气基本上与本周的天气无关。但是，尽管单个天气系统可能会在一周左右的时间内变得混乱和不可预测，但是描述它们的统计数据可能会以确定性和可预测的方式受到干扰，尤其是海洋。过去，大气变量的季节性预测主要基于经验关系，而经验关系在世界上大多数地区都很薄弱。最近，已经使用了由假定的或预测的海洋条件强迫的大气模型。在这里，使用完全耦合的全球海洋-大气总环流模型对气候系统进行季节预测，前置时间最长为6个月。这样的模型应该能够模拟季节性气候的可预测扰动，但是要从混乱的天气中提取这些扰动，则需要模型集成的集成，因此需要大量的计算机资源。可靠地验证概率预测是困难的，但是与观察相比，到目前为止获得的结果对于季节性预测的前景是令人鼓舞的。对1997年和1998年上半年的降雨量预测表明，在当前的厄尔尼诺现象期间，具有统计意义的异常的空间范围显着增加，并且包括整个欧洲的强烈信号。

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