Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?
Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?
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DOI：
10.1002/2014gl061146
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发表时间：
2014-08-16
影响因子：
5.2
通讯作者：
Robinson, Niall
中科院分区：
文献类型：
--
作者：
Eade, Rosie;Smith, Doug;Scaife, Adam;Wallace, Emily;Dunstone, Nick;Hermanson, Leon;Robinson, Niall
季节性到年代际性的预测不可避免地是不确定的，这取决于可预测信号相对于不可预测混沌的大小。不确定性可以使用集合技术来解释，从而允许定量概率预报。在一个完美的系统中，每一个集合成员都代表着气候系统真实演变的一种潜在实现，模型和现实中的可预测成分是相等的。然而，我们发现，可预测的组件有时低于模型比观测，特别是北大西洋涛动的季节性预测和多年预测北大西洋温度和压力。在这些情况下，预测是不自信的，每个集合成员包含太多的噪音。因此，大多数确定性和概率性的措施低估了潜在的技能和理想化的模型实验低估了可预测性。然而，熟练和可靠的预测可以实现使用一个大的合奏，以减少噪音和调整预测方差，通过后处理技术在这里提出。
Seasonal-to-decadal predictions are inevitably uncertain, depending on the size of the predictable signal relative to unpredictable chaos. Uncertainties can be accounted for using ensemble techniques, permitting quantitative probabilistic forecasts. In a perfect system, each ensemble member would represent a potential realization of the true evolution of the climate system, and the predictable components in models and reality would be equal. However, we show that the predictable component is sometimes lower in models than observations, especially for seasonal forecasts of the North Atlantic Oscillation and multiyear forecasts of North Atlantic temperature and pressure. In these cases the forecasts are underconfident, with each ensemble member containing too much noise. Consequently, most deterministic and probabilistic measures underestimate potential skill and idealized model experiments underestimate predictability. However, skilful and reliable predictions may be achieved using a large ensemble to reduce noise and adjusting the forecast variance through a postprocessing technique proposed here.