每日气候指数季节内持续性的状态空间模拟：季节预报的数据驱动方法
State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting
摘要：
诊断气候指数可预测性的现有方法往往对气候系统作出一些不合理的假设，可能导致误导性的结论。我们提出了一个灵活的家庭的状态空间模型，能够分离的影响，外部强迫年际时间尺度，从长期趋势和十年变化，短期天气噪声，观测误差和自相关的变化。标准的潜在可预测性模型仅估计指数总方差中归因于外部强迫的部分。此外，我们的方法允许我们将各个季节性平均值划分为强制，缓慢，快速和错误组件。也可以估计季节内可预测信号的变化。该模型还可以用于预测模式，以评估季节内和季节间的可预测性。我们将所提出的方法应用于1948 - 2017年的北大西洋涛动指数。12月至1月至2月平均北大西洋涛动的年际变化中，约60%可归因于外部强迫，8%可归因于较长时间尺度上的趋势。在某些年份，外部强迫在整个冬季保持相对恒定，在其他年份，它在冬季发生变化。从11月底开始，对12月至1月至2月的平均北大西洋涛动进行熟练的统计预报是可能的，可预测性延伸到3月。12月-1月-2月平均值的统计预测与观测值的相关性为0.48。
英文摘要：
Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separating the effects of external forcing on inter-annual time scales, from long-term trends and decadal variability, short term weather noise, observational errors and changes in autocorrelation. Standard potential predictability models only estimate the fraction of the total variance in the index attributable to external forcing. In addition, our methodology allows us to partition individual seasonal means into forced, slow, fast and error components. Changes in the predictable signal within the season can also be estimated. The model can also be used in forecast mode to assess both intra- and inter-seasonal predictability. We apply the proposed methodology to a North Atlantic Oscillation index for the years 1948-2017. Around 60% of the inter-annual variance in the December-January-February mean North Atlantic Oscillation is attributable to external forcing, and 8% to trends on longer time-scales. In some years, the external forcing remains relatively constant throughout the winter season, in others it changes during the season. Skillful statistical forecasts of the December-January-February mean North Atlantic Oscillation are possible from the end of November onward and predictability extends into March. Statistical forecasts of the December-January-February mean achieve a correlation with the observations of 0.48.
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