A Bayesian Approach to Atmospheric Circulation Regime Assignment
A Bayesian Approach to Atmospheric Circulation Regime Assignment
复制标题
DOI：
10.1175/jcli-d-22-0419.1
复制
发表时间：
2022-06
影响因子：
4.9
通讯作者：
Swinda K. J. Falkena;J. Wiljes;A. Weisheimer;T. Shepherd
中科院分区：
文献类型：
--
作者：
Swinda K. J. Falkena;J. Wiljes;A. Weisheimer;T. Shepherd
研究大气环流状况及其动力学的标准方法是使用硬状况分配，即每个大气状态被分配到它距离最近的状况。然而，这可能并不总是最合适的方法，因为制度分配可能会受到噪声造成的制度距离微小偏差的影响。为了缓解这一问题，我们使用贝叶斯定理开发了一种序贯概率制度分配，该方法可以应用于先前定义的制度，并在新数据可用时实时实施。贝叶斯定理告诉我们，在给定数据的情况下，处于某种状态的概率可以通过结合气候可能性和先验信息来确定。可以使用时间t处的制度概率来通知时间t+1处的先验概率，然后使用该先验概率来顺序地更新制度概率。我们将这种方法应用于再分析数据和包含政权之间转移概率知识的季节性后播合奏。此外，利用集合中存在的信号更好地告知先验概率，可以识别更显著的年际变化。使用分类和回归方法对冬季北大西洋环流制度年际变化中的信号进行了评估，其中在非常强烈的厄尔尼诺年发现了最强的信号。
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes Theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes Theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.