Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models
Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models
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DOI：
10.1002/met.2178
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发表时间：
2024-01-01
影响因子：
2.7
通讯作者：
Hanna，Edward
中科院分区：
文献类型：
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作者：
Sun，Yiming;Simpson，Ian;Hanna，Edward
动态季节预报模型随着时间的推移而不断改进，但往往低估了大气环流变化的幅度，预测夏季变化的能力低于冬季。在这里，我们构建了具有外源输入的非线性自回归移动平均模型（NARMAX）来分析北大西洋大气环流和急流变率的驱动因素，重点关注东大西洋（EA）和斯堪的纳维亚（SCA）模式以及北大西洋涛动（NAO）指数。这些指数的新的时间序列是从经验正交函数（ERF）分析。ERA 5再分析的位势高度数据用于生成EOF。开发了与这些驱动因素具有已知关联的预测因子集，并用于制定滑动窗口NARMAX模型。该模型具有较高的预测精度，其在测试期间（2006-2021年）的平均相关系数表明：NAO为0.78，EA为0.83，SCA为0.68。相比之下，SEAS 5和GloSea 5动力预报模式与观测环流变化的相关性较低：对于NAO，SEAS 5和GloSea 5的相关系数分别为0.51和0.34，对于EA，它们分别为0.15和0.09，对于SCA，它们分别为0.28和0.24。NARMAX预测与SEAS 5和GloSea 5模型的预测和后报的比较突出了NARMAX可用于帮助提高季节预报技能并为动态模型的开发提供信息的领域，特别是在夏季。
Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet‐stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from empirical orthogonal function (EOF) analysis. Geopotential height data from the ERA5 reanalysis are used to generate the EOFs. Sets of predictors with known associations with these drivers are developed and used to formulate a sliding‐window NARMAX model. This model demonstrates a high degree of predictive accuracy, as indicated by its average correlation coefficients over the testing period (2006–2021): 0.78 for NAO, 0.83 for EA and 0.68 for SCA. In comparison, the SEAS5 and GloSea5 dynamical forecast models exhibit lower correlations with observed circulation changes: for NAO, the correlation coefficients are 0.51 for SEAS5 and 0.34 for GloSea5, for EA they are 0.15 and 0.09, respectively, and for SCA, they are 0.28 and 0.24, respectively. Comparison of NARMAX predictions with forecasts and hindcasts from the SEAS5 and GloSea5 models highlights areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer.