Optimal Precursors Identification for North Atlantic Oscillation Using the Parallel Intelligence Algorithm
The North Atlantic Oscillation (NAO) with abnormal sea level pressure (SLP) differences influence the westerly wind strength and storm tracks in the North Atlantic, which further affects the winter climate in the northern hemisphere. The predictability of NAO has become an important area of climate...
| Published in: | Scientific Programming |
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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Hindawi Limited
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.1155/2022/3215039 https://doaj.org/article/4d0c177077114036b5a5a7dd6a333e7c |
| Summary: | The North Atlantic Oscillation (NAO) with abnormal sea level pressure (SLP) differences influence the westerly wind strength and storm tracks in the North Atlantic, which further affects the winter climate in the northern hemisphere. The predictability of NAO has become an important area of climate research in recent years. The identification of optimal precursors (OPRs) would help to investigate the dynamics of atmospheric and oceanic motions, as well as the nonlinear characteristic. The conditional nonlinear optimal perturbation (CNOP) method has been widely used in research on the OPR of the climatic event to explore which kind of initial perturbation is most likely to trigger climate events. However, the previous works on NAO’s OPR are based on a simple ideal model, which cannot describe the evolution process of NAO. Moreover, the commonly used algorithms that rely on the adjoint model are not suitable for the large complicated numerical models that do not have corresponding adjoint models, like Community Earth System Model (CESM). To break through the limitation, this paper proposes the parallel principal component analysis (PCA)-based particle swarm optimization (PSO) and genetic algorithm (GA) hybrid algorithm (PGAPSO) algorithm to identify the OPR of NAO using CESM. For different initial conditions, OPRs identified by the proposed method can always make the basic state develop into NAO events with corresponding phases. Compared with other adjoint-free approaches, the proposed method has relatively better fitness values and more mature patterns. The results also reveal that the proposed method can avoid falling into a local optimum and has strong robustness. In terms of performance, the proposed method searches in feature space with a much lower number of dimensions, thus improving efficiency. In addition, the proposed method is accelerated with multiple parallel frameworks to enhance performance, and it achieves the speedup ratio of 40.2. |
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