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Volume 22, issue 10 | Copyright
Hydrol. Earth Syst. Sci., 22, 5069-5079, 2018
https://doi.org/10.5194/hess-22-5069-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 02 Oct 2018

Technical note | 02 Oct 2018

Technical note: An improved Grassberger–Procaccia algorithm for analysis of climate system complexity

Chongli Di1, Tiejun Wang1, Xiaohua Yang2, and Siliang Li1 Chongli Di et al.
  • 1Institute of Surface-Earth System Science, Tianjin University, Tianjin, 300072, P. R. China
  • 2State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, P. R. China

Abstract. Understanding the complexity of natural systems, such as climate systems, is critical for various research and application purposes. A range of techniques have been developed to quantify system complexity, among which the Grassberger–Procaccia (G-P) algorithm has been used the most. However, the use of this method is still not adaptive and the choice of scaling regions relies heavily on subjective criteria. To this end, an improved G-P algorithm was proposed, which integrated the normal-based K-means clustering technique and random sample consensus (RANSAC) algorithm for computing correlation dimensions. To test its effectiveness for computing correlation dimensions, the proposed algorithm was compared with traditional methods using the classical Lorenz and Henon chaotic systems. The results revealed that the new method outperformed traditional algorithms in computing correlation dimensions for both chaotic systems, demonstrating the improvement made by the new method. Based on the new algorithm, the complexity of precipitation, and air temperature in the Hai River basin (HRB) in northeastern China was further evaluated. The results showed that there existed considerable regional differences in the complexity of both climatic variables across the HRB. Specifically, precipitation was shown to become progressively more complex from the mountainous area in the northwest to the plain area in the southeast, whereas the complexity of air temperature exhibited an opposite trend, with less complexity in the plain area. Overall, the spatial patterns of the complexity of precipitation and air temperature reflected the influence of the dominant climate system in the region.

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The original Grassberger–Procaccia algorithm for complex analysis was modified by incorporating the normal-based K-means clustering technique and the RANSAC algorithm. The calculation accuracy of the proposed method was shown to outperform traditional algorithms. The proposed algorithm was used to diagnose climate system complexity in the Hai He basin. The spatial patterns of the complexity of precipitation and air temperature reflected the influence of the dominant climate system.
The original Grassberger–Procaccia algorithm for complex analysis was modified by incorporating...
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