Classification of synoptic circulation patterns with a two-stage clustering algorithm using the modified structural similarity index metric (SSIM)

Abstract. We develop a new classification method for synoptic circulation patterns with the aim to extend the evaluation routine for climate simulations. This classification is applicable to any region of the globe of any size given the reference data. Its unique novelty is the use of the modified structural similarity index metric (SSIM) instead of traditional distance metrics for cluster building. This classification method combines two classical clustering algorithms used iteratively, hierarchical agglomerative clustering (HAC) and k-medoids, with only one pre-set parameter – the threshold on the similarity between two synoptic patterns expressed as the structural similarity index measure SSIM. This threshold is set by the user to imitate the human perception of the similarity between two images (similar structure, luminance and contrast), whereby the number of final classes is defined automatically.

We apply the SSIM-based classification method to the geopotential height at the pressure-level of 500 hPa from the reanalysis data ERA-Interim 1979–2018 and demonstrate that the built classes are 1) consistent to the changes in the input parameter, 2) well separated, 3) spatially stable, 4) temporally stable, and 5) physically meaningful.

We demonstrate an exemplary application of the synoptic circulation classes obtained with the new classification method for evaluating CMIP6 historical climate simulations and an alternative reanalysis (for comparison purposes): output fields of CMIP6 simulations (and of the alternative reanalysis) are assigned to the classes and a quality index is computed for the match in frequency and duration probability of these classes. We propose using this quality index to supplement a set of commonly used metrics for model evaluation.