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Abstract Climate classification is a commonly used tool to simplify, visualize and make sense of an otherwise unwieldy amount of climate data in applied climate science research. Typically, these classifications have stemmed from one of two perspectives, either a circulation‐to‐environment (C2E) approach, or an environment‐to‐circulation approach (E2C), each with advantages and drawbacks. This research discusses a novel environment‐to‐circulation‐to‐environment (ECE) perspective to applied climate classification, and develops a specific ECE methodology that utilizes canonical correlation and discriminant analysis—the CANDECE method. The benefits of the ECE approach generally, and the CANDECE methodology specifically, are demonstrated in applying climate classification to aid in modelling anomalous water levels (AWLs) along portions of the East and West coasts of the United States. Results show that the CANDECE method performs better than two traditional classification methods ( k ‐means and self‐organizing maps SOMs) at relating AWLs to their broad‐scale atmospheric setups, especially with regard to both high and low extreme AWLs. It is further demonstrated that, compared with the West coast, the CANDECE method is particularly advantageous along the southeastern US coast, where the primary modes of atmospheric variability (which drive the classifications produced by SOMs and k ‐means) do not align with the relevant circulation‐based factors driving AWL variability. While AWLs were utilized for demonstrating the ECE proof‐of‐concept herein, ECE and CANDECE are designed to be useful for any climate application.
Lee et al. (Thu,) studied this question.