An Objective Weather Regime Classification for Aotearoa New Zealand Using a Two-Tiered K-Means Clustering Approach

Abstract Weather regimes (WRs), also known as synoptic types, are defined recurrent patterns that have been used to categorize variability in atmospheric circulation. However, defining the optimal number of can often be arbitrary, and there common shortcomings when oversimplifying a wide range conditions weather outcomes. We build on previous work has regional WRs objectively ascribe an once-daily for Aotearoa New Zealand (ANZ) using affinity propagation combined with K -means clustering. Nine primary ANZ were classified based geopotential height spatial patterns, but these still retained degree variability. Subsidiary clusters subsequently within each WR by applying clustering reveal largest within-cluster differences joint daily temperature precipitation anomalies. Up three subsidiary revealed, total 21 unique emerging from two-tier classification. subtle location intensity regional-scale pressure anomalies, gradients, wind flow over both main islands lead large surface Impacts related exemplified different outcomes rainfall (including anomalies) at subregional levels. The approach presented this study utility enhancing prediction outcomes, including extreme weather, applied more widely time scales improve understanding climate linkages.