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

Abstract

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