A climate region classification for California’s warm season: apparent temperature clustering to support heat-health epidemiology

Abstract

Climate classification enhances our understanding of regional climate patterns and enables a science-based framework for assessing environmental and public health relationships. Prior climate classification systems are limited in their ability to capture variation across dynamic subclimates, particularly in the context of complex topographical, elevation, and meteorological characteristics such as California, United States. In this study, we spatially classified climate regions in California during the warm season (May through September) in 2021 and 2022. We applied principal component analysis with k-means clustering algorithms to gridded data consisting of apparent temperature during the study period as a monthly time series. We then performed statistical and spatial analysis to delineate the geographical extent of climate regions with shared apparent temperature distributions. The results of this study include a statewide map of 30 warm season climate regions based on meteorological data characterized by homogenous temperature patterns that are distinct from one another. The climate regions demonstrate highly variable spatial patterns of heat exposures and often span across and within multiple county boundaries. The methods we present within a complex geography can be readily adapted to other regional settings and updated to other temporal periods. This study informs our understanding of regional climate patterns during the warm season and is applicable to the development of early warning systems and quantifying extreme heat impacts across statewide populations.

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