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Journal of Southern Hemisphere Earth Systems Science Journal of Southern Hemisphere Earth Systems Science SocietyJournal of Southern Hemisphere Earth Systems Science Society
A journal for meteorology, climate, oceanography, hydrology and space weather focused on the southern hemisphere
RESEARCH ARTICLE (Open Access)

Redefining southern Australia’s climatic regions and seasons

Sonya Fiddes https://orcid.org/0000-0002-2752-0845 A B D , Acacia Pepler https://orcid.org/0000-0002-1478-2512 A , Kate Saunders https://orcid.org/0000-0002-1436-7802 C and Pandora Hope https://orcid.org/0000-0002-9631-8181 A
+ Author Affiliations
- Author Affiliations

A Bureau of Meteorology, Melbourne, Australia.

B Present Address. Australian Antarctic Program Partnership, Institute of Marine and Antarctic Studies, University of Tasmania, Hobart, Australia.

C Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands.

D Corresponding author. Email: sonya.fiddes@utas.edu.au

Journal of Southern Hemisphere Earth Systems Science 71(1) 92-109 https://doi.org/10.1071/ES20003
Submitted: 5 August 2020  Accepted: 16 February 2021   Published: 16 March 2021

Journal Compilation © BoM 2021 Open Access CC BY-NC-ND

Abstract

Climate scientists routinely rely on averaging over time or space to simplify complex information and to concisely communicate findings. Currently, no consistent definitions of ‘warm’ or ‘cool’ seasons for southern Australia exist, making comparisons across studies difficult. Similarly, numerous climate studies in Australia use either arbitrarily defined areas or the Natural Resource Management (NRM) clusters to perform spatial averaging. While the NRM regions were informed by temperature and rainfall information, they remain somewhat arbitrary. Here we use weather type influence on rainfall and clustering methods to quantitatively define climatic regions and seasons over southern Australia. Three methods are explored: k-means clustering and two agglomerative clustering methods, Ward linkage and average linkage. K-means was found to be preferred in temporal clustering, while the average linkage method was preferred for spatial clustering. For southern Australia as a whole, we define the cool season as April–September and warm season as October–March, though we note that a three-season split may provide more nuanced climate analysis. We also show that different regions across southern Australia experience different seasons and demonstrate the changing spatial influence of weather types with the seasons, which may aid regionally or seasonally specific climate analysis. Division of southern Australia into 15 climatic regions shows localised agreement with the NRM clusters where distinct differences in rainfall amounts exist. However, the climate regions defined here better represent the importance of topographical aspect on weather type influence and the inland extent of particular weather types. We suggest that the use of these regions would provide consistent climate analysis across studies if widely adopted. A key requirement for climate scientists is the simplification of data sets into both seasonally or regionally averaged subsets. This simplification, by grouping like regions or seasons, is done for a number of reasons both scientific and practical, including to help understand patterns of variability, underlying drivers and trends in climate and weather, to communicate large amounts of data concisely, to reduce the amount of data required for processing (which becomes increasingly important with higher resolution climate model output), or to more simply draw a physical boundary between regions for other purposes, such as flora and fauna habitat analysis, appropriate agricultural practices or water management.

Keywords: climate regions, Natural Resource Management (NRM), rainfall, topography, weather type, Southern Australia, k-means clustering, seasons, regions.


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