Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches

  • Steven Crimp
  • Huidong Jin
  • Philip Kokic
  • Shuvo Bakar
  • Neville Nicholls
Article

Abstract

Anthropogenic climate change has already been shown to effect the frequency, intensity, spatial extent, duration and seasonality of extreme climate events. Understanding these changes is an important step in determining exposure, vulnerability and focus for adaptation. In an attempt to support adaptation decision-making we have examined statistical modelling techniques to improve the representation of global climate model (GCM) derived projections of minimum temperature extremes (frosts) in Australia. We examine the spatial changes in minimum temperature extreme metrics (e.g. monthly and seasonal frost frequency etc.), for a region exhibiting the strongest station trends in Australia, and compare these changes with minimum temperature extreme metrics derived from 10 GCMs, from the Coupled Model Inter-comparison Project Phase 5 (CMIP 5) datasets, and via statistical downscaling. We compare the observed trends with those derived from the “raw” GCM minimum temperature data as well as examine whether quantile matching (QM) or spatio-temporal (spTimerQM) modelling with Quantile Matching can be used to improve the correlation between observed and simulated extreme minimum temperatures. We demonstrate, that the spTimerQM modelling approach provides correlations with observed daily minimum temperatures for the period August to November of 0.22. This represents an almost fourfold improvement over either the “raw” GCM or QM results. The spTimerQM modelling approach also improves correlations with observed monthly frost frequency statistics to 0.84 as opposed to 0.37 and 0.81 for the “raw” GCM and QM results respectively. We apply the spatio-temporal model to examine future extreme minimum temperature projections for the period 2016 to 2048. The spTimerQM modelling results suggest the persistence of current levels of frost risk out to 2030, with the evidence of continuing decadal variation.

Keywords

Frost Daily minimum temperatures Spatio-temporal modelling Quantile matching Future projections 

Notes

Acknowledgements

The authors would like to acknowledge the Australian Bureau of Meteorology (BoM) for provision of its Australian Climate Observations Reference Network—Surface Air Temperature (ACORN-SAT) data for analysis. We would also like to acknowledge that this research was made possible via financial support from the Australian Grains Research and Development Corporation (GRDC).

Supplementary material

382_2018_4188_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 24 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Climate Change InstituteAustralian National UniversityCanberraAustralia
  2. 2.CSIRO DATA61CanberraAustralia
  3. 3.Monash UniversityMelbourneAustralia
  4. 4.CSR&M, Australian National UniversityCanberraAustralia

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