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


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.


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



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

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Supplementary material 1 (DOCX 24 KB)


  1. Allen MJ, Sheridan SC (2016) Evaluating changes in season length, onset, and end dates across the United States (1948–2012). Int J Climatol 36:1268–1277. CrossRefGoogle Scholar
  2. Anderson WK, Garlinge JR (2000) The Wheat book : principles and practice. Department of Agriculture and Food, Western Australia, Perth. Bulletin 4443. Accessed 28 Mar 2017
  3. Angélil O, Perkins-Kirkpatrick S, Alexander LV, Stone D, Donat MG, Wehner M, Shiogama H, Ciavarella A, Christidis N (2016) Comparing regional precipitation and temperature extremes in climate model and reanalysis products. Weather Clim Ext 13:35–43. (ISSN 2212 – 0947)CrossRefGoogle Scholar
  4. Bakar KS, Kokic P (2017) Bayesian Gaussian models for point referenced spatial and spatio-temporal data. J Stat Res 51(1):17–40Google Scholar
  5. Bakar KS, Sahu SK (2015) spTimer: Spatio-temporal bayesian modelling using r. J Stat Soft 63(15):1–32 (ISSN: 1548–7660)CrossRefGoogle Scholar
  6. Bakar KS, Kokic P, Jin H (2015) A spatiodynamic model for assessing frost risk in south-eastern Australia. J Royal Stat Soc: Series C (Applied Statistics) 64(5):755–778. CrossRefGoogle Scholar
  7. Bakar KS, Kokic P, Jin H (2016) Hierarchical spatially varying coefficient and temporal dynamic process models using spTDyn. J Stat Comp Sim 86(4):820–840. CrossRefGoogle Scholar
  8. Banerjee S, Carlin BP, Gelfand AE (2004) Hierarchical modeling and analysis for spatial data. Monographs on Statistics and Applied Probability 101. Chapman & Hall/CRC Press LLC, Boca RatonGoogle Scholar
  9. Bhend J, Whetton PH (2015) Evaluation of simulated recent climate change in Australia. Aus Met Ocean J 65:4–18Google Scholar
  10. Chatterjee S, Hadi A, Price B (2000) Regression analysis by Example. Wiley, London (ISBN13 9780471319467$4)Google Scholar
  11. Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35:L20709. CrossRefGoogle Scholar
  12. Cressie NAC, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, HobokenGoogle Scholar
  13. Crimp S, Bakar KS, Kokic P, Jin H, Nicholls N, Howden M (2015) Bayesian space–time model to analyse frost risk for agriculture in Southeast Australia. Int J Clim 35(8):2092–2108. CrossRefGoogle Scholar
  14. Crimp SJ, Gobbett D, Kokic P, Nidumolu U, Howden M, Nicholls N (2016) Recent seasonal and long-term changes in southern Australian frost occurrence. Clim Change 139(1): 115–128. CrossRefGoogle Scholar
  15. Crimp S, Nicholls N, Kokic P, Risbey JS, Gobbett D, Howden M (2017) Synoptic to large-scale drivers of minimum temperature variability in Australia—long-term changes. Int J Clim. Google Scholar
  16. CSIRO (2007) Climate Change in Australia. Technical Report 2007. (Eds KB Pearce, PN Holper, M Hopkins, WJ Bouma, PH Whetton, KJ Hennessy, SB Power) p. 148. (CSIRO Marine and Atmospheric Research: Aspendale)Google Scholar
  17. Diggle P, Ribeiro PJ (2007) Model-based geostatistics. Springer, New YorkGoogle Scholar
  18. Dosio A (2016) Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models. JGR: Atmos 121(10):5488–5511. Google Scholar
  19. Drosdowsky W (2005) The latitude of the subtropical ridge over eastern Australia: the L index revisited. Int J Clim 25(10):1291–1299. CrossRefGoogle Scholar
  20. Eccel E, Rea R, Caffarra A, Crisci A (2009) Risk of spring frost to apple production under future climate scenarios: the role of phenological acclimation. Int J Biometeorol 53:273. CrossRefGoogle Scholar
  21. Fischer EM, Knutti R (2013) Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys Res Lett 41:1–8. Google Scholar
  22. Grotjahn R, Black R, Leung R, Wehner MF, Barlow M, Bosilovich M, Gershunov A, Gutowski WJ Jr, Gyakum JR, Katz RW, Lee YY (2016) North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends. Clim Dyn 46(3–4):1151–1184. CrossRefGoogle Scholar
  23. Gudmundsson L (2014) qmap: Statistical transformations for post-processing climate model output. R package version 1:0–4Google Scholar
  24. Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations—a comparison of methods. Hydro Earth Sys Sci 16:3383–3390. CrossRefGoogle Scholar
  25. Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM (2013) Observations: atmosphere and surface. In: Stocker TF, Qin D, Plattner G, Tignor MMB, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley P (eds) Climate Change 2013: the physical science basis—contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York, pp 159–254. Google Scholar
  26. IPCC (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Field CB, Barros V. Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor S, Midgley PM (eds) Cambridge University Press, CambridgeGoogle Scholar
  27. IPCC (2013) Climate Change 2013: the physical science basis—contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker TF, Qin D, Plattner G, Tignor MMB, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley P (eds), Cambridge University Press, Cambridge UK and New York.
  28. Kalma JD, Laughlin GP, Caprio JM, Hamer PJC (1992) Advances in Bioclimatology, 2. The Bioclimatology of Frost. Springer, BerlinCrossRefGoogle Scholar
  29. Kingsborough A, Jenkins K, Hall JW (2017) Development and appraisal of long-term adaptation pathways for managing heat-risk in London. Clim Risk Manag. Google Scholar
  30. Knutti R, Sedlacek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Ch 3(4):369–373. CrossRefGoogle Scholar
  31. Kokic P, Jin H, Crimp S (2013) Improved point scale climate projections using a block bootstrap simulation and quantile matching method. Clim Dyn 41(3–4):853–866. CrossRefGoogle Scholar
  32. Kunsch H (1989) The jack-knife and the bootstrap for general stationary observations. Ann Stat 17:1217–1241CrossRefGoogle Scholar
  33. Larsen SH, Nicholls N (2009) Southern Australian rainfall and the subtropical ridge: variations, interrelationships, and trends. Geophys Res Lett 36.
  34. Lee J, Li S, Lund R (2015) Trends in extreme U.S. temperatures. Am Met Soc 27:4209–4225. Google Scholar
  35. Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys R: Atmos 115(D10).
  36. Liang L, Zhang X (2015) Coupled spatiotemporal variability of temperature and spring phenology in the Eastern United States. Int J Clim. Google Scholar
  37. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. CrossRefGoogle Scholar
  38. Marotzke J, Jakob C, Bony S, Dirmeyer PA, O’Gorman PA, Hawkins E, Perkins-Kirkpatrick S, Le Quéré C, Nowicki S, Paulavets K, Seneviratne SI, Stevens B, Tuma M (2017) Climate research must sharpen its view. Nat Clime Ch 7:89–91. CrossRefGoogle Scholar
  39. Meehl GA, Stocker TF, Collins WD, Friedlingstein AT, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SC, Watterson IG, Weaver AJ, Zhao Z-C (2007). Global climate projections. In: Solomon S‚ Qin D‚ Manning M‚ Chen Z‚ Marquis M‚ Averyt KB‚ Tignor M‚ Miller HL (eds) Climate Change 2007: the Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press‚ CambridgeGoogle Scholar
  40. Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kudzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Climate change: stationarity is DEAD: Whither Water Management? Science 319:573–574. CrossRefGoogle Scholar
  41. Moise A, Wilson L, Grose M, Whetton P, Watterson I, Bhend J, Bathols J, Hanson L, Erwin T, Bedin T, Heady C (2015) Evaluation of CMIP3 and CMIP5 models over the Australian region to inform confidence in projections. Aus Meteor Ocean J 65:19–53CrossRefGoogle Scholar
  42. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756. CrossRefGoogle Scholar
  43. R-Development-Core-Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  44. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P (2011) RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clima Change. Google Scholar
  45. Sahu SK, Bakar KS (2012a) A comparison of Bayesian models for daily ozone concentration levels. Stat Method 9(1–2):144–157. CrossRefGoogle Scholar
  46. Sahu SK, Bakar KS (2012b) Hierarchical Bayesian auto-regressive models for large space time data with applications to ozone concentration modelling by Sujit Kumar Sahu and Khandoker Shuvo Bakar: Rejoinder. Appl Stoch Models Bus Industry 28(5):418–419. CrossRefGoogle Scholar
  47. Sahu SK, Gelfand AE, Holland DM (2007) High-resolution space-time ozone modeling for assessing trends. J Am Stat Assoc 102(480):1221–1234. CrossRefGoogle Scholar
  48. Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013a) Climate extremes indices in the CMIP5 multi model ensemble: Part 1. Model evaluation in the present climate. J Geophys Res 118:1716–1733. Google Scholar
  49. Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013b) Climate extremes indices in the CMIP5 multi model ensemble: Part 2. Future climate projections. J Geophys Res 118:2473–2493. Google Scholar
  50. Smith I, Syktus J, Rotstayn L, Jeffrey S (2013) The relative performance of Australian CMIP5 models based on rainfall and ENSO metrics. Aust Meteor Ocean J 63:205–212. doiCrossRefGoogle Scholar
  51. Trenberth KE (1997) The Definition of El Niño. Bull Amer Met Soc 78:2771–2777. CrossRefGoogle Scholar
  52. Trenberth KE, Fasullo JT, Shepherd TG (2015) Attribution of climate extreme events. Nat Clim Ch 5(8):725–730. CrossRefGoogle Scholar
  53. Trewin BC (2012). Techniques used in developing the Australian Climate Observations Reference Network—Surface Air Temperature (ACORN-SAT) dataset. CAWCR Technical Report 49. Centre for Australian Weather and Climate Research, Melbourne. Accessed 1 Aug 2016
  54. Vrac M, Vaittinada Ayar P (2017) Influence of bias correcting predictors on statistical downscaling models. J App Meteorol Clim 56(1):5–26. CrossRefGoogle Scholar
  55. Watterson IG, Hirst AC, Rotstayn LD (2013) A skill-score based evaluation of simulated Australian climate. Aust Meteorol Ocean J 63:181–190CrossRefGoogle Scholar
  56. Westby RM, Lee YY, Black RX (2013) Anomalous temperature regimes during the cool season: long-term trends, low-frequency mode modulation, and representation in CMIP5 simulations. J Clim 26:9061–9076. CrossRefGoogle Scholar
  57. Whan K, Timbal B, Lindesay J (2014) Linear and nonlinear statistical analysis of the impact of sub-tropical ridge intensity and position on south-east Australian rainfall. Int J Clim 34(2):326–342. CrossRefGoogle Scholar
  58. Wuebbles D, Meehl G, Hayhoe K, Karl TR, Kunkel K, Santer B, Wehner M, Colle B, Fischer EM, Fu R, Goodman A, Janssen E, Lee H, Li W, Long LN, Olsen S, Seth A, Sheffield J, Sun L (2014) CMIP5 climate model analyses: climate extremes in the United States. Bull Am Met Soc 95:571–583. CrossRefGoogle Scholar
  59. Zheng B, Chapman SC, Christopher JT, Fredricks TM, Chenu K (2015) Frost trends and their estimated impact on yield in the Australian wheatbelt. J Exp Bot 66:3611–3623. CrossRefGoogle Scholar

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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