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

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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.

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References

  • 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. https://doi.org/10.1002/joc.4422

    Article  Google Scholar 

  • Anderson WK, Garlinge JR (2000) The Wheat book : principles and practice. Department of Agriculture and Food, Western Australia, Perth. Bulletin 4443. https://researchlibrary.agric.wa.gov.au/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1005&context=bulletins. Accessed 28 Mar 2017

  • 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. https://doi.org/10.1016/j.wace.2016.07.001 (ISSN 2212 – 0947)

    Article  Google Scholar 

  • Bakar KS, Kokic P (2017) Bayesian Gaussian models for point referenced spatial and spatio-temporal data. J Stat Res 51(1):17–40

    Google Scholar 

  • Bakar KS, Sahu SK (2015) spTimer: Spatio-temporal bayesian modelling using r. J Stat Soft 63(15):1–32 (ISSN: 1548–7660)

    Article  Google Scholar 

  • 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. https://doi.org/10.1111/rssc.12103

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/00949655.2015.1038267

    Article  Google Scholar 

  • 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 Raton

    Google Scholar 

  • Bhend J, Whetton PH (2015) Evaluation of simulated recent climate change in Australia. Aus Met Ocean J 65:4–18

    Google Scholar 

  • Chatterjee S, Hadi A, Price B (2000) Regression analysis by Example. Wiley, London (ISBN13 9780471319467$4)

    Google Scholar 

  • 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. https://doi.org/10.1029/2008GL035694

    Article  Google Scholar 

  • Cressie NAC, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, Hoboken

    Google Scholar 

  • 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. https://doi.org/10.1002/joc.4109

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s10584-016-1763-5

    Article  Google Scholar 

  • 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. https://doi.org/10.1002/joc.5365

    Article  Google Scholar 

  • 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)

  • Diggle P, Ribeiro PJ (2007) Model-based geostatistics. Springer, New York

    Google Scholar 

  • 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. https://doi.org/10.1002/2015JD024411

    Article  Google Scholar 

  • Drosdowsky W (2005) The latitude of the subtropical ridge over eastern Australia: the L index revisited. Int J Clim 25(10):1291–1299. https://doi.org/10.1002/joc.1196

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s00484-009-0213-8

    Article  Google Scholar 

  • Fischer EM, Knutti R (2013) Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys Res Lett 41:1–8. https://doi.org/10.1002/2013GL058499

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s00382-015-2638-6

    Article  Google Scholar 

  • Gudmundsson L (2014) qmap: Statistical transformations for post-processing climate model output. R package version 1:0–4

    Google Scholar 

  • 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. https://doi.org/10.5194/hess-16-3383-2012

    Article  Google Scholar 

  • 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. https://doi.org/10.1017/CBO9781107415324.008

    Chapter  Google Scholar 

  • 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, Cambridge

    Google Scholar 

  • 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. http://www.ipcc.ch/report/ar5/wg1

  • Kalma JD, Laughlin GP, Caprio JM, Hamer PJC (1992) Advances in Bioclimatology, 2. The Bioclimatology of Frost. Springer, Berlin

    Book  Google Scholar 

  • Kingsborough A, Jenkins K, Hall JW (2017) Development and appraisal of long-term adaptation pathways for managing heat-risk in London. Clim Risk Manag. https://doi.org/10.1016/j.crm.2017.01.001

    Article  Google Scholar 

  • Knutti R, Sedlacek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Ch 3(4):369–373. https://doi.org/10.1038/nclimate1716

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s00382-013-1791-z

    Article  Google Scholar 

  • Kunsch H (1989) The jack-knife and the bootstrap for general stationary observations. Ann Stat 17:1217–1241

    Article  Google Scholar 

  • Larsen SH, Nicholls N (2009) Southern Australian rainfall and the subtropical ridge: variations, interrelationships, and trends. Geophys Res Lett 36. https://doi.org/10.1029/2009GL037786

  • Lee J, Li S, Lund R (2015) Trends in extreme U.S. temperatures. Am Met Soc 27:4209–4225. https://doi.org/10.1175/JCLI-D-13-00283.1

    Article  Google Scholar 

  • 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). https://doi.org/10.1007/s00382-016-3510-z

  • Liang L, Zhang X (2015) Coupled spatiotemporal variability of temperature and spring phenology in the Eastern United States. Int J Clim. https://doi.org/10.1002/joc.4456

    Article  Google Scholar 

  • 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. https://doi.org/10.1029/2009RG000314

    Article  Google Scholar 

  • 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. https://doi.org/10.1038/nclimate3206

    Article  Google Scholar 

  • 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‚ Cambridge

    Google Scholar 

  • 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. https://doi.org/10.1126/science.1151915

    Article  Google Scholar 

  • 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 65:19–53

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s10980-016-0435-1

    Article  Google Scholar 

  • R-Development-Core-Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • 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. https://doi.org/10.1007/s10584-011-0149-y

    Article  Google Scholar 

  • Sahu SK, Bakar KS (2012a) A comparison of Bayesian models for daily ozone concentration levels. Stat Method 9(1–2):144–157. https://doi.org/10.1016/j.stamet.2011.04.009

    Article  Google Scholar 

  • 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. https://doi.org/10.1002/asmb.1951

    Article  Google Scholar 

  • Sahu SK, Gelfand AE, Holland DM (2007) High-resolution space-time ozone modeling for assessing trends. J Am Stat Assoc 102(480):1221–1234. https://doi.org/10.1198/016214507000000031

    Article  Google Scholar 

  • 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. https://doi.org/10.1002/jgrd.50203

    Article  Google Scholar 

  • 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. https://doi.org/10.1002/jgrd.50188

    Article  Google Scholar 

  • 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. https://doi.org/10.22499/2.6301.013 doi

    Article  Google Scholar 

  • Trenberth KE (1997) The Definition of El Niño. Bull Amer Met Soc 78:2771–2777. https://doi.org/10.1029/95GL03602

    Article  Google Scholar 

  • Trenberth KE, Fasullo JT, Shepherd TG (2015) Attribution of climate extreme events. Nat Clim Ch 5(8):725–730. https://doi.org/10.1002/wcc.380

    Article  Google Scholar 

  • 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. http://cawcr.gov.au/publications/technicalreports/CTR_049.pdf. Accessed 1 Aug 2016

  • Vrac M, Vaittinada Ayar P (2017) Influence of bias correcting predictors on statistical downscaling models. J App Meteorol Clim 56(1):5–26. https://doi.org/10.1175/JAMC-D-16-0079.1

    Article  Google Scholar 

  • Watterson IG, Hirst AC, Rotstayn LD (2013) A skill-score based evaluation of simulated Australian climate. Aust Meteorol Ocean J 63:181–190

    Article  Google Scholar 

  • 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. https://doi.org/10.1175/JCLI-D-13-00003.1

    Article  Google Scholar 

  • 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. https://doi.org/10.1002/joc.3689

    Article  Google Scholar 

  • 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. https://doi.org/10.1175/BAMS-D-12-00172.1

    Article  Google Scholar 

  • 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. https://doi.org/10.1093/jxb/erv163

    Article  Google Scholar 

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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).

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Authors and Affiliations

  1. Climate Change Institute, Australian National University, GPO Box 1700, Canberra, ACT, 2601, Australia

    Steven Crimp & Philip Kokic

  2. CSIRO DATA61, Canberra, Australia

    Huidong Jin & Shuvo Bakar

  3. Monash University, Melbourne, Australia

    Neville Nicholls

  4. CSR&M, Australian National University, Canberra, Australia

    Shuvo Bakar

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  1. Steven Crimp
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Correspondence to Steven Crimp.

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Crimp, S., Jin, H., Kokic, P. et al. Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches. Clim Dyn 52, 1247–1262 (2019). https://doi.org/10.1007/s00382-018-4188-1

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