The Annals of Applied Statistics

Modelling the effect of the El Niño-Southern Oscillation on extreme spatial temperature events over Australia

Hugo C. Winter, Jonathan A. Tawn, and Simon J. Brown

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

When assessing the risk posed by high temperatures, it is necessary to consider not only the temperature at separate sites but also how many sites are expected to be hot at the same time. Hot events that cover a large area have the potential to put a great strain on health services and cause devastation to agriculture, leading to high death tolls and much economic damage. Southeastern Australia experienced a severe heatwave in early 2009; 374 people died in the state of Victoria and Melbourne recorded its highest temperature since records began in 1859 [Nairn and Fawcett (2013)]. One area of particular interest in climate science is the effect of large-scale climatic phenomena, such as the El Niño-Southern Oscillation (ENSO), on extreme temperatures. Here, we develop a framework based upon extreme value theory to estimate the effect of ENSO on extreme temperatures across Australia. This approach permits us to estimate the change in temperatures with ENSO at important sites, such as Melbourne, and also whether we are more likely to observe hot temperatures over a larger spatial extent during a particular phase of ENSO. To this end, we design a set of measures that can be used to effectively summarise many important spatial aspects of an extreme temperature event. These measures are estimated using our extreme value framework and we validate whether we can accurately replicate the 2009 Australian heatwave, before using the model to estimate the probability of having a more severe event than has been observed.

Article information

Source
Ann. Appl. Stat., Volume 10, Number 4 (2016), 2075-2101.

Dates
Received: September 2015
Revised: June 2016
First available in Project Euclid: 5 January 2017

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1483606852

Digital Object Identifier
doi:10.1214/16-AOAS965

Mathematical Reviews number (MathSciNet)
MR3592049

Zentralblatt MATH identifier
06688769

Keywords
Conditional extremes covariates El Niño-Southern Oscillation extremal dependence extreme temperature severity-area-frequency curves spatial extremes

Citation

Winter, Hugo C.; Tawn, Jonathan A.; Brown, Simon J. Modelling the effect of the El Niño-Southern Oscillation on extreme spatial temperature events over Australia. Ann. Appl. Stat. 10 (2016), no. 4, 2075--2101. doi:10.1214/16-AOAS965. https://projecteuclid.org/euclid.aoas/1483606852


Export citation

References

  • Alexander, L. V. and Arblaster, J. M. (2009). Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. International Journal of Climatology 29 417–435.
  • Avila, F. B., Dong, S., Menang, K. P., Rajczak, J., Renom, M., Donat, M. G. and Alexander, L. V. (2015). Systematic investigation of gridding-related scaling effects on annual statistics of daily temperature and precipitation maxima: A case study for south-east Australia. Weather and Climate Extremes 9 6–16.
  • Caesar, J., Alexander, L. and Vose, R. (2006). Large-scale changes in observe daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. Journal of Geophysical Research: Atmospheres 111 1–10.
  • Chavez-Demoulin, V. and Davison, A. C. (2005). Generalized additive modelling of sample extremes. J. R. Stat. Soc. Ser. C. Appl. Stat. 54 207–222.
  • Coles, S. G. (1993). Regional modelling of extreme storms via max-stable processes. J. R. Stat. Soc. Ser. B. Stat. Methodol. 55 797–816.
  • Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer, London.
  • Coles, S. G., Heffernan, J. E. and Tawn, J. A. (1999). Dependence measures for extreme value analyses. Extremes 2 339–365.
  • Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York.
  • Davis, R. A., Klüppelberg, C. and Steinkohl, C. (2013). Statistical inference for max-stable processes in space and time. J. R. Stat. Soc. Ser. B. Stat. Methodol. 75 791–819.
  • Davison, A. C., Padoan, S. A. and Ribatet, M. (2012). Statistical modeling of spatial extremes. Statist. Sci. 27 161–186.
  • Davison, A. C. and Smith, R. L. (1990). Models for exceedances over high thresholds. J. R. Stat. Soc. Ser. B. Stat. Methodol. 52 393–442.
  • Dombry, C., Éyi-Minko, F. and Ribatet, M. (2013). Conditional simulation of max-stable processes. Biometrika 100 111–124.
  • Eastoe, E. F. and Tawn, J. A. (2009). Modelling non-stationary extremes with application to surface level ozone. J. R. Stat. Soc. Ser. C. Appl. Stat. 58 25–45.
  • Heffernan, J. E. and Resnick, S. I. (2007). Limit laws for random vectors with an extreme component. Ann. Appl. Probab. 17 537–571.
  • Heffernan, J. E. and Tawn, J. A. (2004). A conditional approach for multivariate extreme values. J. R. Stat. Soc. Ser. B. Stat. Methodol. 66 497–546.
  • Henriques, A. G. and Santos, M. J. J. (1999). Regional drought distribution model. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 24 19–22.
  • Huser, R. and Davison, A. C. (2014). Space–time modelling of extreme events. J. R. Stat. Soc. Ser. B. Stat. Methodol. 76 439–461.
  • Jones, D. A. and Trewin, B. C. (2000). On the relationships between the El Nino-Southern Oscillation and Australian land surface temperature. International Journal of Climatology 20 697–719.
  • Keef, C., Papastathopoulos, I. and Tawn, J. A. (2013). Estimation of the conditional distribution of a multivariate variable given that one of its components is large: Additional constraints for the Heffernan and Tawn model. J. Multivariate Anal. 115 396–404.
  • Kenyon, J. and Hegerl, G. C. (2008). Influence of modes of climate variability on global temperature extremes. Journal of Climate 21 3872–3889.
  • Ledford, A. W. and Tawn, J. A. (1996). Statistics for near independence in multivariate extreme values. Biometrika 83 169–187.
  • Min, S., Cai, W. and Whetton, P. (2013). Influence of climate variability on seasonal extremes over Australia. Journal of Geophysical Research: Atmospheres 118 643–654.
  • Nairn, J. and Fawcett, R. (2013). Defining heatwaves: Heatwave defined as a heat-impact event servicing all community and business sectors in Australia. Centre for Australian Weather and Climate Research, Technical Report, 060 1–96.
  • Northrop, P. J. and Jonathan, P. (2011). Threshold modelling of spatially dependent non-stationary extremes with application to hurricane-induced wave heights. Environmetrics 22 799–809.
  • Perkins, S. E. and Alexander, L. V. (2013). On the measurement of heat waves. Journal of Climate 26 4500–4517.
  • Schlather, M. (2002). Models for stationary max-stable random fields. Extremes 5 33–44.
  • Self, S. G. and Liang, K.-Y. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J. Amer. Statist. Assoc. 82 605–610.
  • Smith, R. L. (1990). Max-stable processes and spatial extremes. 1–32. Unpublished manuscript.
  • Wang, C. and Picaut, J. (2004). Understanding ENSO physics—A review. Geophysical Monograph Series 147 21–48.
  • Winter, H. C. (2016). Extreme value modelling of heatwaves. Ph.D. thesis. Lancaster Univ.
  • Winter, H. C. and Tawn, J. A. (2016). Modelling heatwaves in central France: A case study in extremal dependence. J. R. Stat. Soc. Ser. C. Appl. Stat. 65 345–365.
  • Winter, H. C., Tawn, J. A. and Brown, S. J. (2016). Detecting changing behaviour of heatwaves with climate change. Preprint.