• Allen, M. R., and P. A. Stott, 2003: Estimating signal amplitudes in optimal fingerprinting, part I: Theory. Climate Dyn., 21, 477491, doi:10.1007/s00382-003-0313-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, S. J., J. Caesar, and C. A. T. Ferro, 2008: Global changes in extreme daily temperature since 1950. J. Geophys. Res., 113, D05115, doi:10.1029/2006JD008091.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Christidis, N., P. A. Stott, S. J. Brown, G. C. Hegerl, and J. Caesar, 2005: Detection of changes in temperature extremes during the second half of the 20th century. Geophys. Res. Lett., 32, L20716, doi:10.1029/2005GL023885.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Christidis, N., P. A. Stott, and S. J. Brown, 2011: The role of human activity in the recent warming of extremely warm daytime temperatures. J. Climate, 24, 19221930, doi:10.1175/2011JCLI4150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dey, D. K., D. Roy, and J. Yan, 2015: Univariate extreme value analysis. Extreme Value Modeling and Risk Analysis: Methods and Applications, D. K. Dey and J. Yan, Eds., CRC Press, 1–22.

    • Crossref
    • Export Citation
  • Donat, M. G., and Coauthors, 2013: Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J. Geophys. Res. Atmos., 118, 20982118, doi:10.1002/jgrd.50150.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: A regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Climate Dyn., 16, 169182, doi:10.1007/PL00013733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1997: Multi-pattern fingerprint method for detection and attribution of climate change. Climate Dyn., 13, 601611, doi:10.1007/s003820050185.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., and F. Zwiers, 2011: Use of models in detection and attribution of climate change. Wiley Interdiscip. Rev.: Climate Change, 2, 570591, doi:10.1002/wcc.121.

    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., and Coauthors, 2007: Understanding and attributing climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 663–745.

  • Hegerl, G. C., O. Hoegh-Guldberg, G. Casassa, M. P. Hoerling, R. S. Kovats, C. Parmesan, D. W. Pierce, and P. A. Stott, 2010: Good practice guidance paper on detection and attribution related to anthropogenic climate change. IPCC Expert Meeting on Detection and Attribution of Anthropogenic Climate Change, Geneva, Switzerland, WMO, 1–8.

  • Heo, J.-H., H. Shin, W. Nam, J. Om, and C. Jeong, 2013: Approximation of modified Anderson–Darling test statistics for extreme value distributions with unknown shape parameter. J. Hydrol., 499, 4149, doi:10.1016/j.jhydrol.2013.06.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2012: Summary for policymakers. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, 1–19.

  • Katz, R. W., and B. G. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21, 289302, doi:10.1007/BF00139728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., and F. W. Zwiers, 2000: Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere–ocean GCM. J. Climate, 13, 37603788, doi:10.1175/1520-0442(2000)013<3760:CITEIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., and F. W. Zwiers, 2005: Estimating extremes in transient climate change simulations. J. Climate, 18, 11561173, doi:10.1175/JCLI3320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., F. W. Zwiers, and X. Zhang, 2005: Intercomparison of near-surface temperature and precipitation extremes in AMIP-2 simulations, reanalyses, and observations. J. Climate, 18, 52015223, doi:10.1175/JCLI3597.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, Y.-H., S.-K. Min, X. Zhang, F. Zwiers, L. V. Alexander, M. G. Donat, and Y.-S. Tung, 2016: Attribution of extreme temperature changes during 1951–2010. Climate Dyn., 46, 17691782, doi:10.1007/s00382-015-2674-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livezey, R. E., and W. Y. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 111, 4659, doi:10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., W. M. Washington, C. M. Ammann, J. M. Arblaster, T. M. L. Wigley, and C. Tebaldi, 2004: Combinations of natural and anthropogenic forcings in twentieth-century climate. J. Climate, 17, 37213727, doi:10.1175/1520-0442(2004)017<3721:CONAAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Min, S.-K., X. Zhang, F. W. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multimodel detection and attribution of extreme temperature changes. J. Climate, 26, 74307451, doi:10.1175/JCLI-D-12-00551.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morak, S., G. C. Hegerl, and J. Kenyon, 2011: Detectable regional changes in the number of warm nights. Geophys. Res. Lett., 38, L17703, doi:10.1029/2011GL048531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morak, S., G. C. Hegerl, and N. Christidis, 2013: Detectable changes in the frequency of temperature extremes. J. Climate, 26, 15611574, doi:10.1175/JCLI-D-11-00678.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pachauri, R. K., and Coauthors, 2014: Climate Change 2014: Synthesis Report. Cambridge University Press, 151 pp.

  • Ribes, A., S. Planton, and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Climate Dyn., 41, 28172836, doi:10.1007/s00382-013-1735-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., 2014: Arctic amplification decreases temperature variance in northern mid- to high-latitudes. Nat. Climate Change, 4, 577582, doi:10.1038/nclimate2268.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land–atmosphere coupling and climate change in Europe. Nature, 443, 205209, doi:10.1038/nature05095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiogama, H., N. Christidis, J. Caesar, T. Yokohata, T. Nozawa, and S. Emori, 2006: Detection of greenhouse gas and aerosol influences in changes in temperature extremes. SOLA, 2, 152155, doi:10.2151/sola.2006-039.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sippel, S., D. Mitchell, M. T. Black, A. J. Dittus, L. Harrington, N. Schaller, and F. E. L. Otto, 2015: Combining large model ensembles with extreme value statistics to improve attribution statements of rare events. Wea. Climate Extremes, 9, 2535, doi:10.1016/j.wace.2015.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stone, D. A., and M. R. Allen, 2005: The end-to-end attribution problem: From emissions to impacts. Climatic Change, 71, 303318, doi:10.1007/s10584-005-6778-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tippett, L. H. C., 1931: The Methods of Statistics. Williams and Norgate, 222 pp.

  • Tseng, P., 2001: Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl., 109, 475494, doi:10.1023/A:1017501703105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tseng, P., and S. Yun, 2009: A coordinate gradient descent method for nonsmooth separable minimization. Math. Program., 117, 387423, doi:10.1007/s10107-007-0170-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., J. Yan, and X. Zhang, 2014: Toward optimal fingerprinting in detection of changes in climate extremes with combined score equations. University of Connecticut Dept. of Statistics Tech. Rep. 10, 41 pp.

  • Wilks, D. S., 2006: On “field significance” and the false discovery rate. J. Appl. Meteor. Climatol., 45, 11811189, doi:10.1175/JAM2404.1.

  • Zwiers, F. W., X. Zhang, and Y. Feng, 2011: Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Climate, 24, 881892, doi:10.1175/2010JCLI3908.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., G. C. Hegerl, X. Zhang, and Q. Wen, 2014: Quantifying the human and natural contributions to observed climate change. Statistics in Action, J. F. Lawless, Ed., Chapman and Hall/CRC Press, 349–370.

    • Crossref
    • Export Citation
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Detection and Attribution of Changes in Extreme Temperatures at Regional Scale

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  • 1 College of Economics, Shenzhen University, Shenzhen, Guangdong, China
  • 2 Department of Statistics, University of Connecticut, Storrs, Connecticut
  • 3 Climate Data Analysis Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
  • 4 Department of Statistics, University of Connecticut, Storrs, Connecticut
  • 5 Climate Data Analysis Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
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Abstract

This paper improves an extreme-value-theory-based detection and attribution method and then applies it to four types of extreme temperatures, annual minimum daily minimum (TNn) and maximum (TXn) and annual maximum daily minimum (TNx) and maximum (TXx), using the HadEX2 observation and the CMIP5 multimodel simulation datasets of the period 1951–2010 at 17 subcontinent regions. The methodology is an analog of the fingerprinting method adapted to extremes using the generalized extreme value (GEV) distribution. The signals are estimated as the time-dependent location parameters of GEV distributions fitted to extremes simulated by multimodel ensembles under anthropogenic (ANT), natural (NAT), or combined anthropogenic and natural (ALL) external forcings. The observed extremes are modeled by GEV distributions whose location parameters incorporate the signals as covariates. A coordinate descent algorithm improves both computational efficiency and accuracy in comparison to the existing method, facilitating detection of multiple signals simultaneously. An overall goodness-of-fit test was performed at the regional level. The ANT signal was separated from the NAT signal in four to six regions. In these analyses, the waiting times of the 1951–55 20-yr return level in the 2006–10 climate for the temperature of the coldest night and day were found to have increased to over 20 yr; the corresponding waiting times for the warmest night and day were found to have dropped below 20 yr in a majority of the regions.

Denotes content that is immediately available upon publication as open access.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0835.s1.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jun Yan, jun.yan@uconn.edu

Abstract

This paper improves an extreme-value-theory-based detection and attribution method and then applies it to four types of extreme temperatures, annual minimum daily minimum (TNn) and maximum (TXn) and annual maximum daily minimum (TNx) and maximum (TXx), using the HadEX2 observation and the CMIP5 multimodel simulation datasets of the period 1951–2010 at 17 subcontinent regions. The methodology is an analog of the fingerprinting method adapted to extremes using the generalized extreme value (GEV) distribution. The signals are estimated as the time-dependent location parameters of GEV distributions fitted to extremes simulated by multimodel ensembles under anthropogenic (ANT), natural (NAT), or combined anthropogenic and natural (ALL) external forcings. The observed extremes are modeled by GEV distributions whose location parameters incorporate the signals as covariates. A coordinate descent algorithm improves both computational efficiency and accuracy in comparison to the existing method, facilitating detection of multiple signals simultaneously. An overall goodness-of-fit test was performed at the regional level. The ANT signal was separated from the NAT signal in four to six regions. In these analyses, the waiting times of the 1951–55 20-yr return level in the 2006–10 climate for the temperature of the coldest night and day were found to have increased to over 20 yr; the corresponding waiting times for the warmest night and day were found to have dropped below 20 yr in a majority of the regions.

Denotes content that is immediately available upon publication as open access.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0835.s1.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jun Yan, jun.yan@uconn.edu

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