Short Warm-Side Temperature Distribution Tails Drive Hot Spots of Warm Temperature Extreme Increases under Near-Future Warming

Paul C. Loikith Department of Geography, Portland State University, Portland, Oregon

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J. David Neelin Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Joyce Meyerson Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Jacob S. Hunter Department of Systems Science, Portland State University, Portland, Oregon

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Abstract

Regions of shorter-than-Gaussian warm-side temperature anomaly distribution tails are shown to occur in spatially coherent patterns in global reanalysis. Under such conditions, future warming may be manifested in more complex ways than if the underlying distribution were close to Gaussian. For example, under a uniform warm shift, the simplest prototype for future warming, a location with a short tail would experience a greater increase in extreme warm exceedances relative to a fixed threshold compared to if the distribution were Gaussian. The associated societal and environmental impacts make realistic representation of these short tails an important target for climate models. Global evaluation of the ability for a suite of global climate models (GCMs) contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that most models approximately capture the principal observed coherent regions of short tails. This suggests the underlying dynamics and physics occur on scales resolved by the models, and helps build confidence in model simulations of extremes. Furthermore, most GCMs show more rapid future increases in exceedances of the historical 95th percentile in regions exhibiting short tails in the historical climate. These regions, where the ratio of exceedances projected by the GCM compared to that expected from a Gaussian sometimes exceeds 1.5, are termed hot spots. Prominent hot spots include western North America, Central America, a broad swath of northwestern Eurasia, and the Indochina Peninsula during boreal winter. During boreal summer, central and western Australia, parts of southern Africa, and portions of central South America are major hot spots.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0878.s1.

© 2018 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: Paul C. Loikith, ploikith@pdx.edu

Abstract

Regions of shorter-than-Gaussian warm-side temperature anomaly distribution tails are shown to occur in spatially coherent patterns in global reanalysis. Under such conditions, future warming may be manifested in more complex ways than if the underlying distribution were close to Gaussian. For example, under a uniform warm shift, the simplest prototype for future warming, a location with a short tail would experience a greater increase in extreme warm exceedances relative to a fixed threshold compared to if the distribution were Gaussian. The associated societal and environmental impacts make realistic representation of these short tails an important target for climate models. Global evaluation of the ability for a suite of global climate models (GCMs) contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that most models approximately capture the principal observed coherent regions of short tails. This suggests the underlying dynamics and physics occur on scales resolved by the models, and helps build confidence in model simulations of extremes. Furthermore, most GCMs show more rapid future increases in exceedances of the historical 95th percentile in regions exhibiting short tails in the historical climate. These regions, where the ratio of exceedances projected by the GCM compared to that expected from a Gaussian sometimes exceeds 1.5, are termed hot spots. Prominent hot spots include western North America, Central America, a broad swath of northwestern Eurasia, and the Indochina Peninsula during boreal winter. During boreal summer, central and western Australia, parts of southern Africa, and portions of central South America are major hot spots.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0878.s1.

© 2018 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: Paul C. Loikith, ploikith@pdx.edu

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  • Abatzoglou, J. T., and A. P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. USA, 113, 11 77011 775, https://doi.org/10.1073/pnas.1607171113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290.

    • Search Google Scholar
    • Export Citation
  • Anderson, B. G., and M. L. Bell, 2009: Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States. Epidemiology, 20, 205213, https://doi.org/10.1097/EDE.0b013e318190ee08.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bador, M., L. Terray, and J. Boé, 2016: Emergence of human influence on summer record-breaking temperature over Europe. Geophys. Res. Lett., 43, 404412, https://doi.org/10.1002/2015GL066560.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ballester, J., F. Giogi, and X. Rodo, 2010: Changes in European temperature extremes can be predicted from changes in PDF central statistics. Climatic Change, 98, 277, https://doi.org/10.1007/s10584-009-9758-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, A., B. R. Lintner, K. L. Findell, S. Malyshev, P. C. Loikith, and P. Gentine, 2014: Impact of soil moisture–atmosphere interactions on surface temperature distributions. J. Climate, 27, 79767993, https://doi.org/10.1175/JCLI-D-13-00591.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Borodina, A., E. M. Fischer, and R. Knutti, 2017: Potential to constrain projections of hot temperature extremes. J. Climate, 30, 99499964, https://doi.org/10.1175/JCLI-D-16-0848.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cattiaux, J., H. Douville, R. Schoetter, S. Parey, and P. Yiou, 2015: Project increase in diurnal and interdiurnal variations of European summer temperatures. Geophys. Res. Lett., 42, 899907, https://doi.org/10.1002/2014GL062531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavanaugh, N. R., and S. S. P. Shen, 2014: Northern Hemisphere climatology and trends of statistical moments documented from GHCN-Daily surface air temperature station data from 1950 to 2010. J. Climate, 27, 53965410, https://doi.org/10.1175/JCLI-D-13-00470.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ciais, Ph., and Coauthors, 2005: Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529533, https://doi.org/10.1038/nature03972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coumou, D., and A. Robinson, 2013: Historic and future increase in the global land area affected by monthly heat extremes. Environ. Res. Lett., 8, 041001, https://doi.org/10.1088/1748-9326/8/3/034018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diffenbaugh, N. S., and Coauthors, 2017: Quantifying the influence of global warming on unprecedented extreme climate events. Proc. Natl. Acad. Sci. USA, 114, 48814886, https://doi.org/10.1073/pnas.1618082114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dole, R., and Coauthors, 2011: Was there a basis for anticipating the 2010 Russian heat wave? Geophys. Res. Lett., 38, L06702, https://doi.org/10.1029/2010GL046582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M. G., and L. V. Alexander, 2012: The shifting probability distribution of global daytime and nighttime temperatures. Geophys. Res. Lett., 39, L14707, https://doi.org/10.1029/2012GL052459.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M. G., L. V. Alexander, N. Herold, and A. J. Dittus, 2016: Temperature and precipitation extremes in century-long gridded observations, reanalyses, and atmospheric model simulations. J. Geophys. Res. Atmos., 121, 11 17411 189, https://doi.org/10.1002/2016JD025480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M. G., A. J. Pitman, and S. I. Seneviratne, 2017: Regional warming of hot extremes accelerated by surface energy fluxes. Geophys. Res. Lett., 44, 70117019, https://doi.org/10.1002/2017GL073733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, B., R. Sutton, L. Shaffrey, and L. Wilcox, 2016: The 2015 European heat wave. Bull. Amer. Meteor. Soc., 97, S57S62, https://doi.org/10.1175/BAMS-D-16-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., and C. Schär, 2009: Future changes in daily summer temperature variability: Driving processes and role for temperature extremes. Climate Dyn., 33, 917935, https://doi.org/10.1007/s00382-008-0473-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Climate Change, 5, 560564, https://doi.org/10.1038/nclimate2617.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., S. I. Seneviratne, D. Lüthi, and C. Schär, 2007: The contribution of land-atmosphere coupling to recent European summer heat waves. Geophys. Res. Lett., 34, L06707, https://doi.org/10.1029/2006GL029068.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., K. W. Oleson, and D. M. Lawrence, 2012: Contrasting urban and rural heat stress responses to climate change. Geophys. Res. Lett., 39, L03705, https://doi.org/10.1029/2011GL050576.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garfinkel, C. I., and N. Harnik, 2017: The non-Gaussianity and spatial asymmetry of temperature extremes relative to the storm track: The role of horizontal advection. J. Climate, 30, 445464, https://doi.org/10.1175/JCLI-D-15-0806.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guirguis, K., A. Gershunov, D. R. Cayan, and D. W. Pierce, 2017: Heat wave probability in the changing climate of the Southwest US. Climate Dyn., 50, 38533864, https://doi.org/10.1007/s00382-017-3850-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horton, R. M., J. S. Mankin, C. Lesk, E. Coffel, and C. Raymond, 2016: A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep., 2, 242259, https://doi.org/10.1007/s40641-016-0042-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huybers, P., K. A. McKinnon, A. Rhines, and M. Tingley, 2014: U.S. daily temperatures: The meaning of extremes in the context of nonnormality. J. Climate, 27, 73687384, https://doi.org/10.1175/JCLI-D-14-00216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Im, E.-S., J. S. Pal, and E. A. B. Eltahir, 2017: Deadly heat waves projected in the densely populated agricultural regions of South Asia. Sci. Adv., 3, e1603322, https://doi.org/10.1126/sciadv.1603322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacob, D. J., and D. A. Winner, 2009: Effect of climate change on air quality. Atmos. Environ., 43, 5163, https://doi.org/10.1016/j.atmosenv.2008.09.051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and R. W. Knight, 1997: The 1995 Chicago heat wave: How likely is a recurrence? Bull. Amer. Meteor. Soc., 78, 11071119, https://doi.org/10.1175/1520-0477(1997)078<1107:TCHWHL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B., and Coauthors, 2013: Near-term climate change: Projections and predictability. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 953–1028.

  • Krueger, O., G. C. Hegerl, and S. F. B. Tett, 2015: Evaluation of mechanisms of hot and cold days in climate models over central Europe. Environ. Res. Lett., 10, 014002, https://doi.org/10.1088/1748-9326/10/1/014002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., and M. J. Nath, 2012: A model study of heat waves over North America: Meteorological aspects and projections for the twenty-first century. J. Climate, 25, 47614784, https://doi.org/10.1175/JCLI-D-11-00575.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., and M. J. Nath, 2014: Model simulation and projection of European heat waves in present and future climates. J. Climate, 27, 37133730, https://doi.org/10.1175/JCLI-D-13-00284.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., and A. J. Broccoli, 2015: Comparison between observed and model-simulated atmospheric circulation patterns associated with extreme temperature days over North America using CMIP5 historical simulations. J. Climate, 28, 20632079, https://doi.org/10.1175/JCLI-D-13-00544.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., and J. D. Neelin, 2015: Short-tailed temperature distributions over North America and implications for future changes in extremes. Geophys. Res. Lett., 42, 85778585, https://doi.org/10.1002/2015GL065602

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., B. R. Lintner, J. Kim, H. Lee, J. D. Neelin, and D. E. Waliser, 2013: Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis. Geophys. Res. Lett., 40, 37103714, https://doi.org/10.1002/grl.50688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., D. E. Waliser, H. Lee, J. D. Neelin, B. R. Lintner, S. McGinnis, L. O. Mearns, and J. Kim, 2015a: Evaluation of large-scale meteorological patterns associated with temperature extremes in the NARCCAP regional climate model simulations. Climate Dyn., 45, 32573274, https://doi.org/10.1007/s00382-015-2537-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., and Coauthors, 2015b: Surface temperature probability distributions in the NARCCAP hindcase experiments: Evaluation methodology, metrics, and results. J. Climate, 28, 978997, https://doi.org/10.1175/JCLI-D-13-00457.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKinnon, K. A., A. Rhines, M. P. Tingley, and P. Huybers, 2016: The changing shape of Northern Hemisphere summer temperature distributions. J. Geophys. Res. Atmos., 121, 88498868, https://doi.org/10.1002/2016JD025292G.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Tebaldi, G. Walton, D. Easterling, and L. McDaniel, 2009: Relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S. Geophys. Res. Lett., 36, L23701, https://doi.org/10.1029/2009GL040736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25, 693712, https://doi.org/10.1002/joc.1181.

    • 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, https://doi.org/10.1175/JCLI-D-11-00678.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perkins, S. E., L. V. Alexander, and J. R. Nairn, 2012: Increasing frequency intensity and duration of observed global heatwaves and warm spells. Geophys. Res. Lett., 39, L20714, https://doi.org/10.1029/2012GL053361.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perron, M., and P. Sura, 2013: Climatology of non-Gaussian atmospheric statistics. J. Climate, 26, 10631083, https://doi.org/10.1175/JCLI-D-11-00504.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rhines, A., and P. Huybers, 2013: Frequent summer temperature extremes reflect changes in the mean, not the variance. Proc. Natl. Acad. Sci. USA, 110, E546, https://doi.org/10.1073/pnas.1218748110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robine, J.-M., S. L. Cheung, S. Le Roy, H. Van Oyen, and F. R. Herrmann, 2007: Report on excess mortality in Europe during summer 2003. Rep. prepared for EU Community Action Programme for Public Health, 14 pp.

  • Rowe, C. M., and L. E. Derry, 2012: Trends record-breaking temperatures for the conterminous United States. Geophys. Res. Lett., 39, L16703, https://doi.org/10.1029/2012GL052775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruff, T. W., and J. D. Neelin, 2012: Long tails in regional surface temperature probability distributions with implications for extremes under global warming. Geophys. Res. Lett., 39, L04704, https://doi.org/10.1029/2011GL050610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russo, S., and Coauthors, 2014: Magnitude of extreme heat waves in present climate and their projection in a warming world. J. Geophys. Res. Atmos., 119, 12 50012 512, https://doi.org/10.1002/2014JD022098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sardeshmukh, P. D., G. P. Compo, and C. Penland, 2015: Need for caution in interpreting extreme weather statistics. J. Climate, 28, 91669187, https://doi.org/10.1175/JCLI-D-15-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Nature, 99, 125161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., and M. Huber, 2010: An adaptability limit to climate change due to heat stress. Proc. Natl. Acad. Sci. USA, 107, 95529555, https://doi.org/10.1073/pnas.0913352107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos., 118, 17161733, https://doi.org/10.1002/jgrd.50203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stefanova, L., P. Sura, and M. Griffin, 2013: Quantifying the non-Gaussianity of wintertime daily maximum and minimum temperatures in the Southeast. J. Climate, 26, 838850, https://doi.org/10.1175/JCLI-D-12-00161.1.

    • 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, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, A., and X. Zeng, 2013: Development of global hourly 0.5° land surface air temperature datasets. J. Climate, 26, 76767691, https://doi.org/10.1175/JCLI-D-12-00682.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, A., and X. Zeng, 2014a: Global hourly 0.5-degree land surface air temperature datasets. Research Data Archive, Computational and Information Systems Laboratory, National Center for Atmospheric Research, accessed 24 June 2014, https://doi.org/10.5065/D6PR7SZF.

    • Crossref
    • Export Citation
  • Wang, A., and X. Zeng, 2014b: Range of monthly mean hourly land surface air temperature diurnal cycle over high northern latitudes. J. Geophys. Res. Atmos., 119, 58365844, https://doi.org/10.1002/2014JD021602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, A., and X. Zeng, 2015: Global hourly land surface air temperature datasets: Inter-comparison and climate change. Int. J. Climatol., 35, 39593968, https://doi.org/10.1002/joc.4257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weaver, S. J., A. Kumar, and M. Chen, 2014: Recent increases in extreme temperature occurrence over land. Geophys. Res. Lett., 41, 46694675, https://doi.org/10.1002/2014GL060300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wehner, M., D. Stone, H. Krishnan, K. AchutaRao, and F. Castillo, 2016: The deadly combination of heat and humidity in India and Pakistan in summer 2015. Bull. Amer. Meteor. Soc., 97, S81S86, https://doi.org/10.1175/BAMS-D-16-0145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wuebbles, D. J., and Coauthors, 2014: CMIP5 climate model analyses: Climate extremes in the United States. Bull. Amer. Meteor. Soc., 95, 571583, https://doi.org/10.1175/BAMS-D-12-00172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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