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Global trends in the frequency and duration of temperature extremes

Climatic Change volume 166, Article number: 1 (2021) Cite this article

Abstract

Anthropogenic climate change has affected the frequency and duration of extreme climate events, including extreme heat events (EHE) and extreme cold events (ECE). How the frequency and duration of both EHE and ECE have changed over time within both terrestrial and marine environments globally has not been fully explored. Here, we use detrended daily estimates of minimum and maximum temperature from the ERA5 reanalysis over a 70-year period (1950–2019) to estimate the daily occurrence of EHE and ECE across the globe. We measure the frequency and duration of EHE and ECE by season across years and estimate how these measures have changed over time. Frequency and duration for both EHE and ECE presented similar patterns characterized by low spatial heterogeneity and strong seasonal variation. High EHE frequency and duration occurred within the Antarctic during the austral summer and winter and within the Arctic Ocean during the boreal winter. High ECE frequency and duration occurred within the Nearctic and Palearctic during the boreal winter and the Arctic Ocean during the boreal summer. The trend analysis presented pronounced differences between frequency and duration, high spatial heterogeneity, especially within terrestrial environments, and strong seasonal variation. Positive EHE trends, primarily in duration within marine environments, occurred during the boreal summer within the mid-latitudes of the Northern Hemisphere and during the austral summer within the mid-latitudes of the Southern Hemisphere. The eastern tropical Pacific contained positive EHE and ECE trends, primary in duration during the boreal winter. Our findings emphasize the many near-term challenges that extreme temperature events are likely to pose for human and natural systems within terrestrial and marine environments, and the need to advance our understanding of the developing long-term implications of these changing dynamics as climate change progresses.

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References

  1. AghaKouchak A et al (2020) Climate extremes and compound hazards in a warming world. Annu Rev Earth Planet Sci 48:519–548. https://doi.org/10.1146/annurev-earth-071719-055228

    Article  Google Scholar 

  2. Alexander LV et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos 111:D05109. https://doi.org/10.1029/2005JD006290

    Article  Google Scholar 

  3. Anderson GB, Bell Michelle L (2011) Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities. Environ Health Perspect 119:210–218. https://doi.org/10.1289/ehp.1002313

    Article  Google Scholar 

  4. Bailey LD, van de Pol M (2016) Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. J Anim Ecol 85:85–96. https://doi.org/10.1111/1365-2656.12451

    Article  Google Scholar 

  5. Battisti DS, Naylor RL (2009) Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323:240. https://doi.org/10.1126/science.1164363

    Article  Google Scholar 

  6. Bell B et al (2020) ERA5 hourly data on single levels from 1950 to 1978 (preliminary version). Copernicus climate change service (C3S) climate data store (CDS). (accessed on < 12-01-2020 >), https://cds.climate.copernicus-climate.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-preliminary-back-extension?tab=overview

  7. Buehler T, Raible CC, Stocker TF (2011) The relationship of winter season North Atlantic blocking frequencies to extreme cold or dry spells in the ERA-40. Tellus A: Dynamic Meteorology and Oceanography 63:174–187. https://doi.org/10.1111/j.1600-0870.2010.00492.x

    Article  Google Scholar 

  8. Cai W et al (2014) Increasing frequency of extreme El Nino events due to greenhouse warming. Nature Clim Change 4:111–116. https://doi.org/10.1038/nclimate2100

    Article  Google Scholar 

  9. Cai W et al (2015) ENSO and greenhouse warming. Nat Clim Chang 5:849–859. https://doi.org/10.1038/nclimate2743

    Article  Google Scholar 

  10. Cayan DR (1980) Large-scale relationships between sea surface temperature and surface air temperature. Mon Weather Rev 108:1293–1301. https://doi.org/10.1175/1520-0493(1980)108<1293:LSRBSS>2.0.CO;2

    Article  Google Scholar 

  11. Colominas MA, Schlotthauer G, Torres M, Flandrin P (2012) Noise-assisted EMD methods in action. Advances in Data Science and Adaptive Analysis:4

  12. Coumou D, Robinson A (2013) Historic and future increase in the global land area affected by monthly heat extremes. Environ Res Lett 8:034018. https://doi.org/10.1088/1748-9326/8/3/034018

    Article  Google Scholar 

  13. Cremonese E, Filippa G, Galvagno M, Siniscalco C, Oddi L, Morra di Cella U, Migliavacca M (2017) Heat wave hinders green wave: the impact of climate extreme on the phenology of a mountain grassland. Agric For Meteorol 247:320–330. https://doi.org/10.1016/j.agrformet.2017.08.016

    Article  Google Scholar 

  14. Cribari-Neto F, Zeileis A (2010) Beta Regression in R. J Stat Softw 34:1–24

    Article  Google Scholar 

  15. Diffenbaugh NS et al (2017) Quantifying the influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci U S A 114:4881. https://doi.org/10.1073/pnas.1618082114

    Article  Google Scholar 

  16. Fenner D, Holtmann A, Krug A, Scherer D (2019) Heat waves in Berlin and Potsdam, Germany – Long-term trends and comparison of heat wave definitions from 1893 to 2017. Int J Climatol 39:2422–2437. https://doi.org/10.1002/joc.5962

    Article  Google Scholar 

  17. Ferrari S, Cribari-Neto F (2004) Beta regression for modelling rates and proportions. J Appl Stat 31:799–815. https://doi.org/10.1080/0266476042000214501

    Article  Google Scholar 

  18. Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat Clim Chang 5:560. https://doi.org/10.1038/nclimate2617

    Article  Google Scholar 

  19. Flanders Marine Institute (2018) IHO Sea Areas, version 3. Available online at http://www.marineregions.org/. doi:https://doi.org/10.14284/323

  20. Frölicher TL, Fischer EM, Gruber N (2018) Marine heatwaves under global warming. Nature 560:360–364. https://doi.org/10.1038/s41586-018-0383-9

    Article  Google Scholar 

  21. Garrabou J et al (2009) Mass mortality in northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. Glob Change Biol 15:1090–1103. https://doi.org/10.1111/j.1365-2486.2008.01823.x

    Article  Google Scholar 

  22. Grant PR, Grant BR, Huey RB, Johnson MTJ, Knoll AH, Schmitt J (2017) Evolution caused by extreme events. Philos Trans R Soc Lond Ser B Biol Sci 372:20160146. https://doi.org/10.1098/rstb.2016.0146

    Article  Google Scholar 

  23. Guo Y et al (2017) Heat wave and mortality: a multicountry, multicommunity study. Environ Health Perspect 125:087006. https://doi.org/10.1289/EHP1026

    Article  Google Scholar 

  24. Gutschick VP, BassiriRad H (2003) Extreme events as shaping physiology, ecology, and evolution of plants: toward a unified definition and evaluation of their consequences. New Phytol 160:21–42. https://doi.org/10.1046/j.1469-8137.2003.00866.x

    Article  Google Scholar 

  25. Harris RMB et al (2018) Biological responses to the press and pulse of climate trends and extreme events. Nat Clim Chang 8:579–587. https://doi.org/10.1038/s41558-018-0187-9

    Article  Google Scholar 

  26. Helske J, Luukko P (2018) Rlibeemd: ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN). R package version 1(4):1 https://github.com/helske/Rlibeemd

    Google Scholar 

  27. Hersbach H et al. (2019a) ERA5 monthly averaged data on single levels from 1979 to present. Copernicus climate change service (C3S) climate data store (CDS). (accessed on < 02-14-2020 >). DOI: https://doi.org/10.24381/cds.f17050d7

  28. Hersbach H et al (2019b) Global reanalysis: goodbye ERA-interim, hello ERA5. ECMWF Newsletter:17–24

  29. Hoffmann L et al (2019) From ERA-interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations. Atmos Chem Phys 19:3097–3124. https://doi.org/10.5194/acp-19-3097-2019

    Article  Google Scholar 

  30. Huang NE et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc Lond A Mat 454:903–995. https://doi.org/10.1098/rspa.1998.0193

    Article  Google Scholar 

  31. Kolstad EW, Breiteig T, Scaife AA (2010) The association between stratospheric weak polar vortex events and cold air outbreaks in the northern hemisphere. Q J R Meteorol Soc 136:886–893. https://doi.org/10.1002/qj.620

    Article  Google Scholar 

  32. Kretschmer M, Cohen J, Matthias V, Runge J, Coumou D (2018) The different stratospheric influence on cold-extremes in Eurasia and North America. Climate and Atmospheric Science 1:44. https://doi.org/10.1038/s41612-018-0054-4

    Article  Google Scholar 

  33. La Sorte FA, Hochachka WM, Farnsworth A, Dhondt AA, Sheldon D (2016) The implications of mid-latitude climate extremes for north American migratory bird populations. Ecosphere 7:e01261

    Article  Google Scholar 

  34. La Sorte FA, Fink D, Johnston A (2018) Seasonal associations with novel climates for north American migratory bird populations. Ecol Lett 21:845–856. https://doi.org/10.1111/ele.12951

    Article  Google Scholar 

  35. Lambert D (1992) Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34:1–14. https://doi.org/10.2307/1269547

    Article  Google Scholar 

  36. Lupo AR, Jensen AD, Mokhov II, Timazhev AV, Eichler T, Efe B (2019) Changes in global blocking character in recent decades. Atmosphere 10:92

    Article  Google Scholar 

  37. Luukko PJ, Helske J, Räsänen E (2016) Introducing libeemd: a program package for performing the ensemble empirical mode decomposition. Comput Stat 31:545–557. https://doi.org/10.1007/s00180-015-0603-9

    Article  Google Scholar 

  38. Mair P, Wilcox R (2019) Robust statistical methods in R using the WRS2 package. Beh Res Meth. https://doi.org/10.3758/s13428-019-01246-w

  39. Maron M, McAlpine CA, Watson JEM, Maxwell S, Barnard P (2015) Climate-induced resource bottlenecks exacerbate species vulnerability: a review. Divers Distrib 21:731–743. https://doi.org/10.1111/ddi.12339

    Article  Google Scholar 

  40. Maxwell SL et al (2019) Conservation implications of ecological responses to extreme weather and climate events. Divers Distrib 25:613–625. https://doi.org/10.1111/ddi.12878

    Article  Google Scholar 

  41. McPhillips LE et al (2018) Defining extreme events: a cross-disciplinary review. Earth’s Future 6:441–455. https://doi.org/10.1002/2017EF000686

    Article  Google Scholar 

  42. Mendes MCD, Cavalcanti IFA (2014) The relationship between the Antarctic oscillation and blocking events over the South Pacific and Atlantic Oceans. Int J Climatol 34:529–544. https://doi.org/10.1002/joc.3729

    Article  Google Scholar 

  43. Mitchell D et al (2016) Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ Res Lett 11:074006. https://doi.org/10.1088/1748-9326/11/7/074006

    Article  Google Scholar 

  44. Molla MKI, Sumi A, Rahman MS (2007) Analysis of temperature change under global warming impact using empirical mode decomposition. Int J Inf Technol 3:131–139

    Google Scholar 

  45. Oliver ECJ et al (2018) Longer and more frequent marine heatwaves over the past century. Nat Commun 9:1324. https://doi.org/10.1038/s41467-018-03732-9

    Article  Google Scholar 

  46. Olson DM, Dinerstein E (2002) The global 200: priority ecoregions for global conservation. Ann Mo Bot Gard 89:199–224

    Article  Google Scholar 

  47. Oswald EM (2018) An analysis of the prevalence of heat waves in the United States between 1948 and 2015. J Appl Meteorol Clim 57:1535–1549. https://doi.org/10.1175/JAMC-D-17-0274.1

    Article  Google Scholar 

  48. Parker WS (2016) Reanalyses and observations: what’s the difference? B Am Meteorol Soc 97:1565–1572. https://doi.org/10.1175/bams-d-14-00226.1

    Article  Google Scholar 

  49. Perkins-Kirkpatrick SE, Lewis SC (2020) Increasing trends in regional heatwaves. Nat Commun 11:3357. https://doi.org/10.1038/s41467-020-16970-7

    Article  Google Scholar 

  50. Pfahl S, Wernli H (2012) Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-)daily time scales. Geophys Res Lett 39. https://doi.org/10.1029/2012GL052261

  51. Pielou EC (1979) Biogeography. Wiley, NY

    Google Scholar 

  52. R Development Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/, Vienna, Austria

  53. Röthlisberger M, Pfahl S, Martius O (2016) Regional-scale jet waviness modulates the occurrence of midlatitude weather extremes. Geophys Res Lett 43:10,989–910,997. https://doi.org/10.1002/2016GL070944

    Article  Google Scholar 

  54. Röthlisberger M, Frossard L, Bosart LF, Keyser D, Martius O (2019) Recurrent synoptic-scale Rossby wave patterns and their effect on the persistence of cold and hot spells. J Clim 32:3207–3226. https://doi.org/10.1175/JCLI-D-18-0664.1

    Article  Google Scholar 

  55. Sillmann J, Croci-Maspoli M, Kallache M, Katz RW (2011) Extreme cold winter temperatures in Europe under the influence of North Atlantic atmospheric blocking. J Clim 24:5899–5913. https://doi.org/10.1175/2011JCLI4075.1

    Article  Google Scholar 

  56. Simas AB, Barreto-Souza W, Rocha AV (2010) Improved estimators for a general class of beta regression models. Computational Statistics & Data Analysis 54:348–366. https://doi.org/10.1016/j.csda.2009.08.017

    Article  Google Scholar 

  57. Smale DA et al (2019) Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat Clim Chang 9:306–312. https://doi.org/10.1038/s41558-019-0412-1

    Article  Google Scholar 

  58. Smith ET, Sheridan SC (2018) The characteristics of extreme cold events and cold air outbreaks in the eastern United States. Int J Climatol 38:e807–e820. https://doi.org/10.1002/joc.5408

    Article  Google Scholar 

  59. Smith ET, Sheridan SC (2019) The influence of extreme cold events on mortality in the United States. Sci Total Environ 647:342–351. https://doi.org/10.1016/j.scitotenv.2018.07.466

    Article  Google Scholar 

  60. Smith TT, Zaitchik BF, Gohlke JM (2013) Heat waves in the United States: definitions, patterns and trends. Clim Chang 118:811–825. https://doi.org/10.1007/s10584-012-0659-2

    Article  Google Scholar 

  61. Smithson M, Verkuilen J (2006) A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol Methods 11:54–71. https://doi.org/10.1037/1082-989X.11.1.54

    Article  Google Scholar 

  62. Stallone A, Cicone A, Materassi M (2020) New insights and best practices for the successful use of empirical mode decomposition, iterative filtering and derived algorithms. Sci Rep 10:15161. https://doi.org/10.1038/s41598-020-72193-2

    Article  Google Scholar 

  63. Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 4144–4147. https://doi.org/10.1109/ICASSP.2011.5947265

    Chapter  Google Scholar 

  64. Trenberth KE, Caron JM, Stepaniak DP, Worley S (2002) Evolution of El Niño–Southern oscillation and global atmospheric surface temperatures. J Geophys Res Atmos 107:AAC 5–1-AAC 5–17. https://doi.org/10.1029/2000JD000298

    Article  Google Scholar 

  65. Trenberth KE, Stepaniak DP, Smith L (2005) Interannual variability of patterns of atmospheric mass distribution. J Clim 18:2812–2825. https://doi.org/10.1175/jcli3333.1

    Article  Google Scholar 

  66. Walsh JE, Phillips AS, Portis DH, Chapman WL (2001) Extreme cold outbreaks in the United States and Europe, 1948–99. J Clim 14:2642–2658. https://doi.org/10.1175/1520-0442(2001)014<2642:ECOITU>2.0.CO;2

    Article  Google Scholar 

  67. Wang G et al (2017) Continued increase of extreme El Niño frequency long after 1.5 °C warming stabilization. Nat Clim Chang 7:568–572. https://doi.org/10.1038/nclimate3351

    Article  Google Scholar 

  68. Warton DI, Lyons M, Stoklosa J, Ives AR (2016) Three points to consider when choosing a LM or GLM test for count data. Methods Ecol Evol 7:882–890. https://doi.org/10.1111/2041-210X.12552

    Article  Google Scholar 

  69. Weiss NA (2015) wPerm: permutation tests. R package version 1.0.1. https://CRAN.R-project.org/package=wPerm

  70. Wernberg T et al (2013) An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat Clim Chang 3:78–82. https://doi.org/10.1038/nclimate1627

    Article  Google Scholar 

  71. Whan K, Zwiers F, Sillmann J (2016) The influence of atmospheric blocking on extreme winter minimum temperatures in North America. J Clim 29:4361–4381. https://doi.org/10.1175/JCLI-D-15-0493.1

    Article  Google Scholar 

  72. Wheeler DD, Harvey VL, Atkinson DE, Collins RL, Mills MJ (2011) A climatology of cold air outbreaks over North America: WACCM and ERA-40 comparison and analysis. J Geophys Res Atmos 116. https://doi.org/10.1029/2011JD015711

  73. Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological surprises. Front Ecol Environ 5:475–482

    Article  Google Scholar 

  74. Williams JW, Jackson ST, Kutzbach JE (2007) Projected distributions of novel and disappearing climates by 2100 AD. Proc Natl Acad Sci U S A 104:5738–5742

    Article  Google Scholar 

  75. Wood SN (2017) Generalized additive models: an introduction with R, 2nd edn. Chapman & Hall/CRC, Boca Raton, FL

    Book  Google Scholar 

  76. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01:1–41. https://doi.org/10.1142/s1793536909000047

    Article  Google Scholar 

  77. Wu Z, Huang NE, Long SR, Peng C-K (2007) On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci U S A 104:14889. https://doi.org/10.1073/pnas.0701020104

    Article  Google Scholar 

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Acknowledgements

We thank two anonymous reviewers for the construction comments on an earlier draft and D. Sheldon and The College of Information and Computer Sciences, University of Massachusetts, for the computational support.

Funding

This research was supported by The Wolf Creek Charitable Foundation and the National Science Foundation (DBI-1939187; DEB-2017817).

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  1. Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA

    Frank A. La Sorte & Alison Johnston

  2. Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, 14853, USA

    Toby R. Ault

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  1. Frank A. La Sorte
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Correspondence to Frank A. La Sorte.

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La Sorte, F.A., Johnston, A. & Ault, T.R. Global trends in the frequency and duration of temperature extremes. Climatic Change 166, 1 (2021). https://doi.org/10.1007/s10584-021-03094-0

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Keywords

  • Cold-air outbreaks
  • Climate change
  • Climate extremes
  • Detrended temperature
  • Heat waves
  • Temperature extremes