Advertisement

Regional trend changes in recent surface warming

  • Christian S. Zang
  • Susanne Jochner-Oette
  • José Cortés
  • Anja Rammig
  • Annette Menzel
Article

Abstract

It has been argued in the literature that global temperature increased at a reduced rate between approximately 1998 and 2013, a phenomenon known as the ‘global warming hiatus’. Statistically motivated studies searching for numerical evidence for this episode typically argue against the detectability of a slowdown. At the same time, process-oriented studies on potential causes of the slowdown are more focused on establishing the consistency between observations and model-based expectations of surface warming. Here, we employ three different gridded temperature data sets, two different statistical tests, and four different sets of periods for defining a baseline period and slowdown period associated to the recent slowdown in surface warming to provide strong evidence against consistent regional patterns of significant changes in the warming trend. Roughly half of the earth’s surface has experienced a reduced warming trend during the slowdown period, which is strongly connected to the Interdecadal Pacific Oscillation as a main source of internal variability of the climate system. This finding is consistent with the understanding of the role of internal decadal variability of the climate obtained from modelling and attribution studies. However, controlling for false discovery rates in the context of multiple testing in the spatial domain, we found that only less than 5% of the earth’s surface have experienced a significant regional slowdown in surface warming. At the same time, for roughly the same small number of grid cells we found the opposite pattern of a significant acceleration in warming. In both cases, the identified areas are not robust against the choice of temperature data set and statistical test, as well as against the delineation of the testing periods for baseline and slowdown periods. Our results demonstrate that similar to previous findings at the global level, statistical testing accounting for the multiple testing nature of the problem argues against a robust detectability of the ‘warming hiatus’ at the regional level.

Keywords

Regional temperature Slowdown Hiatus 

Notes

Acknowledgements

AM acknowledges support by the TUM Institute for Advanced Study, AM and CZ support through the (FP7/2007–2013)/ERC Grant 282250 “E3-Extreme Event Ecology”. Code for the numerical analyses is available online as a public GitHub repository at https://github.com/cszang/regional-trends-surface-warming. All used data are in the public domain. Supporting information for this research is provided in the supplementary materials. We thank two anonymous reviewers for their insightful and constructive comments that helped to improve an earlier version of this manuscript.

Supplementary material

382_2018_4524_MOESM1_ESM.pdf (3.9 mb)
Supplementary material 1 (PDF 4017 KB)

References

  1. Ballantyne A, Smith W, Anderegg W et al (2017) Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat Clim Change 7:148–152.  https://doi.org/10.1038/nclimate3204 CrossRefGoogle Scholar
  2. Cahill N, Rahmstorf S, Parnell AC (2015) Change points of global temperature. Environ Res Lett 10:084002.  https://doi.org/10.1088/1748-9326/10/8/084002 CrossRefGoogle Scholar
  3. Cerioli A (1997) Modified tests of independence in 2 × 2 tables with spatial data. Biometrics 53:619.  https://doi.org/10.2307/2533962 CrossRefGoogle Scholar
  4. Chow GC (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28:591–605.  https://doi.org/10.2307/1910133 CrossRefGoogle Scholar
  5. Compo GP, Sardeshmukh PD (2010) Removing ENSO-related variations from the climate record. J Clim 23:1957–1978.  https://doi.org/10.1175/2009JCLI2735.1 CrossRefGoogle Scholar
  6. Cowtan K, Way RG (2014) Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q J R Meteorol Soc 140:1935–1944.  https://doi.org/10.1002/qj.2297 CrossRefGoogle Scholar
  7. Cowtan K, Jacobs P, Thorne P, Wilkinson R (2018) Statistical analysis of coverage error in simple global temperature estimators. Dyn Stat Clim Syst.  https://doi.org/10.1093/climsys/dzy003 CrossRefGoogle Scholar
  8. Dong B, Dai A (2015) The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe. Clim Dyn 45:2667–2681.  https://doi.org/10.1007/s00382-015-2500-x CrossRefGoogle Scholar
  9. England MH, McGregor S, Spence P et al (2014) Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat Clim Change 4:222–227.  https://doi.org/10.1038/nclimate2106 CrossRefGoogle Scholar
  10. England MH, Kajtar JB, Maher N (2015) Robust warming projections despite the recent hiatus. Nat Clim Change 5:394CrossRefGoogle Scholar
  11. Foster G, Rahmstorf S (2011) Global temperature evolution 1979–2010. Environ Res Lett 6:044022.  https://doi.org/10.1088/1748-9326/6/4/044022 CrossRefGoogle Scholar
  12. Fyfe JC, Meehl GA, England MH et al (2016) Making sense of the early-2000 s warming slowdown. Nat Clim Change 6:224–228CrossRefGoogle Scholar
  13. GISTEMP Team (2017) GISS surface temperature analysis (GISTEMP)Google Scholar
  14. Hankey BF, Ries LA, Kosary CL et al (2000) Partitioning linear trends in age-adjusted rates. Cancer Causes Control 11:31–35CrossRefGoogle Scholar
  15. Hansen J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48:RG4004.  https://doi.org/10.1029/2010RG000345 CrossRefGoogle Scholar
  16. Harris I, Jones P, Osborn T j., Lister D (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int J Climatol 34:623–642.  https://doi.org/10.1002/joc.3711 CrossRefGoogle Scholar
  17. IPCC (2013) Summary for Policymakers. In: Stocker TF, Qin D, Plattner G-K et al. (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, CambridgeGoogle Scholar
  18. Karl TR, Arguez A, Huang B et al (2015) Possible artifacts of data biases in the recent global surface warming hiatus. Science 348:1469–1472.  https://doi.org/10.1126/science.aaa5632 CrossRefGoogle Scholar
  19. Lewandowsky S, Risbey JS, Oreskes N (2015) On the definition and identifiability of the alleged “hiatus” in global warming. Sci Rep 5:16784.  https://doi.org/10.1038/srep16784 CrossRefGoogle Scholar
  20. Li Q, Yang S, Xu W et al (2015) China experiencing the recent warming hiatus. Geophys Res Lett 42:889–898CrossRefGoogle Scholar
  21. Liu Z (2011) Dynamics of interdecadal climate variability: a historical perspective. J Clim 25:1963–1995.  https://doi.org/10.1175/2011JCLI3980.1 CrossRefGoogle Scholar
  22. Medhaug I, Stolpe MB, Fischer EM, Knutti R (2017) Reconciling controversies about the ‘global warming hiatus’. Nature 545:41–47.  https://doi.org/10.1038/nature22315 CrossRefGoogle Scholar
  23. Meehl GA, Hu A, Santer BD, Xie S-P (2016) Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends. Nat Clim Change 6:1005–1008.  https://doi.org/10.1038/nclimate3107 CrossRefGoogle Scholar
  24. Miranda PM, Tome AR (2009) Spatial structure of the evolution of surface temperature (1951–2004). Clim Change 93:269–284CrossRefGoogle Scholar
  25. Morice CP, Kennedy JJ, Rayner NA, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J Geophys Res Atmos 117:D08101.  https://doi.org/10.1029/2011JD017187 CrossRefGoogle Scholar
  26. Nieves V, Willis JK, Patzert WC (2015) Recent hiatus caused by decadal shift in Indo-Pacific heating. Science 349:532–535.  https://doi.org/10.1126/science.aaa4521 CrossRefGoogle Scholar
  27. NOAA National Centers for Environmental Information (2017) State of the Climate: Global Climate Report for Annual 2016Google Scholar
  28. Phipson B, Smyth GK (2010) Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Stat Appl Genet Mol Biol.  https://doi.org/10.2202/1544-6115.1585 CrossRefGoogle Scholar
  29. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  30. Rahmstorf S, Foster G, Cahill N (2017) Global temperature evolution: recent trends and some pitfalls. Environ Res Lett 12:054001.  https://doi.org/10.1088/1748-9326/aa6825 CrossRefGoogle Scholar
  31. Rajaratnam B, Romano J, Tsiang M, Diffenbaugh NS (2015) Debunking the climate hiatus. Clim Change 133:129–140.  https://doi.org/10.1007/s10584-015-1495-y CrossRefGoogle Scholar
  32. Rayner NA (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res.  https://doi.org/10.1029/2002JD002670 CrossRefGoogle Scholar
  33. Risbey JS, Lewandowsky S (2017) Climate science: the “pause” unpacked. Nature 545:37–39.  https://doi.org/10.1038/545037a CrossRefGoogle Scholar
  34. Risbey JS, Grose MR, Monselesan DP et al (2017) Transient response of the global mean warming rate and its spatial variation. Weather Clim Extrem 18:55–64.  https://doi.org/10.1016/j.wace.2017.11.002 CrossRefGoogle Scholar
  35. Santer BD, Mears C, Doutriaux C et al (2011) Separating signal and noise in atmospheric temperature changes: the importance of timescale. J Geophys Res Atmos 116:D22105.  https://doi.org/10.1029/2011JD016263 CrossRefGoogle Scholar
  36. Sgubin G, Swingedouw D, Drijfhout S et al (2017) Abrupt cooling over the North Atlantic in modern climate models. Nat Commun 8:ncomms14375.  https://doi.org/10.1038/ncomms14375 CrossRefGoogle Scholar
  37. Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J Clim 21:2283–2296.  https://doi.org/10.1175/2007JCLI2100.1 CrossRefGoogle Scholar
  38. Trenberth KE (2015) Has there been a hiatus? Science 349:691–692.  https://doi.org/10.1126/science.aac9225 CrossRefGoogle Scholar
  39. Vose RS, Arndt D, Banzon VF et al (2012) NOAA’s merged land–ocean surface temperature analysis. Bull Am Meteorol Soc 93:1677–1685.  https://doi.org/10.1175/BAMS-D-11-00241.1 CrossRefGoogle Scholar
  40. Wilks DS (2016) “The stippling shows statistically significant grid points”: how research results are routinely overstated and overinterpreted, and what to do about it. Bull Am Meteorol Soc 97:2263–2273.  https://doi.org/10.1175/BAMS-D-15-00267.1 CrossRefGoogle Scholar
  41. Wolter K, Timlin MS (2011) El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int J Climatol 31:1074–1087.  https://doi.org/10.1002/joc.2336 CrossRefGoogle Scholar
  42. Ying L, Shen Z, Piao S (2015) The recent hiatus in global warming of the land surface: scale-dependent breakpoint occurrences in space and time. Geophys Res Lett 42:2015GL064884.  https://doi.org/10.1002/2015GL064884 CrossRefGoogle Scholar
  43. Zeileis A, Leisch F, Hornik K, Kleiber C (2001) strucchange. An R package for testing for structural change in linear regression models. http://epub.wu.ac.at/1124/. Accessed 8 Sep 2017

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Technical University of Munich, TUM School of Life Sciences WeihenstephanFreisingGermany
  2. 2.Physical Geography/Landscape Ecology and Sustainable Ecosystem DevelopmentCatholic University of Eichstätt-IngolstadtEichstättGermany
  3. 3.Department of GeographyFriedrich Schiller University JenaJenaGermany
  4. 4.Institute for Advanced StudyTechnical University of MunichGarchingGermany

Personalised recommendations