Climate Dynamics

, Volume 42, Issue 11–12, pp 2841–2866 | Cite as

An evaluation of the statistical homogeneity of the Twentieth Century Reanalysis



The Twentieth Century Reanalysis (20CR) holds the distinction of having the longest record length (140-year; 1871–2010) of any existing global atmospheric reanalysis. If the record can be shown to be homogenous, then it would be the first reanalysis suitable for long-term trend assessments, including those of the regional hydrologic cycle. On the other hand, if discontinuities exist, then their detection and attribution—either to artificial observational shocks or climate change—is critical to their proper treatment. Previous research suggested that the quintupling of 20CR’s assimilated observation counts over the central United States was the primary cause of inhomogeneities for that region. The same work also revealed that, depending on the season, the complete record could be considered homogenous. In this study, we apply the Bai-Perron structural change point test to extend these analyses globally. A rigorous evaluation of 20CR’s (in)homogeneity is performed, composed of detailed quantitative analyses on regional, seasonal, inter-variable, and intra-ensemble bases. The 20CR record is shown to be homogenous (natural) for 69 (89) years at 50 % of land grids, based on analysis of the July 2 m air temperature. On average 54 % (41 %) of the grids between 60°S and 60°N are free from artificial inhomogenetites in their February (July) time series. Of the more than 853,376 abrupt shifts detected in 26 variable fields over two monthly time series, approximately 72 % are non-climate in origin; 25 % exceed 1.8 standard deviations of the preceding time series. The knock-on effect of inhomogeneities in 20CR’s boundary forcing and surface pressure data inputs to its surface analysis fields is implicated. In the future, reassessing these inhomogeneities will be imperative to achieving a more definitive attribution of 20CR’s abrupt shifts.


Twentieth Century Reanalysis Change point detection Climate trend analysis Observational shocks Sparse data assimilation 



We would like to thank Gilbert Compo and Prashant Sardeshmukh for valuable discussions on this topic. The lead author was supported by Japan Society for the Promotion of Science Postdoctoral Fellowship for Foreign Researchers P10379: Climate change and the potential acceleration of the hydrological cycle. The second author received financial support from the Iowa Flood Center, IIHR-Hydroscience & Engineering. Support for the Twentieth Century Reanalysis (20CR) Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office. The 20CR version 2.0 and HadISST v1.1 data were obtained from the Research Data Archive (RDA;, which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR) and sponsored by the National Science Foundation (NSF). 20CR every-member data was obtained from the National Energy Research Scientific Computing Center (NERSC; Chesley McColl provided the 20CR assimilated observation count dataset. HadSLP2 was obtained from the Met Office Hadley Centre for Climate Change ( The CRU TS3.1 dataset was obtained in May 2011 from the British Atmospheric Data Centre (BADC; The GPCC v6 Full Data Reanalysis was obtained from the Deutscher Wetterdienst (DWD;, operated under the auspices of the World Meteorological Organization (WMO). The COBE SST dataset was obtained from the Japan Meteorological Agency Tokyo Climate Center ( ERSST v3b was obtained from the NOAA National Climate Data Center (

Supplementary material

382_2013_1996_MOESM1_ESM.pdf (7.4 mb)
Supplementary material 1 (PDF 7585 kb)


  1. Allan R, Ansell T (2006) A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850-2004. J Climate 19(22):5816–5842. doi: 10.1175/Jcli3937.1 CrossRefGoogle Scholar
  2. Andrews DWK (1993) Tests for parameter instability and structural-change with unknown change-point. Econometrica 61(4):821–856CrossRefGoogle Scholar
  3. Ansell TJ, Jones PD, Allan RJ, Lister D, Parker DE, Brunet M, Moberg A, Jacobeit J, Brohan P, Rayner NA, Aguilar E, Alexandersson H, Barriendos M, Brandsma T, Cox NJ, Della-Marta PM, Drebs A, Founda D, Gerstengarbe F, Hickey K, Jonsson T, Luterbacher J, Nordli O, Oesterle H, Petrakis M, Philipp A, Rodwell MJ, Saladie O, Sigro J, Slonosky V, Srnec L, Swail V, Garcia-Suarez AM, Tuomenvirta H, Wang X, Wanner H, Werner P, Wheeler D, Xoplaki E (2006) Daily mean sea level pressure reconstructions for the European-North Atlantic region for the period 1850-2003. J Climate 19(12):2717–2742. doi: 10.1175/Jcli3775.1 CrossRefGoogle Scholar
  4. Bai J (1997) Estimation of a change point in multiple regression models. Rev Econ Stat 79(4):551–563CrossRefGoogle Scholar
  5. Bai J, Perron P (2003) Computation and analysis of multiple structural change models. J Appl Econom 18(1):1–22. doi: 10.1002/Jae.659 CrossRefGoogle Scholar
  6. Becker A, Finger P, Meyer-Christoffer A, Rudolf B, Schamm K, Schneider U, Ziese M (2013) A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst Sci Data Discuss 5:921–998. doi: 10.5194/essd-5-71-2013 CrossRefGoogle Scholar
  7. Betts AK (2009) Land-surface-atmosphere coupling in observations and models. J Adv Model Earth Syst 1(4):18. doi: 10.3894/JAMES.2009.1.4 Google Scholar
  8. Compo GP, Whitaker JS, Sardeshmukh PD (2006) Feasibility of a 100-year reanalysis using only surface pressure data. Bull Am Meteorol Soc 87(2):175. doi: 10.1175/Bams-87-2-175 CrossRefGoogle Scholar
  9. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Bronnimann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY, Jones PD, Kruk MC, Kruger AC, Marshall GJ, Maugeri M, Mok HY, Nordli O, Ross TF, Trigo RM, Wang XL, Woodruff SD, Worley SJ (2011) The twentieth century reanalysis project. Q J Roy Meteor Soc 137(654):1–28. doi: 10.1002/qj.776 CrossRefGoogle Scholar
  10. Compo GP, Whitaker JS, Sardeshmukh PD, Giese B (2012) Developing the Sparse Input Reanalysis for Climate Applications (SIRCA) 1850-2014. Paper presented at the 4th World Climate Research Programme International Conference on Reanalyses, Silver Spring, Maryland, USA, 7–11 May 2012Google Scholar
  11. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thepaut JN, Vitart F (2011a) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J Roy Meteor Soc 137(656):553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  12. Dee DP, Kallen E, Simmons AJ, Haimberger L (2011b) Comments on “Reanalyses suitable for characterizing long-term trends”. Bull Am Meteorol Soc 92(1):65–70CrossRefGoogle Scholar
  13. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res Atmos 108 (D22)Google Scholar
  14. Ferguson CR, Villarini G (2012) Detecting inhomogeneities in the twentieth century reanalysis over the central United States. J Geophys Res-Atmos 117:D05123. doi: 10.1029/2011jd016988 CrossRefGoogle Scholar
  15. Ferguson CR, Wood EF (2011) Observed land-atmosphere coupling from satellite remote sensing and re-analysis. J Hydrometeorol 12(6):1221–1254. doi: 10.1175/2011JHM1380.1 CrossRefGoogle Scholar
  16. Ferguson CR, Wood EF, Vinukollu RV (2012) A global inter-comparison of modeled and observed land-atmosphere coupling. J Hydrometeorol early-online. doi: 10.1175/JHM-D-11-0119.1 Google Scholar
  17. Folland CK, Parker DE (1995) Correction of instrumental biases in historical sea-surface temperature data. Q J Roy Meteor Soc 121(522):319–367CrossRefGoogle Scholar
  18. Giorgi F, Francisco R (2000) Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Clim Dyn 16(2–3):169–182CrossRefGoogle Scholar
  19. Hsiang SM, Meng KC, Cane MA (2011) Civil conflicts are associated with the global climate. Nature 476(7361):438–441. doi: 10.1038/Nature10311 CrossRefGoogle Scholar
  20. Huntington TG (2006) Evidence for intensification of the global water cycle: review and synthesis. J Hydrol 319(1–4):83–95CrossRefGoogle Scholar
  21. Ishii M, Shouji A, Sugimoto S, Matsumoto T (2005) Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe collection. Int J Climatol 25(7):865–879CrossRefGoogle Scholar
  22. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471CrossRefGoogle Scholar
  23. Kanamitsu M, Alpert JC, Campana KA, Caplan PM, Deaven DG, Iredell M, Katz B, Pan HL, Sela J, White GH (1991) Recent changes implemented into the global forecast system at NMC. Weather Forecast 6(3):425–435CrossRefGoogle Scholar
  24. Kaplan A, Kushnir Y, Cane MA, Blumenthal MB (1997) Reduced space optimal analysis for historical data sets: 136 years of Atlantic sea surface temperatures. J Geophys Res-Oceans 102(C13):27835–27860CrossRefGoogle Scholar
  25. Kennedy JJ, Rayner NA, Smith RO, Parker DE, Saunby M (2011a) Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 1. Measurement and sampling uncertainties. J Geophys Res-Atmos 116:D14103. doi: 10.1029/2010jd015218 CrossRefGoogle Scholar
  26. Kennedy JJ, Rayner NA, Smith RO, Parker DE, Saunby M (2011b) Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 2. Biases and homogenization. J Geophys Res-Atmos 116:D14104. doi: 10.1029/2010jd015220 CrossRefGoogle Scholar
  27. Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82(2):247–267CrossRefGoogle Scholar
  28. Lawrimore JH, Menne MJ, Gleason BE, Williams CN, Wuertz DB, Vose RS, Rennie J (2011) An overview of the global historical climatology network monthly mean temperature data set, version 3. J Geophys Res-Atmos 116:D19121. doi: 10.1029/2011jd016187 CrossRefGoogle Scholar
  29. Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319(5863):607–610. doi: 10.1126/science.1152339 CrossRefGoogle Scholar
  30. McVicar TR, Van Niel TG, Li LT, Roderick ML, Rayner DP, Ricciardulli L, Donohue RJ (2008) Wind speed climatology and trends for Australia, 1975-2006: capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophys Res Lett 35 (20)Google Scholar
  31. Meehl GA, Hu AX, Santer BD (2009) The Mid-1970s climate shift in the pacific and the relative roles of forced versus inherent decadal variability. J Climate 22(3):780–792. doi: 10.1175/2008jcli2552.1 CrossRefGoogle Scholar
  32. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25(6):693–712CrossRefGoogle Scholar
  33. Mitchell TD, Carter TR, Jones PD, Hulme M, New M (2004) A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). Tyndall Centre Working Papers No 55 (Available at
  34. Moorthi S, Pan H-L, Caplan P (2001) Changes to the 2001 NCEP oerational MRF/AVN global analysis/forecast system. Technical Procedures Bulletin 484, NOAA, NWS: Silver Spring, MD Available from
  35. Moritz MA, Parisien M-A, Batllori E, Krawchuk MA, Dorn JV, Ganz DJ, Hayhoe K (2012) Climate change and disruptions to global fire activity. Ecosphere 3(6). doi: 10.1890/ES11-00345.1
  36. New M, Hulme M, Jones P (1999) Representing twentieth-century space-time climate variability. Part I: development of a 1961-90 mean monthly terrestrial climatology. J Climate 12(3):829–856CrossRefGoogle Scholar
  37. New M, Hulme M, Jones P (2000) Representing twentieth-century space-time climate variability. Part II: development of 1901-96 monthly grids of terrestrial surface climate. J Climate 13(13):2217–2238CrossRefGoogle Scholar
  38. Pettitt AN (1979) A non-parametric approach to the change-point problem. Appl Stat 28:126–135. doi: 10.2307/2346729 CrossRefGoogle Scholar
  39. Powell AM, Xu JJ (2011) A new assessment of the mid-1970s abrupt atmospheric temperature change in the NCEP/NCAR reanalysis and associated solar forcing implications. Theor Appl Climatol 104(3–4):443–458. doi: 10.1007/s00704-010-0344-1 CrossRefGoogle Scholar
  40. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (eds) (1992) Numerical recipes in c: the art of scientific computing, 2 edn. Cambridge University Press, 994 ppGoogle Scholar
  41. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res-Atmos 108(D14):4407. doi: 10.1029/2002jd002670 CrossRefGoogle Scholar
  42. Rienecker MM, Suarez MJ, Todling R, Bacmeister J, Takacs L, Liu H-C, Gu W, Sienkiewicz M, Koster RD, Gelaro R, Stajner I, Nielson E (2008) The GEOS-5 data assimilation system-documentation of versions 5.0.1 and 5.1.0 NASA GSFC technical report series on global modeling and data assimilation. NASA/TM-2007-104606 27:92Google Scholar
  43. Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich M, Schubert SD, Takacs L, Kim G-K, Bloom S, Chen J, Collins D, Conaty A, da Silva AM, Gu W, Joiner J, Koster RD, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder CR, Reichle R, Robertson FR, Ruddick AG, Sienkiewicz M, Woollen J (2011) MERRA-NASA’s modern-era retrospective analysis for research applications. J Clim. doi: 10.1175/JCLI-D-11-00015.1 Google Scholar
  44. Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van den Dool HM, Pan HL, Moorthi S, Behringer D, Stokes D, Pena M, Lord S, White G, Ebisuzaki W, Peng P, Xie P (2006) The NCEP climate forecast system. J Climate 19(15):3483–3517CrossRefGoogle Scholar
  45. Saha S, Moorthi S, Pan HL, Wu XR, Wang JD, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu HX, Stokes D, Grumbine R, Gayno G, Wang J, Hou YT, Chuang HY, Juang HMH, Sela J, Iredell M, Treadon R, Kleist D, Van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei HL, Yang RQ, Lord S, Van den Dool H, Kumar A, Wang WQ, Long C, Chelliah M, Xue Y, Huang BY, Schemm JK, Ebisuzaki W, Lin R, Xie PP, Chen MY, Zhou ST, Higgins W, Zou CZ, Liu QH, Chen Y, Han Y, Cucurull L, Reynolds RW, Rutledge G, Goldberg M (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91(8):1015–1057. doi: 10.1175/2010BAMS3001.1 CrossRefGoogle Scholar
  46. Schar C, Vidale PL, Luthi D, Frei C, Haberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427(6972):332–336. doi: 10.1038/Nature02300 CrossRefGoogle Scholar
  47. Schneider U, Becker A, Finger F, Meyer-Christoffer A, Ziese M, Rudolf B (2013) GPCC’s new land-surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor Appl Climatol. doi: 10.1007/s00704-013-0860-x
  48. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar
  49. Sheffield J, Wood EF (2008) Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim Dynam 31(1):79–105. doi: 10.1007/s00382-007-0340-z CrossRefGoogle Scholar
  50. Smith TM, Reynolds RW (2003) Extended reconstruction of global sea surface temperatures based on COADS data (1854-1997). J Climate 16(10):1495–1510CrossRefGoogle Scholar
  51. Team RDC (2008) R: a language and environment for statistical computing. R Found. For Stat. Comput., ViennaGoogle Scholar
  52. Thorne PW, Vose RS (2010) Reanalyses suitable for characterizing long-term trends are they really achievable? Bull Am Meteorol Soc 91(3):353. doi: 10.1175/2009BAMS2858.1 CrossRefGoogle Scholar
  53. Troy TJ, Sheffield J, Wood EF (2012) The role of winter precipitation and temperature on northern Eurasian streamflow trends. J Geophys Res-Atmos 117:D05131. doi: 10.1029/2011jd016208 Google Scholar
  54. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western US forest wildfire activity. Science 313(5789):940–943. doi: 10.1126/science.1128834 CrossRefGoogle Scholar
  55. Whitaker JS, Hamill TM (2002) Ensemble data assimilation without perturbed observations. Mon Weather Rev 130(7):1913–1924CrossRefGoogle Scholar
  56. Willmott CJ, Rowe CM, Philpot WD (1985) Small-scale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Am Cartographer 12(1):5–16CrossRefGoogle Scholar
  57. Worley SJ, Woodruff SD, Reynolds RW, Lubker SJ, Lott N (2005) ICOADS release 2.1 data and products. Int J Climatol 25(7):823–842. doi: 10.1002/joc.1166 CrossRefGoogle Scholar
  58. Zeileis A, Kleiber C (2005) Validating multiple structural change models: a case study. J Appl Econom 20(5):685–690CrossRefGoogle Scholar
  59. Zeileis A, Kleiber C, Kraemer W, Hornik K (2003) Testing and dating of structural changes in practice. Comput Stat Data Anal 44:109–123CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Hydrology and Water Resources Engineering, Institute of Industrial ScienceThe University of TokyoTokyoJapan
  2. 2.Department of Environmental Resources EngineeringThe State University of New York College of Environmental Science and ForestrySyracuseUSA
  3. 3.IIHR, Hydroscience & EngineeringThe University of IowaIowa CityUSA

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