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Surface temperature dataset for North America obtained by application of optimal interpolation algorithm merging tree-ring chronologies and climate model output

Abstract

A new dataset of surface temperature over North America has been constructed by merging climate model results and empirical tree-ring data through the application of an optimal interpolation algorithm. Errors of both the Community Climate System Model version 4 (CCSM4) simulation and the tree-ring reconstruction were considered to optimize the combination of the two elements. Variance matching was used to reconstruct the surface temperature series. The model simulation provided the background field, and the error covariance matrix was estimated statistically using samples from the simulation results with a running 31-year window for each grid. Thus, the merging process could continue with a time-varying gain matrix. This merging method (MM) was tested using two types of experiment, and the results indicated that the standard deviation of errors was about 0.4 °C lower than the tree-ring reconstructions and about 0.5 °C lower than the model simulation. Because of internal variabilities and uncertainties in the external forcing data, the simulated decadal warm–cool periods were readjusted by the MM such that the decadal variability was more reliable (e.g., the 1940–1960s cooling). During the two centuries (1601–1800 AD) of the preindustrial period, the MM results revealed a compromised spatial pattern of the linear trend of surface temperature, which is in accordance with the phase transition of the Pacific decadal oscillation and Atlantic multidecadal oscillation. Compared with pure CCSM4 simulations, it was demonstrated that the MM brought a significant improvement to the decadal variability of the gridded temperature via the merging of temperature-sensitive tree-ring records.

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References

  1. Ahmed M, Anchukaitis KJ, Asrat A et al (2013) Continental-scale temperature variability during the past two millennia. Nat Geosci 6:339–346

    Article  Google Scholar 

  2. Ammann CM, Joos F, Schimel DS, Otto-Bliesner BL, Tomas RA (2007) Solar influence on climate during the past millennium: results from transient simulations with the NCAR Climate System Model. Proc Natl Acad Sci 104:3713–3718

    Article  Google Scholar 

  3. Barkmeijer J, Iversen T, Palmer T (2003) Forcing singular vectors and other sensitive model structures. Quart J Roy Meteor Soc 129:2401–2423

    Article  Google Scholar 

  4. Bauer E (2003) Assessing climate forcings of the Earth system for the past millennium. Geophys Res Lett 30(6):1276–1279

    Article  Google Scholar 

  5. Bengtsson L, Ghil M, Källén E (1981) Dynamic meteorology: data assimilation methods, vol 36. Springer, New York

    Book  Google Scholar 

  6. Briffa KR, Jones P (1993) Global surface air temperature variations during the twentieth century: part 2, implications for large-scale high-frequency palaeoclimatic studies. The Holocene 3:77–88

    Article  Google Scholar 

  7. Briffa KR, Osborn TJ, Schweingruber FH, Harris IC, Jones PD, Shiyatov SG, Vaganov EA (2001) Low-frequency temperature variations from a northern tree ring density network. J Geophys Res 106:2929–2941

    Article  Google Scholar 

  8. Bürger G, Fast I, Cubasch U (2006) Climate reconstruction by regression—32 variations on a theme. Tellus A 58:227–235

    Article  Google Scholar 

  9. Burgers G, van Leeuwen PJ, Evensen G (1998) Analysis scheme in the ensemble Kalman filter. Mon Weather Rev 126:1719–1724

    Article  Google Scholar 

  10. Cook ER, Anchukaitis KJ, Buckley BM, D’Arrigo RD, Jacoby GC, Wright WE (2010) Asian monsoon failure and megadrought during the last millennium. Science 328:486–489

    Article  Google Scholar 

  11. Cook ER, Briffa KR, Jones PD (1994) Spatial regression methods in dendroclimatology: a review and comparison of two techniques. Int J Climatol 14:379–402

    Article  Google Scholar 

  12. Crowley TJ, Zielinski G, Vinther B, Udisti R, Kreutz K, Cole-Dai J, Castellano E (2008) Volcanism and the Little Ice Age. PAGES News 16:22–23

    Google Scholar 

  13. Daley R (1997) Atmospheric data assimilation. J Meteorol Soc Jpn Ser 2(75):209–219

    Google Scholar 

  14. D’Arrigo R, Villalba R, Wiles G (2001) Tree-ring estimates of Pacific decadal climate variability. Climate Dynam 18:219–224

    Article  Google Scholar 

  15. D’Arrigo R, Wilson R, Jacoby GC (2006) On the long-term context for late twentieth century warming. J Geophys Res 111(D03103)

  16. Esper J, Cook ER, Schweingruber FH (2002) Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability. Science 295:2250–2253

    Article  Google Scholar 

  17. Esper J, Frank DC, Wilson RJ, Briffa KR (2005) Effect of scaling and regression on reconstructed temperature amplitude for the past millennium. Geophys Res Lett 32(L07711)

  18. Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res: Oceans (1978–2012) 99:10143–10162

    Article  Google Scholar 

  19. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynam 53:343–367

    Article  Google Scholar 

  20. Gandin LS, Hardin R (1965) Objective analysis of meteorological fields. Israel Program for Scientific Translations, Jerusalem

    Google Scholar 

  21. Gao C, Robock A, Ammann C (2008) Volcanic forcing of climate over the past 1500 years: an improved ice core-based index for climate models. J Geophys Res Atmospheres (1984–2012) 113(D23), D23111

    Article  Google Scholar 

  22. Gonzalez-Rouco J et al (2011) Medieval Climate Anomaly to Little Ice Age transition as simulated by current climate models. PAGES News 19:7–8

    Google Scholar 

  23. Goosse H et al (2012a) The role of forcing and internal dynamics in explaining the “Medieval Climate Anomaly”. Climate Dynam 39:2847–2866

    Article  Google Scholar 

  24. Goosse H, Crowley T, Zorita E, Ammann C, Renssen H, Driesschaert E (2005) Modelling the climate of the last millennium: what causes the differences between simulations? Geophys Res Lett 32(6), L06710

    Article  Google Scholar 

  25. Goosse H, Guiot J, Mann ME, Dubinkina S, Sallaz-Damaz Y (2012b) The medieval climate anomaly in Europe: comparison of the summer and annual mean signals in two reconstructions and in simulations with data assimilation. Global Planet Change 84:35–47

    Article  Google Scholar 

  26. Goosse H, Renssen H, Timmermann A, Bradley RS, Mann ME (2006) Using paleoclimate proxy-data to select optimal realisations in an ensemble of simulations of the climate of the past millennium. Climate Dynam 27:165–184

    Article  Google Scholar 

  27. Gray ST, Graumlich LJ, Betancourt JL, Pederson GT (2004) A tree-ring based reconstruction of the Atlantic Multidecadal Oscillation since 1567 AD. Geophys Res Lett 31(12), L12205

    Article  Google Scholar 

  28. Hamill TM, Whitaker JS, Snyder C (2001) Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon Weather Rev 129(11):2776–2790

    Article  Google Scholar 

  29. Hanhijärvi S, Tingley MP, Korhola A (2013) Pairwise comparisons to reconstruct mean temperature in the Arctic Atlantic Region over the last 2,000 years. Climate Dynam 41:2039–2060

    Article  Google Scholar 

  30. Hansen J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48(4), RG4004

    Article  Google Scholar 

  31. Hansen J, Sato M, Ruedy R, Lo K, Lea DW, Medina-Elizade M (2006) Global temperature change. Proc Natl Acad Sci 103(39):14288–14293

    Article  Google Scholar 

  32. Hodson DR, Sutton R, Cassou C, Keenlyside N, Okumura Y, Zhou T (2010) Climate impacts of recent multidecadal changes in Atlantic Ocean Sea Surface Temperature: a multimodel comparison. Climate Dynam 34:1041–1058

    Article  Google Scholar 

  33. Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Weather Rev 126(3):796–811

    Article  Google Scholar 

  34. Jiang D et al (2015) Paleoclimate modeling in China: a review. Adv Atmos Sci 32:250–275

    Article  Google Scholar 

  35. Jones PD, Briffa KR, Barnett TP, Tett SFB (1998) High-resolution palaeoclimatic records for the last millennium: interpretation, integration and comparison with General Circulation Model control-run temperatures. The Holocene 8(4):455–471

    Article  Google Scholar 

  36. Jones PD et al (2009) High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects. The Holocene 19(1):3–49

    Article  Google Scholar 

  37. Jones PD, Lister DH, Osborn TJ, Harpham C, Salmon M, Morice CP (2012) Hemispheric and large-scale land surface air temperature variations: an extensive revision and an update to 2010. J Geophys Res (1984–2012) 117(D5), D05127

    Google Scholar 

  38. Jones PD, Mann ME (2004) Climate over past millennia. Rev Geophys 42(2):1–42

    Article  Google Scholar 

  39. Juckes MN et al (2007) Millennial temperature reconstruction intercomparison and evaluation. Clim Past 3(4):591–609

    Article  Google Scholar 

  40. Jungclaus J et al (2010) Climate and carbon-cycle variability over the last millennium. Clim Past 6(3):1009–1044

    Article  Google Scholar 

  41. 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 (1978–2012) 102(C13):27835–27860

    Article  Google Scholar 

  42. Knudsen MF, Jacobsen BH, Seidenkrantz M-S, Olsen J (2014) Evidence for external forcing of the Atlantic Multidecadal Oscillation since termination of the Little Ice Age. Nat Commun 5:3323

    Article  Google Scholar 

  43. Landrum L, Otto-Bliesner BL, Wahl ER, Conley A, Lawrence PJ, Rosenbloom N, Teng H (2013) Last millennium climate and its variability in CCSM4. J Clim 26(4):1085–1111

    Article  Google Scholar 

  44. Lean J, Rind D (1999) Evaluating sun–climate relationships since the Little Ice Age. J Atmos Sol Terr Phy 61:25–36

    Article  Google Scholar 

  45. Liang E et al (2006) The 1920s drought recorded by tree rings and historical documents in the semi-arid and arid areas of northern China. Clim Chang 79:403–432

    Article  Google Scholar 

  46. MacDonald GM (2005) Variations in the Pacific Decadal Oscillation over the past millennium. Geophys Res Lett 32(8), L08703

    Article  Google Scholar 

  47. Man W, Zhou T (2011) Forced response of atmospheric oscillations during the last millennium simulated by a climate system model. Chinese Sci Bull 56:3042–3052

    Article  Google Scholar 

  48. Man W, Zhou T, Jungclaus JH (2012) Simulation of the East Asian summer monsoon during the last millennium with the MPI Earth system model. J Clim 25:7852–7866

    Article  Google Scholar 

  49. Man W, Zhou T, Jungclaus JH (2014) Effects of large volcanic eruptions on global summer climate and East Asian monsoon changes during the last millennium: analysis of MPI-ESM simulations. J Clim 27(19):7394–7409

    Article  Google Scholar 

  50. Mann ME, Zhang Z, Hughes MK, Bradley RS, Miller SK, Rutherford S, Ni F (2008) Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. Proc Natl Acad Sci 105:13252–13257

    Article  Google Scholar 

  51. Mann ME et al. (2009) Global signatures and dynamical origins of the Little Ice Age and Medieval Climate Anomaly. Science 326(5957):1256–1260

  52. Mantua NJ, Hare SR (2002) The Pacific decadal oscillation. J Phys Oceanogr 58:35–44

    Article  Google Scholar 

  53. Massey FJ Jr (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46:68–78

    Article  Google Scholar 

  54. Meehl GA et al (2012) Climate system response to external forcings and climate change projections in CCSM4. J Clim 25:3661–3683

    Article  Google Scholar 

  55. Melnichenko O, Hacker P, Maximenko N, Lagerloef G, Potemra J (2014) Spatial optimal interpolation of Aquarius sea surface salinity: algorithms and implementation in the North Atlantic. J Atmos Ocean Technol 31:1583–1600

    Article  Google Scholar 

  56. Menemenlis D, Fieguth P, Wunsch C, Willsky A (1997) Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data. J Geophys Res Oceans (1978–2012) 102:10573–10584

    Article  Google Scholar 

  57. Michaelsen J (1987) Cross-validation in statistical climate forecast models. J Clim Appl Meteorol 26:1589–1600

    Article  Google Scholar 

  58. Phipps S, Rotstayn L, Gordon H, Roberts J, Hirst A, Budd W (2011) The CSIRO Mk3L climate system model version 1.0—part 1: description and evaluation. Geosci Model Dev 4:483–509

    Article  Google Scholar 

  59. Phipps S, Rotstayn L, Gordon H, Roberts J, Hirst A, Budd W (2012) The CSIRO Mk3L climate system model version 1.0—part 2: response to external forcings. Geosci Model Dev 5:649–682

    Article  Google Scholar 

  60. Pozo-Vázquez D, Esteban-Parra M, Rodrigo F, Castro-Diez Y (2001) A study of NAO variability and its possible non-linear influences on European surface temperature. Climate Dynam 17:701–715

    Article  Google Scholar 

  61. Rohde R et al (2013a) A new estimate of the average earth surface land temperature spanning 1753 to 2011. Geoinfor Geostat An Overview 1(1):1–7

    Google Scholar 

  62. Rohde R et al (2013b) Berkeley earth temperature averaging process. Geoinfor Geostat An Overview 1(2):1–13

    Article  Google Scholar 

  63. Salzer MW, Hughes MK (2007) Bristlecone pine tree rings and volcanic eruptions over the last 5000 yr. Quaternary Res 67:57–68

    Article  Google Scholar 

  64. Schmidt G et al (2012) Climate forcing reconstructions for use in PMIP simulations of the last millennium (v1. 1). Geosci Model Dev 5:185–191

    Article  Google Scholar 

  65. Schurer AP, Hegerl GC, Mann ME, Tett SF, Phipps SJ (2013) Separating forced from chaotic climate variability over the past millennium. J Clim 26:6954–6973

    Article  Google Scholar 

  66. Sigl M, McConnell JR, Toohey M et al (2014) Insights from Antarctica on volcanic forcing during the Common Era. Nat Clim Chang 4:693–697

    Article  Google Scholar 

  67. Slonosky V, Yiou P (2002) Does the NAO index represent zonal flow? The influence of the NAO on North Atlantic surface temperature. Climate Dynam 19:17–30

    Article  Google Scholar 

  68. Smith TM, Reynolds RW (2005) A global merged land–air–sea surface temperature reconstruction based on historical observations (1880–1997). J Clim 18:2021–2036

    Article  Google Scholar 

  69. 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

    Article  Google Scholar 

  70. Sutton RT, Hodson DL (2005) Atlantic Ocean forcing of North American and European summer climate. Science 309:115–118

    Article  Google Scholar 

  71. van der Schrier G, Barkmeijer J (2005) Bjerknes’ hypothesis on the coldness during AD 1790–1820 revisited. Climate Dynam 25:537–553

    Article  Google Scholar 

  72. von Storch H, Cubasch U, Gonzalez-Rouco J, Jones J, Voss R, Widmann M, Zorita E (2000) Combining paleoclimatic evidence and GCMs by means of data assimilation through upscaling and nudging. pp. 28–31, Proc. 11th symposium on global climate change studies, AMS Long Beach, CA, 2000

  73. Widmann M, Goosse H, van der Schrier G, Schnur R, Barkmeijer J (2010) Using data assimilation to study extratropical Northern Hemisphere climate over the last millennium. Clim Past 6:627–644

    Article  Google Scholar 

  74. Zhou T, Yu R (2006) Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J Clim 19:5843–5858

    Article  Google Scholar 

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grants 41175066, 41275076), China Meteorological Administration Special Public Welfare Research Fund (GYHY201306019), National Basic Research Program of China (2010CB950102), and China Postdoctoral Science Foundation (Grant 2014M550711). We acknowledge the International Tree-Ring Data Bank (ITRDB) from where we obtained the tree-ring data of North America (http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring). We thank all the data contributors and related scholars. The CRUTEM4v data were obtained from http://www.cru.uea.ac.uk/cru/data/temperature/, the Mlost data were from NOAA’s National Climatic Data Center (NCDC): ftp://ftp.ncdc.noaa.gov/pub/data/paleo/reconstructions/pcn/instrumental/MLOST/, Gistemp data were from http://data.giss.nasa.gov/gistemp/, and the CCSM4 simulation data were from http://www.cesm.ucar.edu/experiments/cesm1.0/#paleo.

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Correspondence to Yong Luo.

Appendix/notation

Appendix/notation

The variables mentioned in Section 2.4 are listed and defined explicitly. First, we assume that the background field (referred to as the climate model-simulated gridded data) has N grids. Second, as the number of tree-ring sites is a dependent variable from grid to grid (as stated in Section 2.4), only those chronologies within the prescribed range are available, and thus, M chronologies at some time point can be assumed.

T a i : surface temperature of final analysis results at grid i.

T b i : surface temperature of background at grid i.

W: optimal weight matrix with N × M elements.

R: observation error covariance matrix. In this study, only diagonal elements are preserved, indicating that the errors of different tree rings are noncorrelated.

B: background error covariance matrix. This is computed from the covariance calculations based on a moving time window ensemble.

H: observation operator. In this study, it is used to interpolate the data in the grids at the tree-ring sites using a bilinear method; it can be regarded as a linear operator.

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Chen, X., Xing, P., Luo, Y. et al. Surface temperature dataset for North America obtained by application of optimal interpolation algorithm merging tree-ring chronologies and climate model output. Theor Appl Climatol 127, 533–549 (2017). https://doi.org/10.1007/s00704-015-1634-4

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Keywords

  • Pacific Decadal Oscillation
  • Error Covariance Matrix
  • Atlantic Multidecadal Oscillation
  • Merging Method
  • Optimal Interpolation Scheme