• Ault, T. R., , C. Deser, , M. Newman, , and J. Emile-Geay, 2013: Characterizing decadal to centennial variability in the equatorial Pacific during the last millennium. Geophys. Res. Lett., 40, 34503456, doi:10.1002/grl.50647.

    • Search Google Scholar
    • Export Citation
  • Bentsen, M., and Coauthors, 2013: The Norwegian Earth System Model, NorESM1-M—Part 1: Description and basic evaluation of the physical climate. Geosci. Model Dev., 6, 687720, doi:10.5194/gmd-6-687-2013.

    • Search Google Scholar
    • Export Citation
  • Blender, R., , and K. Fraedrich, 2003: Long time memory in global warming simulations. Geophys. Res. Lett., 30, 1769, doi:10.1029/2003GL017666.

    • Search Google Scholar
    • Export Citation
  • Chylek, P., , J. Li, , M. K. Dubey, , M. Wang, , and G. Lesins, 2011: Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmos. Chem. Phys. Discuss., 11, 22 89322 907, doi:10.5194/acpd-11-22893-2011.

    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, doi:10.1002/qj.776.

    • Search Google Scholar
    • Export Citation
  • Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Climate, 24, 34843519, doi:10.1175/2011JCLI3955.1.

    • Search Google Scholar
    • Export Citation
  • Dufresne, J.-L., and Coauthors, 2013: Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5. Climate Dyn., 40, 21232165, doi:10.1007/s00382-012-1636-1.

    • Search Google Scholar
    • Export Citation
  • Fraedrich, K., , and R. Blender, 2003: Scaling of atmosphere and ocean temperature correlations in observations and climate models. Phys. Rev. Lett., 90, 108501, doi:10.1103/PhysRevLett.90.108501.

    • Search Google Scholar
    • Export Citation
  • Fraedrich, K., , U. Luksch, , and R. Blender, 2004: 1/ƒ model for long-time memory of the ocean surface temperature. Phys. Rev., 70E, 037301, doi:10.1103/PhysRevE.70.037301.

    • Search Google Scholar
    • Export Citation
  • Franke, J., , D. Frank, , C. C. Raible, , J. Esper, , and S. Bronnimann, 2013: Spectral biases in tree-ring climate proxies. Nat. Climate Change, 3, 360364, doi:10.1038/nclimate1816.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., 2010: Long-range dependence and climate noise characteristics of Antarctic temperature data. J. Climate, 23, 60746081, doi:10.1175/2010JCLI3654.1.

    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , R. Ruedy, , M. Sato, , and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.

  • Huybers, P., , and W. Curry, 2006: Links between annual, Milankovitch and continuum temperature variability. Nature, 441, 329332, doi:10.1038/nature04745.

    • Search Google Scholar
    • Export Citation
  • Kennedy, J. J., , N. A. Rayner, , R. O. Smith, , D. E. Parker, , and M. Saunby, 2011a: Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 1. Measurement and sampling uncertainties. J. Geophys. Res., 116, D14103, doi:10.1029/2010JD015218.

    • Search Google Scholar
    • Export Citation
  • Kennedy, J. J., , N. A. Rayner, , R. O. Smith, , D. E. Parker, , and M. Saunby, 2011b: Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 2. Biases and homogenization. J. Geophys. Res., 116, D14104, doi:10.1029/2010JD015220.

    • Search Google Scholar
    • Export Citation
  • Koscielny-Bunde, E., , A. Bunde, , S. Havlin, , H. Roman, , Y. Goldreich, , and H.-J. Schellnhuber, 1998: Indication of a universal persistence law governing atmospheric variability. Phys. Rev. Lett., 81, 729732, doi:10.1103/PhysRevLett.81.729.

    • Search Google Scholar
    • Export Citation
  • Laepple, T., , and P. Huybers, 2014: Global and regional variability in marine surface temperatures. Geophys. Res. Lett., 41, 25282534, doi:10.1002/2014GL059345.

    • Search Google Scholar
    • Export Citation
  • Lennartz, S., , and A. Bunde, 2009: Trend evaluation in records with long-term memory: Application to global warming. Geophys. Res. Lett., 36, L16706, doi:10.1029/2009GL039516.

    • Search Google Scholar
    • Export Citation
  • Mantua, N. J., , S. R. Hare, , Y. Zhang, , J. M. Wallace, , and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 10691079, doi:10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Martin, T., , W. Park, , and M. Latif, 2013: Multi-centennial variability controlled by Southern Ocean convection in the Kiel Climate Model. Climate Dyn., 40, 20052022, doi:10.1007/s00382-012-1586-7.

    • Search Google Scholar
    • Export Citation
  • McCarthy, G. D., , I. D. Haigh, , J. J. M. Hirschi, , J. P. Grist, , and D. A. Smeed, 2015: Ocean impact on decadal Atlantic climate variability revealed by sea-level observations. Nature, 521, 508510, doi:10.1038/nature14491.

    • Search Google Scholar
    • Export Citation
  • Monetti, R. A., , S. Havlin, , and A. Bunde, 2003: Long-term persistence in the sea surface temperature fluctuations. Physica A, 320, 581589, doi:10.1016/S0378-4371(02)01662-X.

    • Search Google Scholar
    • Export Citation
  • Morice, C. P., , J. J. Kennedy, , N. A. Rayner, , and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, doi:10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • North, G. R., , J. Wang, , and M. G. Genton, 2011: Correlation models for temperature fields. J. Climate, 24, 58505862, doi:10.1175/2011JCLI4199.1.

    • Search Google Scholar
    • Export Citation
  • Østvand, L., , T. Nilsen, , K. Rypdal, , D. Divine, , and M. Rypdal, 2014: Long-range memory in internal and forced dynamics of millennium-long climate model simulations. Earth Syst. Dyn., 5, 295308, doi:10.5194/esd-5-295-2014.

    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., , and R. S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Meteor. Soc., 78, 28372849, doi:10.1175/1520-0477(1997)078<2837:AOOTGH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Raddatz, T., and Coauthors, 2007: Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century? Climate Dyn., 29, 565565, doi:10.1007/s00382-007-0247-8.

    • Search Google Scholar
    • Export Citation
  • Rohde, R., , R. Muller, , R. Jacobsen, , S. Perlmutter, , and S. Mosher, 2013a: Berkeley Earth temperature averaging process. Geoinf. Geostat: An Overview, 1 (2), doi:10.4172/2327-4581.1000103.

    • Search Google Scholar
    • Export Citation
  • Rohde, R., and Coauthors, 2013b: A new estimate of the average earth surface land temperature spanning 1753 to 2011. Geoinf. Geostat: An Overview, 1 (1), doi:10.4172/2327-4581.1000101.

    • Search Google Scholar
    • Export Citation
  • Rybski, D., , A. Bunde, , S. Havlin, , and H. von Storch, 2006: Long-term persistence in climate and the detection problem. Geophys. Res. Lett., 33, L06718, doi:10.1029/2005GL025591.

    • Search Google Scholar
    • Export Citation
  • Rybski, D., , A. Bunde, , and H. von Storch, 2008: Long-term memory in 1000-year simulated temperature records. J. Geophys. Res., 113, D02106, doi:10.1029/2007JD008568.

    • Search Google Scholar
    • Export Citation
  • Rypdal, K., 2012: Global temperature response to radiative forcing: Solar cycle versus volcanic eruptions. J. Geophys. Res., 117, D06115, doi:10.1029/2011JD017283.

    • Search Google Scholar
    • Export Citation
  • Rypdal, K., , L. Østvand, , and M. Rypdal, 2013: Long-range memory in Earth’s surface temperature on time scales from months to centuries. J. Geophys. Res. Atmos., 118, 70467062, doi:10.1002/jgrd.50399.

    • Search Google Scholar
    • Export Citation
  • Rypdal, K., , M. Rypdal, , and H.-B. Fredriksen, 2015: Spatiotemporal long-range persistence in Earth’s temperature field: Analysis of stochastic-diffusive energy balance models. J. Climate, 28, 83798395, doi:10.1175/JCLI-D-15-0183.1.

    • Search Google Scholar
    • Export Citation
  • Rypdal, M., , and K. Rypdal, 2014: Long-memory effects in linear response models of Earth’s temperature and implications for future global warming. J. Climate, 27, 52405258, doi:10.1175/JCLI-D-13-00296.1.

    • Search Google Scholar
    • Export Citation
  • Sen Gupta, A., , N. C. Jourdain, , J. N. Brown, , and D. Monselesan, 2013: Climate drift in the CMIP5 models. J. Climate, 26, 85978615, doi:10.1175/JCLI-D-12-00521.1.

    • Search Google Scholar
    • Export Citation
  • Slutz, R. J., , S. J. Lubker, , J. D. Hiscox, , S. D. Woodruff, , R. L. Jenne, , D. H. Joseph, , P. M. Steurer, , and J. D. Elms, 1985: COADS: Comprehensive Ocean–Atmosphere Data Set, release 1. NOAA Environmental Research Laboratories, Climate Research Program, 262 pp.

  • Smith, T. M., , R. W. Reynolds, , T. C. Peterson, , and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 22832296, doi:10.1175/2007JCLI2100.1.

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

    • Search Google Scholar
    • Export Citation
  • Voldoire, A., and Coauthors, 2013: The CNRM-CM5.1 global climate model: Description and basic evaluation. Climate Dyn., 40, 20912121, doi:10.1007/s00382-011-1259-y.

    • Search Google Scholar
    • Export Citation
  • Vyushin, D. I., , P. J. Kushner, , and F. Zwiers, 2012: Modeling and understanding persistence of climate variability. J. Geophys. Res., 117, D21106, doi:10.1029/2012JD018240.

    • Search Google Scholar
    • Export Citation
  • Watanabe, S., and Coauthors, 2011: MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev., 4, 845872, doi:10.5194/gmd-4-845-2011.

    • Search Google Scholar
    • Export Citation
  • Woodruff, S. D., and Coauthors, 2011: ICOADS Release 2.5: Extensions and enhancements to the surface marine meteorological archive. Int. J. Climatol., 31, 951967, doi:10.1002/joc.2103.

    • Search Google Scholar
    • Export Citation
  • Yukimoto, S., and Coauthors, 2011: Meteorological Research Institute-Earth System Model version 1 (MRI-ESM1)—Model description. MRI Tech. Rep. 64, 71 pp. [Available online at http://www.mri-jma.go.jp/Publish/Technical/DATA/VOL_64/index_en.html.]

  • Zorita, E., , F. González-Rouco, , and S. Legutke, 2003: Testing the Mann et al. (1998) approach to paleoclimate reconstructions in the context of a 1000-yr control simulation with the ECHO-G coupled climate model. J. Climate, 16, 13781390, doi:10.1175/1520-0442(2003)16<1378:TTMEAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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Spectral Characteristics of Instrumental and Climate Model Surface Temperatures

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  • 1 Department of Mathematics and Statistics, University of Tromsø, Tromsø, Norway
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Abstract

The spatiotemporal temperature variability for several gridded instrumental and general circulation climate model data is characterized, contrasting power spectra of local and global temperatures, land and sea temperatures, and temperatures of different regions. There is generally a high degree of agreement between the spectral characteristics of instrumental and climate model data. All but the equatorial spectra exhibit a power-law shape and are hence more consistent with the spectra expected from long-memory processes than from short-memory processes. The power-law exponent β of the spectra is a measure of memory, or persistence, of the temperatures and is observed to be about twice as large for global temperature than for local temperatures. However, there are large variations, in particular between land and sea surface temperatures. This is shown by estimates of the spectra for different regions and global maps of β. It is also demonstrated that global spectra are related to local spectra via teleconnections between local temperatures.

Denotes Open Access content.

Corresponding author address: Hege-Beate Fredriksen, Department of Mathematics and Statistics, University of Tromsø, Hansine Hansens Vei 54, N-9037 Tromsø, Norway. E-mail: hege-beate.fredriksen@uit.no

Abstract

The spatiotemporal temperature variability for several gridded instrumental and general circulation climate model data is characterized, contrasting power spectra of local and global temperatures, land and sea temperatures, and temperatures of different regions. There is generally a high degree of agreement between the spectral characteristics of instrumental and climate model data. All but the equatorial spectra exhibit a power-law shape and are hence more consistent with the spectra expected from long-memory processes than from short-memory processes. The power-law exponent β of the spectra is a measure of memory, or persistence, of the temperatures and is observed to be about twice as large for global temperature than for local temperatures. However, there are large variations, in particular between land and sea surface temperatures. This is shown by estimates of the spectra for different regions and global maps of β. It is also demonstrated that global spectra are related to local spectra via teleconnections between local temperatures.

Denotes Open Access content.

Corresponding author address: Hege-Beate Fredriksen, Department of Mathematics and Statistics, University of Tromsø, Hansine Hansens Vei 54, N-9037 Tromsø, Norway. E-mail: hege-beate.fredriksen@uit.no
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