• Barnett, T. P. 1986: Detection of changes in the global troposphere temperature field induced by greenhouse gases. J. Geophys. Res.,91 (D6), 6659–6667.

  • ——, and M. E. Schlesinger, 1987: Detecting changes in global climate induced by greenhouse gases. J. Geophys. Res.,92, 14 772–14 780.

  • ——, and Coauthors, 1991: Greenhouse signal detection. Greenhouse-Gas-Induced-Climatic Change: A Critical Appraisal of Simulations and Observations, M. E. Schlesinger, Ed., Elsevier Science, 593–602.

  • ——, B. D. Santer, P. D. Jones, R. S. Bradley, and K. R. Briffa, 1996: Estimates of low frequency natural variability in near-surface air temperature. Holocene,6, 255–263.

  • Bell, T. L., 1982: Optimal weighting of data to detect climatic change: Application to the carbon dioxide problem. J. Geophys. Res.,87, 11 161–11 170.

  • ——, 1986: Theory of optimal weighting of data to detect climatic change. J. Atmos. Sci.,43, 1694–1710.

  • Cubasch, U., K. Hasselmann, H. Höck, E. Maier-Reimer, U. Mikolajewicz, B. D. Santer, and R. Sausen, 1992: Time-dependant greenhouse warming computations with a coupled ocean–atmosphere model. Climate Dyn.,8, 55–69.

  • ——, B. D. Santer, A. Hellbach, G. C. Hegerl, H. Höck, E. Maier-Reimer, U. Mikolajewicz, A. Stössel, and R. Voss, 1994: Monte Carlo climate forecasts with a global coupled ocean–atmosphere model. Climate Dyn.,10, 1–19.

  • Gates, L., and Coauthors, 1996: Climate Models—Evaluation. Climate Change 1995. The IPCC Second Scientific Assessment, J. T. Houghton et al., Eds., Cambridge University Press., 229–284.

  • Hannoschöck, G., and C. Frankignoul, 1985: Multivariate statistical analysis of sea surface temperature anomaly experiments with the GISS general circulation model. J. Atmos. Sci.,42, 1430–1450.

  • Hasselmann, K., 1979: On the signal-to-noise problem in atmospheric response studies. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 251–259.

  • ——, 1993: Optimal fingerprints for the detection of time-dependent climate change. J. Climate,6, 1957–1971.

  • ——, 1997: Detection and attribution of climate change. Climate Dyn., in press.

  • Hegerl, G. C., H. von Storch, K. Hasselmann, U. Cubasch, B. D. Santer, and P. D. Jones, 1996: Detecting anthropogenic climate change with an optimal fingerprint method. J. Climate,9, 2281–2306.

  • ——, K. Hasselmann, U. Cubasch, J. F. B. Mitchell, E. Roeckner, R. Voss, and J. Waszkewitz, 1997: On multi-fingerprint detection and attribution of greenhouse gas- and aerosol forced climate change. Climate Dyn., in press.

  • Jones, P. D., and K. R. Briffa, 1992: Global surface air temperature variations during the twentieth century. Part 1: Spatial, temporal and seasonal details. Holocene,2, 165–179.

  • Kim, K. Y., G. R. North, and G. C. Hegerl, 1996: Comparisons of the second-moments statistics of climate models. J. Climate,9, 2204–2221.

  • Madden, R. A., and V. Ramanathan, 1980: Detecting climate change due to increasing carbon dioxide. Science,209, 763–768.

  • Manabe, S., and R. J. Stouffer, 1996: Low-frequency variability of surface air temperature in a 1000-year integration of a coupled atmosphere–ocean–land surface model. J. Climate,9, 376–393.

  • North, G. R., and K. Y. Kim, 1995: Detection of forced climate signals. Part II: Simulation results. J. Climate,8, 409–417.

  • ——, T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev.,110, 600–706.

  • ——, K. Y. Kim, S. S. P. Shen, and J. W. Hardin, 1995: Detection of forced climate signals. Part I: Filter Theory. J. Climate,8, 401–408.

  • Penner, J. E., and Coauthors, 1994: Quantifying and minimizing uncertainty of climate forcing by anthropogenic aerosols. Bull. Amer. Meteor. Soc.,75, 375–400.

  • Santer, B. D., T. M. L. Wigley, and P. D. Jones, 1993: Correlation methods in fingerprint detection studies. Climate Dyn.,8, 265–276.

  • ——, W. Brüggemann, U. Cubasch, K. Hasselmann, H. Hock, E. Maier-Reimer, and U. Mikolajewicz, 1994: Signal-to-noise analysis of time-dependent greenhouse warming experiments. Part 1: Pattern analysis. Climate Dyn.,9, 267–285.

  • ——, K. E. Taylor, J. E. Penner, T. M. L. Wigley, U. Cubasch, and P. D. Jones, 1995a: Towards the detection and attribution of an anthropogenic effect on climate. Climate Dyn.,12, 77–100.

  • ——, U. Mikolajewicz, W. Brüggemann, U. Cubasch, K. Hasselmann, H. Höck, E. Maier-Reimer, and T. M. L. Wigley, 1995b: Ocean variability and its influence on the detectability of ocean greenhouse warming signals. J. Geophys. Res,100, 10 693–10 725.

  • ——, T. M. L. Wigley, T. P. Barnett, and E. Anyamba, 1996: Detection of climate change and attribution of causes. Climate Change 1995. The IPCC Second Scientific Assessment, J. T. Houghton et al., Eds., Cambridge University Press, 407–444.

  • Stevens, M. J., and G. R. North, 1996: Detection of the climate response to the solar cycle. J. Atmos. Sci.,53, 2594–2608.

  • von Storch, H., and G. Hannoschöck, 1985: Statistical aspects of estimated principal vectors (EOFs) based on small sample sizes. J. Climate Appl. Meteor.,24, 716–724.

  • von Storch, J., V. Kharim, U. Cubasch, G. C. Hegerl, D. Schriever, H. von Storch, and E. Zorita, 1997: A description of a 1260-year integration with the coupled ECHAM1/LSG general circulation model. J. Climate, in press.

  • Tett, S. F. B., T. C. Johns, and J. F. B. Mitchell, 1996: Global and regional variability in a coupled AOGCM. Climate Dyn., in press.

  • Wainstein, L. A., and V. D. Zubakov, 1962: Extraction of Signals from Noise. Prentice-Hall, 362 pp.

  • Wigley, T. M. L., and T. P. Barnett. 1990: Detection of the greenhouse effect in the observations. Climate Change. The IPCC Scientific Assessment, J. T. Houghton, G. L. Jenkins, and J. J. Ephraums, Eds., Cambridge University Press, 239–255.

  • Zwiers, F. W., and H. von Storch, 1995: Taking serial correlation into account in tests of the mean. J. Climate,8, 336–351.

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Comparison of Statistically Optimal Approaches to Detecting Anthropogenic Climate Change

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  • 1 Max-Planck-Institut für Meteorologie, Hamburg, Germany
  • | 2 Climate System Research Program, College of Geosciences and Maritime Studies, Texas AM University, College Station, Texas
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Abstract

Three statistically optimal approaches, which have been proposed for detecting anthropogenic climate change, are intercompared. It is shown that the core of all three methods is identical. However, the different approaches help to better understand the properties of the optimal detection. Also, the analysis allows us to examine the problems in implementing these optimal techniques in a common framework. An overview of practical considerations necessary for applying such an optimal method for detection is given. Recent applications show that optimal methods present some basis for optimism toward progressively more significant detection of forced climate change. However, it is essential that good hypothesized signals and good information on climate variability be obtained since erroneous variability, especially on the timescale of decades to centuries, can lead to erroneous conclusions.

Corresponding author address: Dr. Gabriele C. Hegerl, Max-Planck-Institut für Meteorologie, Bundesstraβe 55, D-20146 Hamburg, Germany.

Email: hegerl@dkrz.de

Abstract

Three statistically optimal approaches, which have been proposed for detecting anthropogenic climate change, are intercompared. It is shown that the core of all three methods is identical. However, the different approaches help to better understand the properties of the optimal detection. Also, the analysis allows us to examine the problems in implementing these optimal techniques in a common framework. An overview of practical considerations necessary for applying such an optimal method for detection is given. Recent applications show that optimal methods present some basis for optimism toward progressively more significant detection of forced climate change. However, it is essential that good hypothesized signals and good information on climate variability be obtained since erroneous variability, especially on the timescale of decades to centuries, can lead to erroneous conclusions.

Corresponding author address: Dr. Gabriele C. Hegerl, Max-Planck-Institut für Meteorologie, Bundesstraβe 55, D-20146 Hamburg, Germany.

Email: hegerl@dkrz.de

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