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Climate Dynamics

, Volume 10, Issue 1–2, pp 1–19 | Cite as

Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model

  • U. Cubasch
  • B. D. Santer
  • A. Hellbach
  • G. Hegerl
  • H. Höck
  • E. Maier-Reimer
  • U. Mikolajewicz
  • A. Stössel
  • R. Voss
Article

Abstract

Four time-dependent greenhouse warming experiments were performed with the same global coupled atmosphere-ocean model, but with each simulation using initial conditions from different “snapshots” of the control run climate. The radiative forcing — the increase in equivalent CO2 concentrations from 1985–2035 specified in the Intergovernmental Panel on Climate Change (IPCC) scenario A — was identical in all four 50-year integrations. This approach to climate change experiments is called the Monte Carlo technique and is analogous to a similar experimental set-up used in the field of extended range weather forecasting. Despite the limitation of a very small sample size, this approach enables the estimation of both a mean response and the “between-experiment” variability, information which is not available from a single integration. The use of multiple realizations provides insights into the stability of the response, both spatially, seasonally and in terms of different climate variables. The results indicate that the time evolution of the global mean warming signal is strongly dependent on the initial state of the climate system. While the individual members of the ensemble show considerable variation in the pattern and amplitude of near-surface temperature change after 50 years, the ensemble mean climate change pattern closely resembles that obtained in a 100-year integration performed with the same model. In global mean terms, the climate change signals for near surface temperature, the hydrological cycle and sea level significantly exceed the variability among the members of the ensemble. Due to the high internal variability of the modelled climate system, the estimated detection time of the global mean temperature change signal is uncertain by at least one decade. While the ensemble mean surface temperature and sea level fields show regionally significant responses to greenhouse-gas forcing, it is not possible to identify a significant response in the precipitation and soil moisture fields, variables which are spatially noisy and characterized by large variability between the individual integrations.

Keywords

Climate Change Signal Model Climate System Climate Change Experiment Temperature Change Signal Climate Change Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bakan S, Chlond A, Cubasch U, Feichter J, Graf HF, Grassl H, Hasselmann K, Kirchner I, Latif M, Roeckner E, Sausen R, Schlese U, Schriever D, Schult I, Schumann U, Sielmann F, Welke W (1991) Climate response to smoke from the burning oil wells in Kuwait. Nature 351:367–371Google Scholar
  2. Baumgartner A, Reichel E (1975) The world water balance: mean annual global continental and maritime precipitation, evaporation and runoff. R Oldenbourg, München-WienGoogle Scholar
  3. Brankovic C, Palmer TN, Molteni F, Tibaldi S, Cubasch U (1990) Extended range predictions with the ECMWF models: time lagged ensemble forecasting. Q J R Meteorol Soc 116:835–866Google Scholar
  4. Cubasch U, Hasselmann K, Höck H, Maier-Reimer E, Mikolajewicz U, Sauter BD, Sausen R (1992) Time-dependent greenhouse warming computations with a coupled ocean-atmosphere model. Clim Dyn 8:55–69Google Scholar
  5. Fichefet T, Tricot C (1992) Influence of the starting date of model integration on projections of greenhouse-gas-induced climatic change. Geophys Res Lett 19:1771–1774Google Scholar
  6. Gloersen P, Campbell WJ (1988) Variations in the Arctic, Antarctic, and global sea ice covers during 1978 to 1987 as observed with the Nimbus 7 scanning multichannel microwave radiometer. J Geophys Res 93:10666–10674Google Scholar
  7. Hansen J, Lacis A, Ruedy R (1990) Comparison of solar and other influences on long-term climate. In: Climate impact of solar variability, Conf. Publn 3086, NASA, Greenbelt, pp 149–159Google Scholar
  8. Hasselmann K, Sausen R, Maier-Reimer E, Voss R (1993) On the cold start problem in transient simulations with coupled ocean-atmosphere models. Clim Dyn 9:53–61Google Scholar
  9. Houghton ST, Jenkins GJ, Ephraums JJ (eds) (1990) Climate change. The IPCC scientific assessment. Cambridge University Press, CambridgeGoogle Scholar
  10. Houghton JT, Callander BA, Varney SK (eds) (1992) Climate Change 1992. The supplementary report to the IPCC scientific assessment. Cambridge University Press, CambridgeGoogle Scholar
  11. Livezey RE, Chen WY (1983) Statistical field significance and its determination by Monte Carlo techniques. Mon Weather Rev 111:46–59CrossRefGoogle Scholar
  12. Loewe P (1987) Sea ice simulations performed with forcing fields specified from a GCM. Techn. Rep. 6/87, Cold Regions Research Lab. 72, Hanover, New HampshireGoogle Scholar
  13. Lorenz EN (1975) Climatic predictability. In: The physical basis of climate and climate modelling. GARP publication series No. 16, WMO Geneva, pp 132–137Google Scholar
  14. Lorenz EN (1984) Irregularity: a fundamental property of the atmosphere. Tellus 36A:98–110Google Scholar
  15. Lorenz EN (1991) Chaos, spontaneous climatic variations and detection of the greenhouse effect. In: Schlesinger M (ed) Greenhouse-gas-induced climatic change: a critical appraisal of simulations and observations. Elsevier, Amsterdam, pp 445–453Google Scholar
  16. Maier-Reimer E, Mikolajewicz U, Hasselmann K (1993) Mean circulation of the Hamburg LSG OGCM and its sensitivity of the thermohaline surface forcing. J Phys Oceanogr 23:731–757CrossRefGoogle Scholar
  17. Manabe S, Spelman MJ, Stouffer RJ (1992) Transient responses of a coupled ocean-atmosphere model for gradual changes of atmospheric CO2. Part 11: seasonal response. J Clim 5:105–126Google Scholar
  18. Mikolajewicz U, Maier-Reimer E (1990) Internal secular variability in an ocean circulation model. Clim Dyn 4:145–156Google Scholar
  19. Mikolajewicz U, Santer BD, Maier-Reimer E (1990) Ocean response to greenhouse warming. Nature 354:589–593Google Scholar
  20. Mitchell JFB, Ingram WJ (1992) On CO2 and climate: mechanisms of changes in cloud. J Clim 5:5–21Google Scholar
  21. Molteni F, Cubasch U, Tibaldi S (1988) 30- and 60-day forecast experiments with the ECMWF spectral models. In: Chargas C, Puppi G (eds) Persistent meteo-oceanographic anomalies and teleconnections. Pontif Acad Sci Scripta Varia, MCMLXXXVIII, 505–555Google Scholar
  22. Murphy JM (1992) A prediction of the transient response of climate. Climate Research Technical Note 32, Hadley Centre, Bracknell, UKGoogle Scholar
  23. Parkinson CA (1991) Interannual variability of the spatial distribution of sea ice in the North Pole region. J Geophys Res 96:4791–4801PubMedGoogle Scholar
  24. Parkinson CA (1992) Interannual variability of monthly southern ocean sea ice distributions. J Geophys Res 97:5349–5363Google Scholar
  25. Parkinson CA, Bindschadler RA (1984) Response of Antarctic sea ice to uniform atmospheric temperature increases. Geophys Monogr Am Geophys Union 29:254–264Google Scholar
  26. Santer BD, Brüggemann W, Cubasch U, Hasselmann K, Höck H, Maier-Reimer E, Mikolajewicz U (1993a) Signal-to-noise analysis of time-dependent greenhouse warming experiments. Part 1. Pattern analysis. Clim Dyn 9:267–285Google Scholar
  27. Santer BD, Cubasch U, Mikolajewicz U, Hegerl G (1993b) The use of general circulation models in detecting climate change induced by greenhouse gases. PCMDI Report No. 10, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, LivermoreGoogle Scholar
  28. Sausen R, Barthel K, Hasselmann K (1988) Coupled ocean-atmosphere models with flux correction. Clim Dyn 2:154–163Google Scholar
  29. Storch H (1982) A remark on Chervin-Schneider's algorithm to test significance of climate experiments with GCMs. J Atmos Sci 39:187–189Google Scholar
  30. Stouffer RJ, Manabe S, Bryan K (1989) Interhemispheric asymmetry in climate response to a gradual increase of atmospheric CO2. Nature 342:660–662Google Scholar
  31. Stössel A, Lemke P, Owens WB (1990) Coupled sea ice-mixed layer simulations for the Southern Ocean. J Geophys Res 95:9539–9555Google Scholar
  32. Washington WM, Meehl GA (1989) Climate sensitivity due to increased CO2: experiments with a coupled atmosphere and ocean general circulation model. Clim Dyn 4:1–38Google Scholar
  33. Wigley TML, Raper SCB (1990) Natural variability of the climate system and detection of the greenhouse effect. Nature 344:324–327Google Scholar
  34. Wigley TML, Santer BD (1990) Statistical comparison of spatial fields in model validation, perturbation and predictability experiments. J Geophys Res 95:851–865Google Scholar
  35. Zebiak SE, Cane MA (1987) A Model El Niño-Southern Oscillation, Mon Weather Rev 115:2262–2278Google Scholar
  36. Zwally HJ, Comiso JC, Parkinson CL, Carsey FD, Campbell WJ, Gloersen P (1983) Antarctic sea ice 1973–1976: satellite passive-microwave observations, NASA Spec Publ SP-459:1206Google Scholar

Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • U. Cubasch
    • 1
  • B. D. Santer
    • 2
  • A. Hellbach
    • 1
  • G. Hegerl
    • 3
  • H. Höck
    • 3
  • E. Maier-Reimer
    • 3
  • U. Mikolajewicz
    • 3
  • A. Stössel
    • 3
  • R. Voss
    • 4
  1. 1.Deutsches KlimarechenzentrumHamburgGermany
  2. 2.Lawrence Livermore National LaboratoryProgram for Climate Model Diagnosis and IntercomparisonLivermoreUSA
  3. 3.Max-Planck-Institut für MeteorologieHamburgGermany
  4. 4.Meteorologisches Institut der Universität HamburgHamburgGermany

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