Abstract
This paper describes a methodology that combines the outputs of (1) the Integrated Model to Assess the Greenhouse Effect (IMAGE Version 1.0) of the Netherlands National Institute of Public Health and Environmental Protection (RIVM) (given a greenhouse gas emission policy, this model can estimate the effects such as global mean surface air temperature change for a wide variety of policies) and (2) ECHAM-1/LSG, the Global Circulation Model (GCM) of the Max-Planck Institute for Meteorology in Hamburg, Germany. The combination enables one to calculate grid point surface air temperature changes for different scenarios with a turnaround time that is much quicker than that for a GCM. The methodology is based upon a geographical pattern of the ratio of grid point temperature change to global mean values during a certain period of the simulation, as calculated by ECHAM-1/LSG for the 1990 Scenarios A and D of the Intergovernmental Panel on Climate Change (IPCC). A procedure, based upon signal-to noise ratios in the outputs, enabled us to estimate where we have confidence in the methodology; this is at about 23% to 83% of the total of 2,048 grid points, depending upon the scenario and the decade in the simulation. It was found that the methodology enabled IMAGE to provide useful estimates of the GCM-predicted grid point temperature changes. These estimates were within 0.5K (0.25K) throughout the 100 years of a given simulation for at least 79% (74%) of the grid points where we are confident in applying the methodology. The temperature ratio pattern from Scenario A enabled IMAGE to provide useful estimates of temperature change within 0.5K (0.25K) in Scenario D for at least 88% (68%) of the grid points where we have confidence; indicating that the methodology is transferable to other scenarios. Tests with the Geophysical Fluid Dynamics Laboratory GCM indicated, however, that a temperature ratio pattern may have to be developed for each GCM. The methodology, using a temperature ratio pattern from the 1990 IPCC Scenario A and involving IMAGE, gave gridded surface air temperature patterns for the 1992 IPCC radiative-forcing Scenarios C and E and the RIVM emission Scenario B; none of these scenarios has been simulated by ECHAM-1/LSG. The simulations reflect the uncertainty range of a future warming.
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References
Barnett, T. P., Santer, B. D., Jones, P. D., Bradley, R. S., and Briffa, K. R.: 1995, ‘Estimates of Low Frequency Natural Variability in Near-surface Air Temperature’, Science (submitted).
Cubasch, U., Hasselmann, K., Höck, H., Maier-Reimer, E., Mikolajewicz, U., Santer, B. D., and Sausen, R.: 1992, ‘Time-dependent Greenhouse Warming Computations with a Coupled Ocean- Atmosphere Model’, Clim. Dyn. 8, 55–69.
Cubasch, U., Santer, B. D., Hellbach, A., Hegerl, G., Höck, H., Maier-Reimer, E., Mikolajewicz, U., Stössel, A., and Voss, R., 1994, ‘Monte Carlo Climate Change Forecasts with a Global Coupled Ocean-Atmosphere Model’, Clim. Dyn. 10, 1–19.
Giorgi, F., and Mearns, L. O.: 1991, ‘Approaches to the Simulation of Regional Climate Change: A Review’, Rev. Geophys. 29(2), 191.
Harvey, L. D. D.: 1989, ‘Transient Climatic Response to an Increase of Greenhouse Gases’, Clim. Change 15, 15.
Hasselmann, K.: 1992, ‘Optimal Fingerprints for the Detection of Time Dependent Climate Change’, MPI Report 88, MPI für Meteorologie, Hamburg, Germany.
Hasselmann, K., Sausen, R., Maier-Reimer, E., and Voss, R.: 1992, ‘On the Cold Start Problem in Transient Simulations with Coupled Atmosphere-Ocean Models’, MPI Report 83, MPI für Meteorologie, Hamburg, Germany.
Houghton, J. T., Callander, B. A., and Varney, S. K.: 1992, Climate Change 1992: The Supplementary Report to The IPCC Assessment, Cambridge University Press, Cambridge, UK.
IPCC (Intergovernmental Panel on Climate Change): 1991, Climate Change: The IPCC Response Strategies, Island Press, Washington, DC.
Manabe, S., Stouffer, R. J., Spelman, M. J., and Bryan, K.: 1991, ‘Transient Responses of a Coupled Ocean-Atmosphere Model to Gradual Changes of Atmospheric CO2. Part I: Annual Mean Response’, J. Clim. 4, 785.
Robinson, P. J., and Finkelstein, P. L.: 1991, ‘The Development of Impact-oriented Climate Scenarios’, Bull. Amer. Meteor. Soc. 72(4), 481.
Robock, A., Turco, R. P., Harwell, M. A., Ackerman, T. P., Andressen, R., Chang, H.-S., and Sivakumar, M. V. K.: 1993, ‘Use of General Circulation Model Output in the Creation of Climate Change Scenarios for Impact Analysis’, Clim. Change 23, 293.
Rotmans, J.: 1990, IMAGE: An Integrated Model to Assess the Greenhouse Effect, Academic Publishers, Dordrecht, Netherlands.
Rotmans, J., De Boois, H., and Swart, R. J.: 1990, ‘An Integrated Model for the Assessment of the Greenhouse Effect: The Dutch Approach’, Clim. Change 16, 331.
Rotmans, J., and Swart, R. J.: 1991, ‘Modelling Tropical Deforestation and its Consequences for Global Climate’, Ecological Modelling 58, 217.
Rotmans, J., and Den Elzen, M. G. J.: 1992a, ‘A Model-based Approach to the Calculation of Global Warming Potentials (GWP)’, Internat. J. Clim. 12, 865.
Rotmans, J., Den Elzen, M. G. J., Krol, M. S., Swart, R. J., and Van Der Woerd, H. J.: 1992b, ‘Stabilizing Atmospheric Concentrations: Towards International Methane Control’, Ambio 21(6), 404.
Santer, B. D., Wigley, T. M. L., Schlesinger, M. E., and Mitchell, J. F. B.: 1990, ‘Developing Climate Scenarios from Equilibrium GCM Results’, MPI Report 47, MPI für Meteorolgie, Hamburg, Germany.
Santer, B. D., Brüggemann, W., Cubasch, U., Hasselmann, K., Höck, H., Maier-Reimer, E., and Mikolajewicz, U.: 1994, ‘Signal-to-Noise Analysis of Time-dependent Greenhouse Warming Experiments, Part 1: Pattern Analysis’, Clim. Dyn. 9, 267–285.
Santer, B., Taylor, K. E., Wigley, T. M. L., Penner, J. E., Cubasch, U., and Jones, P. D.: 1995, ‘Towards Ihe Detection and Attribution of an Anthropogenic Effect on Climate’, Clim. Dyn. 12(2), 77–100.
Schneider, S. H.: 1994, Detecting Climatic Change Signals: Are There Any ‘Fingerprints?’ Science 263, 341–347.
Tegart, W. J. McG., Sheldon, G. W., and Griffiths, D. C.: 1990, Climate Change: The IPCC Impacts Assessment, Australian Government Publishing Service, Canberra, Australia.
Wigley, T. M. L., and Raper, S. C. B.: 1990, ‘Natural Variability of the Climate System and Detection of me Greenhouse Effect’, Nature 344, 324.
Wigley, T. M. L., and Raper, S. C. B.: 1991, ‘Internally Generated Natural Variability of Global-ean Temperatures’, in: M. E. Schlesinger, ed., Greenhouse-Gas-Induced Climatic Change: A Critical Appraisal of Simulations and Observations, Elsevier Science Publishers, Amsterdam, Netherlands.
Wigley, T. M. L., and Raper, S. C. B.: 1992, ‘Implications for Climate and Sea Level of Revised IPCC Emission Scenarios’, Nature 357, 293.
Wigley, T. M. L., and Schlesinger, M. E.: 1985, ‘Analytical Solution for the Effect of Increasing CO2 on Global Mean Temperature’, Nature 315, 649.
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The work reported by the authors was carried out during their stay at the project “Forestry and Climate Change” of the International Institute for Applied Systems Analysis, Laxenburg, Austria.
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Jonas, M., Fleischmann, K., Ganopolski, A.V. et al. Grid point surface air temperature calculations with a fast turnaround: Combining the results of IMAGE and a GCM. Climatic Change 34, 479–512 (1996). https://doi.org/10.1007/BF00139303
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DOI: https://doi.org/10.1007/BF00139303