34 An Introduction to Simple Climate Models used in the IPCC Second Assessment Report change are: the regional distribution of the temperature changes; the initial volume of the glaciers and ice-caps, and their sensitivity to increases in temperature; and the initial state of balance of the Greenland and Antarctic ice sheets and their sensitivity to temperature changes. Uncertainties in sea level rise cannot, therefore, be separated from uncertainties in global mean temperature change. However, changes in accumulation will also affect the volume of land-based ice. For the glaciers and icecaps and for the Greenland ice sheet, accumulation has been assumed constant, where for the Antarctic ice sheet, accumulation is assumed to increase as temperature increases. Figures 11 and 12 express the uncertainty in temperature and sea level rise. 5. COMPARISON OF SURFACE TEMPERATURE CHANGES AND OCEAN THERMAL EXPANSION AS SIMULATED BY AOGCMS AND SCMs Simple climate models have been, and will continue to be, used for analysis of the global scale implications of alternative emissions scenarios or of alternative assumptions concerning the properties of individual model components. It is, therefore, pertinent to compare the global mean temperature and sea level projections as simulated by one- and two-dimensional upwelling-diffusion models on the one hand, and AOGCMs on the other hand. Figure 15 compares the change in global mean surface air temperature as simulated by several different AOGCMs with that of the one-dimensional upwelling-diffusion model with a CO2 doubling climate sensitivity of 2.5°C and that of the twodimensional climate model (whose sensitivity is fixed at 2.2°C). The spread in the AOGCM results can be largely explained by the differences in the model climate sensitivity, which varies from 2.1 to 4.6°C. Note that the interannual variability in the AOGCM response is absent in the SCM response, which increases smoothly but is otherwise similar to the AOGCM response. Comparison of Figure 15 with Figure 11 illustrates the ability of upwelling-diffusion models to span the results of most AOGCMs when a range of values for the climate sensitivity is used. A further illustration of the comparability of AOGCM and SCM time-dependent behaviour is given in Figure 16, which compares the global mean temperature change for the Geophysical Fluid Dynamics Laboratory (GFDL) AOGCM and the upwelling-diffusion climate model when both models are driven by various rates of increase in atmospheric CO2 concentration (see SAR WGI: Section 6.3.1). To ensure a valid comparison, the SCM climate sensitivity was set at the GFDL model value of 3.7°C. All other parameter values remained unchanged. The value of the land/ocean sensitivity differential (1.3), chosen on the basis of other GCM results (Raper, et al., 1996), is similar to that for the GFDL model. The thermohaline circulation in the SCM was made to vary with surface warming in a manner that closely approximated the variation in the GFDL model (Manabe and Stouffer, 1994). The surface temperature responses are seen to agree well over a wide range of forcings. Global temperature change (°C) Figure 15. Comparison of global mean surface air temperature change as simulated by several different AOGCMs (with climate sensitivity varying from 2.1 to 4.6°C), the one-dimensional upwelling-diffusion climate model (climate sensitivity of 2.5°C), and the two-dimensional upwelling-diffusion model (climate sensitivity of 2.2°C), in each case driven by a 1 per cent per year (compounded) CO2 concentration Year from start of experiment Figure 16. Global mean surface air temperature increase as computed by the GFDL AOGCM (solid lines) and the one-dimensional upwelling-diffusion climate model with a CO2 doubling sensitivity of 3.7°C. Results are shown for cases in which the atmospheric CO2 concentration increases by 0.25, 0.5, 1, 2 and 4 per cent per year (reproduced from SAR WGI, Figure 6.13). An Introduction to Simple Climate Models used in the IPCC Second Assessment Report Oceanic thermal expansion (cm) 80 160 240 320 400 480 Year from start of experiment Year from start of experiment Figure 17. Comparison between the AOGCM results of Manabe and Stouffer (1994) and the one-dimensional upwelling-diffusion model for cases in which the atmospheric CO2 concentration increases by I per cent per year (compounded) until the concentration has either doubled or quadrupled and is then stabilized (a) global mean surface air temperature; (b) sea level rise due to ocean thermal expansion (reproduced from SAR WGI, Figure 6.17). with the one-dimensional model results in Figure 17. For the 2xCO2 case, the agreement is excellent for both the global mean temperature and the thermal expansion results. For the 4xCO2 case, the one-dimensional model gives lower warming and higher expansion, implying that the flux of heat into the deeper layers of the ocean is greater than in the AOGCM. Other comparisons have been carried out by Raper and Cubasch (1996) using the Max Plank Institute (MPI) AOGCM described in Cubasch, et al. (1992). When the climate sensitivity is adjusted to give reasonably good agreement between the two models for temperature, the thermal expansion estimate from the SCM is greater than that in the AOGCM. This again implies that there is a greater flux of heat into the ocean in the one-dimensional model than in this particular AOGCM. The reason for this was not resolved at the time of publication of the SAR WGI. Note that when the heat flux into the ocean is larger (smaller) the surface temperature change is smaller (larger) and the thermal expansion is larger (smaller) (Harvey, 1994). To summarize, it is possible to replicate the behaviour of a wide range of complex AOGCMs with SCMs. Of even greater importance than the ability to replicate the behaviour of any one AOGCM is the ability of SCMs to span the range of results that are obtained with different AOGCMs. Thus, SCMs provide a convenient and computationally fast tool for use in scenario and sensitivity analyses, in which a large number of model runs is required to cover the different emissions scenarios and to span the uncertainties inherent in AOGCMs. |