Climate Change 2001:
Working Group I: The Scientific Basis
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9.3 Projections of Climate Change

9.3.1 Global Mean Response

Figure 9.4: Intercomparison statistics for seasonal and annual (a) temperature and (b) precipitation changes in nineteen CMIP2 experiments at the doubling of CO2 (years 61 to 80). The total length of the bars shows the mean squared amplitude of the simulated local temperature and precipitation changes averaged over all experiments and over the whole world. The lowermost part of each bar represents a nominally unbiased "common signal", the mid-part directly model-related variance and the top part the inter-experiment variance attributed to internal variability. Precipitation changes are defined as 100% x (PG-PCTRL) / Max(PCTRL, 0.25 mm/day), where the lower limit of 0.25 mm/day is used to reduce the sensitivity of the global statistics to areas with very little control run precipitation.

Since the SAR, there have been a number of new AOGCM climate simulations with various forcings that can provide estimates of possible future climate change as discussed in Section 9.1.2. For the first time we now have a reasonable number of climate simulations with different forcings so we can begin to quantify a mean climate response along with a range of possible outcomes. Here each model's simulation of a future climate state is treated as a possible outcome for future climate as discussed in the previous section.

These simulations fall into three categories (Table 9.1):

Table 9.1 gives a detailed overview of all experiments assessed in this report. 1%/yr CO2 increase (CMIP2) experiments

Figure 9.3 shows the global average temperature and precipitation changes for the nineteen CMIP2 simulations. At the time of CO2 doubling at year 70, the 20-year average (years 61 to 80) global mean temperature change (the transient climate response TCR; see Section 9.2) for these models is 1.1 to 3.1°C with an average of 1.8°C and a standard deviation of 0.4°C (Figure 9.7). This is similar to the SAR results (Figure 6.4 in Kattenberg et al., 1996).

At the time of CO2 doubling at year 70, the 20-year average (years 61 to 80) percentage change of the global mean precipitation for these models ranges from -0.2 to 5.6% with an average of 2.5% and a standard deviation of 1.5%. This is similar to the SAR results.

For a hypothetical, infinite ensemble of experiments, in which Tm and T'' are uncorrelated and both have zero means,

{T2} = Tf2 + {Tm2} + {T''2} = Tf2+ 2M + 2N.

The ensemble mean square climate change is thus the sum of contributions from the common forced component (Tf2), model differences (2M), and internal variability (2N ). This framework is applied to the CMIP2 experiments in Figure 9.4. These components of the total change are estimated for each grid box separately, using formulas that allow for unbiased estimates of these when a limited number of experiments are available (Räisänen 2000, 2001). The variance associated with internal variability 2N is inferred from the temporal variability of detrended CO2 run minus control run differences and the model-related variance 2M as a residual. Averaging the local statistics over the world, the relative agreement between the CMIP2 experiments is much higher for annual mean temperature changes (common signal makes up 86% of the total squared amplitude) than for precipitation (24%) (Figure 9.4).

The relative agreement on seasonal climate changes is slightly lower, even though the absolute magnitude of the common signal is in some cases larger in the individual seasons than in the annual mean. Only 10 to 20% of the inter-experiment variance in temperature changes is attributable to internal variability, which indicates that most of this variance arises from differences between the models themselves. The estimated contribution of internal variability to the inter-experiment variance in precipitation changes is larger, from about a third in the annual mean to about 50% in individual seasons. Thus there is more internal variability and model differences and less common signal indicating lower reliability in the changes of precipitation compared to temperature.

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