approaches are taken for this analysis, each influencing uncertainty and variability of the results. The two approaches can be described as follows:

1. _Holding future variability constant:_ In this approach, the consequences of a mean change in the future are examined while the variance is held constant, i.e., the probability distribution of rainfall in the future has the same variance as the corresponding distribution of the past as calculated from the empirical data.

2. _Changing future variability:_ It is likely that future variability also increases; yet, the RCMs do not calculate variability. In order to account for such potential changes, the variance is changed by way of sensitivity analysis as discussed further below.

The original projections lie in the distant future. To study the effects of climate change in the near future, i.e. the next 10 years, the following approach is adopted.

- The empirical mean distribution is set as the baseline case for the year 2005. Observe that this distribution was calculated based on the time series 1961-2005. According to Tadross et al. (2005) climate change effects were already observed in this time period. However, most of the variables were not significant, and those that were significant had very low correlations, so it appears reasonable that the empirical mean distribution is considered as the baseline case for 2005. The mean distributions from the two models, Precis and MM5, serve as the future distributions for 2080 and 2075, respectively.

- The difference between each dekad mean of the empirical and future distribution serve as the incremental steps from 2005 to 2080 and from 2005 to 2075, respectively. One simplistic assumption is that the steps are proportional to the future year minus the base year 2005. However, because the GCM is driven by the mean temperature rise over the time horizon, the incremental steps are assumed to be proportional to the temperature rise from the HadAM3P for the A2 scenario, which was also used for the Precis and MM5 projections. Therefore, for each year a new distribution is used to simulate the rainfall amount for each dekad. Again, first the mean value is changed over the years and the variance is held constant, so that the ‘a’ parameter can be estimated. Afterwards, also the effects of an additional increase or decrease in the variance are analyzed. The output uncertainty is measured by confidence regions.

For a preliminary examination of the contract based on the rainfall data of the Chitedze station, for each phase a gamma distribution<sup>4</sup> is fitted. Figure 6 shows the distributions for each phase with the estimated parameter. Due to the longer time horizon of phase three (80 days compared to 30 days for phase 1 and 2), the distribution for phase 3 is skewed to the right, indicating that the rainfall amount is generally higher compared to the other phases. Furthermore, phase 2 seems to have a higher rainfall amount compared to phase 1. For example, with 90 percent probability, the rainfall amount in phase 1 is below 306 mm, whereas for phase 2, the rainfall amount is below 340 mm. Given the upper trigger for each phase, one can determine the probability that the amount of rainfall

> 4 A gamma distribution was chosen because it is standardly used for describing rainfall totals.

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