No abstract

Global climate change is a detectable and attributable global phenomenon, yet its manifestation at the regional scale, especially within the rainfall record, can be difficult to identify. This problem is particularly acute over southern Africa, a region characterised by a low density of observations and highly dependent on rural agriculture, where the impact of rainfall changes on maize cultivation critically depends on the timing with respect to the crop phenological cycle. To evaluate changes in rainfall affecting maize cropping, daily rainfall observations from 104 stations across Malawi, Mozambique, Zambia and Zimbabwe were used to detect trends in planting dates, rainfall cessation and duration of the rainfall season, as well as number of dry days, length of dry spells and measures of rainfall intensity during critical periods for growing maize. Correlations with the Southern Oscillation Index (SOI) and Antarctic Oscillation (AAO) were used to infer how large-scale climate variability affects these attributes of rainfall and highlight where (and when) trends may contribute to more frequent crossings of critical thresholds. The El Niño Southern Oscillation (ENSO) was associated with changes in planting and cessation dates as well as the frequency of raindays during the rainfall season (particularly early in the season). AAO mainly affected raindays towards the end of the season when maize was planted late. Trends are discussed relative to changes projected in empirically downscaled scenarios of rainfall from 7 general circulation models for the 2046-2065 period, assuming an SRES A2 emissions scenario.

No abstract

The potential for long-range prediction of Zimbabwe summer rainfall is investigated using an analysis of variance approach. It is assumed that the variance between seasons (inter-annual variability) is made up of two components: the climate noise (intra-seasonal variability) and signal (any variance above the noise). The magnitude of the climate noise is estimated by computing the variance of time-averaged rainfall data based on a statistical model whose parameters are derived from observed daily values at each station. Results from the study indicate that up to approximately 70% of the total variance in Zimbabwe summer rainfall is potentially predictable at long range. Predictability is greatest during the last half of the rainy season, January to March, and much lower during October to December. The south section of the country shows relatively more predictability than the north. Comparisons between signal and noise are discussed in the context of long-range predictability. The natural variability (noise) is proposed as a lower limit of the standard error of the estimate for any long-range precipitation forecast for the country.

No abstract