Global climate change is a detectable and attributable phenomenon,
yet its manifestation at the regional scale, especially within the
rainfall record, can be difficult to characterize due to the high degree
of variability. At the same time, an understanding of historical change
is essential in order to provide context for climate change projections,
as well as a credible basis for adaptation. Over southern Africa this
task is complicated by particularly erratic rainfall, the presence of
multidecadal trends, and limited data with sparse spatial coverage.
Furthermore, climate models suggest that competing responses (such
as increasing numbers of dry days, coupled with increases in rainfall
intensity), working at different timescales, will serve to additionally
mask any signals in the time-averaged total rainfall.
Ideally, the changes in the secondary attributes can then be compared
with equivalent attributes from downscaled climate scenarios to
assess agreement and contribute to confidence in projected downscaled
data. However, downscaled scenarios may suffer from varied
biases and limitations due to their methodological approach, e.g.
rainfall parameterisations within RCMs and neglected land-climate
interactions within empirically derived scenarios. These limitations
may lead to inconsistencies between different sources of information.
The comparisons described earlier, based on secondary attributes and
sub-seasonal timescales, facilitate highlighting areas of weakness in
each downscaling, which may not be readily manifest in the more
commonly used time-averaged products.
These issues are explored using data from southern Africa;
station observations of daily rainfall, dynamical scenarios from 2
regional climate models and empirical scenarios from 6 general circulation
models. The observations are analysed for detectable trends in
the start, end, 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 agriculture. Correlations with the Southern
Oscillation Index (SOI) and Antarctic Oscillation (AAO) are 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. Trends are also
compared with and discussed in relation to changes projected in the
downscaled scenarios, highlighting inconsistencies, which may be
due to systematic biases in each methodology. The implications for
agricultural adaptation are examined and discussed. The paper suggests
that when building scenarios of climate change, a wide range of
models and methodologies should be interrogated and their interpretation
relies heavily on understanding the methods and underlying
physical processes. Furthermore, such processes act across political
boundaries and collaboration across institutions, as highlighted here,
is needed to confidently make regional assessments.