1. INTRODUCTION AND BACKGROUND
4.4 Input Data Acquisition and Processing
4.4.2 Climate Change Projections Considered
The climate change projections used in this study were derived from seven downscaled global circulation models (GCMs) employed in the Intergovernmental Panel on Climate Change (IPCC) Fourth (AR4) Assessment Report (IPCC, 2007). The downscaled climate change projections were obtained from the Council for Scientific and Industrial Research (CSIR) and from the Climate Systems Analysis Group (CSAG) based at the University of Cape Town (UCT). The CSIR GCM projections were dynamically downscaled to a 0.5 degree horizontal resolution grid across southern Africa (Engelbrecht et al., 2009) and the CSAG GCM projections were empirically downscaled to regional scales (Hewitson, 2012, pers. comm.), also encompassing all of southern Africa.
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The differences between these downscaling techniques are detailed in Table 4.2, which also includes a summary of the relevant information regarding the selected GCM projections and the downscaling institutions.
Table 4.2 Information on selected downscaled GCMs, the downscaling institutions and projected trends in mean annual precipitation.
Downscaled GCM Downscaling Institute Time Periods Considered MAP Trend
GDFL2.1 CSIR 1971-1990 Wetting
2046-2065
MIROC CSIR 1971-1990 Drying
2046-2065
CSIRO CSAG-UCT 1971-1990 Wetting
2046-2065
ECHO CSAG-UCT 1971-1990 Wetting
2046-2065
IPSL CSAG-UCT 1971-1990 Wetting
2046-2065
ECH5 CSAG-UCT 1971-1990 No Appreciable
Change 2046-2065
MRI CSAG-UCT 1971-1990 Wetting
The selection of the downscaled GCMs was based on projected changes in mean annual precipitation (MAP) and mean annual runoff (MAR) described by each downscaled GCM projection. An increasing trend in MAP and MAR between the present and the future essentially indicates a wet climate change projection or wetting pattern of change, whereas a declining trend in MAP and MAR suggests a dry climate change projection or drying pattern of change. The GFDL2.1 and MIROC GCMs downscaled by the CSIR represented wet and dry climate change projections respectively. With the exception of the ECH5 GCM, all GCMs downscaled by CSAG represented wet climate change projections, with variations in the degrees of change.
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Owing to time constraints, not all GCMs downscaled by the CSIR could be included in this study. Therefore, based on projected changes in MAP, only the “wettest” downscaled GCM (i.e. the GFDL2.1 GCM) and the “driest” downscaled GCM (i.e. the MIROC GCM), were selected from the ensemble of CSIR GCMs (see Engelbrecht et al., 2009). Further, the majority of the GCMs used in this study are GCMs downscaled by CSAG. These particular projections represent regional/point scales, a condition which makes them more suitable for application in hydrological modelling. All GCMs used in this study were originally derived from coupled GCM (CGCM) projections that contributed to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) (IPCC, 2007). It is important to note that these projections represent a range in global models (including their various sensitivities, parameterizations, etc.), downscaling methodologies and institutions and, therefore, reflect the uncertainty that is inherent in climate change projections at present. This was considered critical since using projections from a single combination of GCM- downscaling institution runs the risk of obtaining a biased view of the future.
The GFDL2.1 and MIROC GCM projections were dynamically downscaled by the CSIR using the high-resolution conformal-cubic atmospheric model (CCAM), developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia. This was performed by forcing the CCAM model using bias-corrected sea-surface temperatures and sea-ice fields of CGCMs to produce regional scale climate change projections (Engelbrecht et al., 2009). The CSAG GCM projections were empirically downscaled by using observed data to derive relationships between synoptic scale climates and local climates and applying the resulting relationships to GCM output to generate higher resolution local scale climate change projections (Hewitson et al., 2005).
It is critical to note at this juncture that this study used GCMs to provide an understanding of the likely behaviour of point specific catchment processes. It is recognised that GCMs are inherently limited by their spatial resolutions. A single GCM generally represents a spatial grid of ~300km (Hewitson et al., 2005). This means that a particular GCM will provide one average value that theoretically represents a 300km by 300km grid pixel. At this resolution, this average value is of little value in the investigation of point specific (i.e. local) catchment processes.
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Therefore, the use of downscaled GCM data was considered useful in understanding the likely changes in water quality responses to future changes in rainfall and temperature regimes through modelling exercises, rather than representing a precise simulation of Mkabela Catchment itself. The GCM projections used in this study were downscaled to
“regional” scales, which in this case was at the scale of the Nagle WMU (Figures 4.1 and 4.2). The ability of the downscaled GCM projections to sufficiently reproduce regional scale climatic data has been verified elsewhere (Engelbrecht et al., 2009; Hewitson et al., 2005) and these projections were considered to be credible for their application in this study.
The spatial resolution limitation of GCMs mentioned above implies that when compared to actual point specific measured data, GCM-derived data are not infallible and must be applied with some caution (see Chapter 5). This can be attributed to a number of issues ranging from the effects of natural variability, which may not be entirely captured by the downscaled GCMs and land use-climate feedback mechanisms which may also influence local scale climates and thus be potentially omitted in the downscaled GCMs. These issues introduce some uncertainty with respect to the application of downscaled GCM output. This discussion is expanded on in Section 4.6.4 and Chapter 5.
The climate change projections used in this study considered present (1971-1990) and future (2046-2065) time periods. The projections are based on the Special Report on Emission Scenarios (SRES) A2 storyline and emission scenario (Nakićenović et al., 2000), which assumes that global efforts to reduce greenhouse gas emissions are relatively ineffective. The SRES scenarios are considered to be plausible alternative futures based on current emission trends rather than specific predictions of the future (Woznicki and Nejadhashemi, 2012). The A2 scenario used in this study assumes a “business as usual” approach in which developing countries continue to use ever-increasing quantities of fossil fuels and developed countries make little effort in altering their energy consumption patterns or developing efficient fuel technologies. By deduction, the A2 scenario assumes conditions which favour increased anthropogenic pressure on environmental resources. Climatic data and, in particular, rainfall data, obtained from the downscaled GCMs for the Nagle WMU was not adjusted or post- processed in any way for the Mkabela Catchment, since the catchment was considered to be located in relatively close enough proximity to the points of interest of the downscaled GCMs (i.e. the Nagle WMU).
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