Water research commission

Climate change scenarios

1. Global Climate Models (GCMs)

The resolving scale of GCMs has improved significantly in the last 10 years with many state of the art GCMs able to resolve at a scale of around 100 km (Louw, et al., 2012). Downscaling is a concept based on the assumption that local scale climate is largely a function of the large scale climate, modified by some local forcing such as topography. Downscaled climate data (rainfall and temperature) were obtained from the Climate System Analysis Group (CSAG) at the University of Cape Town.

Table 4 provides a condensed description of the information on GCMs, the global climate change scenarios of which were statistically downscaled by CSAG to point scale for application in this study. The climate change scenarios developed by CSAG for application in this project were derived from global scenarios produced by five GCMs, all of which were applied in the IPCC’s (2007) Fourth Assessment Report [AR4] (Schulze et al., 2011).

Table 4: Global Circulation Model (GCM) description

The statistically downscaled climate data from the various GCMs include daily minimum and maximum temperatures and rainfall. The climate change scenarios were developed for the “present” (1971 – 1990) and “intermediate future” (2046 – 2065).

These statistically downscaled GCMs values were used in various modelling phases including determining:

• 
  Climate change impacts on yield and quality of crops
  
• 
  Climate change impacts on crop irrigation requirements
  
• 
  Climate change impacts on irrigation water availability.
  

2. A note of caution on the GCMs used in this study

Overall changes in future scenarios of climate depend strongly on (Schulze et al., 2011):

• 
  which GCMs were used, and
  
• 
  how many GCMs were in the ensemble used.
  

The five GCMs which were available for use in this study, viz. CGCM3.1(T47), CNRM-CM3, ECHAM5/MPI-OM, GISS-ER and IPSL-CM4 are considered by climatologists to produce rainfall output possibly on the wetter side of the spectrum (Hewitson, 2010. Personal communication with Prof Schulze), and this has to be borne in mind in interpreting any impacts in which rainfall is an input variable. Furthermore, an error in GISS GCM’s rainfall values for parts of South Africa was reported during the course of the project and all statistics from multiple GCMs involving rainfall had to be re-calculated in order to eliminate the known error from that GCM (Schulze et al., 2011).

However, the reader should note that the main contribution of this study is to develop the methodology to analyse the financial vulnerability of farmers on a micro level. The accuracy of the selected GCMs and the error which was discovered in one of the GCMs is therefore irrelevant for the purpose of this study - the methodology developed in this study can use the data/information generated by any existing/future GCM. However, at the time of this analysis, the GCMs used remain the only credible tools we had for climate change impact studies (Schulze, 2014).

3. Climate projections

The climate projections for the study regions for the period 2040-2060 are presented below. They are median results from multiple models of the IPCC 4AR, nine downscaled in the case of rainfall and thirteen in the case of temperature.

The projections are presented for the four seasons and in the case of rainfall the 10th and 90th (extreme) percentiles are also given to indicate the range within the model projection outputs.

In the case of temperature, it can be seen that, during all seasons, increases in average temperature of around 2 degrees is projected. It is also inferred that the frequency of very hot days will increase significantly.

DJF MAM

JJA SON

Figure 11: Median of 2040-2060 average seasonal Temperature anomalies for the SRES A2 scenario

Figure 12: Rainfall projections for the eastern study area showing the median and 10^th and 90^th percentiles.

Yellow indicates 50mm or more per month less, red 10-20mm per month less, light blue 10mm per month more, dark blue 10-20 mm more, and turquoise 35mm or more per month more.
The rainfall projections show a range of possibilities for each season indicate uncertainty but the median values indicate a drying in DJF and a wetting in SON, with little change in the other 2 seasons. The high variability of the region and its current exposure to droughts and floods is thus likely to continue.

The sensitivity of the summer rainfall crops to these changes will be further explored with the help of crop modelling.

Figure 13: Rainfall projections for the western study area showing the median and 10^th and 90^th percentiles. Yellow indicates 50mm or more per month less, red 10-20mm per month less, light blue 10mm per month more, and dark blue 10-20 mm more.

The rainfall projections show a range of possibilities for each season indicate uncertainty but the median values indicate a drying in MAM, that is early winter, and a slight wetting in JJA (midwinter), with little change in the other 2 seasons. The high rainfall variability of the region and its current exposure to droughts is unlikely to change.

The early season drying will be a factor in determining the sensitivity of winter crops and the availability of irrigation during the dry season of early and middle summer in this region.

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