AFRICA
2.3 Temperature Data and Climate Change Detection
Analysis of trends in observational records can provide insights into the characteristics and magnitude of global and regional climatic variations during the past decade to century (Frei and Schar, 2001). However, most attempts to characterise trends in climatic parameters have been plagued by the lack of long-term, high quality daily datasets (DeGaentano and Allen, 2002). With this in mind, problems relating to temperature datasets such as those mentioned above are discussed in this section and the temperature dataset used in this study is described.
2.3.1 Data problems to be aware of
No extensive temperature dataset can be totally void of any errors. However, with knowledge of the manner by which likely errors could arise and by careful quality control, the errors can be minimised and more confidence placed in the integrity of the dataset. Often the data available contains inhomogeneities as a result, inter alia, of changes in observational practices, changes in instrumentation type, instrument exposure, changes in location or changes in a location's characteristics, for example urbanization (Akimemi et al., 1999; King'uyu et aI., 2000). Furthermore, if data are available, they are often not available in electronic form and/or the quality of the data are often questionable (Easterling et al., 2000). For example, of 973 temperature stations in South Africa with daily records used in a study by Schulze and Maharaj (2004), only 299 stations were actually recording in the year 2000 and only 22 stations have actual (i.e. non-infilled or extended) records longer than 50 years. Furthermore, an apparent availability of data can be misleading, as in many parts of the world the spatial distribution of climate stations is uneven, with a high proportion of stations located close to urban areas and along coastlines (Groisman and Legates, 1995).
Other problems relating to data include the following:
• The exchange of international data is often hampered by cost recovery policies for high-resolution historical climate records.
• Data on short-term weather events (e.g. tropical cyclones) are largely unavailable owing to an absence of electronic or automatic recording instruments.
• A major problem affecting virtually every analysis of climatic records relates to undocumented or unknown effects of inhomogeneities in the datasets (Karl and Easterling, 1999).
To address the problems of inhomogeneities in datasets, a set of climate monitoring principles was proposed by Karl and Easterling (1999). The principles are aimed at reducing inhomogeneities in climatic records in order to allow unbiased analysis of these records to take place and trends in climatic variables to be detected. The principles include the following:
• An overlapping period of measurement for both old and new observing systems should be standard practice prior to implementing changes, in order to develop appropriate transfer functions from the old to new observing systems.
• Calibration, validation, processing algorithms, knowledge of instrumentation, station history and any other information relevant to interpreting the measured variable should be mandatory information that is recorded and archived with the original data.
• Routine assessment of both the random and systematic errors should take place in order to adequately monitor environmental variations and change.
• Observation stations with long, uninterrupted records should be maintained and protected to ensure a long-term homogenous climate record from that station.
• When implementing or installing new observation systems, priority should be given to either data poor regions, variables or regions sensitive to change or key measurements with inadequate temporal resolution.
• Data management systems should be guided by freedom of access, low-cost mechanisms that facilitate use and quality control (Karl and Easterling, 1999).
These principles, in particular the principles relating to the protection of stations and data management systems, should be applied to observing climatic variables in South Africa. Discussed below is the temperature dataset used for this study.
2.3.2 Temperature dataset used in this study
Daily maximum and minimum temperature records were obtained from a CD-ROM which accompanied a recently completed report to the Water Research Commission (WRC) titled "The Development of a Database of Gridded Daily Temperature for Southern Africa" (Schulze and Maharaj, 2004). This report formed a component of a WRC project titled "The Development of a Revised Spatial Database of Annual, Monthly and Daily Rainfall and other Hydroclimatic Variables for South Africa." The project developed procedures to infill missing daily maximum and minimum temperature values and to extend temperature records to a common, preselected base period from 1950 to 2000, for those stations qualifying on the strength of existing records.
Daily temperature data were obtained from three main sources, viz. the South African Weather Service (SAWS), the Institute for Soil, Climate and Water (ISCW) and the South African Sugarcane Research Institute (SASRI). Of the 1053 stations which initially qualified following quality control of data, only 23 had a record length of greater than 50 years of daily temperature data, with only 4 stations having a record length in excess of 75 years of daily data. The network of temperature stations is densest in the KwaZulu-Natal, Gauteng and Western Cape provinces and sparsest in the Northern Cape province and Lesotho (Schulze and Maharaj, 2004).
Following initial quality controls, further stringent quality controls were undertaken on all daily temperature data included in the database. Thereafter, missing or discarded temperature values were infilled and records extended by using data from nine control stations for each target station, selected according to criteria, distance from the target station and difference in altitude. Infilling/record extension of the target station was undertaken by using the "difference in standard deviation method" (described in Schulze and Maharaj, 2004) after adjusting the control stations records' for differences in altitude using regional and monthly lapse rates. Where the target station could not be infilled using this method, Fourier Analysis was used (Schulze and Maharaj, 2004).
For this study on detection of trends in temperature a 51-year record of daily temperature for the common period 1950 to 2000 was extracted from the database of daily temperatures for southern Mrica (Schulze and Maharaj, 2004) for stations with an actual data record length of 20 years or longer. Furthermore, stations were eliminated if any of their records were infilled using Fourier Analysis, as the infilled values of temperature for a given day of the year remain the same year to year, and, therefore nullify any trend. Following this elimination process, each station's record of montWy means of daily values (e.g. as in Table 2.5 for one station) was checked manually. Any anomalies found in the time series plots were then investigated at a daily level. As a result, five stations in Lesotho,viz. 0204819 A,0207337 W 0263859 W 0267137 W- - , - , - and 0297083_W were identified as stations with questionable data.
A further data check undertaken was to identify the land use surrounding each temperature station, using the 1996 Landsat TM National Land Cover Image from the CSIR. If the land use was urban, the station was flagged, as the temperature record
could have been influenced by an urban heat island effect. Owing to the small number of stations located in heavily built-up urban areas, the effect of land use was not considered any further at this stage.
The initial 1053 temperature stations whose records qualified were reduced to 342 once further quality controls had been effected and a minimum 20 year actual record length was considered. These 342 stations were further reduced to 209 when those stations with records which at any stage in the 51 year period 1950 - 2000 had been infilledlextended by Fourier Analysis had been removed. Thus the temperature stations used in the analysis contained few infilled values.
Following this data collation and quality control, the next step was to analyse the datasets for trends over time, using the methods outlined in Section 2.4 below.