The use of such std.dev.-based and symmetric thresholds for the categories may not be ideal, leading to pseudo-documentaries with very different distributions of categories compared to the documentary data, most probably not reflecting real climatic shifts. We therefore used a “original” slightly different approach to define the categories, making use of the available but short overlap periods of instrumental and documentary data. We defined the thresholds in a way that the fraction of years allocated to each category remained the same for the original documentary data and the categorised instrumental data. For example, in the case of Lesotho, three years of the overlap period have the category -2 in the documentary data, reflecting a fraction of 9%. The other categories -1, 0, +1 and +2 have fractions of 35%, 26%, 21% and 9%, respectively. The percentiles of each category in the overlap periods of all regions are shown in Table S2.3. In the example of Lesotho, all years with rainfall anomalies smaller than -52mm (with respect to 1901-1990; representing the 9th percentile of rainfall amounts in the 1866-1899 overlap period) were allocated to the category -2. Years with anomalies between -52mm and 55mm (representing the 44th percentile (9th + 35th percentile)) were allocated to an index of -1. Accordingly, the thresholds of 121mm, representing the 70th percentile (100-21-9) and 238mm, representing the 91st percentile (100-9) were used as thresholds for the categories +1 and +2, respectively. Note that we used the raw and non-degraded instrumental data to define the whereas the allocation to categories for the pseudo-documentaries was calculated after adding white noise to the instrumental data. The disadvantage of this percentile-based categorisation approach is that it depends strongly on the overlap periods, which are relatively short, so that each individual year can affect the outcome (such as outliers or problematic years in terms of amount or coherence of the available documentary information). The categorisation can have a substantial influence on the relation of the pseudo-documentaries to the original instrumental data. To avoid biases introduced by this step, we repeated the white noise degrading and categorisation until three criteria were fulfilled:
The use of such std.dev.-based and symmetric thresholds for the categories may not be ideal, leading to pseudo-documentaries with very different distributions of categories compared to the “original” documentary data, most probably not reflecting real climatic shifts. We therefore used a slightly different approach to define the categories, making use of the available but short overlap periods of instrumental and documentary data. We defined the thresholds in a way that the fraction of years allocated to each category remained the same for the original documentary data and the categorised instrumental data. For example, in the case of Lesotho, three years of the overlap period have the category -2 in the documentary data, reflecting a fraction of 9%. The other categories -1, 0, +1 and +2 have fractions of 35%, 26%, 21% and 9%, respectively. The percentiles of each category in the overlap periods of all regions are shown in Table S2.3. In the example of Lesotho, all years with rainfall anomalies smaller than -52mm (with respect to 1901-1990; representing the 9th percentile of rainfall amounts in the 1866-1899 overlap period) were allocated to the category -2. Years with anomalies between -52mm and 55mm (representing the 44th percentile (9th + 35th percentile)) were allocated to an index of -1. Accordingly, the thresholds of 121mm, representing the 70th percentile (100-21-9) and 238mm, representing the 91st percentile (100-9) were used as thresholds for the categories +1 and +2, respectively. Note that we used the raw and non-degraded instrumental data to define the whereas the allocation to categories for the pseudo-documentaries was calculated after adding white noise to the instrumental data. The disadvantage of this percentile-based categorisation approach is that it depends strongly on the overlap periods, which are relatively short, so that each individual year can affect the outcome (such as outliers or problematic years in terms of amount or coherence of the available documentary information). The categorisation can have a substantial influence on the relation of the pseudo-documentaries to the original instrumental data. To avoid biases introduced by this step, we repeated the white noise degrading and categorisation until three criteria were fulfilled:
SOM_CD_R5.docx