Abstract:
A climatology of synoptic drylines on the subtropical southern African interior plateau (SAP) is developed
using ERA5 reanalysis specific humidity and surface temperature gradients and an objective detection algorithm. Drylines
are found to occur regularly during spring and summer (September–March), and almost daily during December of that
period, but rarely in winter. A westward shift in peak dryline frequency takes place through the summer. Drylines peak
first over the eastern parts of the SAP during November with a mean of 10 drylines and then over the central (mean of 12)
and western SAP (mean of 20) in December. During midsummer, drylines over the eastern SAP are negatively correlated
with drylines in the west. Between 1980 and 2020, a significant correlation exists between ENSO and dryline days over the
eastern (r = 0.44; p value = 0.004) and central (r = 0.41; p value = 0.008) SAP with fewer drylines (up to 10) occurring during
years with increased surface moisture and more drylines (up to 45) occurring during years with decreased surface moisture.
Drylines forming over the eastern parts of the SAP were more likely to move westward than drylines over the central
and western parts. Onset times across the SAP show that drylines have a tendency to form during either the late morning
to early afternoon (1100 and 1400 LST) or during the early evening hours (1700 and 2000 LST), suggesting that the surface
heat trough (Kalahari heat low) and westward moisture transport mechanisms, such as the Limpopo low-level jet and ridging
highs, are responsible for the formation of most drylines across the SAP.
Description:
DATA AVAILABILITY STATEMENT : The ERA5 data (Hersbach. et al. 2018) used were downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store. Satellite imagery was downloaded from EUMETSAT Data Store. Station data in this study are available on request from the South AfricanWeather Service (SAWS) (http://www.weathersa. co.za). Other surface observation data were downloaded from online (www.ogimet.com), as were monthly ERSSTv5 data for the Ni ˜no-3.4 region (Huang et al. 2017) (https://climexp. knmi.nl/).