cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/saeon.egagasini.10000108
title: Western Indian Ocean Energy Sinks (WOES) 0.25: a CROCO numerical ocean model hindcast at 1/12 degree monthly mean for the south western Indian Ocean from 1993-2014 nested in a 1/4 degree WOES run.

description: This realistic ocean simulation was run using the Coastal and Regional Ocean COmmunity model (CROCO), based on the Regional Ocean Modelling System (ROMS), which has 60 terrain-following vertical levels. This output (WOES 0.25) is the largest grid of a triply nested configuration: WOES I, WOES II and WOES III, with horizontal resolutions of ~22.5, 7.5 and 2.5 km respectively. Monthly ouputs of the 0.25 degree GLORYS ocean reanalysis is used to force the boundaries of WOES I. The surface forcing for this model is provided by a bulk formulation using daily ERA-Interim atmospheric reanalysis (with a resolution of ~80 km) and using a relative wind approach. The output is saved as daily averages, in monthly netcdf files spanning January 1993 - December 2014. WOES 0.25 spans 55.7degS to 3.18388 degS and 10degW to 102.25degE and covers most of the Southern Subtropical Indian Ocean and a part of the Southern Atlantic Ocean. Model output includes: averaged free-surface (zeta), averaged vertically integrated u-momentum component (ubar), averaged vertically integrated v-momentum component (vbar), averaged u-momentum component (u), averaged v-momentum component (v), averaged potential temperature (temp), averaged salinity (salt), averaged vertical momentum component (w). 

Numerical computations were performed on the IDRIS (Institut du Developpement et des Ressources en Informatique Scientifique) IBM "ADA" computer facility (under grant A0020107630)
DOI: 10.15493/sarva.saws.10000194
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the 90% percentile for 2036 - 2065 relative to 1976-2005, for the DJF season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (DJF) rainfall (mm per month) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.beeh.10000188
title: Positive Chill Units - June

description: Certain biennial plants which have a dormant (i.e. rest) season during winter may require a period of accumulated minimum temperatures below a threshold in order to stimulate growth, develop leaves, flower or set fruit. Deciduous trees, for example, need a certain period of winter chilling for completion of their seasonal dormancy. Insufficient winter chilling may result in delayed foliation, reduced fruit set (including fruits remaining small and being misshapen as they ripen) and reduced fruit quality (including reduced firmness, and/or greener fruit coloration). The required amount of chilling for completion of the rest period varies between species, cultivars and different locations. Many chill accumulation models have been formulated, most of them requiring hourly temperatures to have either been observed or estimated. The Linsley-Noakes PCU model was used to produce maps of chill units in this Atlas. The techniques by which PCUs were computed and then mapped are described in the shaded sections which follow.

Derivation of hourly values of temperature for the determination of PCUs hourly values of temperature were determined from the 50 year time series of daily maximum and minimum temperatures generated at each of the 429 700 raster points covering South Africa, using the techniques described by Schulze and Maharaj (2004). For each daytime and night-time cycle the sine-log equations derived by Linsley-Noakes et al. (1995) were employed. In these equations the estimation of hourly temperatures is based on the observation that the daytime solar cycle follows a sine curve from sunrise to sunset, whereas night-time cooling may be described by a logarithmic equation.
DOI: 10.15493/dea.carbon.10000069
title: South African Land Cover 2014 Class C Indigenous Forest Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 4 of the 2013-14 South African National land cover dataset was used to derive South African land cover 2014 Class C Indigenous Forest for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs
DOI: 10.15493/saeon.fynbos.10000003
title: Langrivier rain gauge 14B (472m), event rainfall, 06/2011 - 06/2019

description: Event rainfall data collected by Davis Instruments (2011-2017) and Texas Electronics (2017- current) tipping bucket gauge installed at 1.2m above ground at 472 masl in the Langriver catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by a Pendant Event logger (UA-003-64, Onset, Massachusetts, U.S.A).  Tipping bucket calibrated to a 0.2 mm (Davis) or 0.254 mm (Texas) per tip event.  (Collection continuing, contact SAEON for updated datasets)
DOI: 10.15493/sarva.csag.10000204
title: Number of days for Minimum Temperature under 10th percentile for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000286
title: Maximum of Maximum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000263
title: Precipitation 90th percentile for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/saeon.egagasini.10000034
title: The ASCA April 2015 raw dissolved oxygen data for station Stn007A - Stn022A

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data collected during the ASCA project have been obtained from various oceanographic instrumentation.This raw data was collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor, from which water samples were collected and analysed for dissolved oxygen for the following stations: Stn007A, Stn008A, Stn009A, Stn010A, Stn011A, Stn012A, Stn013A, Stn014A, Stn015A, Stn016A, Stn017A, Stn018A, Stn019A, Stn020A, Stn021A and Stn022A
DOI: 10.15493/sarva.csag.10000397
title: Total precipitation for the month (mm) for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.gb.10000016
title: Sectoral Gross Value Added (GVA) for community social and personal services, as well as government services (SIC9&10) as a stock from 1995 to 2016

description: The South African Gross Geographic Value Added (GVA) data for each economic sector per year since 1995 to 2016 was supplied by Quantec to the CSIR built environment. This data was disaggregated to a 500m grid using a dasymetric mapping algorithm (https://gap.csir.co.za/gap/images/documents/developing-a-geo-data-frame-using-dasymetric-mapping-principles-to-facilitate-data-integration). and is provided for each year
DOI: 10.15493/saeon.fynbos.bi1120
title: Biesievlei stream flow and stage height August 2011 - August 2020

description: Hourly average and halfhourly sample stream flow in cumec estimated from stage height measured with an OTT Orpheus Mini in the stilling well at a V-notch weir in Biesievlei catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000167
title: Maximum of Maximum Temperature for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.231120-6
title: The distribution of Quintile 2 Secondary Schools per District Municipality

description: School Quintiles are an indication of the socio-economic status of the school. Quintile 1 schools are the poorest, while quintile 5 schools are the least poor .Schools are ranked according to poverty of the school community . This dataset indicates the distribution of schools according to quintiles using the data from the master list of schools obtained from the Department of Basic Education.
DOI: 10.15493/sarva.saws.10000261
title: Seasonal percentage (%) change in rainfall from the median projected for 2036-2065, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway

description: Seasonal percentage (%) change in rainfall from the median projected for 2036-2065, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.beeh.10000287
title: Extremes (1950 - 2000) Of Maximum Temperatures (C) All 12 Months

description: When days with very low or very high temperatures are experienced, the question is invariably asked as to whether that `extreme` minimum or maximum temperature was a record, either for the country or province, or for a specific location or month of the year. It is for that reason that `extremes` of temperatures, at both ends of the spectrum, were extracted from the daily temperature database generated by Schulze and Maharaj (2004) for the 51 year period (in this case) from 1950 to 2000, and analysed in tabular and mapped form. While these maps/tables are informative and interesting, there are nevertheless some limitations as to their accuracy, for reasons which are discussed in the shaded blocks containing some scientific background to the analyses.Limitations over South Africa to mapping extremes of temperatures, the generation of daily temperature time series at one arc minute spatial resolution at the 429 700 grid points covering South Africa depends on the selection of "best" control stations, extrapolating to altitudes above that of the highest-lying station in a particular region, as well as computational procedures regarding initial and final daily temperature values (Schulze and Maharaj, 2004). With respect to extreme minimum temperatures, no account has been taken of cold air drainage into valleys, which may aggravate minimum extremes.

Extremes of temperatures were derived from over 970 qualifying stations' records for the 51 year (1950 - 2000) time series of quality controlled daily maximum and minimum values generated at each of the 429 700 one arc minute (i.e. 1.7 x 1.7 km) raster points covering South Africa, using the regional/seasonal lapse rate and infilling techniques developed by Schulze and Maharaj (2004). In order to avoid analysing and mapping anomalous temperature extremes resulting from incorrect recordings, the extremes which were derived are therefore taken as the means of the two highest or lowest values found in the temperature time series at each grid point.
DOI: 10.15493/nccis.data.10000023
title: Projected changes in annual mean temperature (°C) under RCP 4.5 for the 2080-2099 time period based on the Australian Community Climate and Earth System Simulator (ACCESS1-0)

description: The analyses of future climate change over South Africa as described in the Third National Communication, are from the projections of the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Assessment Report (AR) 5 of the Intergovernmental Panel on Climate Change (IPCC). These projections are used to inform on the uncertainty range of the large-scale climate change futures over the southern African region. At the Council for Scientific Industrial Research (CSIR), a dynamic regional climate model CCAM (conformal-cubic atmospheric model) of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was used to downscale CMIP5 CGCM projections to 50 km resolution over Africa. These downscalings were for both Representative Concentration Pathway (RCP) 8.5 and Representative Concentration Pathway (RCP) 4.5 of AR5 of the IPCC. RCP 4.5 describes a future with relatively ambitious emission reductions whereas RCP 8.5 describes a future with no reductions in emissions. Emissions in RCP 4.5 peak around 2040, then decline and in RCP 8.5 emissions continue to rise throughout the 21st century. The change in temperature is expressed as an anomaly, the difference between the average climate over a period of the last several decades (1971-2000), and the projected climate (short to medium term 2021 to 2050). The simulations were performed on supercomputers of the CSIRO and on the Centre for High Performance Computing (CHPC) of the Meraka Institute of the CSIR in South Africa.
DOI: 10.15493/nccis.data.10000003
title: Linear trend in the daytime hottest temperature (1931-2015) expressed as deg C and defined as the maximum value of maximum temperature (TX).

description: An updated analysis of trends in South African temperatures is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). 

A total of 27 weather stations was used for for temperature trends analysis spanning the period 1931-2015. Apart from the trends in the annual mean minimum, maximum and average temperatures, and average diurnal temperature range, the analyses also includes the determination of trends in extreme temperatures, of which the indices considered are based on those developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013).  The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level.

The analysis demonstrates that, South Africa has been warming significantly over the period 1931-2015. Over the western parts of the country, including much of the Western and Northern Cape, and also in the east over Gauteng, Limpopo and the east coast of KwaZulu-Natal, the observed rate of warming has been 2 °C/century or even higher – in the order of twice the global rate of temperature increase.  Associated increases in the annual number of hot days have also occurred, but there have been decreases in the annual number of cold nights over most of the country.
DOI: 10.15493/sarva.beeh.10000371
title: Monthly mean of diurnal (daily) temperature ranges for the 50 year (1950 - 1999) period (C) - October

description: The diurnal temperature range is the difference between the maximum and minimum temperatures on a given day. Some plants respond to the rhythmic variations brought about by shifts from high temperatures during the day to low temperatures during the night. The main long term factors affecting diurnal temperature ranges over South Africa are continentality and altitude (Schulze, 1997). It nevertheless needs emphasising here that short term, irregular diurnal conditions are often controlled by the passage of cold fronts or other air masses which may be associated with strongly contrasting temperature regimes.

"Monthly means of diurnal (daily) temperature ranges were derived from the 50 year (1950 - 1999) time series of quality controlled daily  maximum and minimum temperature values generated at each of the 429 700 one arc minute (i.e.1.7 x 1.7 km) raster points covering South Africa, using the regional/seasonal lapse rate and infilling techniques developed by Schulze and Maharaj (2004). From theoretical considerations, experience and analyses of high quality daily temperature datasets, the diurnal temperature range is very seldom < 2 degree Celsius. When, therefore, on a given day the temperature range value was computed to be < 1.5 degree Celsius, this value was `forced` to be 1.5 degree Celsius, with equal compensatory adjustments being made to that day's maximum and minimum temperature values."
DOI: 10.15493/sarva.saws.10000153
title: Seasonal mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.