A new perspective of the climatological features of upper-level cut-off lows in the Southern Hemisphere

This study presents a detailed view of the seasonal variability of upper-level cut-off lows (COLs) in the Southern Hemisphere. The COLs are identified and tracked using data from a 36-year period of the European Centre for Medium Range Weather Forecast reanalysis (ERA-Interim). The objective identification of the COLs uses a new approach, which is based on 300 hPa relative vorticity minima, and three restrictive criteria of the presence of a cold-core, stratospheric potential vorticity intrusion, and cut-off cyclonic circulation. The highest COL activity is in agreement with previous studies, located near three main continental areas (Australia, South America, and Africa), with maximum frequencies usually observed in the austral autumn. The COL mean intensity values show a marked seasonal and spatial variation, with maximum (minimum) values during the austral winter (summer), a unique feature that has not been observed previously in studies based on the geopotential. The link between intensity and lysis is examined, and finds that weaker systems are more susceptible to lysis in the vicinity of the Andes Cordillera, associated with the topographic Rossby wave. Lysis and genesis regions are close to each other, confirming that COLs are quasi-stationary systems. Also, COLs tend to move eastward and are faster over the higher latitudes. The mean growth/decay rates coincide with the major genesis and lysis density regions, such as the significant decay values across the Andes all year. As a consequence of using vorticity for the tracking method a longer lifetime of COLs is detected than in other studies, but this does not affect the total frequency of occurrence. Comparisons with other studies suggest that the differences in seasonality are due to uncertainties in the reanalyses and the methods used to identify COLs

Rainfall simulations of high-impact weather in South Africa with the conformal cubic atmospheric model (CCAM)

Warnings of severe weather with a lead time longer that two hours require the use of skillful numerical weather prediction (NWP) models. In this study, we test the performance of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric Model (CCAM) in simulating six high-impact weather events, with a focus on rainfall predictions in South Africa. The selected events are tropical cyclone Dineo (16 February 2017), the Cape storm (7 June 2017), the 2017 Kwa-Zulu Natal (KZN) floods (10 October 2017), the 2019 KZN floods (22 April 2019), the 2019 KZN tornadoes (12 November 2019) and the 2020 Johannesburg floods (5 October 2020). Three configurations of CCAM were compared: a 9 km grid length (MN9km) over southern Africa nudged within the Global Forecast System (GFS) simulations, and a 3 km grid length over South Africa (MN3km) nudged within the 9 km CCAM simulations. The last configuration is CCAM running with a grid length of 3 km over South Africa, which is nudged within the GFS (SN3km). The GFS is available with a grid length of 0.25 , and therefore, the configurations allow us to test if there is benefit in the intermediate nudging at 9 km as well as the effects of resolution on rainfall simulations. The South AfricanWeather Service (SAWS) station rainfall dataset is used for verification purposes. All three configurations of CCAM are generally able to capture the spatial pattern of rainfall associated with each of the events. However, the maximum rainfall associated with two of the heaviest rainfall events is underestimated by CCAM with more than 100 mm. CCAM simulations also have some shortcomings with capturing the location of heavy rainfall inland and along the northeast coast of the country. Similar shortcomings were found with other NWP models used in southern Africa for operational forecasting purposes by previous studies. CCAM generally simulates a larger rainfall area than observed, resulting in more stations reporting rainfall. Regarding the different configurations, they are more similar to one another than observations, however, with some suggestion that MN3km outperforms other configurations, in particular with capturing the most extreme events. The performance of CCAM in the convective scales is encouraging, and further studies will be conducted to identify areas of possible improvement.The AIMS NEI Women in Climate Change Science (WiCCS) fellowship and the Water Research Commission.https://www.mdpi.com/journal/atmospheream2023Geography, Geoinformatics and Meteorolog

Climate change and maize production in the Vaal catchment of South Africa: assessment of farmers’ awareness, perceptions and adaptation strategies

In recent years, maize production in South Africa has faced challenges related to climate change which have prompted farmers to adapt their production activities. We assessed factors informing the adaptive decision-making of maize farmers in the Vaal catchment by examining linkages between farmers’ experiences, their perceptions of climate change and the adaptation strategies they use. Data were collected through semi-structured household-level interviews, key informant interviews and focus group discussions. Catchment climate data were also collected to determine key 30 yr trends (1989-2018) and the farmers’ level of awareness about these trends. Data were analysed using descriptive statistics, Mann-Kendall (MK) test, Sen’s slope test, climate anomalies and multinomial logit modelling. Results suggest that maize farmers in the catchment are aware of climate change (95%), with many of them referring to it as ‘a shift in climate’. This perceived ‘shift’ is supported by meteorological data, as the MK test confirmed a decreasing inter-annual precipitation trend (-0.149) and a decreasing trend at the onset of the maize planting season (-0.167), with temperature showing an increasing trend (0.470). These trends have inspired the adoption of a range of timing-related responses and other farming and off-farm adaptations. Modelling results revealed farmer perception, farmer typology and the nature of maize production (rainfed) as some of the variables with a deciding influence on the nature of the adaptation employed. The study confirms the importance of understanding intersections between qualitative and quantitative variables in triggering adaptive responses. Current strategies need to be expanded and supplemented to improve resilience and prevent maladaptation.</jats:p

Integrated assessment of the influence of climate change on current and future intra-annual water availability in the Vaal River catchment

Abstract Increasing water demand due to population growth, economic expansion and the need for development puts a strain on the supply capacity of the Vaal River catchment in South Africa. Climate change presents additional challenges in the catchment which supports the country's economic hub, more than 30% of its population and over 70% of its maize production. This study evaluates the influence of climate change on current and future intra-annual water availability and demand using a multi-tiered approach where climate scenarios, hydrological modelling and socio-economic considerations were applied. Results shows exacerbated water supply challenges for the future. Temperature increases of between 0.07 and 5 °C and precipitation reductions ranging from 0.4 to 30% for Representative Concentration Pathways (RCPs) 4.5 and 8.5, respectively, are also predicted by the end of the century. The highest monthly average streamflow reductions (8–10%) are predicted for the summer months beyond 2040. Water Evaluation and Planning (WEAP) simulations project an increase in future water requirements, gaps in future water assurance and highlight limitations in existing management strategies. The study recommends a combination of adaptation plans, climatic/non-climatic stressor monitoring, wastewater-reuse, conservation, demand management and inter-basin transfers to reduce future uncertainty in monthly water sustainability.</jats:p

Performance assessment of three convective parameterization schemes in WRF for downscaling summer rainfall over South Africa

Austral summer rainfall over the period 1991/1992 to 2010/2011 was dynamically downscaled by the weather research and forecasting (WRF) model at 9 km resolution for South Africa. Lateral boundary conditions for WRF were provided from the European Centre for medium-range weather (ECMWF) reanalysis (ERA) interim data. The model biases for the rainfall were evaluated over the South Africa as a whole and its nine provinces separately by employing three different convective parameterization schemes, namely the (1) Kain–Fritsch (KF), (2) Betts–Miller–Janjic (BMJ) and (3) Grell–Devenyi ensemble (GDE) schemes. All three schemes have generated positive rainfall biases over South Africa, with the KF scheme producing the largest biases and mean absolute errors. Only the BMJ scheme could reproduce the intensity of rainfall anomalies, and also exhibited the highest correlation with observed interannual summer rainfall variability. In the KF scheme, a significantly high amount of moisture was transported from the tropics into South Africa. The vertical thermodynamic profiles show that the KF scheme has caused low level moisture convergence, due to the highly unstable atmosphere, and hence contributed to the widespread positive biases of rainfall. The negative bias in moisture, along with a stable atmosphere and negative biases of vertical velocity simulated by the GDE scheme resulted in negative rainfall biases, especially over the Limpopo Province. In terms of rain rate, the KF scheme generated the lowest number of low rain rates and the maximum number of moderate to high rain rates associated with more convective unstable environment. KF and GDE schemes overestimated the convective rain and underestimated the stratiform rain. However, the simulated convective and stratiform rain with BMJ scheme is in more agreement with the observations. This study also documents the performance of regional model in downscaling the large scale climate mode such as El Niño Southern Oscillation (ENSO) and subtropical dipole modes. The correlations between the simulated area averaged rainfalls over South Africa and Nino3.4 index were −0.66, −0.69 and −0.49 with KF, BMJ and GDE scheme respectively as compared to the observed correlation of −0.57. The model could reproduce the observed ENSO-South Africa rainfall relationship and could successfully simulate three wet (dry) years that are associated with La Niña (El Niño) and the BMJ scheme is closest to the observed variability. Also, the model showed good skill in simulating the excess rainfall over South Africa that is associated with positive subtropical Indian Ocean Dipole for the DJF season 2005/2006

A dynamic and thermodynamic analysis of the 11 December 2017 tornadic supercell in the Highveld of South Africa

On 11 December 2017, a tornadic supercell initiated and moved through the northern Highveld region of South Africa for 7 h. A tornado from this supercell led to extensive damage to infrastructure and caused injury to and displacement of over 1000 people in Vaal Marina, a town located in the extreme south of the Gauteng Province. In this study we conducted an analysis in order to understand the conditions that led to the severity of this supercell, including the formation of a tornado. The dynamics and thermodynamics of two configurations of the Unified Model (UM) were also analysed to assess their performance in predicting this tornadic supercell. It was found that this supercell initiated as part of a cluster of multicellular thunderstorms over a dry line, with three ingredients being important in strengthening and maintaining it for 7 h: significant surface to mid-level vertical shear, an abundance of low-level warm moisture influx from the tropics and Mozambique Channel, and steep mid-level lapse rates. It was also found that the 4.4 km grid spacing configuration of the model (SA4.4) performed better than the 1.5 km grid spacing version. SA1.5 underestimated the low-level warm moisture advection and convergence, and missed the storm initiation. SA4.4 captured the supercell; however, the mid-level vorticity was found to be 1 order of magnitude smaller than that of a typical mesocyclone. A grid length of 4.4 km is too coarse to fully capture the details of a mesocyclone, which may also explain why the model underestimated the surface to mid-level wind shear and low-level horizontal mass and moisture flux convergence. Future investigations will involve experimental research over the Highveld region of South Africa to understand mesoscale and local dynamics processes responsible for tornadogenesis in some severe storms. Such a study, to the best of our knowledge, has never been conducted.</p

Rainfall Simulations of High-Impact Weather in South Africa with the Conformal Cubic Atmospheric Model (CCAM)

Warnings of severe weather with a lead time longer that two hours require the use of skillful numerical weather prediction (NWP) models. In this study, we test the performance of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric Model (CCAM) in simulating six high-impact weather events, with a focus on rainfall predictions in South Africa. The selected events are tropical cyclone Dineo (16 February 2017), the Cape storm (7 June 2017), the 2017 Kwa-Zulu Natal (KZN) floods (10 October 2017), the 2019 KZN floods (22 April 2019), the 2019 KZN tornadoes (12 November 2019) and the 2020 Johannesburg floods (5 October 2020). Three configurations of CCAM were compared: a 9 km grid length (MN9km) over southern Africa nudged within the Global Forecast System (GFS) simulations, and a 3 km grid length over South Africa (MN3km) nudged within the 9 km CCAM simulations. The last configuration is CCAM running with a grid length of 3 km over South Africa, which is nudged within the GFS (SN3km). The GFS is available with a grid length of 0.25°, and therefore, the configurations allow us to test if there is benefit in the intermediate nudging at 9 km as well as the effects of resolution on rainfall simulations. The South African Weather Service (SAWS) station rainfall dataset is used for verification purposes. All three configurations of CCAM are generally able to capture the spatial pattern of rainfall associated with each of the events. However, the maximum rainfall associated with two of the heaviest rainfall events is underestimated by CCAM with more than 100 mm. CCAM simulations also have some shortcomings with capturing the location of heavy rainfall inland and along the northeast coast of the country. Similar shortcomings were found with other NWP models used in southern Africa for operational forecasting purposes by previous studies. CCAM generally simulates a larger rainfall area than observed, resulting in more stations reporting rainfall. Regarding the different configurations, they are more similar to one another than observations, however, with some suggestion that MN3km outperforms other configurations, in particular with capturing the most extreme events. The performance of CCAM in the convective scales is encouraging, and further studies will be conducted to identify areas of possible improvement