Cut-Off Lows (COLs) are known for heavy rainfall in the Western Cape, but there is a dearth of information on COL rainfall characteristics over the Cape. To bridge this gap we analysed three types of ...datasets (observation, satellite and reanalysis) to study the characteristics of COLs that occurred in the vicinity of the Western Cape over 37 years (1981–2017) and applied a self-organising Map (SOM) to classify the COLs into groups based on their rainfall patterns. The results show about 10 COLs (per year) occur over the Western Cape and contribute about 11% of the annual rainfall in the cape but with a large inter-annual variability. In 2015 and 2016, the COLs occurred more frequently and contributed more rainfall than normal which reduced the drought severity. But, in 2017, the frequency and rainfall contribution of the COLs were below normal. Nevertheless, more COLs does not always mean higher COL rainfall as >45% of the COLs over the Western Cape produce little or no rainfall. The SOM results reveal that the spatial distribution of COL rainfall can be grouped into four major patterns. The first pattern indicates rainfall over the entire Western Cape, while the second shows little or no rainfall; the third and fourth patterns feature rainfall over the south-east coast and south-west coast, respectively. Wet COLs are associated with more southward transport of warm, moist tropical air into the Western Cape and greater contrasts between the warm and cold air masses compared to the dry COLs.
•COLs account for 10% of annual rainfall in Western Cape but with large variability.•COLs reduced drought severity over Western Cape in 2015 and 2016 but not in 2017•COLs over the area can be classified into four groups based on their rainfall patterns.•>45% of COLs over Western Cape produce with little or no rainfall.•Wet COLs require transport of warm moist tropical air towards Western Cape.
The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° ...resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.
Cut-off lows (COLs) cause hazardous weather but also play a crucial role in the annual rainfall of the Western Cape, especially during a drought year. However, there is a dearth of information on the ...capability of atmospheric models to reproduce the characteristics of COLs over this region. This study evaluates the capability of a regional climate model (WRF) in simulating COLs over the Western Cape, with emphasis on the drought periods. Observation, reanalysis and simulations datasets were analysed for the study. The simulated climatology and inter-annual variability of COL with the associated patterns were compared to the reanalysis and observation results. The Self Organising Map (SOM) was used to group the observed and simulated COL rainfall to similar patterns. The results of the analysis show that WRF captures the seasonal and annual climatology of COL and the associated rainfall, but the model struggles to simulate the inter-annual variability of the systems. The model reproduces all the COL rainfall patterns well, though it under-estimates the frequency of dry COLs. However, WRF simulation agrees with the reanalysis that wetter COLs over the Western Cape are associated with more transport of warm, moist air from the tropics. The results of the study have application in improving weather and seasonal forecasting of COLs over the Western Cape.
Cut‐off lows (COLs) over Southern Africa are known for their extreme rainfall, but the reliable prediction of COL rainfall remains a challenge because of complex interactions with topography. As ...there is a dearth of information on these interactions over this region, this study examines the influence of topography on the characteristics of COLs over Southern Africa. Multi‐ensemble simulations from the Weather Research Forecasting (WRF) model were run to simulate three COLs over Southern Africa, using the real topography CONTROL and three idealized terrains (i.e., “no topography” NoTOPO, “only west topography” WTOPO and “only east topography” ETOPO). A COL tracking algorithm was used to track COLs in the simulation dataset. The characteristics of the examined COLs include their formation, track, rainfall and vertical structure. The results show that topography influences the characteristics of COLs, although such influence varies with individual COLs. The influence of topography on COL formation is more pronounced on COLs that form in the vicinity of the sub‐continent than the ones that form far from the sub‐continent. Topography promotes uplift of warm moist air and initiation of deep convection, which may weaken the zonal strength of the westerly jet and allow for the formation of the COL. As the COLs approach topography, their tracks become more southerly. Topography decelerates the westerly wave systems, hence the system moves faster in NoTOPO experiments than in CONTROL experiments. While topography seems not to influence the timing of the peaks in rainfall during the COL lifecycle, it does influence the spatial distribution of rainfall. The presence of western topography enhances the rainfall over the western part of Southern Africa, whereas the presence of eastern topography reduces the rainfall over the western parts of Southern Africa. These results have applications in improving the forecasting of COLs over the complex terrain of Southern Africa.
Cut‐off lows (COLs) are often difficult to predict due to their complicated relationship with the surface terrain. This study has explored how the mountains of Southern Africa influence COLs by performing a series of sensitivity experiments with different topographic configurations. The results show that topography influences the formation, tracks, rainfall and vertical structure of COLs over Southern Africa.
ABSTRACT
This study evaluates the capability of regional climate models (RCMs) in simulating extreme rainfall events over Southern Africa, and in reproducing the characteristics of widespread extreme ...rainfall events (WERE) in the Western Cape (South Africa). We obtained simulation datasets of nine RCMs from the Co‐ordinated Regional Downscaling Experiment (CORDEX) and compared them with observation datasets from the Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) as well as with the reanalysis dataset (ERAINT) that forced the simulations. A self‐organising map was used to classify the WEREs into 12 nodes. The contribution of each RCM to each node was compared with the contributions of GPCP, TRMM and ERAINT. Using ERAINT dataset, we analysed the synoptic‐scale atmospheric condition associated with each node.
The results show that, in simulating the spatial distribution of the extreme rainfall event over Southern Africa, only four RCMs perform better than the forcing reanalysis (ERAINT) while two RCMs perform worse than the reanalysis. All the RCMs underestimate the threshold of extreme rainfall over Western Cape, poorly simulate the inter‐annual variability of the WEREs (r ≤ 0.3), but correctly reproduce the maximum frequency of the WEREs in Autumn (March–May). The WEREs in the Western Cape may be broadly grouped into four synoptic rainfall patterns. The first pattern links WEREs with tropical rainfall activities (tropical‐temperate troughs); the second pattern shows isolated WEREs; the third and fourth patterns link WEREs with rainfall activities in the mid‐latitudes (frontal systems) and over the Agulhas Current, respectively. While most RCMs overestimate the frequency of the first pattern, they all underestimate the frequency of the second pattern but simulate the frequencies of the third and fourth patterns well. The results of this study should help in improving the RCM simulations over Southern Africa and in downscaling impacts of climate change on extreme rainfall events over the region.
This study assesses the capability of regional climate models (RCMs) in simulating the characteristics of widespread extreme rainfall events over the East Coast of South Africa. Simulations of nine ...RCMs from the Coordinated Regional Downscaling Experiment (CORDEX) were analyzed for the study. All the simulations cover 12 years (1996–2008). Using the 95th percentile of daily rainfall as the threshold of extreme events and the simultaneous occurrence of extreme events over 50 % of the East Coast as widespread extreme events (WERE), we compared the characteristics of simulated WERE with observations (GPCP and TRMM) and with the reanalysis (ERAINT) that forced the simulations. Most RCMs perform well in simulating the seasonal variation of WEREs over the East Coast but perform poorly in simulating the interannual variability. Based on their rainfall synoptic patterns over Southern Africa, the WEREs in the East Coast can be generally classified into four groups. The first group connects the WEREs with tropical rainfall activities over the subcontinent. The second group links WEREs with frontal rainfall south of the subcontinent. The third group links the WEREs with both tropical and temperate rainfall activities while the fourth group represents isolated WEREs. The RCMs show different capabilities in simulating the frequency of WERE in each group, some perform better than ERAINT while some perform worse. Results of this study could provide information on the usability of RCMs in downscaling the impact of climate change on widespread extreme rainfall events over South Africa.
Abstract We describe the first effort within the Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of ...twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ∼25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed.
Includes bibliographical references.
In South Africa, extreme rainfall events often lead to widespread destruction, damage infrastructure, displace communities, strain water management and even ...destroy lives. Past studies have shown that reliable predictions of extreme rainfall events from regional climate models (RCMs) could help reduce the impact of these events. The present study evaluates the ability of nine RCMs in simulating extreme rainfall events over South Africa, focusing on the Western Cape (WC) and east coast (EC) areas. This study defines an extreme rainfall over a location as rainfall that is equal to or above the 95th percentile of the rainfall distribution at that location, and defines widespread extreme rainfall events (WEREs) over an area as events during which more than 50 of the grid-points in the area experience extreme rainfall. The 95th percentile threshold values were calculated over 11 years (1998-2008) of South Africa’s daily rainfall data from the nine RCMs (CCLM, REMO, PRECIS, CRCM5, ARPEGE, REGCM3, WRF, RACMO and RCA35), which participated in the Coordinated Regional Climate Downscaling Experiment (CORDEX) and used ERA-Interim (ERAINT) as their boundary forcing. The simulations were compared to two observation datasets (TRMM and GPCP), and to ERAINT rainfall data to understand whether these RCMs improve on the results from ERAINT. A self organizing map (SOM) was used to characterize WEREs identified in all the datasets into archetypal groups, and ERAINT data is used to describe the underlying circulations for each archetypal rainfall pattern. The number of WEREs mapped to each rainfall pattern for each dataset allows us to get an idea of whether certain RCMs are more likely to simulate certain rainfall patterns.
Cut-off lows (COLs) are an important rainfall source in the Western Cape. While several
studies have examined the devastating impacts of COLs during extreme rainfall events,
little is known about the ...characteristics of COLs during droughts and how the
characteristics are influenced by the South African complex topography. This thesis
investigates the interannual variability of COLs and COL precipitation over Western Cape,
with a focus on the 2015 - 2017 drought that affected the region and examines how well
climate models simulate the variability. It also studies how the complex topography of
South Africa influences the COLs characteristics. Four types of datasets (observation,
satellite, reanalysis, and simulation) were analysed for the thesis. The observation,
satellite and reanalysis data were analysed from the period 1979-2017, while two
simulations were performed using a regional climate model (called WRF) and a variable
grid model (called MPAS) for the period 2007-2017. A COL tracking algorithm was used
to extract all the COLs that occurred in the vicinity of the Western Cape during the study
periods. The Self Organising Map (SOM) was used to classify the COLs into groups
based on their precipitation patterns. The upper-air data was analysed to study the
characteristics of the COLs in each group. To examine the role of topography on COLs,
WRF was applied to simulate three COLs over real and three idealised terrains (i.e. "no
topography", "only-west-topography" and "only east topography").
The results show that, on average, the Western Cape experiences 10 COLs per year and
the COLs contribute about 11% of the annual precipitation over the province, although
with a large interannual variability. In 2015 and 2016, the COLs occurred more frequently
than normal, with more than normal precipitation contribution, thereby reducing the
drought severity in the two years. Contrarily, in 2017, the COL frequency and precipitation
contribution were less than normal, because COLs were mainly seen further south.
Nevertheless, we found that an increase in annual COL frequency does not always lead
to an increase in the annual COL precipitation, because the COLs produce different
amounts of precipitation. More than 45% of the COLs over the Western Cape produces
little or no precipitation. The SOM results reveal that the spatial distribution of COL
iv
precipitation can be grouped into four major patterns. The first pattern indicates
precipitation over the entire Western Cape while the second shows little or no
precipitation; the third and fourth patterns feature precipitation over south-east coast and
south-west coast, respectively. The major difference between the first pattern (i.e. wet
cols) and the second pattern (dry COLs) is that while the wet COL is associated with a
southward transport of warm and moist tropical air towards the Western Cape, the dry
COL is not. Hence, the contrast between the warm and cold air mass is weaker in dry
COLs than in its wet counterpart.
The models (WRF and MPAS) capture the seasonal and annual climatologies of COLs
and their precipitation. However, they do not always capture the inter-annual variability,
with WRF outperforming MPAS in general and during the drought period. Both models
represented all the COL precipitation patterns well but under-estimated the frequency of
dry COLs throughout the seasons. However, the models were able to simulate the
general observed differences between dry and wet COLs. WRF simulation shows that
topography influences the precipitation, track, formation and vertical structure of COLs.
Topography provides the additional forcing needed for COL formation. The results of this
study may be applied to improve monitoring and prediction of extreme rainfall events over
the Western Cape.
PDF
