Simulating present and future climates of southern Africa using general circulation models

Joubert, A. M.; Hewitson, Bruce

doi:10.1177/030913339702100104

Cited by 73 publications

(54 citation statements)

References 67 publications

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“…Considerable uncertainty exists in relation to largescale precipitation changes simulated by GCMs for Africa (Hudson 1997, Hudson & Hewitson 1997, Joubert & Hewitson 1997, Feddema 1999. Joubert & Hewitson (1997) nevertheless conclude that, in general, precipitation is simulated to increase over much of the African continent by the year 2050.…”

Section: Review Of Previous African Climate Change Scenario Workmentioning

confidence: 99%

African climate change: 1900-2100

Hulme¹,

Doherty²,

Ngara³

et al. 2001

Clim. Res.

1,083

689

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This paper reviews observed and possible future (2000-2100) continentwide changes in temperature and rainfall for Africa. For the historic period we draw upon a new observed global climate data set which allows us to explore aspects of regional climate change related to diurnal temperature range and rainfall variability. The latter includes an investigation of regions where seasonal rainfall is sensitive to El Niño climate variability. This review of past climate change provides the context for our scenarios of future greenhouse gas-induced climate change in Africa. These scenarios draw upon the draft emissions scenarios prepared for the Intergovernmental Panel on Climate Change's Third Assessment Report, a suite of recent global climate model experiments, and a simple climate model to link these 2 sets of analyses. We present a range of 4 climate futures for Africa, focusing on changes in both continental and regional seasonal-mean temperature and rainfall. Estimates of associated changes in global CO 2 concentration and global-mean sea-level change are also supplied. These scenarios draw upon some of the most recent climate modelling work. We also identify some fundamental limitations to knowledge with regard to future African climate. These include the often poor representation of El Niño climate variability in global climate models, and the absence in these models of any representation of regional changes in land cover and dust and biomass aerosol loadings. These omitted processes may well have important consequences for future African climates, especially at regional scales. We conclude by discussing the value of the sort of climate change scenarios presented here and how best they should be used in national and regional vulnerability and adaptation assessments.

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Section: Review Of Previous African Climate Change Scenario Workmentioning

confidence: 99%

African climate change: 1900-2100

Hulme¹,

Doherty²,

Ngara³

et al. 2001

Clim. Res.

1,083

689

View full text Add to dashboard Cite

show abstract

“…Rainfall forecasts, however, are still heavily dependent on sub-grid scale parameterisations of the rainfall forming processes; these cannot take complete account of understanding of the behaviour of rainfall cells in different meteorological conditions (see e.g. Hewitson and Crane, 1996;Joubert and Hewitson, 1997). Convective cells, in particular, cannot be resolved at the current mesoscale grid sizes of 1040 km used in 5 to 10 day ahead predictions.…”

Section: Introductionmentioning

confidence: 99%

Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

Pappenberger

Beven

Hunter

et al. 2005

Hydrol. Earth Syst. Sci.

298

213

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The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfallrunoff model (LisFlood) for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE) analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP) which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

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“…The main idea behind statistical downscaling is to use statistical relationships to link resolved behaviours in GCMs with the climate in a study area. This approach encompasses a range of statistical techniques as simple linear regression, delta change method, multiple regression, weather generators, canonical correlation analysis and artificial neural networks (Salathe et al 2007;Kattenberg et al 1996;Giorgi et al 2001;Hewitson and Crane 2006;Joubert and Hewitson 1997;von Storch et al 1993;Crane and Hewitson 1998).…”

Section: Modelling Procedures and Downscalingmentioning

confidence: 99%

Multi-GCM ensembles performance for climate projection on a GIS platform

Gharbia

Gill

Johnston

et al. 2016

Model. Earth Syst. Environ.

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Climate impact studies especially in the field of hydrology often depend on climate change projections at fine spatial resolution. General circulation models (GCMs), which are the tools for estimating future climate scenarios, run on a very coarse scale, so the output from GCMs need to be downscaled to obtain a finer spatial resolution. This paper aims to present GIS platform as a downscaling environment through a suggested algorithm, which applies statistical downscaling models to multidimensional GCMEnsembles simulations. Climate change projections for the Shannon River catchment in Ireland were developed for several climate variables from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP): all these processes are implemented in a GIS platform through designed and developed GIS-based algorithm. This algorithm is used as a downscaling tool in GIS environment, which is unprecedented in literature. Statistical downscaling methods were used in the projection process after a particular verification and performance evaluation using several techniques such as Taylor diagram for each GCM-ensembles within independent sub-periods. The established statistical relationships were used to predict the response of the future climate from simulated climate model changes of the coarse scale variables. Significant changes in temperature, precipitation, wind speed, solar radiation and relative humidity were projected at a very fine spatial scale. It was concluded that the main source of uncertainty was related to the GCMs simulation and selection. In addition, it was obvious to conclude that GIS platform is an efficient tool for spatial downscaling using raster data forms.

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Simulating present and future climates of southern Africa using general circulation models

Cited by 73 publications

References 67 publications

African climate change: 1900-2100

African climate change: 1900-2100

Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

Multi-GCM ensembles performance for climate projection on a GIS platform

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