Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa

Hewitson, Bruce; Crane, Robert G.

doi:10.1002/joc.1314

Cited by 369 publications

(280 citation statements)

References 29 publications

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“…Nonstationarity may be attributed to an incomplete set of predictor variables that exclude low-frequency climate behaviour, inadequate sampling or calibration period, or temporal change in climate system structures (Wilby, 1998). The degree of non-stationarity in projected climate change has recently been assessed by Hewitson and Crane (2006) who found that this is relatively small and that circulation dynamics in particular may be more robust to non-stationarities.…”

Section: Statistical Downscalingmentioning

confidence: 99%

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Fowler

Blenkinsop

Tebaldi

2007

Intl Journal of Climatology

1,860

1,475

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Abstract:There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts.Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the 'downscaling for hydrological impacts' community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system.

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Section: Statistical Downscalingmentioning

confidence: 99%

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Fowler

Blenkinsop

Tebaldi

2007

Intl Journal of Climatology

1,860

1,475

View full text Add to dashboard Cite

show abstract

“…3 × 3 grid boxes of the 2° ERA-Int reanalysis. A similar domain size is used, for example, in the statistical downscaling procedure of Hewitson and Crane (2006). Despite less information (or variance) being contained in the small domain data compared to the large domain data, a SOM of identical size (i.e.…”

Section: Domain Selectionmentioning

confidence: 99%

“…Self-Organizing Maps (SOM, Kohonen 2001) is a pattern clustering method that is used as one of the methods to derive synoptic circulation types in this and other contexts, including statistical downscaling (e.g. Hewitson and Crane 2006;Yin et al 2011;Ohba et al 2016) and process-based validation of GCMs (e.g. Brown et al 2010;Finnis et al 2009;Higgins and Cassano 2010).…”

mentioning

confidence: 99%

Interannual rainfall variability and SOM-based circulation classification

et al. 2017

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synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location. Keywords SOM · Synoptic circulation · Rainfall variability · Southern AfricaThe relationship between synoptic circulation and local expressions of climate is the foundation of synoptic climatology and traditionally underpins the process of interpretative weather forecasting. The basic premise of synoptic climatology is that the large-scale atmospheric circulation exerts some control over weather and related environmental phenomena at the Earth's surface, thereby providing a level of deterministic forcing which influences the local weather. This directly leads to the use of atmospheric circulation patterns as a means to express drivers of weather (and climate) variability. Self-Organizing Maps (SOM, Kohonen 2001) is a pattern clustering method that is used as one of the methods to derive synoptic circulation types in this and other contexts, including statistical downscaling (e.g. Hewitson and Crane 2006;Yin et al. 2011;Ohba et al. 2016) and process-based validation of GCMs (e.g. Brown et al. 2010;Finnis et al. 2009;Higgins and Cassano 2010).In the application of SOM to study climate variability, the evolution of synoptic drivers of weather is typically represented by progression through classes of daily synoptic states identified as SOM nodes. That progression Abstract Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disa...

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“…The selection of potential external predictors was guided by the literature (Cavazos and Hewitson, 2005;Hewitson and Crane, 2006). Large-scale gridded climate data from the European Centre for Medium-range Forecasts re-analysis data , known as the ERA-40 re-analysis data (Uppala et al, 2005) were obtained from the British Atmospheric Data Center (http://badc.nerc.ac.uk).…”

Section: Case Study Descriptionmentioning

confidence: 99%

Stochastic simulation of rainfall in the semi‐arid Limpopo basin, Botswana

Kenabatho

McIntyre

Chandler

et al. 2011

Intl Journal of Climatology

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ABSTRACT:The application of spatial-temporal stochastic rainfall models to semi-arid or arid areas is expected to be particularly challenging because of the high variability of rainfall, sparse rain gauge networks with significant periods of missing rainfall and potential data quality issues. In this article, a generalized linear model (GLM) has been fitted to daily rainfall data from the period 1975-1999 for 13 gauges in a 7660 km 2 sub-basin of the Limpopo basin in Botswana, with the objective of exploring applicability of the GLM for infilling historic records and for climate change analysis. Several relevant statistics of rainfall space-time variability were used to analyse model performance, including use of an independent validation period and sites that were not used in the fitting. The GLM was considered to simulate rainfall adequately for the purpose of sub-basin-scale water resource studies, although the model uncertainty is high. The main factors affecting rainfall space-time variability were found to be seasonality, autocorrelation of daily rainfall, altitude, latitude and longitude. Addition of large-scale drivers of rainfall (pressure, humidity and temperature) further improved representation of inter-annual variability, and this link to large-scale climate potentially facilitates downscaling of global climate model outputs. Although the model was locally sensitive to data quality issues, there was no evidence that these issues affected sub-basin scale analysis.

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Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa

Cited by 369 publications

References 29 publications

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Interannual rainfall variability and SOM-based circulation classification

Stochastic simulation of rainfall in the semi‐arid Limpopo basin, Botswana

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