2006
DOI: 10.1007/s00376-006-0006-5
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Using statistical downscaling to quantify the GCM-related uncertainty in regional climate change scenarios: A case study of Swedish precipitation

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Cited by 47 publications
(36 citation statements)
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“…As with RCMs, SD depends on the validity of the GCM output, and any GCM uncertainties are compounded by the SD process (Wilby et al, 2002). Input data uncertainty from the historical data used in downscaling is another potential source of error (Chen et al, 2006). SD methods may not accurately reflect regional feedback mechanisms (Christensen et al, 2007).…”
Section: Extreme Value Methodsmentioning
confidence: 99%
“…As with RCMs, SD depends on the validity of the GCM output, and any GCM uncertainties are compounded by the SD process (Wilby et al, 2002). Input data uncertainty from the historical data used in downscaling is another potential source of error (Chen et al, 2006). SD methods may not accurately reflect regional feedback mechanisms (Christensen et al, 2007).…”
Section: Extreme Value Methodsmentioning
confidence: 99%
“…The uncertainty introduced by choice of driving GCM was assessed by Chen et al (2006) who used 17 GCMs from CMIP2 and a statistical downscaling method to produce regional precipitation scenarios over Sweden. The uncertainty in estimated precipitation change from different GCMs was larger than that for different regions.…”
Section: Use Of Multiple Model Outputs and Emissions Scenariosmentioning
confidence: 99%
“…Gardner employed exponential equilibrium to assess annual runoff in catchments with a wide range of climatic conditions [13]. This method also has been applied by a wide range of researchers like Graham, Chen, Benestad, and Carter [14][15][16][17]. It's very clear that many factors like land use, agriculture and irrigation patterns which have effects on basin's hydrologic conductivity, are variable in long time.…”
Section: Probability Space and Gcm-scenario Combinationsmentioning
confidence: 99%