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Statistical downscaling of climate impact indices: Testing the direct approach

Revista: Climatic Change
Año: 2014   Volumen: 127
Página inicial: 547   Última página: 560
Estado: Publicado
En este estado desde: 23 Oct 2014
Archivo PDF: 2014_Casanueva_ClimChange_rev.pdf
Enlace al PDF: 2014_Casanueva_ClimChange
DOI: 10.1007/s10584-014-1270-5

Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts to stakeholders.
Typically, CIIs comprise into a single index several weather variables —such as temperature, wind speed, precipitation and humidity— which are relevant for a particular problem of interest. Moreover, most of the CIIs require daily (or monthly) physical coherence among these variables for their proper calculation. This constraints the number of statistical downscaling techniques suitable for a component-wise approach to this problem. We test the suitability of the alternative “direct” downscaling approach in which the downscaling method is applied directly to the CII, thus circumventing the multi-variable problem and allowing the use of a wider range of downscaling methods. For illustrative purposes, we consider two popular CIIs —the FireWeather Index (FWI) and the Physiological Equivalent Temperature (PET), used in the wildfire and tourism sectors, respectively— and compare the performance of the two approaches using the analog method, a simple and popular
method providing inter-variable dependence. The results obtained with ‘perfect’ reanalysis predictors are comparable for both approaches, although smaller accuracy is obtained in general with the direct approach. Moreover, similar climate change ‘deltas’ are obtained with both approaches when applied to an illustrative future global projection using the ECHAM5 model. Overall, there is a trade-off between performance and simplicity which needs to be balanced for each particular application.