Abstract
AbstractThis work tests the suitability of statistical downscaling (SD) approaches to generate local seasonal forecasts of daily maximum temperature and precipitation for a set of selected stations in Senegal for the July–August–September season during the period 1979–2000. Two‐month lead raw daily maximum temperature and precipitation from the five models included in the ENSEMBLES seasonal hindcast are compared against the corresponding downscaled predictions, which are obtained by applying the analog technique based on two different types of predictors: the direct surface variables and a combination of appropriate upper‐air variables. Beyond correcting the large biases of the low‐resolution raw model outputs, SD is found to add noteworthy value in terms of forecast association (as measured by interannual correlation), providing thus suitable (i.e. calibrated) predictions at the local‐scale needed for practical applications, which means a clear advantage for the end‐users of seasonal forecasts over the area of study. Moreover, a recommendation on the adequacy of surface (large‐scale) predictors for SD of maximum temperature (precipitation) is also given.
Highlights
Given their low spatial resolution, the global seasonal forecasts provided by the current climate models need to be satisfactorily translated to the local-scale required for most practical applications
Assessing the suitability of statistical downscaling (SD) approaches for seasonal forecasting over West Africa, where the capacity to invest in regional climate models is limited and the strong interannual climate variations are crucial for various socio-economic sectors (Ndiaye, 2010), is of large interest
The 2-month lead daily downscaled predictions obtained for JAS for the period 1979–2000 were yearly aggregated and validated against the corresponding observations in terms of interannual correlation and mean absolute error (MAE), which account for different aspects of forecast quality: association and accuracy, respectively
Summary
Given their low spatial resolution, the global seasonal forecasts provided by the current climate models need to be satisfactorily translated to the local-scale required for most practical applications (see, e.g. Hanssen-Bauer et al, 2005). One option for this is statistical downscaling (SD), which is based on empirical/statistical relationships linking the global model simulations (predictors) with the local observations of the target predictand variable (e.g. daily maximum temperature and precipitation in this work). Assessing the suitability of SD approaches for seasonal forecasting over West Africa, where the capacity to invest in regional climate models is limited and the strong interannual climate variations are crucial for various socio-economic sectors (Ndiaye, 2010), is of large interest
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