Statistical downscaling in the arid central Andes: uncertainty analysis of multi-model simulated temperature and precipitation |
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| Authors: | Maxime Souvignet J??rgen Heinrich |
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| Institution: | 1. Institute of Geography, University of Leipzig, Johannisallee 19a, 04103, Leipzig, Germany
2. Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), Cologne University of Applied Sciences, Betzdorfer Stra?e 2, 50679, Cologne, Germany
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| Abstract: | Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann?CWhitney and Brown?CForsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area. |
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