Transferability in the future climate of a statistical downscaling method for precipitation in France
Abstract
A statistical downscaling approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with a focus on the transferability of the method to the future climate. First, the realism of downscaled present-day precipitation climatology and inter-annual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained but elaborated predictors do not lead to major and consistent across reanalyses improvements. The downscaling method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in downscaled trends due to the choice of the reanalysis are large andobserved trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect-model framework. The statistical downscaling relationship is built using present-day predictors and precipitation simulated by twelve regional climate models. The entire projections are then downscaled, and future downscaled and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are downscaled thanks to the relationship built with reanalyses and observations, formore »
- Authors:
-
- Centre National de la Recherche Scientifique (CNRS), Toulouse (France). Climate Modelling and Global Change Team
- Centre National de la Recherche Scientifique (CNRS), Toulouse (France)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1565259
- Grant/Contract Number:
- 505539; ANR 2011 JS56 014 01
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Additional Journal Information:
- Journal Volume: 120; Journal Issue: 3; Journal ID: ISSN 2169-897X
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences
Citation Formats
Dayon, G., Boé, J., and Martin, E. Transferability in the future climate of a statistical downscaling method for precipitation in France. United States: N. p., 2015.
Web. doi:10.1002/2014jd022236.
Dayon, G., Boé, J., & Martin, E. Transferability in the future climate of a statistical downscaling method for precipitation in France. United States. https://doi.org/10.1002/2014jd022236
Dayon, G., Boé, J., and Martin, E. Wed .
"Transferability in the future climate of a statistical downscaling method for precipitation in France". United States. https://doi.org/10.1002/2014jd022236. https://www.osti.gov/servlets/purl/1565259.
@article{osti_1565259,
title = {Transferability in the future climate of a statistical downscaling method for precipitation in France},
author = {Dayon, G. and Boé, J. and Martin, E.},
abstractNote = {A statistical downscaling approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with a focus on the transferability of the method to the future climate. First, the realism of downscaled present-day precipitation climatology and inter-annual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained but elaborated predictors do not lead to major and consistent across reanalyses improvements. The downscaling method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in downscaled trends due to the choice of the reanalysis are large andobserved trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect-model framework. The statistical downscaling relationship is built using present-day predictors and precipitation simulated by twelve regional climate models. The entire projections are then downscaled, and future downscaled and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are downscaled thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the downscaling method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.},
doi = {10.1002/2014jd022236},
journal = {Journal of Geophysical Research: Atmospheres},
number = 3,
volume = 120,
place = {United States},
year = {Wed Jan 07 00:00:00 EST 2015},
month = {Wed Jan 07 00:00:00 EST 2015}
}
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