Bias Correction of the ENSEMBLES High-Resolution Climate Change Projections for Use by Impact Models: Evaluation on the Present Climate. Dosio, A. and Paruolo, P.
Bias Correction of the ENSEMBLES High-Resolution Climate Change Projections for Use by Impact Models: Evaluation on the Present Climate [link]Paper  doi  abstract   bibtex   
A statistical bias correction technique is applied to a set of high-resolution climate change simulations for Europe from 11 state-of-the-art regional climate models (RCMs) from the project ENSEMBLES. Modeled and observed daily values of mean, minimum and maximum temperature and total precipitation are used to construct transfer functions for the period 1961-1990, which are then applied to the decade 1991-2000, where the results are evaluated. By using a large ensembles of model runs and a long construction period, we take into account both intermodel variability and longer (e.g., decadal) natural climate variability. Results show that the technique performs successfully for all variables over large part of the European continent, for all seasons. In particular, the probability distribution functions (PDFs) of both temperature and precipitation are greatly improved, especially in the tails, i.e., increasing the capability of reproducing extreme events. When the statistics of bias-corrected results are ensemble averaged, the result is very close to the observed ones. The bias correction technique is also able to improve statistics that depend strongly on the temporal sequence of the original field, such as the number of consecutive dry days and the total amount of precipitation in consecutive heavy precipitation episodes, which are quantities that may have a large influence on, e.g., hydrological or crop impact models. Bias-corrected projections of RCMs are hence found to be potentially useful for the assessment of impacts of climate change over Europe.
@article{dosioBiasCorrectionENSEMBLES2011,
  title = {Bias Correction of the {{ENSEMBLES}} High-Resolution Climate Change Projections for Use by Impact Models: Evaluation on the Present Climate},
  author = {Dosio, A. and Paruolo, P.},
  date = {2011-08},
  journaltitle = {Journal of Geophysical Research},
  volume = {116},
  issn = {0148-0227},
  doi = {10.1029/2011jd015934},
  url = {http://mfkp.org/INRMM/article/14151660},
  abstract = {A statistical bias correction technique is applied to a set of high-resolution climate change simulations for Europe from 11 state-of-the-art regional climate models (RCMs) from the project ENSEMBLES. Modeled and observed daily values of mean, minimum and maximum temperature and total precipitation are used to construct transfer functions for the period 1961-1990, which are then applied to the decade 1991-2000, where the results are evaluated. By using a large ensembles of model runs and a long construction period, we take into account both intermodel variability and longer (e.g., decadal) natural climate variability. Results show that the technique performs successfully for all variables over large part of the European continent, for all seasons. In particular, the probability distribution functions (PDFs) of both temperature and precipitation are greatly improved, especially in the tails, i.e., increasing the capability of reproducing extreme events. When the statistics of bias-corrected results are ensemble averaged, the result is very close to the observed ones. The bias correction technique is also able to improve statistics that depend strongly on the temporal sequence of the original field, such as the number of consecutive dry days and the total amount of precipitation in consecutive heavy precipitation episodes, which are quantities that may have a large influence on, e.g., hydrological or crop impact models. Bias-corrected projections of RCMs are hence found to be potentially useful for the assessment of impacts of climate change over Europe.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14151660,~to-add-doi-URL,bias-correction,climate-change,climate-projections,data-transformation-modelling,e-obs,europe,statistics},
  number = {D16}
}
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