Daily values of mean, minimum, maximum temperature and total precipitation from the ETHZ CLM-HadCM3 transient climate change simulation for the period 1961-2099 have been corrected for biases according to Dosio and Paruolo, 2011: Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate, J. Geophys. Res., 116, D16106, DOI: 10.1029/2011JD015934. These data have been produced from a transient climate change simulation for the period 1951-2099 driven by the coupled global model HadCM3Q3 of the U.K. Met Office Hadley Centre according to the SRES A1B marker scenario. 121 different Lineage: Simulation data from a regional numerical climate model with lateral and sea-surface conditions determined from the output of the HadCM3Q3 coupled global model. The simulation was produced at the Swiss Federal Institute of Technology Zurich (http://www.ethz.ch) with the regional climate model CLM. (UUID: 7284e708-5e2c-11e1-9105-0017085a97ab). Daily values of mean, minimum, maximum temperature and total precipitation from this simulation have been corrected for biases according to Dosio and Paruolo, 2011: Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate, J. Geophys. Res., 116, D16106, DOI: 10.1029/2011JD015934
- Alessandro Dosio
How to cite
Dosio, Alessandro (2015): Bias corrected high resolution temperature and precipitation projection for Europe in daily temporal resolution from the ETHZ CLM regional climate model driven by boundary conditions from the HadCM3 global circulation model according to SRES A1B scenario, 1961-2099 (ENSEMBLES).. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-climate-ethz-clm_scn_hadcm3q0_eobs_1961-1990_1961-2100
atmosphere climatic alteration atmospheric precipitation climatic change air temperature meteorology climatic experiment climatology greenhouse gas man-made climate change meteorological geographical features meteorological parameter
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- AMER GEOPHYSICAL UNION, WASHINGTON, UNITED STATES OF AMERICA
A statistical bias correction technique is applied to a set of high resolution climate change simulations for Europe from 11 state-of-theart regional climate models (RCMs) from the project ENSEMBLES. Modelled 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 inter-model 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.
- AMER GEOPHYSICAL UNION, WASHINGTON, USA
A statistical bias correction technique is applied to twelve high-resolution climate change simulations of temperature and precipitation over Europe, under the SRES A1B scenario, produced for the EU project ENSEMBLES. The bias correction technique is based on a transfer function, estimated on current climate, which affects the whole Probability Distribution Function (PDF) of variables, and which is assumed constant between the current and future climate. The impact of bias correction on 21st Century projections, their inter-model variability, and the climate change signal is investigated, with focus being on discrepancies between the original and the bias-corrected results. As assessing the impact of climate change is significantly dependent on the frequency of extreme events, we also analyze the evolution of the shape of the PDFs, and extreme events indices. Results show that the ensemble mean climate change signal and its inter-model variability are generally conserved. However, the impact of the bias correction varies amongst regions, seasons and models, and differences up to 0.5 C for the summer temperature climate change signal are found in Southern Europe. Finally the bias correction is found to influence the probability of extreme events like extremely hot or frost days, which also impacts the climate change signal.
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