Projections of Climate Change Indices of Temperature and Precipitation from an Ensemble of Bias-Adjusted High-Resolution EURO-CORDEX Regional Climate Models. Dosio, A. 121(10):2015JD024411-5511. ![Projections of Climate Change Indices of Temperature and Precipitation from an Ensemble of Bias-Adjusted High-Resolution EURO-CORDEX Regional Climate Models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Statistical bias-adjustment of climate models' outputs is being increasingly used for assessing the impact of climate change on several sectors. It is known that these techniques may alter the mean climate signal of the adjusted variable; however, the effect on the projected occurrence of climate extremes is less commonly investigated. Here the outputs of an ensemble of high-resolution (0.11°) regional climate models (RCM) from the Coordinated Regional-climate Downscaling Experiment for Europe (EURO-CORDEX) have been bias adjusted, and a number of climate indices from the Expert Team on Climate Change Detection and Indices have been calculated for the present (1981-2010) and future (2071-2100) climate. Indices include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. Results show that absolute-threshold indices are largely affected by bias adjustment, as they depend strongly on both the present mean climate value (usually largely biased in the original RCMs) and its shift under climate change. The change of percentile-based indices is less affected by bias adjustment, as that of indices based on the duration of an event (e.g., consecutive dry days or heat waves) although the present climate value can differ between original and bias-adjusted results. Indices like R95ptot (the total amount of precipitation larger than the 95th reference percentile) are largely affected by bias adjustment, although, when analyzing an ensemble of RCMs, the differences are usually smaller than, or comparable to, the intermodel variability.
@article{dosioProjectionsClimateChange2016,
title = {Projections of Climate Change Indices of Temperature and Precipitation from an Ensemble of Bias-Adjusted High-Resolution {{EURO}}-{{CORDEX}} Regional Climate Models},
author = {Dosio, Alessandro},
date = {2016-05},
journaltitle = {J. Geophys. Res. Atmos.},
volume = {121},
pages = {2015JD024411-5511},
issn = {2169-897X},
doi = {10.1002/2015jd024411},
url = {http://mfkp.org/INRMM/article/14053888},
abstract = {Statistical bias-adjustment of climate models' outputs is being increasingly used for assessing the impact of climate change on several sectors. It is known that these techniques may alter the mean climate signal of the adjusted variable; however, the effect on the projected occurrence of climate extremes is less commonly investigated. Here the outputs of an ensemble of high-resolution (0.11°) regional climate models (RCM) from the Coordinated Regional-climate Downscaling Experiment for Europe (EURO-CORDEX) have been bias adjusted, and a number of climate indices from the Expert Team on Climate Change Detection and Indices have been calculated for the present (1981-2010) and future (2071-2100) climate. Indices include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. Results show that absolute-threshold indices are largely affected by bias adjustment, as they depend strongly on both the present mean climate value (usually largely biased in the original RCMs) and its shift under climate change. The change of percentile-based indices is less affected by bias adjustment, as that of indices based on the duration of an event (e.g., consecutive dry days or heat waves) although the present climate value can differ between original and bias-adjusted results. Indices like R95ptot (the total amount of precipitation larger than the 95th reference percentile) are largely affected by bias adjustment, although, when analyzing an ensemble of RCMs, the differences are usually smaller than, or comparable to, the intermodel variability.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14053888,~to-add-doi-URL,bias-correction,climate-change,ipcc,ipcc-scenarios,precipitation,temperature},
number = {10}
}
Downloads: 0
{"_id":"3k4KcWAfJhh54f4p2","bibbaseid":"dosio-projectionsofclimatechangeindicesoftemperatureandprecipitationfromanensembleofbiasadjustedhighresolutioneurocordexregionalclimatemodels","authorIDs":[],"author_short":["Dosio, A."],"bibdata":{"bibtype":"article","type":"article","title":"Projections of Climate Change Indices of Temperature and Precipitation from an Ensemble of Bias-Adjusted High-Resolution EURO-CORDEX Regional Climate Models","author":[{"propositions":[],"lastnames":["Dosio"],"firstnames":["Alessandro"],"suffixes":[]}],"date":"2016-05","journaltitle":"J. Geophys. Res. Atmos.","volume":"121","pages":"2015JD024411-5511","issn":"2169-897X","doi":"10.1002/2015jd024411","url":"http://mfkp.org/INRMM/article/14053888","abstract":"Statistical bias-adjustment of climate models' outputs is being increasingly used for assessing the impact of climate change on several sectors. It is known that these techniques may alter the mean climate signal of the adjusted variable; however, the effect on the projected occurrence of climate extremes is less commonly investigated. Here the outputs of an ensemble of high-resolution (0.11°) regional climate models (RCM) from the Coordinated Regional-climate Downscaling Experiment for Europe (EURO-CORDEX) have been bias adjusted, and a number of climate indices from the Expert Team on Climate Change Detection and Indices have been calculated for the present (1981-2010) and future (2071-2100) climate. Indices include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. Results show that absolute-threshold indices are largely affected by bias adjustment, as they depend strongly on both the present mean climate value (usually largely biased in the original RCMs) and its shift under climate change. The change of percentile-based indices is less affected by bias adjustment, as that of indices based on the duration of an event (e.g., consecutive dry days or heat waves) although the present climate value can differ between original and bias-adjusted results. Indices like R95ptot (the total amount of precipitation larger than the 95th reference percentile) are largely affected by bias adjustment, although, when analyzing an ensemble of RCMs, the differences are usually smaller than, or comparable to, the intermodel variability.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14053888,~to-add-doi-URL,bias-correction,climate-change,ipcc,ipcc-scenarios,precipitation,temperature","number":"10","bibtex":"@article{dosioProjectionsClimateChange2016,\n title = {Projections of Climate Change Indices of Temperature and Precipitation from an Ensemble of Bias-Adjusted High-Resolution {{EURO}}-{{CORDEX}} Regional Climate Models},\n author = {Dosio, Alessandro},\n date = {2016-05},\n journaltitle = {J. Geophys. Res. Atmos.},\n volume = {121},\n pages = {2015JD024411-5511},\n issn = {2169-897X},\n doi = {10.1002/2015jd024411},\n url = {http://mfkp.org/INRMM/article/14053888},\n abstract = {Statistical bias-adjustment of climate models' outputs is being increasingly used for assessing the impact of climate change on several sectors. It is known that these techniques may alter the mean climate signal of the adjusted variable; however, the effect on the projected occurrence of climate extremes is less commonly investigated. Here the outputs of an ensemble of high-resolution (0.11°) regional climate models (RCM) from the Coordinated Regional-climate Downscaling Experiment for Europe (EURO-CORDEX) have been bias adjusted, and a number of climate indices from the Expert Team on Climate Change Detection and Indices have been calculated for the present (1981-2010) and future (2071-2100) climate. Indices include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. Results show that absolute-threshold indices are largely affected by bias adjustment, as they depend strongly on both the present mean climate value (usually largely biased in the original RCMs) and its shift under climate change. The change of percentile-based indices is less affected by bias adjustment, as that of indices based on the duration of an event (e.g., consecutive dry days or heat waves) although the present climate value can differ between original and bias-adjusted results. Indices like R95ptot (the total amount of precipitation larger than the 95th reference percentile) are largely affected by bias adjustment, although, when analyzing an ensemble of RCMs, the differences are usually smaller than, or comparable to, the intermodel variability.},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14053888,~to-add-doi-URL,bias-correction,climate-change,ipcc,ipcc-scenarios,precipitation,temperature},\n number = {10}\n}\n\n","author_short":["Dosio, A."],"key":"dosioProjectionsClimateChange2016","id":"dosioProjectionsClimateChange2016","bibbaseid":"dosio-projectionsofclimatechangeindicesoftemperatureandprecipitationfromanensembleofbiasadjustedhighresolutioneurocordexregionalclimatemodels","role":"author","urls":{"Paper":"http://mfkp.org/INRMM/article/14053888"},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14053888","~to-add-doi-URL","bias-correction","climate-change","ipcc","ipcc-scenarios","precipitation","temperature"],"downloads":0},"bibtype":"article","biburl":"https://tmpfiles.org/dl/58794/INRMM.bib","creationDate":"2020-07-02T22:41:05.648Z","downloads":0,"keywords":["*imported-from-citeulike-inrmm","~inrmm-mid:c-14053888","~to-add-doi-url","bias-correction","climate-change","ipcc","ipcc-scenarios","precipitation","temperature"],"search_terms":["projections","climate","change","indices","temperature","precipitation","ensemble","bias","adjusted","high","resolution","euro","cordex","regional","climate","models","dosio"],"title":"Projections of Climate Change Indices of Temperature and Precipitation from an Ensemble of Bias-Adjusted High-Resolution EURO-CORDEX Regional Climate Models","year":null,"dataSources":["DXuKbcZTirdigFKPF"]}