Dataset of global extreme climatic indices due to an acceleration of ice sheet melting during the 21st century

• Published in: Data in Brief
• Main Author: Defrance, Dimitri
• Other Authors: Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2019
• Subjects: ice sheet contribution; climate change; extreme events; temperature; precipitation; global climate model; [SDE.MCG]Environmental Sciences/Global Changes; Greenland; Antarc*; Antarctica; Ice Sheet
• Online Access: https://hal.inrae.fr/hal-02620560; https://hal.inrae.fr/hal-02620560/document; https://hal.inrae.fr/hal-02620560/file/1-s2.0-S2352340919309400-main%20%282%29_1.pdf; https://doi.org/10.1016/j.dib.2019.104585

International audience This article describes extreme indices maps (Data Cube, raster X Time) for different scenarios with a more important contribution to the sea level rise from Greenland and/or Antarctica during the 21st century under the Representative Concentration Pathway (RCP) 8.5 emission scenario. The indices are produced annually and globally with a resolution of 0.5_ _ 0.5_ from 1951 to 2099. The data were generated by simulating daily maximum and minimum temperature and precipitation from the IPSL-CM5A-LR model from Coupled Model Intercomparison Project Phase 5 (CMIP5). These climatic data are unbiased and downscaled to the 0.5_x0.5 scale with the Cumulative Distribution Function transform (CDFt) and EWEMBI dataset compiled to support the bias correction of climate input data for ISIMIP. Finally, each extreme indice is computed on the unbiased data on each grid cell on all continents.