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Ensemble of T&P bias corrected CORDEX data for climate change impact modeling

Many open questions and unresolved issues surround the topic of bias correction (BC) in climate change impact studies (CCIS). While there is an increasing body of literature in hydrology, relatively few studies exist to quantify the impacts of post-processing bias correction methods in agricultural impact models, driven by regional climate model (RCM) data. We provide daily T&P bias corrected data (based on 4 different BC methods) from 9 different CORDEX GCM-RCM combinations, following the RCP4.5 emission scenario for the period 2006-2100. The data will be provided to encourage further CCIS across West Africa in agriculture, but also from other disciplines, such as hydrology and energy.

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Cite this as

Laux, Patrick (2020). Dataset: Ensemble of T&P bias corrected CORDEX data for climate change impact modeling. https://doi.org/10.1594/PANGAEA.922245

DOI retrieved: 2020

Additional Info

Field Value
Imported on January 12, 2023
Last update November 29, 2024
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.922245
Author Laux, Patrick
Given Name Patrick
Family Name Laux
Source Creation 2020
Publication Year 2020
Resource Type text/tab-separated-values - filename: Laux_2020
Subject Areas
Name: Agriculture

Name: Atmosphere

Name: Ecology

Related Identifiers
Title: To bias correct or not to bias correct? An agricultural impact modelers' perspective on regional climate model data
Identifier: https://doi.org/10.1016/j.agrformet.2021.108406
Type: DOI
Relation: References
Year: 2021
Source: Agricultural and Forest Meteorology
Authors: Laux Patrick , Rötter Reimund P , Webber Heidi , Dieng Diarra , Rahimi Jaber , Wei Jianhui , Faye Babacar , Srivastava Amit K , Bliefernicht Jan , Adeyeri Oluwafemi , Arnault Joel , Kunstmann Harald .