A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Thomas Noël , Harilaos Loukos , Dimitri Defrance 

Abstract

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

DOI

https://doi.org/10.31223/X5S610

Subjects

Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology

Keywords

climate change, CMIP6, downscaling, projections, downscaling, climate change, adaptation, impact modelling., High-resolution, adaptation, impact modelling, ERA5-Land, impact modelling, projections, CMIP6, ERA5-Land

Dates

Published: 2021-08-30 21:13

Last Updated: 2021-09-16 23:25

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License

CC BY Attribution 4.0 International

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