A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments

Thomas Noël; Harilaos Loukos; Dimitri Defrance; Mathieu Vrac; Guillaume Levavasseur

doi:10.1016/j.dib.2021.106900

A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments

Data Brief. 2021 Feb 21:35:106900. doi: 10.1016/j.dib.2021.106900. eCollection 2021 Apr.

Authors

Thomas Noël¹, Harilaos Loukos¹, Dimitri Defrance¹, Mathieu Vrac², Guillaume Levavasseur³

Affiliations

¹ The Climate Data Factory, Paris, France.
² Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France.
³ Institut Pierre Simon Laplace, SU/CNRS, Paris, France.

Abstract

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). 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 modeling community standards and value checking for outlier detection.

Keywords: Adaptation; CMIP5; Climate change; Downscaling; ERA5; High-resolution; Impact modeling; Projections.