Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-229-2024
https://doi.org/10.5194/gmd-17-229-2024
Model experiment description paper
 | 
12 Jan 2024
Model experiment description paper |  | 12 Jan 2024

High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia

Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova

Data sets

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8314980

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8338468

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340234

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 4 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340250

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340266

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 6 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340274

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340279

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340287

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340297

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 10 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340318

High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11 Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova https://doi.org/10.5281/zenodo.8340338

ERA5 hourly data on single levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.adbb2d47

Iberia01: Daily gridded (0.1{\degree} resolution) dataset of precipitation and temperatures over the Iberian Peninsula José M. Gutiérrez, Sixto Herrera, Rita M. Cardoso, Pedro Matos Soares, Fátima Espírito-Santo, and Pedro Viterbo https://doi.org/10.20350/digitalCSIC/8641

The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data (https://esgf-node.llnl.gov/projects/esgf-llnl/) L. Cinquini https://doi.org/10.1016/j.future.2013.07.002

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Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.