Articles | Volume 18, issue 8
https://doi.org/10.5194/gmd-18-2461-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2461-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
30 Apr 2025
Methods for assessment of models |
| 30 Apr 2025
| 30 Apr 2025
The Earth System Grid Federation (ESGF) Virtual Aggregation (CMIP6 v20240125)
Ezequiel Cimadevilla
CORRESPONDING AUTHOR
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Bryan N. Lawrence
National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK
Antonio S. Cofiño
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
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Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
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Short summary
The Earth System Grid Federation (ESGF) stores an enormous amount of climate data spread across millions of files in data centres all over the world. Accessing and working with this scientific information is quite complex. This work presents ESGF Virtual Aggregation, an approach that combines data from different sources into a format that is ready for analysis straightaway.
The Earth System Grid Federation (ESGF) stores an enormous amount of climate data spread across...
