Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
Model evaluation paper
 | 
13 Jan 2022
Model evaluation paper |  | 13 Jan 2022

Convolutional conditional neural processes for local climate downscaling

Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner

Data sets

The ERA-Interim reanalysis: Configuration and performance of the data assimilation system (https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/) D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N. Thépaut, and F. Vitart https://doi.org/10.1002/qj.828

Global multi-resolution terrain elevation data 2010 (GMTED2010) J. J. Danielson and D. B. Gesch https://developers.google.com/earth-engine/datasets/catalog/USGS_GMTED2010

Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning (https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_mTPI) D. M. Theobald, D. Harrison-Atlas, W. B. Monahan, and C. M. Albano https://doi.org/10.1371/journal.pone.0143619

Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment (https://www.ecad.eu/dailydata/index.php) A. M. G. Klein Tank, J. B. Wijngaard, G. P. Können, R. Böhm, G. Demarée, A. Gocheva, M. Mileta, S. Pashiardis, L. Hejkrlik, C. Kern‐Hansen, and R. Heino https://doi.org/10.1002/joc.773

Model code and software

annavaughan/convCNPClimate: First release (v1.0.0) A. Vaughan https://doi.org/10.5281/zenodo.4554603

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Short summary
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
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