Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6747-2022
© Author(s) 2022. 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-15-6747-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
06 Sep 2022
Model evaluation paper |
| 06 Sep 2022
| 06 Sep 2022
Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Rodrigo Manzanas
Departamento de Matemática Aplicada y Ciencias de la Computación (MACC), Universidad de Cantabria, Santander, Spain
Grupo de Meteorología y Computación, Universidad de Cantabria, Unidad Asociada al CSIC, Santander, Spain
Ezequiel Cimadevilla
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Jesús Fernández
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Jose González-Abad
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Antonio S. Cofiño
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
José Manuel Gutiérrez
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
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Cited articles
Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Båserud, L., Izsák, B., Szentes, O., Tveito, O. E., and Frei, C.: Evaluation of daily precipitation analyses in E-OBS (v19. 0e) and ERA5 by comparison to regional high-resolution datasets in European regions, Int. J. Climatol., 42, 727–747, 2022. a
Baño-Medina, J.: Understanding Deep Learning Decisions in Statistical Downscaling Models, in: Proceedings of the 10th International Conference on Climate Informatics, 79–85, 2020. a
Baño-Medina, J., Manzanas, R., Cimadevilla, E., Fernández, J., González-Abad, J., Cofiño, A. S., and Gutiérrez, J. M.: 2022_Bano_DeepESD_GMD_data (1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6823422, 2022a. a
Baño-Medina, J., Manzanas, R., Cimadevilla, E., Fernández, J., González-Abad, J., Cofiño, A. S., and Gutiérrez, J. M.: Repository supporting the results presented in the manuscript on Downscaling Multi-Model Climate Projection Ensembles with Deep Learning (DeepESD): Contribution to CORDEX EUR-44 (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6828304, 2022b. a
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a
Brands, S., Herrera, S., Fernández, J., and Gutiérrez, J. M.: How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa?, Clim. Dynam., 41, 803–817, 2013. a
Cannon, A. J.: Probabilistic Multisite Precipitation Downscaling by an Expanded Bernoulli–Gamma Density Network, J. Hydrometeorol., 9, 1284–1300, 2008. a
Casanueva, A., Herrera, S., Fernández, J., and Gutiérrez, J. M.: Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative, Climatic Change, 137, 411–426, 2016. a
Christian, J., Arora, V., Boer, G., Curry, C., Zahariev, K., Denman, K., Flato, G., Lee, W., Merryfield, W., Roulet, N., and Scinocca, J.: The global carbon cycle in the Canadian Earth system model (CanESM1): Preindustrial control simulation, J. Geophys. Res.-Biogeo., 115, G03014, https://doi.org/10.1029/2008JG000920, 2010. a
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, 2018. a
Dai, A.: Precipitation characteristics in eighteen coupled climate models, J. Climate, 19, 4605–4630, 2006. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, D. P., and Bechtold, P.: The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Doblas Reyes, F., Acosta Navarro, J. C., Acosta Cobos, M. C., Bellprat, O., Bilbao, R., Castrillo Melguizo, M., Fuckar, N., Guemas, V., Lledó Ponsati, L., Menegoz, M., Prodhomme, C.,
Serradell Maronda, K.,
Tintó Prims, O.,
Batté, L.,
Volpi, D.,
Ceglar, A.,
Haarsma, R., and
Massonnet, F.: Using EC-Earth for climate prediction research, ECMWF Newsletter, 154, 35–40, https://doi.org/10.21957/fd9kz3, 2018. a
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5, Clim. Dynam., 40, 2123–2165, 2013. a
Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke, W., Dunne, K. A., Griffies, S. M., Hallberg, R. W., Harrison, M. J., Levy, H., Wittenberg, A. T., Phillips, P. J., and Zadeh, N.: GFDL's ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon System Formulation and Baseline Simulation Characteristics, J. Climate, 26, 2247–2267, 2013. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fernández, J., Frías, M., Cabos, W., Cofiño, A., Domínguez, M., Fita, L., Gaertner, M., García-Díez, M., Gutiérrez, J. M., Jiménez-Guerrero, P., Liguori, G., Montávez, J. P., Romera, R., and Sánchez, E.: Consistency of climate change projections from multiple global and regional model intercomparison projects, Clim. Dynam., 52, 1139–1156, 2019. a
François, B., Thao, S., and Vrac, M.: Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks, Clim. Dynam., 57, 3323–3353, 2021. a
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, 2019. a
Gutiérrez, C., Somot, S., Nabat, P., Mallet, M., Corre, L., van Meijgaard, E., Perpiñán, O., and Gaertner, M. Á.: Future evolution of surface solar radiation and photovoltaic potential in Europe: investigating the role of aerosols, Environ. Res. Lett., 15, 034035, https://doi.org/10.1088/1748-9326/ab6666, 2020. a, b
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., Martín, D. S., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J., Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado, M. J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T., Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D. E., Fischer, A. M., Cardoso, R. M., Soares, P. M. M., Czernecki, B., and Pagé, C.: An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment, Int. J. Climatol., 39, 3750–3785, 2019. a, b, c
Gutowski, W. J., Ullrich, P. A., Hall, A., Leung, L. R., O'Brien, T. A., Patricola, C. M., Arritt, R. W., Bukovsky, M. S., Calvin, K. V., Feng, Z., Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C., Qian, Y., Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., and Zarzycki, C.: The Ongoing Need for High-Resolution Regional Climate Models: Process Understanding and Stakeholder Information, B. Am. Meteorol. Soc., 101, E664–E683, 2020. a
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández, J., Frías, M. D., Manzanas, R., San-Martín, D., Cimadevilla, E., Cofiño, A. S., and Gutiérrez, J. M.: The R-based climate4R open framework for reproducible climate data access and post-processing, Environ. Modell. Softw., 111, 42–54, 2019. a
Klok, E. and Klein Tank, A.: Updated and extended European dataset of daily climate observations, Int. J. Climatol., 29, 1182–1191, 2009. a
Le Roux, R., Katurji, M., Zawar-Reza, P., Quénol, H., and Sturman, A.: Comparison of statistical and dynamical downscaling results from the WRF model, Environ. Modell. Softw., 100, 67–73, 2018. a
LeCun, Y. and Bengio, Y.: Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, MIT Press,
255–258, 1995. a
Maraun, D. and Widmann, M.: Statistical Downscaling and Bias Correction for Climate Research, Cambridge University Press, Online ISBN 9781107588783, https://doi.org/10.1017/9781107588783, 2018. a, b
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A.: VALUE: A framework to validate downscaling approaches for climate change studies, Earths Future, 3, 1–14, 2015. a
Müller, W., Jungclaus, J., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., and Marotzke, J.: A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM 1.2-HR), J. Adv. Model. Earth Sy., 10, 1383–1413, 2018. a, b
Olmo, M. E., Balmaceda-Huarte, R., and Bettolli, M. L.: Multi-model ensemble of statistically downscaled GCMs over southeastern South America: historical evaluation and future projections of daily precipitation with focus on extremes, Clim. Dynam., online first, https://doi.org/10.1007/s00382-022-06236-x, 2022. a
Pan, B., Hsu, K., AghaKouchak, A., and Sorooshian, S.: Improving Precipitation Estimation Using Convolutional Neural Network, Water Resour. Res., 55, 2301–2321, 2019. a
Schoof, J. T. and Pryor, S. C.: Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks, Int. J. Climatol., 21, 773–790, 2001. a
Sun, L. and Lan, Y.: Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods, Int. J. Climatol., 41, 1128–1147, 2021. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012. a
Vautard, R., Kadygrov, N., Iles, C., Boberg, F., Buonomo, E., Bülow, K., Coppola, E., Corre, L., van Meijgaard, E., Nogherotto, R., Sandstad, M., Schwingshackl, C., Somot, S., Aalbers, E., Christensen, O. B., Ciarlo, J. M., Demory, N.-E., Giorgi, F., Jacob, D., Jones, R. G., Keuler, K., Kjellström, E., Lenderink, G., Levavasseur, G., Nikulin, G., Sillmann, J., Solidoro, C., Lund Sørland, S., Steger, C., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Evaluation of the large EURO-CORDEX regional climate model ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032344, https://doi.org/10.1029/2019JD032344, 2021. a
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global climate model: description and basic evaluation, Clim. Dynam., 40, 2091–2121, 2013. a
Vrac, M. and Ayar, P.: Influence of Bias Correcting Predictors on Statistical Downscaling Models, J. Appl. Meteorol. Clim., 56, 5–26, https://doi.org/10.1175/JAMC-D-16-0079.1,
2016. a, b
Vrac, M., Stein, M., Hayhoe, K., and Liang, X.-Z.: A general method for validating statistical downscaling methods under future climate change, Geophys. Res. Lett., 34, L18701, https://doi.org/10.1029/2007GL030295, 2007. a, b, c
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L.-B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 1–9, https://doi.org/10.1038/sdata.2016.18, 2016.
a
Williams, P. M.: Modelling Seasonality and Trends in Daily Rainfall Data, in: Advances in Neural Information Processing Systems 10, edited by: Jordan, M. I., Kearns, M. J., and Solla, S. A., MIT Press, Proceedings of the Neural Information Processing Systems (NIPS), 985–991,
ISBN 0-262-10076-2,
1998. a
Short summary
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.
Deep neural networks are used to produce downscaled regional climate change projections over...
