Articles | Volume 15, issue 6
https://doi.org/10.5194/gmd-15-2635-2022
© 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-15-2635-2022
© 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
| 
01 Apr 2022
Model evaluation paper |
| 01 Apr 2022
| 01 Apr 2022
Added value of EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited – Part 1: Precipitation
João António Martins Careto
CORRESPONDING AUTHOR
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Ed. C8, 1749-016 Lisbon, Portugal
Pedro Miguel Matos Soares
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Ed. C8, 1749-016 Lisbon, Portugal
Rita Margarida Cardoso
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Ed. C8, 1749-016 Lisbon, Portugal
Sixto Herrera
Meteorology Group, Dept. of Applied Mathematics and Computer Science,
Universidad de Cantabria, Santander, Spain
José Manuel Gutiérrez
Meteorology Group, Instituto de Física de Cantabria,
CSIC-University of Cantabria, Santander, Spain
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Cited articles
Azorin-Molina, C., Tijm, S., Ebert, E. E., Vicente-Serrano, S. M., and Estrela,
M. J.: Sea breeze thunderstorms in the eastern Iberian Peninsula.
Neighborhood verification of HIRLAM and HARMONIE precipitation forecasts,
Atmos. Res., 139, 101–115,
https://doi.org/10.1016/j.atmosres.2014.01.010, 2014.
Ban, N., Schmidli, J., and Schär, C.: Evaluation of the convection-resolving
regional climate modeling approach in decade-long simulations, J.
Geophys. Res.-Atmos., 119, 7889–7907, https://doi.org/10.1002/2014JD021478, 2014.
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi,
M., Ahrens, B., Alias, A., Anders, I., Bastin, S., and Belušić, D.:
The first multi-model ensemble of regional climate simulations at
kilometer-scale resolution, Part I: Evaluation of precipitation, Clim.
Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w,
2021.
Berthou, S., Kendon, E. J., Chan, S. C., Ban, N., Leutwyler, D., Schär,
C., and Fosser, G.: Pan-European climate at convection-permitting scale: a
model intercomparison study, Clim. Dynam., 55, 35–59, https://doi.org/10.1007/s00382-018-4114-6, 2020.
Boberg, F., Berg, P., Thejll, P., Gutowski, W. J., and Christensen, J. H.:
Improved confidence in climate change projections of precipitation evaluated
using daily statistics from the PRUDENCE ensemble, Clim. Dynam., 32,
1097–1106, https://doi.org/10.1007/s00382-008-0446-y, 2009.
Boberg, F., Berg, P., Thejll, P., Gutowski, W. J., and Christensen, J. H.:
Improved confidence in climate change projections of precipitation further
evaluated using daily statistics from ENSEMBLES models, Clim. Dynam.,
35, 1509–1520, https://doi.org/10.1007/s00382-009-0683-8, 2010.
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, https://doi.org/10.1007/s00382-013-1742-8, 2013.
Cardoso, R. M., and Soares, P. M. M.: Is there added value in the EURO-CORDEX hindcast temperature simulations? Assessing the added value using climate distributions in Europe, Int. J. Climatol., 1–16, https://doi.org/10.1002/joc.7472, 2022.
Cardoso, R. M., Soares, P. M. M., Miranda, P. M. A., and Belo-Pereira, M.: WRF
high resolution simulation of Iberian mean and extreme precipitation
climate, Int. J. Climatol., 33, 2591–2608, https://doi.org/10.1002/joc.3616, 2013.
Careto, J. A. M., Soares, P. M. M., Cardoso, R. M., Herrera, S., and Gutiérrez, J. M.: Added value of EURO-CORDEX high-resolution downscaling over the Iberian
Peninsula revisited – Part 2: Max and min temperature, Geosci. Model Dev., 15, 2653–2671, https://doi.org/10.5194/gmd-15-2653-2022, 2022.
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,
https://doi.org/10.1007/s10584-016-1683-4, 2016a.
Casanueva, A., Kotlarski, S., Herrera, S., Fernández, J., Gutiérrez,
J. M., Boberg, F., Colette, A., Christensen, O. B., Goergen, K., Jacob, D., and
Keuler, K.: Daily precipitation statistics in a EURO-CORDEX RCM ensemble:
added value of raw and bias-corrected high-resolution simulations, Clim. Dynam., 47, 719–737, https://doi.org/10.1007/s00382-015-2865-x, 2016b.
Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model
projections of changes in European climate by the end of this century,
Climatic Change, 81, 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007.
Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I.,
Jones, R., Kolli, R. K., Kwon, W.-T., Laprise, R., Magaña Rueda, V.,
Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A., and
Whetton, P.: Regional climate projections, in: Climate Change 2007: The
Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B.,
Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, UK and New
York, NY, TRN: GB07CC205, 2007.
Christensen, O. B., Drews, M., Christensen, J. H., Dethloff, K., Ketelsen, K.,
Hebestadt, I., and Rinke, A.: The HIRHAM regional climate model, Version 5
(beta), https://www.dmi.dk/fileadmin/Rapporter/TR/tr06-17.pdf (last access: 29
April 2021), 2007.
Ciarlo, J. M., Coppola, E., Fantini, A., Giorgi, F., Gao, X., Tong, Y.,
Glazer, R. H., Alavez, J. A. T., Sines, T., Pichelli, E., and Raffaele, F.: A
new spatially distributed added value index for regional climate models: the
EURO-CORDEX and the CORDEX-CORE highest resolution ensembles, Clim. Dynam., 57, 1403–1424, https://doi.org/10.1007/s00382-020-05400-5,
2020.
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders,
I., Ban, N., Bastin, S., Belda, M., Belusic, D., and Caldas-Alvarez, A.: A
first-of-its-kind multi-model convection permitting ensemble for
investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34, https://doi.org/10.1007/s00382-018-4521-8, 2020.
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.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for added value in
precipitation simulated by high-resolution nested regional climate models
and observations, Clim. Dynam., 38, 1229–1247, https://doi.org/10.1007/s00382-011-1068-3, 2012.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for small scale
added value of RCM's downscaled climate change signal, Clim. Dynam., 40,
601–618, https://doi.org/10.1007/s00382-012-1415-z, 2013.
Fosser, G., Khodayar, S., and Berg, P.: Climate change in the next 30 years:
What can a convection-permitting model tell us that we did not already
know?, Clim. Dynam., 48, 1987–2003,
https://doi.org/10.1007/s00382-016-3186-4, 2017.
Froidevaux, P., Schlemmer, L., Schmidli, J., Langhans, W., and Schär, C.:
Influence of the background wind on the local soil moisture–precipitation
feedback, J. Atmos. Sci., 71, 782–799, https://doi.org/10.1175/JAS-D-13-0180.1, 2014.
Fumière, Q., Déqué, M., Nuissier, O., Somot, S., Alias, A.,
Caillaud, C., Laurantin, O., and Seity, Y.: Extreme rainfall in Mediterranean
France during the fall: added value of the CNRM-AROME Convection-Permitting
Regional Climate Model, Clim. Dynam., 55, 77–91, https://doi.org/10.1007/s00382-019-04898-8, 2020.
Giorgi, F. and Bates, G. T.: The climatological skill of a regional model
over complex terrain, Mon. Weather Rev., 117, 2325–2347, https://doi.org/10.1175/1520-0493(1989)117<2325:TCSOAR>2.0.CO;2, 1989.
Giorgi, F. and Mearns, L. O.: Approaches to the simulation of regional
climate change: a review, Rev. Geophys., 29, 191–216, https://doi.org/10.1029/90RG02636, 1991.
Giorgi, F. and Mearns, L. O.: Introduction to special section: Regional
climate modeling revisited, J. Geophys. Res.-Atmos.,
104, 6335–6352, https://doi.org/10.1029/98JD02072, 1999.
Giorgi, F., Jones, C. and Asrar, G. R.: Addressing climate information needs
at the regional level: the CORDEX framework, World Meteorological
Organization (WMO) Bulletin, 58, 175–183, 2009.
Gutowski Jr., W. J., Takle, E. S., Kozak, K. A., Patton, J. C., Arritt, R. W., and
Christensen, J. H.: A possible constraint on regional precipitation intensity
changes under global warming, J. Hydrometeorol., 8, 1382–1396,
https://doi.org/10.1175/2007JHM817.1, 2007.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
Heikkilä, U., Sandvik, A., and Sorteberg, A.: Dynamical downscaling of
ERA-40 in complex terrain using the WRF regional climate model, Clim. Dynam., 37, 1551–1564, https://doi.org/10.1007/s00382-010-0928-6, 2011.
Herrera, S., Cardoso, R. M., Soares, P. M. M., Espírio-Santo, F., Viterbo, P., and Gutiérrez, J. M.: “Iberia01: Daily gridded (0.1º resolution) dataset of precipitation and temperatures over the Iberian Peninsula” DIGITAL.CSIC [data set], https://doi.org/10.20350/digitalCSIC/8641, 2019a.
Herrera, S., Cardoso, R. M., Soares, P. M., Espírito-Santo, F., Viterbo, P., and Gutiérrez, J. M.: Iberia01: a new gridded dataset of daily precipitation and temperatures over Iberia, Earth Syst. Sci. Data, 11, 1947–1956, https://doi.org/10.5194/essd-11-1947-2019, 2019b.
Herrera, S., Soares, P. M., Cardoso, R. M., and Gutiérrez, J. M.: Evaluation
of the EURO-CORDEX Regional Climate Models Over the Iberian Peninsula:
Observational Uncertainty Analysis, J. Geophys. Res.-Atmos., 125, e2020JD032880, https://doi.org/10.1029/2020JD032880, 2020.
Hohenegger, C., Brockhaus, P., Bretherton, C. S., and Schär, C.: The soil
moisture–precipitation feedback in simulations with explicit and
parameterized convection, J. Climate, 22, 5003–5020, https://doi.org/10.1175/2009JCLI2604.1, 2009.
Imamovic, A., Schlemmer, L., and Schär, C.: Collective impacts of
orography and soil moisture on the soil moisture-precipitation feedback,
Geophys. Res. Lett., 44, 11682–11691, https://doi.org/10.1002/2017GL075657, 2017.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., and
Georgopoulou, E.: EURO-CORDEX: new high-resolution climate change
projections for European impact research, Reg. Environ. Change, 14,
563–578, https://doi.org/10.1007252Fs10113-013-0499-2,
2014.
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda,
M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A.,
Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L.,
Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A.,
García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K.,
Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C.,
Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S.,
Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E.,
Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P.,
Montávez, J. P., Navarra, A., Nikulin, G., Noblet-Ducoudré, N.,
Panitz, H. J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J. P.,
Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R.,
Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L.,
Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K.,
and Wulfmeyer, V., Regional climate downscaling over Europe: perspectives
from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020.
Kendon, E. J., Roberts, N. M., Senior, C. A., and Roberts, M. J.: Realism of
rainfall in a very high-resolution regional climate model, J.
Climate, 25, 5791–5806, https://doi.org/10.1175/JCLI-D-11-00562.1, 2012.
Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., and
Senior, C. A.: Heavier summer downpours with climate change revealed by
weather forecast resolution model, Nat. Clim. Change, 4, 570–576,
https://doi.org/10.1038/nclimate2258, 2014.
Khan, M. S., Coulibaly, P., and Dibike, Y.: Uncertainty analysis of
statistical downscaling methods, J. Hydrol., 319, 357–382,
https://doi.org/10.1016/j.jhydrol.2005.06.035, 2006.
Kirshbaum, D. J., Adler, B., Kalthoff, N., Barthlott, C., and Serafin, S.:
Moist orographic convection: Physical mechanisms and links to
surface-exchange processes, Atmosphere, 9, 80, https://doi.org/10.3390/atmos9030080, 2018.
Klein Tank, A. M. G., Wijngaard, J. B., Können, G. P., Böhm, R.,
Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L.,
Kern-Hansen, C., and Heino, R.: Daily dataset of 20th-century surface air
temperature and precipitation series for the European Climate Assessment,
Int. J. Climatol., 22, 1441–1453, https://doi.org/10.1002/joc.773, 2002.
Klok, E. J. and Klein Tank, A. M. G.: Updated and extended European dataset of
daily climate observations, Int. J. Climatol., 29, 1182–1191, https://doi.org/10.1002/joc.1779, 2009.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
Laprise, R.: Regional climate modelling, J. Comput. Phys.,
227, 3641–3666, https://doi.org/10.1016/j.jcp.2006.10.024,
2008.
Leung, L. R., Mearns, L. O., Giorgi, F., and Wilby, R. L.: Regional climate
research: Needs and opportunities, B. Am. Meteorol.
Soc., 84, 89–95, 2003.
Leutwyler, D., Lüthi, D., Ban, N., Fuhrer, O., and Schär, C.:
Evaluation of the convection-resolving climate modeling approach on
continental scales, J. Geophys. Res.-Atmos., 122,
5237–5258, https://doi.org/10.1002/2016JD026013, 2017.
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F.,
Chen, F., Chen, L., Clark, M., Dai, A., and Dudhia, J.: Continental-scale
convection-permitting modeling of the current and future climate of North
America, Clim. Dynam., 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9, 2017.
McGregor, J. L.: Regional climate modelling, Meteorol. Atmos.
Phys., 63, 105–117, https://doi.org/10.1007/BF01025367, 1997.
McSweeney, C. F., Jones, R. G., Lee, R. W., and Rowell, D. P.: Selecting CMIP5
GCMs for downscaling over multiple regions, Clim. Dynam., 44, 3237–3260,
https://doi.org/10.1007/s00382-014-2418-8, 2015.
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T.,
Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., and Raper,
S. C.: Global climate projections In Climate Change 2007: The Physical
Science Basis, Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by: Solomon,
S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M.,
Miller, H. L., Cambridge University Press, Cambridge, UK and New York, NY,
TRN: GB07CC205, 2007.
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: Evaluation of
the AR4 climate models' simulated daily maximum temperature, minimum
temperature, and precipitation over Australia using probability density
functions, J. Climate, 20, 4356–4376, https://doi.org/10.1175/JCLI4253.1, 2007.
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P.,
Alias, A., Belušić, D., Berthou, S., Caillaud, C., and Cardoso, R. M.:
The first multi-model ensemble of regional climate simulations at
kilometer-scale resolution part 2: historical and future simulations of
precipitation, Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021.
Prein, A. F., Gobiet, A., Suklitsch, M., Truhetz, H., Awan, N. K., Keuler, K.,
and Georgievski, G.: Added value of convection permitting seasonal
simulations, Clim. Dynam., 41, 2655–2677, https://doi.org/10.1007/s00382-013-1744-6, 2013a.
Prein, A. F., Holland, G. J., Rasmussen, R. M., Done, J., Ikeda, K., Clark,
M. P., and Liu, C. H.: Importance of regional climate model grid spacing for
the simulation of heavy precipitation in the Colorado headwaters, J.
Climate, 26, 4848–4857, https://doi.org/10.1175/JCLI-D-12-00727.1, 2013b.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., and Brisson, E.: A review
on regional convection-permitting climate modeling: Demonstrations,
prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015.
Prein, A. F., Gobiet, A., Truhetz, H., Keuler, K., Goergen, K., Teichmann,
C., Maule, C. F., Van Meijgaard, E., Déqué, M., Nikulin, G., and
Vautard, R.: Precipitation in the EURO-CORDEX 0.11∘ and 0.44∘
simulations: high resolution, high benefits?, Clim. Dynam., 46, 383,
https://doi.org/10.1007/s00382-015-2589-y, 2016.
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J.,
Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., and Stouffer, R. J.:
Climate models and their evaluation. In Climate change 2007: The
physical science basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B.,
Tignor, M., and Miller, H. L., Cambridge University Press: Cambridge, UK and New
York, NY, TRN: GB07CC205, 2007.
Rios-Entenza, A., Soares, P. M. M., Trigo, R. M., Cardoso, R. M., and
Miguez-Macho, G.: Precipitation recycling in the Iberian Peninsula: spatial
patterns and temporal variability, J. Geophys. Res.-Atmos., 119, 5895–5912, https://doi.org/10.1002/2013JD021274, 2014.
Rummukainen, M.: State-of-the-art with regional climate models, Wires Clim. Change, 1, 82–96, https://doi.org/10.1002/wcc.8, 2010.
Rummukainen, M.: Added value in regional climate modeling, Wires Clim. Change, 7, 145–159, https://doi.org/10.1002/wcc.378, 2016.
Schulzweida, U.: Climate Data Operators, User's Guide, Version 1.1.9,
Max-Planck Institute for Meteorology, Hamburg, Germany, https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf, last access: 29
April 2021.
Soares, P. M. and Cardoso, R. M.: A simple method to assess the added value
using high-resolution climate distributions: application to the EURO-CORDEX
daily precipitation, Int. J. Climatol., 38, 1484–1498,
https://doi.org/10.1002/joc.5261, 2018.
Soares, P. M., Cardoso, R. M., Miranda, P. M., de Medeiros, J., Belo-Pereira,
M., and Espirito-Santo, F: WRF high resolution dynamical downscaling of
ERA-Interim for Portugal, Clim. Dynam., 39, 2497–2522, https://doi.org/10.1007/s00382-012-1315-2, 2012a.
Soares, P. M., Cardoso, R. M., Miranda, P. M., Viterbo, P., and Belo-Pereira, M.:
Assessment of the ENSEMBLES regional climate models in the representation of
precipitation variability and extremes over Portugal, J. Geophys. Res.-Atmos., 117, D07114, https://doi.org/10.1029/2011JD016768, 2012b.
Soares, P. M., Cardoso, R. M., Semedo, Á., Chinita, M. J., and Ranjha, R.:
Climatology of the Iberia coastal low-level wind jet: weather research
forecasting model high-resolution results, Tellus A, 66, 22377, https://doi.org/10.3402/tellusa.v66.22377, 2014.
Soares, P. M., Cardoso, R. M., Lima, D. C., and Miranda, P. M.: Future
precipitation in Portugal: high-resolution projections using WRF model and
EURO-CORDEX multi-model ensembles, Clim. Dynam., 49, 2503–2530,
https://doi.org/10.1007/s00382-016-3455-2, 2017.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M. M., Allen, S. K., Boschung,
J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M. (Eds.): Climate Change
2013: The physical science basis. contribution of working group I to the
fifth assessment report of IPCC the intergovernmental panel on climate
change, Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, https://doi.org/10.1017/CBO9781107415324, 2014.
Torma, C., Giorgi, F., and Coppola, E.: Added value of regional climate
modeling over areas characterized by complex terrain – Precipitation over
the Alps, J. Geophys. Res.-Atmos., 120, 3957–3972,
https://doi.org/10.1002/2014JD022781, 2015.
van der Linden, P. and Mitchell, J. E.: ENSEMBLES: Climate Change and its
Impacts: Summary of research and results from the ENSEMBLES project, Met
Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK, 2009.
Wilby, R. L., Wigley, T. M. L., Conway, D., Jones, P. D., Hewitson, B. C., Main,
J., and Wilks, D. S.: Statistical downscaling of general circulation model
output: A comparison of methods, Water Resour. Res., 34, 2995–3008,
https://doi.org/10.1029/98WR02577, 1998.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences. Academic Press,
Oxford, UK, 467 pp., 1995.
Williams, D. N., Taylor, K. E., Cinquini, L., Evans, B., Kawamiya, M., Lautenschlager, M., Lawrence, B., Middleton, D., and ESGF Contributors: The Earth System Grid Federation: Software framework supporting CMIP5 data analysis and dissemination, ClIVAR Exchanges, 56, 40–42, http://centaur.reading.ac.uk/25732/1/WilEA11_CE.pdf (last access: 29 April
2021), 2011.
Zappa, G., Shaffrey, L. C., and Hodges, K. I.: The ability of CMIP5 models to simulate North Atlantic extratropical cyclones, J. Climate, 26, 5379–5396, https://doi.org/10.1175/JCLI-D-12-00501.1, 2013.
Short summary
This work focuses on the added value of high-resolution models relative to their forcing simulations, with a recent observational gridded dataset as a reference, covering the entire Iberian Peninsula. The availability of such datasets with a spatial resolution close to that of regional climate models encouraged this study. For precipitation, most models reveal added value. The gains are even more evident for precipitation extremes, particularly at a more local scale.
This work focuses on the added value of high-resolution models relative to their forcing...
