Articles | Volume 10, issue 3
Model experiment description paper
31 Mar 2017
Model experiment description paper |  | 31 Mar 2017

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

Paolo Davini, Jost von Hardenberg, Susanna Corti, Hannah M. Christensen, Stephan Juricke, Aneesh Subramanian, Peter A. G. Watson, Antje Weisheimer, and Tim N. Palmer

Related authors

Multi-centennial evolution of the climate response and deep-ocean heat uptake in a set of abrupt stabilization scenarios with EC-Earth3
Federico Fabiano, Paolo Davini, Virna L. Meccia, Giuseppe Zappa, Alessio Bellucci, Valerio Lembo, Katinka Bellomo, and Susanna Corti
Earth Syst. Dynam., 15, 527–546,,, 2024
Short summary
Impact of stochastic physics on the representation of atmospheric blocking in EC-Earth3
Michele Filippucci, Simona Bordoni, and Paolo Davini
EGUsphere,,, 2024
Short summary
Predictable decadal forcing of the North Atlantic jet speed by sub-polar North Atlantic sea surface temperatures
Kristian Strommen, Tim Woollings, Paolo Davini, Paolo Ruggieri, and Isla R. Simpson
Weather Clim. Dynam., 4, 853–874,,, 2023
Short summary
Atmospheric response to cold wintertime Tibetan Plateau conditions over eastern Asia in climate models
Alice Portal, Fabio D'Andrea, Paolo Davini, Mostafa E. Hamouda, and Claudia Pasquero
Weather Clim. Dynam., 4, 809–822,,, 2023
Short summary
Performance-based sub-selection of CMIP6 models for impact assessments in Europe
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483,,, 2023
Short summary

Related subject area

Climate and Earth system modeling
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731,,, 2024
Short summary
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685,,, 2024
Short summary
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532,,, 2024
Short summary
cfr (v2024.1.26): a Python package for climate field reconstruction
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431,,, 2024
Short summary
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445,,, 2024
Short summary

Cited articles

Anstey, J. A., Davini, P., Gray, L. J., Woollings, T. J., Butchart, N., Cagnazzo, C., Christiansen, B., Hardiman, S. C., Osprey, S. M., and Yang, S.: Multi-́model analysis of Northern Hemisphere winter blocking: Model biases and the role of resolution, J. Geophys. Res.-Atmos., 118, 3956–3971, 2013.
Arnold, H., Moroz, I., and Palmer, T.: Stochastic parametrizations and model uncertainty in the Lorenz'96 system, Philos. T. R. Soc. Lond, 371, 20110479,, 2013.
Balsamo, G., Viterbo, P., Beljaars, A., van den Hurk, B., Hirschi, M., Betts, A. K., and Scipal, K.: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, 2009.
Beljaars, A., Bechtold, P., Köhler, M., Morcrette, J.-J., Tompkins, A., Viterbo, P., and Wedi, N.: The numerics of physical parametrization, Proc. of ECMWF Seminar on Recent Developments in Numerical Methods for Atmosphere and Ocean Modelling, ECMWF, Reading, UK, 2004.
Bengtsson, L., Steinheimer, M., Bechtold, P., and Geleyn, J.-F.: A stochastic parametrization for deep convection using cellular automata, Q. J. Roy. Meteor. Soc., 139, 1533–1543, 2013.
Short summary
The Climate SPHINX project is a large set of more than 120 climate simulations run with the EC-Earth global climate. It explores the sensitivity of present-day and future climate to the model horizontal resolution (from 150 km up to 16 km) and to the introduction of two stochastic physics parameterisations. Results shows that the the stochastic schemes can represent a cheaper alternative to a resolution increase, especially for the representation of the tropical climate variability.