Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-269-2022
https://doi.org/10.5194/gmd-15-269-2022
Model evaluation paper
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13 Jan 2022
Model evaluation paper | Highlight paper |  | 13 Jan 2022

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale

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Cited articles

Adam, O., Schneider, T., Brient, F., and Bischoff, T.: Relation of the double-ITCZ bias to the atmospheric energy budget in climate models, Geophys. Res. Lett., 43, 7670–7677, https://doi.org/10.1002/2016GL069465, 2016. 
Adam, O., Schneider, T., and Brient, F.: Regional and seasonal variations of the double-ITCZ bias in CMIP5 models, Clim. Dynam., 51, 101–117, https://doi.org/10.1007/s00382-017-3909-1, 2018. 
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., and Gruber, A.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003 (data available at: https://psl.noaa.gov/data/gridded/data.gpcp.html, last access: 1 August 2020). 
Andrews, T., Andrews, M. B., Bodas-Salcedo, A., Jones, G. S., Kuhlbrodt, T., Manners, J., Menary, M. B., Ridley, J., Ringer, M. A., Sellar, A. A., and Senior, C. A.: Forcings, feedbacks, and climate sensitivity in HadGEM3-GC3. 1 and UKESM1, J. Adv. Model. Earth Sy., 11, 4377–4394, https://doi.org/10.1029/2019MS001866, 2019. 
Bador, M., Boé, J., Terray, L., Alexander, L. V., Baker, A., Bellucci, A., Haarsma, R., Koenigk, T., Moine, M. P., Lohmann, K., and Putrasahan, D. A.: Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models, J. Geophys. Res.-Atmos., 125, e2019JD032184, https://doi.org/10.1029/2019JD032184, 2020. 
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Short summary
Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.