Articles | Volume 19, issue 12
https://doi.org/10.5194/gmd-19-5531-2026
https://doi.org/10.5194/gmd-19-5531-2026
Development and technical paper
 | 
26 Jun 2026
Development and technical paper |  | 26 Jun 2026

A systematic atmospheric parameter optimization method to improve ENSO simulation in the ICON XPP Earth system model

Dakuan Yu, Dietmar Dommenget, Holger Pohlmann, and Wolfgang A. Müller

Related authors

The ICON-based Earth System Model for climate predictions and projections (ICON XPP v1.0)
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblueh, Clarissa A. Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
Geosci. Model Dev., 18, 9385–9415, https://doi.org/10.5194/gmd-18-9385-2025,https://doi.org/10.5194/gmd-18-9385-2025, 2025
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Cited articles

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Bayr, T., Wengel, C., Latif, M., Dommenget, D., Lübbecke, J., and Park, W.: Error compensation of ENSO atmospheric feedbacks in climate models and its influence on simulated ENSO dynamics, Clim. Dynam., 53, 155–172, https://doi.org/10.1007/s00382-018-4575-7, 2019. a, b
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: From CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, https://doi.org/10.1007/s00382-013-1783-z, 2014. a, b, c
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Collins, M., An, S. Il, Cai, W., Ganachaud, A., Guilyardi, E., Jin, F. F., Jochum, M., Lengaigne, M., Power, S., Timmermann, A., Vecchi, G., and Wittenberg, A.: The impact of global warming on the tropical Pacific Ocean and El Niño, Nat. Geosci., 3, 391–397, https://doi.org/10.1038/ngeo868, 2010. a
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We developed a new method to improve how a leading climate model simulates El Niño, a major driver of global weather extremes. By testing how the model responds to small changes in key atmospheric settings, we identified which processes matter most and adjusted them systematically. This approach makes the model’s behavior closer to observations and shows a promising path for building more reliable climate predictions.
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