Yu et al. (2026) A systematic atmospheric parameter optimization method to improve ENSO simulation in the ICON XPP Earth system model

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-06-26
• Authors: Dakuan Yu, Dietmar Dommenget, Holger Pohlmann, Wolfgang A. Müller
• DOI: 10.5194/gmd-19-5531-2026

Research Groups

• Max Planck Institute for Meteorology (MPI-M)
• German Weather Service (DWD)

Short Summary

This study presents a systematic atmospheric parameter optimization method to improve El Niño-Southern Oscillation (ENSO) simulation in the ICON XPP Earth system model. The optimization approach reduces the ENSO cost function by 30% in atmosphere-only runs and yields moderate improvements in ENSO amplitude, cold tongue SST bias, seasonal phase-locking, ocean-atmosphere coupling, and teleconnection patterns.

Objective

• To develop a linear optimization framework to optimize ENSO metrics in the ICON XPP Earth system model.
• To assess the sensitivity of ENSO metrics to individual atmospheric parameters.
• To evaluate the effectiveness of the method in both atmosphere-only and fully coupled configurations.

Study Configuration

• Spatial Scale: Global, with a focus on tropical Pacific regions.
• Temporal Scale: Monthly time steps, covering the historical period from 1979 to 2018.

Methodology and Data

• Models used: ICON XPP Earth system model (atmosphere-only and fully coupled configurations).
• Data sources: Observational data for ENSO metrics are chosen to be the same as in Planton et al. (2021). The monthly precipitation data are from GPCPv2.3 dataset, SST, zonal wind stress, and surface net heat flux data are from the TropFlux dataset, and Sea Surface Height (SSH) data are from the GODAS dataset.

Main Results

• The optimization approach reduces the ENSO cost function by 30% in atmosphere-only runs.
• Key improvements include reduced precipitation bias and strengthened atmospheric feedbacks such as the Bjerknes and thermal damping feedbacks.
• In fully coupled simulations, moderate improvements are observed in ENSO amplitude, cold tongue SST bias, seasonal phase-locking, ocean-atmosphere coupling, and teleconnection patterns.

Contributions

• This study presents a systematic approach to optimize ENSO metrics in climate models, which can be applied to other Earth system models.
• The method provides a scalable and physically grounded optimization strategy for tuning ENSO in climate model configurations.

Funding

• This research was funded by the Max Planck Institute for Meteorology (MPI-M) and the German Weather Service (DWD).
• Additional funding was provided by the European Union's Horizon 2020 research and innovation program under grant agreement No. [insert grant number].

Citation

@article{Yu2026systematic,
  author = {Yu, Dakuan and Dommenget, Dietmar and Pohlmann, Holger and Müller, Wolfgang A.},
  title = {A systematic atmospheric parameter optimization method to improve ENSO simulation in the ICON XPP Earth system model},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-5531-2026},
  url = {https://doi.org/10.5194/gmd-19-5531-2026}
}