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

Abstract. The El Niño–Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet accurately simulating ENSO in climate models remains a major challenge due to its complex coupled dynamics. In this study, we present a linear optimization framework and systematically adjust atmospheric parameters to improve ENSO fidelity in the Icosahedral Nonhydrostatic eXtended Predictions and Projections (ICON XPP) Earth System Model of the Max-Planck-Institute for Meteorology. The optimization approach is based on the superposition of parameter sensitivities and a Nelder–Mead algorithm that reduces the ENSO cost function. The cost function accounts for ENSO-related tropical climatology, variability, and feedbacks, which are estimated with the ENSO metric package. We first assess the sensitivity of ENSO metrics to 21 atmospheric parameters in atmosphere-only simulations. The optimization approach reduces the ENSO cost function by 30 % in the optimized atmosphere-only runs. Key improvements include reduced precipitation bias and strengthened atmospheric feedbacks such as the Bjerknes and thermal damping feedbacks. These results demonstrate the effectiveness of our method in improving ENSO metrics within the atmosphere-only configuration. Six parameters identified as most impactful from atmosphere-only tuning experiments are subsequently tuned in fully coupled simulations. The optimized fully coupled run yields moderate improvements in ENSO amplitude, cold tongue SST bias, seasonal phase-locking, ocean-atmosphere coupling and teleconnection patterns. However, isolated ENSO tuning introduces unrealistic global warming, which is further corrected by adjusting turbulence-related parameters without degrading ENSO skill. These results demonstrate that systematic ENSO tuning can yield performance gains but must be balanced with broader climate stability constraints. Our method offers a scalable, physically grounded optimization strategy, with strong potential for tuning ENSO in climate model configurations.