Task 1.1 Model development - Adjustment of ENSO Dynamics in ICON-Seamless and Predictions
The coupled model ICON-XPP (ICON-Seamless) will form the basis for operational climate forecasts and climate projections for the upcoming CMIP7 ("coupled model inter-comparison project 7"). ICON-XPP was assembled from various ICON components in a completely new condition. The technical development of ICON-XPP for climate experiments is almost complete, but the model still has to be adjusted with regard to a coupled model climate ("tuning"). This tuning process primarily involves the achievement of core mean-climate targets, e.g. radiation balance, global temperature, sea ice, etc. However, the tuning process does not consider climate variability and trends, which are critical to operational climate predictions.
Here, we aim to investigate the "El Nino/Southern Oscillation" (ENSO) in ICON-XPP in more detail and to adapt the model error in the tropical Pacific and ENSO dynamics with suitable changes to the parameter space. ENSO is one of the key processes for climate prediction on seasonal to annual time scales, and is routinely predicted in numerous operational forecasting systems. However, In many forecast systems and their underlying climate models, the mean state and trends of the tropical Pacific and thus the ENSO dynamics are only insufficiently represented (Kim et al., 2017; Saeger et al. 2019; Heede and Fedorov; 2021 L'Heureux et al., 2022). This often affects the simulated development and predictions of an ENSO event. With the help of theoretical models, we investigate the model parameter space and its influence on the mean state in the tropical Pacific and ENSO dynamics. We investigate how these parameters can be used to improve ENSO dynamics and properties in ICON-XPP and predictions. If there is a clear indication, the parameter values are included in the central ICON seamless configuration and used for climate forecasts and future climate scenarios for CMIP7.
Contributors
Max Planck Institute for Meteorology
Dr. Wolfgang Müller
Dr. Holger Pohlmann
Dr. Dakuan Yu
University of Hamburg
Prof. Dr. Johanna Baehr
Dr. Jürgen Bader
Dr. Sebastian Brune