ESM working group: Meeting notes 2024/2025

Earth System Working Group
, ,
50

Date: 4/12/2025
Chair: @ShayneM
Participants: 15

1. Admin and resource usage

  • Science Presentations: We’re looking to schedule in science presentations for early next year. If you have any work that you would like to share during the ESM WG meetings, contact the ESM WG co-chairs or @spencerwong. Presentations from recent conferences and workshops are welcome, as are informal updates on in progress work.

  • LG87 Resource Usage:

    • A couple hundred KSU from the WG allocation still remain, but these will be used up by already running experiments.
    • It’s a good time to put in any experiment proposals for next quarter. Having early proposals helps with allocating working group resources. If you have any experiments you are interested in running with ESM WG resources, follow the proposal guidelines here to apply to use WG compute.
    • Storage usage at 65 TB of 100 TB allocation.

2: 2026 CMIP7/ESM focused workshop

  • ACCESS NRI is planning on hosting a CMIP7/ESM focussed workshop in Melbourne in early September 2026.
  • This will include a hackathon/training day, and two days of workshop.
  • A major goal will be to strengthen connections across the ESM and Land Working Groups, and will cover a range of topics including model development, model evaluation, and experiments for better understanding the model.
  • We are currently looking for people to join the organising committee, and are aiming for the committee to comprise researchers with a range of research focuses, career stages, and genders. Contact one of the co-chairs @dkhutch, @ctychung, @tiloz, @ShayneM or @spencerwong if you are interested in joining the organising committee.

3. Support for data sharing and storage

  • ACCESS-NRI is working to improve the options available for storing and sharing data.
  • A new system will come online early 2026, and will consist of 3 tiers of data storage: Working group resources designed for short term storage (3-12 months), a ACCESS-NRI data sandbox for data widely used across the community (1-2 years), and publication through NCI’s data repository sandbox (yrs+).
  • Documentation on using the different tiers will be available early next year, and @kdruken and @joshuatorrance will be available present more details at a future ESM WG meeting.

3. Science presentation

Dakuan Yu from the Max Planck Institute for Meteorology presented on ENSO Optimisation in ICON XPP Earth System Model.

(Recording to be uploaded shortly)

Introduction

  • The ICON model is the latest climate model developed at MPI. Key new features include an isocahedral (soccer ball) grid, use of non-hydrostatic dynamics, and unifying NWP and climate prediction. The ICON XPP (Extended Prediction and Projection) model is being developed for a range of timescales, from seasonal to decadal.
  • While ENSO is a key part of the climate system, it’s representation in climate models is far from perfect.
  • This talk describes parameter optimisation work used to improve ENSO in the ICON XPP model.

ENSO biases in the ICON XPP model

  • The CLIVAR ESNO Metrics Package (Yann et al.) includes metrics for evaluating ENSO climatology, variability, and processes.
  • At the start of this project, the ICON XPP model showed problems in all these areas, including precipitation, ENSO amplitude, seasonality, skewness, pattern, as well as wind-SST and SST-heat flux feedbacks.

Tuning in an AMIP configuration

  • A cost function was developed which combines the different metrics and 21 tuning parameters.
  • Tuning the parameters to optimise this cost function worked well with the AMIP configuration, and several of the biases were successfully reduced.
  • Sensitivity analysis found the 6 most important tuning parameters.

Tuning the coupled model

  • The 6 parameters identified during the AMIP tuning were then optimised in a coupled configuration.
  • This improved the SST bias, ENSO amplitude, seasonality, ENSO duration, and wind-SSH feedback. However large errors for the double ICTZ bias, precipitation, and skewness.
  • In aggregate, the climatology metrics were not improved much, but the performance and feedback errors were significantly reduced.
  • While ENSO metrics were improved, global mean temperature increased. Further analysis found which parameters GMT was sensitive to, and these were retuned to optimise both GMT and ENSO together.

Conclusions

  • The tuning approach successfully improved several of the ENSO metrics. The improvements were roughly equivalent to those from doubling the resolution. However, biases remained in several of the metrics.

Please feel free to correct any mistakes in these notes directly, or message @spencerwong with corrections.

Additional information

ESM Working group: Meeting notes 2026