Long spin-up times remain a major computational bottleneck in ocean and climate modelling, limiting our ability to investigate past climate states, understand model biases, and quantify parameter uncertainty. Developing more efficient spin-up methods can help overcome these limitations and support the use of past climate information to strengthen confidence in future climate projections.

This session will bring together results from the Past-to-Future Global Ocean Circulation Model Spin-Up Competition. The competition challenges participants to bring a global ocean model to equilibrium using as few computational resources as possible. Participants access the model as a time-forward black box, advancing the ocean state through a prescribed routine without modifying the underlying model or its physical configuration. This common framework enables a systematic comparison of alternative approaches.

The session will focus on three core aspects:

• presenting the methods developed by participating teams and comparing their performance against the common benchmark;
• discussing computational efficiency, convergence, and reproducibility, including the costs associated with training data where machine learning methods are used;
• exploring lessons learned and the potential for applying successful approaches more broadly in ocean and climate modelling.

We welcome competition participants and researchers interested in ocean and climate modelling, numerical analysis, scientific computing, and machine learning. The goal is to identify promising approaches, discuss remaining challenges, and encourage future collaborations on efficient model initialisation. Registration for the competition closes on 1 November 2026, with final results due on 1 March 2027.