The Madden–Julian Oscillation (MJO) is a large-scale tropical wave of enhanced and suppressed rainfall, slowly moving eastward along the equator and influencing the weather and climate globally. We study the MJO using a simplified model designed to capture its large-scale features. We introduce new, more realistic model inputs, show that this enhanced model successfully replicates key characteristics of the MJO, and identify some of its limitations.

We present an alternative statistical methodology to detect whether the Atlantic Ocean’s circulation system is approaching a tipping point. Our approach separates natural variability from real early warning signals of tipping, reducing false alarms. When applied to a proxy of the Atlantic Ocean’s circulation strength, we find significant signs that the system is undergoing destabilization, suggesting that it may be approaching a tipping point, which could have major impacts on global climate patterns.

Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.

Particle-tracking simulations compute how ocean currents transport material. However, initializing these simulations is often ad hoc. Here, we explore how two different strategies (releasing particles over space or over time) compare. Specifically, we compare the variability in particle trajectories to the variability of particles computed in a 50-member ensemble simulation. We find that releasing the particles over 20 weeks gives variability that is most like that in the ensemble.

We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage. We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.

Extreme weather poses serious risks, making prevention crucial. Using the Lorenz 96 model as a testbed, we propose a bottom-up approach to mitigate extreme events via local interventions guided by multi-scenario ensemble forecasts. Unlike control-theoretic methods, our approach selects the best control scenario from available options. It achieves a high success rate of 99.4% while maintaining reasonable costs, offering a practical strategy to reduce extremes under limited control.