Improving accuracy and providing uncertainty estimations: ensemble methodologies for ocean forecasting

<strong class="journal-contentHeaderColor">Abstract.</strong> Ensemble forecasting has emerged as an essential approach for addressing the uncertainties inherent in ocean prediction, offering a probabilistic framework that enhances accuracy of both short-term and long-range forecasts. By more effectively addressing the intrinsic chaotic nature of mesoscale and submesoscale variability, ensemble methods offer critical insights into forecast errors and improve the reliability of predictions. This paper reviews the ensemble methodologies currently used in ocean forecasting, including techniques borrowed from weather prediction, such as virtual ensembles and Monte Carlo (MC) methods. It also explores the latest advancements in ensemble data assimilation (DA), which have been successfully integrated into both ocean general circulation models (OGCMs) and operational forecasting systems. These advancements enable more accurate representation of forecast uncertainties (error of the day) by sampling perturbations conditioned on available observations. Despite the progress made, challenges remain in fully realizing the potential of ensemble forecasting, particularly in developing tools for analyzing results and incorporating them into decision-making processes. This paper highlights the crucial role of ensemble forecasting in improving ocean predictions and advocates for its wider adoption in operational systems.