Evaluating the Impact of Relaxation Experiments on Subseasonal Forecast Skill Using Machine Learning Weather Models

Subseasonal weather predictions for the extratropics remain a significant challenge. Although sources of extratropical subseasonal predictability are linked to slowly evolving components of the atmospheric system, such as tropical modes of variability (e.g., the Madden-Julian Oscillation and tropical waves), these sources are not yet fully leveraged due to systematic  errors in numerical weather prediction models. In particular, short-term errors in the tropics can degrade forecast skill in the extratropics on subseasonal timescales. However, the specific tropical regions where such errors most strongly influence extratropical forecast skill remain unclear. Relaxation experiments using NWP models provide a means to identify these key regions, though such experiments are computationally expensive, especially when run in ensemble mode. In this study, we utilize machine learning-based weather prediction models to perform relaxation experiments across various tropical regions on the subseasonal timescale. Probabilistic forecasts are generated using initial conditions from the Ensemble of Data Assimilations (EDA) system of the European Centre for Medium-Range Weather Forecasts (ECMWF). By evaluating reforecasts for a five year period (2020-2024), which lies outside the training period of the models, this study systematically investigates the role of nudging techniques and the influence of tropical variability, particularly the MJO, on subseasonal forecast skill. Additionally, we evaluate the experiments conditioned on the state of the MJO to assess the regional contributions of tropical predictability to forecast improvements in the extratropics. Our results underscore the potential of nudging as both a diagnostic tool and a means to enhance extended-range forecasting skill, especially when combined with emerging machine learning-based prediction approaches.