Abstract
Marine Heatwaves (MHWs) have emerged as one of the most important threat for marine ecosystems, with impacts such as coral bleaching, massive fish mortality and displacement of mobile fauna. In the context of climate change, it is urgent to develop strategies such as subseasonal to seasonal forecasting to help human societies adapt and react to the increasing frequency, duration and intensity of these events. Here we evaluate the predictability of MHWs at the scale of a South Pacific island country, New Caledonia, using ensemble forecasts from a dynamical coupled ocean-atmosphere model. We show that implementing a probabilistic approach where we extract information from the dispersion in the ensemble results in a higher skill than a deterministic approach where we simply compute the ensemble average. We find that longer, more intense, and wider MHWs, are more predictable than weaker, less intense, and shorter MHWs. We also find that the longest and widest MHWs occur in the cold season (June–October) during strong La Niña episodes, and that they can successfully be predicted up to 7 months in advance. In contrast, MHWs occurring during the warm season have poor or no predictability of more than a few weeks in advance. We discuss how this information can be efficiently transferred to marine stakeholders in terms of the usefulness and useability of the forecast. We recommend that future research should focus on identifying the drivers of different types of MHWs in order to understand their sources of predictability.