Coupled Prediction: Integrated Atmosphere-Wave-Ocean forecasting.

It is difficult to overestimate the importance of the ocean for mankind and animal life on our planet. Covering 70% of the Earth’s surface, the ocean is the world's largest source of oxygen and absorbs 50 times more carbon dioxide than the atmosphere. Climate is regulated by the ocean heat transport, making our world a habitable place. For human beings, the ocean is also a source of food, economic resources, travel, and leisure activities. Fish accounts for about 17% of the animal protein consumed globally (FAO, 2020). The economic activities associated with the ocean are many and of crucial importance. For example, the FAO estimates that 59.6 million people in the world are engaged in fisheries activities. Only in the European Union, it is estimated that the blue sector employs almost three and a half million workers. World commerce heavily depends on marine transportation; approximately 50 thousand ships trade internationally, representing 80-90% of world trade (Schnurr and Walker, 2019). About 40% of the world population lives along coasts, which are now endangered by the sea level rise due to climate change and are of paramount importance for economic activity. These facts and figures highlight that ocean forecasting is considered as a vital activity. The first scientific successful ocean forecasting method was developed during the second world war to facilitate the landings of the US Navy. Swell forecasts were produced by analyzing wind speeds and fetch extension. The first modern approaches, based on the use of numerical models (Pinardi et al., 2017), were developed during the 1950s with the establishment of the basics of storm surge forecasting (Hansen, 1956). Other relevant advances, including first 3-dimensional simulations, took place during the 1960s. Good examples of these achievements can be found on estuarine (Shubinski et al., 1965) and general circulation modeling (McWilliams, 1966). Since those early successes, thanks to an ever-increasing computing capacity, ocean forecast techniques have evolved to what is today a complex body of codes, data and technologies able to deal with the non-linear and chaotic nature of ocean processes. Today, the scientific modeling community is committed to improve the reliability of the forecasts, mainly advancing on three leading edge areas: data assimilation, coupled forecasting and ensemble modeling.