摘要：The feasibility of global ocean weather prediction was just emerging as the Global Ocean Data Assimilation Experiment (GODAE) began in 1997. Ocean weather includes phenomena such as meandering currents and fronts, eddies, the surface mixed layer and sea surface temperature (SST), equatorial and coastally trapped waves, upwelling of cold water, and Rossby waves, all influencing ocean variables such as temperature (T), salinity (S), currents, and sea surface height (SSH). Adequate real-time data input, computing power, numerical ocean models, data assimilation capabilities, atmospheric forcing, and bathymetric/boundary constraints are essential to make such prediction possible. The key observing systems and real-time data inputs are SSH from satellite altimetry, satellite and in situ SST, T, or T and S profiles (e.g., Argo, TAO/Triton, PIRATA moored array in the Atlantic, bathythermographs), and atmospheric forcing. The ocean models dynamically interpolate data in conjunction with data assimilation, convert atmospheric forcing into oceanic responses, and forecast the ocean weather, applying bathymetric/boundary constraints in the process. The results are substantially influenced by ocean model simulation skill and it is advantageous to use an ocean model that is eddy-resolving (nominally 1/10° or finer), not just eddy-permitting. Because the most abundant ocean observations are satellite surface data, and subsurface data are very sparse in relation to the spatial scales of the mesoscale ocean features that dominate the ocean interior, downward projection of surface data is a key challenge in ocean data assimilation. The need for accurate prediction of ocean features that are inadequately observed, such as mixed layer depth, places a major burden on the ocean model, data assimilation, and atmospheric forcing. The sensitivity of ocean phenomena to atmospheric forcing and the time scale for response affect the time scale for oceanic predictive skill, sensitivity to the initial state versus the atmospheric forcing as a function of forecast length, and thus oceanic data requirements and prediction system design. Outside of surface boundary layers and shallow regions, forecast skill is about one month globally and over many subregions, and is only modestly reduced by using climatological forcing after the end of atmospheric forecasts versus using analysis-quality forcing for the duration. In addition, global ocean prediction systems must demonstrate the ability to provide initial and boundary conditions to nested regional and coastal models that enhance their predictive skill. Demonstrations of feasibility in relation to the preceding phenomena, requirements, and challenges are drawn from the following global and basin-scale ocean prediction systems: BLUElink> (Australia), the HYbrid Coordinate Ocean Model (HYCOM; USA), Mercator (France), Multivariate Ocean Variational Estimation/Meteorological Research Institute Community Ocean Model (MOVE/MRI.COM; Japan), and the Naval Research Laboratory Layered Ocean Model (NLOM; USA).
