Abstract
Abstract : 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, the surface mixed layer and sea surface temperature (SST), waves, upwelling of cold water, 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 ocean models dynamically interpolate data in conjunction with data assimilation, convert atmospheric forcing into oceanic responses, and forecast the ocean weather. The results are substantially influenced by ocean model simulation skill and it is advantageous to use an ocean model that is eddy-resolving, not just eddy-permitting. Because the most abundant ocean observations are satellite surface data, and subsurface data are very sparse, 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 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 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. 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.
Highlights
The results shown are from the Australian BLUElink> operational prediction system (Brassington et al, 2007) and ocean reanalysis system (Schiller et al, 2008), which are based on the Geophysical Fluid Dynamics Laboratory Modular Ocean Model version 4 (MOM4) (Griffies et al, 2004) and the BLUElink> ocean data assimilation system (BODAS; Oke et al, 2008), which uses ensemble optimal interpolation
Based on relatively long time scales for the evolution of Kuroshio meanders south of Japan observed by Ambe et al (2004) plus the 1/10° MRI.COM simulation skill and realistic dynamics for such features demonstrated by Tsujino et al (2006), Usui et al (2006) used this model to investigate the potential for longerrange forecasts of these features
The first was a demonstration of eddyresolving global ocean modeling without ocean data assimilation, because an eddy-resolving model that performs well is a key component of a global prediction system for mesoscale ocean features, a point verified in subsequent examples
Summary
Feasibility demonstrations cover the key capabilities needed for global and basin-scale ocean prediction systems They must have the ability to nowcast and forecast (1) deep ocean mesoscale variability, including individual eddies and meanders of ocean currents and fronts, (2) sea surface temperature (SST) with accuracy sufficient for user applications and future coupled atmosphere-ocean and Earth system prediction systems, and (3) coastal region phenomena, such as upwelling of cold water and the generation and propagation of coastally trapped waves, with skill sufficient to provide useful results for applications and useful boundary and initial conditions for nested coastal models with higher resolution or added capability, such as tides. Two other articles in this issue provide additional information about GODAE-related ocean prediction systems (Dombrowsky et al.) and the data assimilation techniques employed (Cummings et al.), including Mercator and the other prediction systems discussed in this article
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