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The Future of Ocean Modeling

Sonya Legg,* Alistair Adcroft,* Whit Anderson,* V. Balaji,* John Dunne,* Stephen Griffies,* Robert Hallberg,* Matthew Harrison,* Isaac Held,* Tony Rosati,* Robbie Toggweiler,* Geoff Vallis,* Laurent White*


In this white paper we explore the possible trends in ocean modeling, to complement other white papers in this volume which focus on observational trends. Numerical models and the computers they run on are now integral to ocean science. Models can be used for both short term prediction and long term projection, as low cost testbeds for observational scenarios, and to integrate and synthesize observations, as well as to explore the fundamental physics and biogeochemistry of the ocean.

In the future, oceanography as a whole is likely to become much more applied, with increasing demand to apply oceanographic understanding to societal and commercial needs. Ocean models will likely be used by non-experts interested in a variety of different applications from coastal fisheries to storm surge prediction. Ideally the best ocean models available should be used—usually such models are created in the academic/public research sector, with an open-source structure which encourages continual improvement. To make these models accessible to applied users they will have to be more flexible and easier to use as black boxes, perhaps running on a remote computer through a web-based service. A technological development which would make models easier to tailor for different applications is the standardization of code to make model modules interchangeable. A user would then be able to take model components “off the shelf” and put together the right combination of

*

Atmospheric and Ocean Sciences Program, Princeton University



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The Future of Ocean Modeling Sonya Legg,* Alistair Adcroft,* Whit Anderson,* V. Balaji,* John Dunne,* Stephen Griffies,* Robert Hallberg,* Matthew Harrison,* Isaac Held,* Tony Rosati,* Robbie Toggweiler,* Geoff Vallis,* Laurent White* In this white paper we explore the possible trends in ocean model- ing, to complement other white papers in this volume which focus on observational trends. Numerical models and the computers they run on are now integral to ocean science. Models can be used for both short term prediction and long term projection, as low cost testbeds for observational scenarios, and to integrate and synthesize observations, as well as to explore the fundamental physics and biogeochemistry of the ocean. In the future, oceanography as a whole is likely to become much more applied, with increasing demand to apply oceanographic under- standing to societal and commercial needs. Ocean models will likely be used by non-experts interested in a variety of different applications from coastal fisheries to storm surge prediction. Ideally the best ocean models available should be used—usually such models are created in the academic/public research sector, with an open-source structure which encourages continual improvement. To make these models accessible to applied users they will have to be more flexible and easier to use as black boxes, perhaps running on a remote computer through a web-based service. A technological development which would make models easier to tailor for different applications is the standardization of code to make model modules interchangeable. A user would then be able to take model components “off the shelf” and put together the right combination of *Atmospheric and Ocean Sciences Program, Princeton University 

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 OCEANOGRAPHY IN 2025 dynamical core, parameterizations, and data assimilation infrastructure for a particular application. In the past few decades great advances in ocean modeling have been enabled simply by the increase in resolution possible as computer power increased. It seems likely however that a wall is being reached in terms of the speed of individual processors. Increased resolution will now be achieved largely through the use of greater numbers of proces- sors. Distributed computing, running different components of the model at different sites, is not likely to help increase resolution, due to the problems of latency. The addition of more components to the modeling system (i.e., biological and chemical, as well as physical) will require dramatically greater computational resources and improvements in data management. With the likely limitations on raw computing power, new numeri- cal techniques will be investigated to allow better representation of the ocean. An example is the use of unstructured meshes, which have now reached the stage of being used for idealized process study simulations. In 2025 there will likely be global circulation models employing such techniques, just as the once experimental isopycnal vertical discretization is now being employed in climate models today. However, it is unlikely that adaptive grids will be used in global climate models due to lingering concerns about their use with complex subgridscale parameterizations. Instead adaptive grids are more likely to be useful in local area models, such as in coastal storm surge forecasting and hurricane forecasting. Con- tinued improvement in vertical coordinates and advection schemes will help to reduce numerical errors. Techniques for bridging between global and smaller scales are likely to be in more widespread use in the future, so that for example global climate predictions can be applied to scales where user communities (e.g., ,fisheries) need information. These include techniques for nesting differ- ent models within global models and for locally enhancing the resolution of global models. Data assimilation will become a more indispensible part of ocean modeling, including the coupled assimilation necessary for decadal pre- dictions, and employing real time use of subsurface data (e.g., from auton- omous platforms and other new observing technology as it comes on line). Continuation of observing and monitoring technologies which have proved their worth for oceanography and climate science (e.g., satellite altimetry, Argo profilers, TAO moorings) is essential for modern coupled data assimilation. Model biases will likely continue to be a significant challenge for coupled assimilation as well as simulation and prediction, providing motivation for continued process studies. Process studies will likely focus

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9 SONYA LEGG ET AL. on still smaller scales as model resolutions increase, and process studies will be strongly integrated with models. In turn model improvements are likely to be strongly linked to the improved physical understanding of processes provided by such process studies. As model parameteriza- tions become more physically based, they will incorporate fewer arbitrary dimensional constants. The remaining non-dimensional constants will be determined from a combination of observations, laboratory experiments and LES modeling. In this way model credibility will no longer be con- fined to one realization (e.g., the current climate) and simulations of both future and paleo scenarios will become more credible. In the future, observational oceanography will likely make much greater use of models in planning of observing programs and interpreta- tion of observational data. Real time communication between models and observing systems will become more important, for example, in using model predictions to guide intelligent observing platforms. KEY SCIENCE qUESTIONS As global ocean modeling becomes both more interdisciplinary and higher resolution, questions such as the role of mesoscale eddies in bio- geochemical cycles, air-sea interaction and climate will be able to be examined. New observations such as global measurements of the spatial and temporal variability of turbulent mixing, measurements of currents and mixing under ice sheets, and continuous measurements of the fluxes into and out of geostrophic eddies, will stimulate modeling studies of the role of the ocean on the ice sheets, and the importance of tidal mixing and mesoscale eddies to the global circulation. As models begin to employ parameterizations without tunable dimensional parameters they will be able to be applied to paleooceanographic problems such as deglaciation and CO2 variations. With a longer observational record, better coupled model initialization systems and higher resolution coupled models, we will have a much better, although probably still incomplete, understand- ing of the processes involved in decadal variability. EDUCATIONAL NEEDS New trends in ocean modeling will require more recruits with train- ing in computational fluid dynamics techniques, as well as software engi- neers who can develop the web interfaces to make models accessible to a broader user base. More scientists well versed in interdisciplinary work, at the interface between physical and biological oceanography, will be needed. At the same time the academic and research community will need to learn to communicate with applied scientists using models

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0 OCEANOGRAPHY IN 2025 for practical purposes. One recruitment strategy is to encourage teach- ing of upper-level oceanography classes to physical science/engineering majors, something which will require the cooperation of the faculty of those departments. All these needs have to be balanced against maintain- ing rigor in fundamental oceanographic understanding, and the reward structure of the research and academic institutions.