Oceanography Vol. 19, No. 1, Mar. 2006 5
WAY BACK, before the dawn of supercomputers and clusters, models of the oceans were simple equations with analytical solutions, or spinning tanks, appropriately scaled to represent the depth and width of a basin. Oceanographers have evolved from creating simple representations (mod- els) to using numerical solutions of coupled equations to understand the complex interactions of the ocean and its forcing and ecosystems. Before, we sought to understand the structure of the ocean circulation systems; now, we seek to predict Earth’s climate (its atmosphere, ocean, ecosys- tems, and cryosphere coupled appropriately in physics and numerics) or forecast the next phy- toplankton bloom. As computational power has increased to the point where your desktop word processor has more power than the 1970 main frame, the fi eld of computational oceanography has grown in sophistication and complexity.
Practitioners of virtually every discipline boast their own language and terms of art. Computa- tional oceanographers, or ocean modelers (as we refer to them) are no exception. For the uniniti- ated, a typical modeling article in a technical journal appears, at fi rst and perhaps second blush, to be written in gibberish. Readers require decoders for terms such as “data-assimilative hybrid isopycnal sigma-pressure coordinate ocean model” and “zero mean-random process exponentially decorrelated in time”—not to mention “discretization,” “parameterization” and “3-D eigen de- composition.” What seems perfectly sensible and precise to a scientist within the discipline may be utterly incomprehensible to an outsider, even a well-trained, highly intelligent outsider.
More and more, a new generation of scientists weaves together numerical representations of the oceanic systems and the data that captures the essence of the system at a particular time and place.
The use of data and models together allows scientists to project what has been and what will be. To further this fi eld, observationalists need to understand how ocean modelers use the data they col- lect (“data assimilation”), just as modelers need to understand how ocean observations are made and where uncertainties exist in the data they use. All scientists also need to keep up with fast- moving information technology trends, driven largely by outside forces. What worked best for the ocean sciences community yesterday may not be ideal tomorrow. In this magazine issue, our col- leagues describe several new and exciting ocean models and modeling approaches and forecast fu- ture developments. The contributors and editors have done their best to decode modeling language for a broader audience, to facilitate important cross-disciplinary communication. While these translations are no doubt imperfect, this issue should provoke some exciting discussions.
Q U A R T E R D E C K
DECODING MODELS
E L L E N S . K A P P E L , E D I T O R T E R R I P A L U S Z K I E W I C Z , G U E S T E D I T O R
This article has been published in Oceanography, Volume 19, Number 1, a quarterly journal of The Oceanography Society. Copyright 2006 by The Oceanography Society. All rights reserved. Permission is granted to copy this article for use in teaching and research. Republication, systemmatic reproduction, or collective redistirbution of any portion of this article by photocopy machine, reposting, or other means is permitted only with the approval of The Oceanography Society. Send all correspondence to: info@tos.org or Th e Oceanography Society, PO Box 1931, Rockville, MD 20849-1931, USA.
