 |
| Titel |
Theoretical comparison of subgrid turbulence in the atmosphere and ocean |
| VerfasserIn |
V. Kitsios, J. S. Frederiksen, M. J. Zidikheri |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
2198-5634
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 6 ; Nr. 2, no. 6 (2015-12-10), S.1675-1704 |
| Datensatznummer |
250115205
|
| Publikation (Nr.) |
 copernicus.org/npgd-2-1675-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Due to the massive disparity between the largest and smallest eddies
in the atmosphere and ocean, it is not possible to simulate these
flows by explicitly resolving all scales on a computational grid.
Instead the large scales are explicitly resolved, and the
interactions between the unresolved subgrid turbulence and large
resolved scales are parameterised. If these interactions are not
properly represented then an increase in resolution will not
necessarily improve the accuracy of the large scales. This has been
a significant and long standing problem since the earliest climate
simulations. Historically subgrid models for the atmosphere and
ocean have been developed in isolation, with the structure of each
motivated by different physical phenomena. Here we solve the
turbulence closure problem by determining the parameterisation
coefficients (eddy viscosities) from the subgrid statistics of high
resolution quasi-geostrophic atmospheric and oceanic simulations.
These subgrid coefficients are characterised into a set of simple
unifying scaling laws, for truncations made within the enstrophy
cascading inertial range. The ocean additionally has an inverse
energy cascading range, within which the subgrid model coefficients
have alternative scaling properties. Simulations adopting these
scaling laws are shown to reproduce the statistics of the reference
benchmark simulations across resolved scales, with orders of
magnitude improvement in computational efficiency. This reduction
in both resolution dependence and computational effort will improve
the efficiency and accuracy of geophysical research and operational
activities that require data generated by general circulation
models, including: weather, seasonal and climate prediction;
transport studies; and understanding natural variability and extreme
events. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|