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| Titel |
Toward unification of the multiscale modeling of the atmosphere |
| VerfasserIn |
A. Arakawa, J.-H. Jung, C.-M. Wu |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 11, no. 8 ; Nr. 11, no. 8 (2011-04-21), S.3731-3742 |
| Datensatznummer |
250009645
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| Publikation (Nr.) |
 copernicus.org/acp-11-3731-2011.pdf |
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| Zusammenfassung |
| As far as the representation of deep moist convection is
concerned, only two kinds of model physics are used at present: highly
parameterized as in the conventional general circulation models (GCMs) and
explicitly simulated as in the cloud-resolving models (CRMs). Ideally, these
two kinds of model physics should be unified so that a continuous transition
of model physics from one kind to the other takes place as the resolution
changes. With such unification, the GCM can converge to a global CRM (GCRM)
as the grid size is refined. This paper suggests two possible routes to
achieve the unification. ROUTE I continues to follow the parameterization
approach, but uses a unified parameterization that is applicable to any
horizontal resolutions between those typically used by GCMs and CRMs. It is
shown that a key to construct such a unified parameterization is to
eliminate the assumption of small fractional area covered by convective
clouds, which is commonly used in the conventional cumulus parameterizations
either explicitly or implicitly. A preliminary design of the unified
parameterization is presented, which demonstrates that such an assumption
can be eliminated through a relatively minor modification of the existing
mass-flux based parameterizations. Partial evaluations of the unified
parameterization are also presented. ROUTE II follows the "multi-scale
modeling framework (MMF)" approach, which takes advantage of explicit
representation of deep moist convection and associated cloud-scale processes
by CRMs. The Quasi-3-D (Q3-D) MMF is an attempt to broaden the applicability
of MMF without necessarily using a fully three-dimensional CRM. This is
accomplished using a network of cloud-resolving grids with large gaps. An
outline of the Q3-D algorithm and highlights of preliminary results are
reviewed. |
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