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| Titel |
Shallow cumuli ensemble statistics for development of a stochastic parameterization |
| VerfasserIn |
Mirjana Sakradzija, Axel Seifert, Thijs Heus |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250092778
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| Publikation (Nr.) |
 EGU/EGU2014-7139.pdf |
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| Zusammenfassung |
| According to a conventional deterministic approach to the parameterization of moist
convection in numerical atmospheric models, a given large scale forcing produces an unique
response from the unresolved convective processes. This representation leaves out the
small-scale variability of convection, as it is known from the empirical studies of deep and
shallow convective cloud ensembles, there is a whole distribution of sub-grid states
corresponding to the given large scale forcing. Moreover, this distribution gets broader with
the increasing model resolution. This behavior is also consistent with our theoretical
understanding of a coarse-grained nonlinear system. We propose an approach to
represent the variability of the unresolved shallow-convective states, including the
dependence of the sub-grid states distribution spread and shape on the model horizontal
resolution.
Starting from the Gibbs canonical ensemble theory, Craig and Cohen (2006) developed a
theory for the fluctuations in a deep convective ensemble. The micro-states of a deep
convective cloud ensemble are characterized by the cloud-base mass flux, which, according to
the theory, is exponentially distributed (Boltzmann distribution). Following their work, we
study the shallow cumulus ensemble statistics and the distribution of the cloud-base mass
flux. We employ a Large-Eddy Simulation model (LES) and a cloud tracking algorithm,
followed by a conditional sampling of clouds at the cloud base level, to retrieve the
information about the individual cloud life cycles and the cloud ensemble as a whole. In the
case of shallow cumulus cloud ensemble, the distribution of micro-states is a generalized
exponential distribution.
Based on the empirical and theoretical findings, a stochastic model has been developed to
simulate the shallow convective cloud ensemble and to test the convective ensemble theory.
Stochastic model simulates a compound random process, with the number of convective
elements drawn from a Poisson distribution, and cloud properties sub-sampled from a
generalized ensemble distribution. We study the role of the different cloud subtypes in a
shallow convective ensemble and how the diverse cloud properties and cloud lifetimes affect
the system macro-state. To what extent does the cloud-base mass flux distribution deviate
from the simple Boltzmann distribution and how does it affect the results from the
stochastic model? Is the memory, provided by the finite lifetime of individual clouds, of
importance for the ensemble statistics? We also test for the minimal information
given as an input to the stochastic model, able to reproduce the ensemble mean
statistics and the variability in a convective ensemble. An important property of the
resulting distribution of the sub-grid convective states is its scale-adaptivity - the
smaller the grid-size, the broader the compound distribution of the sub-grid states. |
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