 |
| Titel |
Subgrid-scale parameterization and low-frequency variability: a response theory approach |
| VerfasserIn |
Jonathan Demaeyer, Stéphane Vannitsem |
| Konferenz |
EGU General Assembly 2016
|
| Medientyp |
Artikel
|
| Sprache |
en
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250123363
|
| Publikation (Nr.) |
 EGU/EGU2016-2598.pdf |
|
|
|
|
|
| Zusammenfassung |
| Weather and climate models are limited in the possible range of resolved spatial and temporal
scales. However, due to the huge space- and time-scale ranges involved in the Earth System
dynamics, the effects of many sub-grid processes should be parameterized. These
parameterizations have an impact on the forecasts or projections. It could also affect the
low-frequency variability present in the system (such as the one associated to ENSO or
NAO). An important question is therefore to know what is the impact of stochastic
parameterizations on the Low-Frequency Variability generated by the system and its model
representation.
In this context, we consider a stochastic subgrid-scale parameterization based on the
Ruelle’s response theory and proposed in Wouters and Lucarini (2012). We test this
approach in the context of a low-order coupled ocean-atmosphere model, detailed in
Vannitsem et al. (2015), for which a part of the atmospheric modes is considered as
unresolved. A natural separation of the phase-space into a slow invariant set and its fast
complement allows for an analytical derivation of the different terms involved in
the parameterization, namely the average, the fluctuation and the long memory
terms.
Its application to the low-order system reveals that a considerable correction of the
low-frequency variability along the invariant subset can be obtained. This new approach of
scale separation opens new avenues of subgrid-scale parameterizations in multiscale systems
used for climate forecasts.
References:
Vannitsem S, Demaeyer J, De Cruz L, Ghil M. 2015. Low-frequency variability
and
heat transport in a low-order nonlinear coupled ocean-atmosphere model.
Physica D: Nonlinear Phenomena 309: 71–85.
Wouters J, Lucarini V. 2012. Disentangling multi-level systems: averaging,
correlations and memory. Journal of Statistical Mechanics: Theory and Experiment
2012(03): P03 003. |
|
|
|
|
|
|