Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRoutes to long-term atmospheric predictability in reduced-order coupled ocean-atmosphere systems -- Impact of the ocean basin boundary conditions
107
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The predictability of the atmosphere at short and long time scales, associated with the coupling to the ocean, is explored in a new version of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), based on a 2-layer quasi-geostrophic atmosphere and a 1-layer reduced-gravity quasi-geostrophic ocean. This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this paper considers a low-order version of the model with 40 dynamical variables. First the increase of surface friction (and the associated heat flux) with the ocean can either induce chaos when the aspect ratio between the meridional and zonal directions of the domain of integration is small, or suppress chaos when it is large. This reflects the potentially counter-intuitive role that the ocean can play in the coupled dynamics. Second, and perhaps more importantly, the emergence of long-term predictability within the atmosphere for specific values of the friction coefficient occurs through intermittent excursions in the vicinity of a (long-period) unstable periodic solution. Once close to this solution the system is predictable for long times, i.e. a few years. The intermittent transition close to this orbit is, however, erratic and probably hard to predict. This new route to long-term predictability contrasts with the one found in the closed ocean-basin low-order version of MAOOAM, in which the chaotic solution is permanently wandering in the vicinity of an unstable periodic orbit for specific values of the friction coefficient. The model solution is thus at any time influenced by the unstable periodic orbit and inherits from its long-term predictability.
rate research
Read More
This paper describes a reduced-order quasi-geostrophic coupled ocean-atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher-resolution configurations. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An optimal configuration is proposed for which the ocean resolution is sufficiently high, while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics.
A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of MAOOAM, a coupled ocean-atmosphere model of intermediate complexity. Two physically-based parameterizations are investigated, the first one based on the singular perturbation of Markov operator, also known as homogenization. The second one is a recently proposed parameterization based on the Ruelles response theory. The two parameterization are implemented in a rigorous way, assuming however that the unresolved scale relevant statistics are Gaussian. They are extensively tested for a low-order version known to exhibit low-frequency variability, and some preliminary results are obtained for an intermediate-order version. Several different configurations of the resolved-unresolved scale separations are then considered. Both parameterizations show remarkable performances in correcting the impact of model errors, being even able to change the modality of the probability distributions. Their respective limitations are also discussed.
One the major factors determining the development and evolution of atmospheric convection is the sea surface temperature and its variability. Results of this thesis show that state of atmospheric convection impacts the diurnal distribution of thermal energy in the upper ocean. Under calm and clear sky conditions a shallow warm layer of several meters depth develops on the surface of the ocean. This warm layer drives an anomalous flux from the ocean to the atmosphere. A novel Kelvin wave trajectory database based on satellite data is introduced in this study. The investigation of its data shows that substantial fraction of Kelvin waves is initiated as a result of interaction with another Kelvin wave. Two distinct categories are defined and analyzed: the two- and multiple Kelvin wave initiations, and a spin off initiation. Results show that primary forcing of such waves are high diurnal cycle and/or increased wind speed and latent heat flux at the ocean surface. Variability of the ocean surface and subsurface along Kelvin wave trajectories over Indian Ocean is investigated: wind speed and latent heat flux increase and a sea surface temperature anomaly decreases during a wave passage. It is also shown that Kelvin waves are longitude-diurnal cycle phase locked over the Maritime Continent. This cycle phase locking is such that it agrees with mean, local diurnal cycle of convection in the atmosphere. The strength of the longitude-diurnal cycle phase locking differs between non-blocked Kelvin waves, which make successful transition over the Maritime Continent, and blocked waves that terminate within it. The distance between the islands of Sumatra and Borneo agrees with the distance travelled by an average Kelvin wave in one day. This suggests that the Maritime Continent may act as a filter, favoring successful propagation waves, which are in phase with the local diurnal cycle of convection.
A stochastic subgrid-scale parameterization based on the Ruelles response theory and proposed in Wouters and Lucarini [2012] is tested in the context of a low-order coupled ocean-atmosphere model for which a part of the atmospheric modes are considered as unresolved. A natural separation of the phase-space into an invariant set and its complement allows for an analytical derivation of the different terms involved in the parameterization, namely the average, the fluctuation and the long memory terms. In this case, the fluctuation term is an additive stochastic noise. Its application to the low-order system reveals that a considerable correction of the low-frequency variability along the invariant subset can be obtained, provided that the coupling is sufficiently weak. This new approach of scale separation opens new avenues of subgrid-scale parameterizations in multiscale systems used for climate forecasts.
Dynamical systems theory approach has been successfully used in physical oceanography for the last two decades to study mixing and transport of water masses in the ocean. The basic theoretical ideas have been borrowed from the phenomenon of chaotic advection in fluids, an analogue of dynamical Hamiltonian chaos in mechanics. The starting point for analysis is a velocity field obtained by this or that way. Being motivated by successful applications of that approach to simplified analytic models of geophysical fluid flows, researchers now work with satellite-derived velocity fields and outputs of sophisticated numerical models of ocean circulation. This review article gives an introduction to some of the basic concepts and methods used to study chaotic mixing and transport in the ocean and a brief overview of recent results with some practical applications of Lagrangian tools to monitor spreading of Fukushima-derived radionuclides in the ocean.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
