56,770 research outputs found
Low-frequency variability and heat transport in a low-order nonlinear coupled ocean-atmosphere model
We formulate and study a low-order nonlinear coupled ocean-atmosphere model
with an emphasis on the impact of radiative and heat fluxes and of the
frictional coupling between the two components. This model version extends a
previous 24-variable version by adding a dynamical equation for the passive
advection of temperature in the ocean, together with an energy balance model.
The bifurcation analysis and the numerical integration of the model reveal
the presence of low-frequency variability (LFV) concentrated on and near a
long-periodic, attracting orbit. This orbit combines atmospheric and oceanic
modes, and it arises for large values of the meridional gradient of radiative
input and of frictional coupling. Chaotic behavior develops around this orbit
as it loses its stability; this behavior is still dominated by the LFV on
decadal and multi-decadal time scales that is typical of oceanic processes.
Atmospheric diagnostics also reveals the presence of predominant low- and
high-pressure zones, as well as of a subtropical jet; these features recall
realistic climatological properties of the oceanic atmosphere.
Finally, a predictability analysis is performed. Once the decadal-scale
periodic orbits develop, the coupled system's short-term instabilities --- as
measured by its Lyapunov exponents --- are drastically reduced, indicating the
ocean's stabilizing role on the atmospheric dynamics. On decadal time scales,
the recurrence of the solution in a certain region of the invariant subspace
associated with slow modes displays some extended predictability, as reflected
by the oscillatory behavior of the error for the atmospheric variables at long
lead times.Comment: v1: 41 pages, 17 figures; v2-: 42 pages, 15 figure
Comparison of stochastic parameterizations in the framework of a coupled ocean-atmosphere model
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 Ruelle's 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.Comment: 44 pages, 12 figures, 4 table
Multi-level Dynamical Systems: Connecting the Ruelle Response Theory and the Mori-Zwanzig Approach
In this paper we consider the problem of deriving approximate autonomous
dynamics for a number of variables of a dynamical system, which are weakly
coupled to the remaining variables. In a previous paper we have used the Ruelle
response theory on such a weakly coupled system to construct a surrogate
dynamics, such that the expectation value of any observable agrees, up to
second order in the coupling strength, to its expectation evaluated on the full
dynamics. We show here that such surrogate dynamics agree up to second order to
an expansion of the Mori-Zwanzig projected dynamics. This implies that the
parametrizations of unresolved processes suited for prediction and for the
representation of long term statistical properties are closely related, if one
takes into account, in addition to the widely adopted stochastic forcing, the
often neglected memory effects.Comment: 14 pages, 1 figur
Data assimilation in slow-fast systems using homogenized climate models
A deterministic multiscale toy model is studied in which a chaotic fast
subsystem triggers rare transitions between slow regimes, akin to weather or
climate regimes. Using homogenization techniques, a reduced stochastic
parametrization model is derived for the slow dynamics. The reliability of this
reduced climate model in reproducing the statistics of the slow dynamics of the
full deterministic model for finite values of the time scale separation is
numerically established. The statistics however is sensitive to uncertainties
in the parameters of the stochastic model. It is investigated whether the
stochastic climate model can be beneficial as a forecast model in an ensemble
data assimilation setting, in particular in the realistic setting when
observations are only available for the slow variables. The main result is that
reduced stochastic models can indeed improve the analysis skill, when used as
forecast models instead of the perfect full deterministic model. The stochastic
climate model is far superior at detecting transitions between regimes. The
observation intervals for which skill improvement can be obtained are related
to the characteristic time scales involved. The reason why stochastic climate
models are capable of producing superior skill in an ensemble setting is due to
the finite ensemble size; ensembles obtained from the perfect deterministic
forecast model lacks sufficient spread even for moderate ensemble sizes.
Stochastic climate models provide a natural way to provide sufficient ensemble
spread to detect transitions between regimes. This is corroborated with
numerical simulations. The conclusion is that stochastic parametrizations are
attractive for data assimilation despite their sensitivity to uncertainties in
the parameters.Comment: Accepted for publication in Journal of the Atmospheric Science
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