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Variability of the North-Atlantic storm track

Objective

To develop our understanding of the factors that control the
length and intensity of storm tracks in general and the North
Atlantic storm track in particular, and to suggest the likely
range of impacts of increased greenhouse gases on the dynamics of
the storm track.


A method for objective identification of cyclones will be
developed to yield information about the location, the life time,
and the intensity of the individual cyclones from atmospheric
circulation data. This information leads to the distribution of
cyclone activity, period lengths, recurrence times, and
intensity.

The cyclone trajectories, positions and intensity,
will be subjected to further analysis based on nonlinear
techniques and Lagrangian diagnostics. Eddy-mean flow diagnostic
tools are applied to the comprehensive and the idealized data
sets to interpret the underlying physical mechanisms, their
differences and to distinguish the anthropogenic and natural
forcing on storm track variability.

The physical interpretation may necessitate further experiments
to isolate mechanisms that transport climate signals over large
distances, such as from the Tropical Pacific to the Northeast
Atlantic/European sector. A reduced GCM (SGCM) of the general
circulation model ECHAM will be used for idealized experiments.
The SGCM-experiments are carried out on an aqua-planet;
greenhouse gas forcing is modified during these runs (1 x
CO2, 2 x CO2). These experiments will be completed
by simulations of point vortex models on periodic domain and on
the sphere.

The nonlinear dynamics of storm-track variability will be studied
as well by simplified nonlinear modelling based on point-vortex
systems and turbulence simulations. Using the outputs of the
above models and real atmospheric data, information is derived of
the large scale dynamics based on probability measures in
state-space applying (a) phase-space reconstructions based on new
techniques aimed at reducing noise level and making optimal use
of information. Subsequently, (b) dimension and entropy
estimates of time series and Lagrangian cyclone trajectories can
be deduced to obtain (c) some nonlinear systems analysis
diagnostics including tests to distinguish random from
deterministic signals.

Funding Scheme

CSC - Cost-sharing contracts

Coordinator

UNIVERSITY OF HAMBURG
Address
Bundesstrasse 55
20146 Hamburg
Germany

Participants (2)

Consiglio Nazionale delle Ricerche (CNR)
Italy
Address
Corso Fiume 4
10133 Torino
UNIVERSITY OF READING
United Kingdom
Address
Earley Gate 2, Whiteknights, Palmer Building
RG6 2AU Reading / Silchester