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Large-scale SST variability in the midlatitudes and in the tropical Atlantic

MPS-Authors

Dommenget,  Dietmar
MPI for Meteorology, Max Planck Society;

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Citation

Dommenget, D. (2000). Large-scale SST variability in the midlatitudes and in the tropical Atlantic. PhD Thesis, University of Hamburg, Hamburg.


Cite as: https://hdl.handle.net/21.11116/0000-0009-E320-2
Abstract
In this these the SST variability of the northern midlatitudes and the tropical Atlantic have
been analysed. The analysis has been based on the comparison of the observations with a
hierarchy of different coupled simulations.
The analysis of the midlatitude SST variability has shown that the large-scale features of
the SST variability cannot be simulated by a fixed depth mixed layer ocean model and that
the spectral distribution of the SST is significantly different for an AR-l process on time
scales from seasons to decades. The processes that are important for these differences are
the seasonal variability of the mixed layer depth, the wind induced mixing, which entrains
water from the sub-mixed layer ocean, and the heat exchange between the mixed layer and
the sub-mixed layer ocean. The observed increase in the SST variance from the interannual
to the decadal time scale is due to the heat exchange between the sub-mixed layer ocean and
the mixed layer and not, as in the AI I X50 simulation, merely an effect of the integration of
atmospheric noise. All these processes can be simulated by the local air-sea interactions in
the dynamical ocean mixed layer IUIIX¿unarnàc. The analysis of the seasonal predictability
of the SST in the MIX¿,,no ¿.simulation indicates that the knowledge of the actual mixed
layer depth is important to predict the SST development in summer and fall.
In the analysis of the tropical Atlantic SST variability, it was found that the two dominant
SST patterns of the observed SST and in all analysed CGCMs are centred in the northern
and in the southern trade wind zones, whereas the correlation between the two patterns is
not significantly different from zero. An interhemispheric dipole, or stated differently, an
anti-correlation of the SSTs in the northern and southern trade wind zones, which could
be important for rainfall anomalies in e.g. north-east BrazTl, does therefore not exist. I
conclude that the often cited dipole pattern is an artifact of the EOF analysis technique
used. The fact that the simple slab ocean model produces the same pattern, indicates that
the SST anomalies are forced by the atmosphere consistent with the Null hypothesis of SST
variability.
In the final chapter of this work I have introduced a new technique to study the response
of the atmosphere to a given SST pattern in a coupled simulation. In this new technique the
SST anomaly patterns or historical SST time series is introduced by an additional heat flux
into the seasonal mixed layer ocean model. The comparison of the atmospheric response in
the coupled simulation with the usual AMIP-type simulation has shown that the response in
the midlatitudes can be significantly different and that the response of the atmosphere is very
sensible to the structure of the given SST anomaly pattern. In general the new technique
seems to be a good tool to study the atmospheric response to SST anomaly pattern in the
midlatitudes and instead of introducing a fixed SST pattern or a given historical SST time
series, the mixed layer simulation offers many other possibilities.