150 Mojib Latif, Axei Timmennann, Anselm Grotzner, Christian Eckert, Reinhard Voss
with a change in the NAO, the SSTs in the northern North Atlantic cooled, while
they warmed further to the south (Fig. 9.3a). On the one hand, the surface temperature pattern can be explained as the response of the ocean to the surf ace heat flux
changes that result from the change in the NAO. On the other hand, the SST anomaly pattern may well force a change in the NAO, as shown by Rodwell et al.
(1999). Thus, one has to consider the interdecadal variability within the framework
oflarge-scale air-sea interactions, as originally suggested by Bjerknes (1964).
We like to note one further interesting feature. As described above, the NAO
exhibits quite strong quasi-decadal variations during the recent decades (Fig. 9.la).
These fluctuations are not seen in the convection indices of the Labrador and
Greenland Seas (Figs. 9.2c and 9.2d), while decadal variations are obvious in the
upper ocean salinity time series ofthe Sargasso Sea (Fig. 9.2e) and the subsurface
temperature at ocean weather ship 'C' (52.5°N, 35.5°W) which is shown in Fig. 9.4.
Furthermore, the quasi-decadal variations in the NAO and those shown in Figs.
9.2e and Fig. 9.4 are not coherent. This may point towards different mechanisms
for the generation ofthe decadal variabilities.
Different mechanisms were put forward to explain the interdecadal variability in
general. On the one hand, external forcing mechanisms have been discussed for a
long time. Variations in the incoming solar radiation, for instance, were proposed
as one of the major sources of interdecadal variability (e.g. Labitzke (1987), Lean
et al. (1995». Cubasch et al. (1997) show indeed that some climate impact ofthe
sun might exist on time scales of many decades and longer by means of a coupled
model experiment prescribing variations in the incoming solar radiation, but it is
fairly controversial at present how strong the fluctuations in the solar insolation
actually are. The forcing of interdecadal climate variability by vo1canos is well
established and therefore less controversial, and major vo1canic eruptions can be
easily seen in regional and globally averaged temperature records (e. g. Robock
and Mao (1995».
On the other hand, interdecadal variability arises from interactions within and
between the different climate sub-systems. The two most important climate subsystems are the ocean and the atmosphere. Non-linear interactions between different space and time scales can produce interdecadal variability in both the ocean
(e.g. Jiang et al. (1996), Spall (1996» and the atmosphere (e. g. Lorenz (1963),
James and James (1989» as shown by many modeling studies. More important in
the generation of interdecadal variability, however, seem to be the interactions
between the ocean and the atmosphere. The stochastic climate model scenario proposed by Hasselmann (1976) is a 'one-way' interaction: The atmospheric 'noi se'
(the random weather fluctuations) drives low-frequency changes in the ocean, leading to a red spectrum in the ocean's sea surf ace temperature (SST), for instance. It
has been shown that the interannual variability in the midlatitudinal upper oceans is
consistent with Hasselmann's (1976) stochastic climate model (e.g. Frankignoul
and Hasselmann (1977». This concept has been generalized recently by Frankignoul et al. (1997) who incorporated the wind-driven ocean gyres into the stochastic climate model concept, which extends the applicability of the stochastic climate
with a change in the NAO, the SSTs in the northern North Atlantic cooled, while
they warmed further to the south (Fig. 9.3a). On the one hand, the surface temperature pattern can be explained as the response of the ocean to the surf ace heat flux
changes that result from the change in the NAO. On the other hand, the SST anomaly pattern may well force a change in the NAO, as shown by Rodwell et al.
(1999). Thus, one has to consider the interdecadal variability within the framework
oflarge-scale air-sea interactions, as originally suggested by Bjerknes (1964).
We like to note one further interesting feature. As described above, the NAO
exhibits quite strong quasi-decadal variations during the recent decades (Fig. 9.la).
These fluctuations are not seen in the convection indices of the Labrador and
Greenland Seas (Figs. 9.2c and 9.2d), while decadal variations are obvious in the
upper ocean salinity time series ofthe Sargasso Sea (Fig. 9.2e) and the subsurface
temperature at ocean weather ship 'C' (52.5°N, 35.5°W) which is shown in Fig. 9.4.
Furthermore, the quasi-decadal variations in the NAO and those shown in Figs.
9.2e and Fig. 9.4 are not coherent. This may point towards different mechanisms
for the generation ofthe decadal variabilities.
Different mechanisms were put forward to explain the interdecadal variability in
general. On the one hand, external forcing mechanisms have been discussed for a
long time. Variations in the incoming solar radiation, for instance, were proposed
as one of the major sources of interdecadal variability (e.g. Labitzke (1987), Lean
et al. (1995». Cubasch et al. (1997) show indeed that some climate impact ofthe
sun might exist on time scales of many decades and longer by means of a coupled
model experiment prescribing variations in the incoming solar radiation, but it is
fairly controversial at present how strong the fluctuations in the solar insolation
actually are. The forcing of interdecadal climate variability by vo1canos is well
established and therefore less controversial, and major vo1canic eruptions can be
easily seen in regional and globally averaged temperature records (e. g. Robock
and Mao (1995».
On the other hand, interdecadal variability arises from interactions within and
between the different climate sub-systems. The two most important climate subsystems are the ocean and the atmosphere. Non-linear interactions between different space and time scales can produce interdecadal variability in both the ocean
(e.g. Jiang et al. (1996), Spall (1996» and the atmosphere (e. g. Lorenz (1963),
James and James (1989» as shown by many modeling studies. More important in
the generation of interdecadal variability, however, seem to be the interactions
between the ocean and the atmosphere. The stochastic climate model scenario proposed by Hasselmann (1976) is a 'one-way' interaction: The atmospheric 'noi se'
(the random weather fluctuations) drives low-frequency changes in the ocean, leading to a red spectrum in the ocean's sea surf ace temperature (SST), for instance. It
has been shown that the interannual variability in the midlatitudinal upper oceans is
consistent with Hasselmann's (1976) stochastic climate model (e.g. Frankignoul
and Hasselmann (1977». This concept has been generalized recently by Frankignoul et al. (1997) who incorporated the wind-driven ocean gyres into the stochastic climate model concept, which extends the applicability of the stochastic climate