A. Heading
Coupled Mechanisms and Predictability of Tropical
Atlantic Variability
Name of the Lecturer: Ping Chang
Notes by: Young-Oh Kwon,
Alfredo Ruiz-Barradas
Date of Lecture: July 28, 2000
B. Introduction
Climate variability on interannual and decadal time scales in the tropical Atlantic region
appears in two forms – one is in association with meridional variation of the Atlantic
ITCZ and interhemispheric SST gradient and the other is in association with trade wind
and equatorial SST variability similar to the Pacific ENSO.
These climate variations
have a significant impact on rainfall variability of the surrounding countries in Central
and South America, the Caribbean and Africa. However, their physics are not well
understood. Many questions remain unanswered. For example, is Tropical Atlantic
Variability (TAV) simply a response to forcing induced by other climate modes, such as
ENSO and NAO ? or is it also influenced by “active” air-sea feedbacks local to tropical
Atlantic ?
If latter is true, how do these “active” feedbacks affect the climate
predictability of the system ?
In this lecture, we shall explore and contrast some different ideas on how TAV may be
driven and what may be the essential elements. We shall begin with a simple system
where the atmosphere and ocean are “passively” coupled. Then, we shall examine a
number of “active” coupling mechanisms that may be potentially important to TAV.
Finally, we shall illustrate, using a simple “actively” coupled ocean-atmosphere model,
how “active” coupling may influence the predictability of TAV
C. Lecture
“Passive” Coupling
The simplest coupled ocean-atmosphere system is perhaps the coupled mixed layer
model presented by Barsugli and Battisti (1998).
The model is a straightforward
extension of the stochastic climate model for SST originally formulated by Hasselmann
(1976) and Frankignoul and Hasselmann (1977) and includes a thermodynamic air-sea
coupling through surface heat exchange. Assuming that the surface heat exchange is
predominately controlled by air-sea temperature differences, then the ocean and the
atmosphere are subject to a negative feedback. This is because for a given atmospheric
temperature, the ocean temperature will always tend to relax toward that.
The
atmospheric temperature is subject to a similar constraint if an oceanic temperature is
given. This leads to the so-called “passive” coupling between the ocean and the
atmosphere. This simple coupled model has been successful in explaining some basic
characteristics of climate variability in extratropical regions.
However, in the tropics the surface heat flux variability is not only influenced by air-sea
temperature differences, but also influenced by surface winds mainly through latent heat
flux. In fact, the effect of surface winds often dominates over the effect of air-sea
temperature differences on surface heat flux variability in the tropics, as shown in figure
1 (Cayan, 1992). Furthermore, there is modeling and observational evidence that the
large-scale surface circulation of the tropical atmosphere is responsive to SST anomalies
in the tropics. For example, it has shown that cross-equatorial wind variability is tightly
coupled to local SST gradient in tropical Atlantic Ocean. This raises the possibility that
other air-sea feedbacks, particularly those involving interactions between surface wind
and SST anomalies, may be important contributing factors to TAV. In other words,
“passive” coupling alone may not be sufficient to explain the observed climate variability
in the tropical Atlantic and other “active” coupling mechanisms may be important.
"Active" Coupling
"Active" coupling takes place through dynamical and thermodynamical interactions
between the ocean and the atmosphere.
In TAV context, two forms of dynamical
feedbacks may be of particular interest. The first involves interactions between zonal
wind stress and equatorial SST anomalies, akin to the feedback mechanism proposed by
Bjerknes (1969) for ENSO. Indeed, the modeling works by Zebiak (1993), Carton and
Huang (1994) and Delecluse et al. (1994) suggest that an ENSO-like feedback does
operate in the equatorial Atlantic, albeit much weaker than the Pacific counterpart. More
recent studies (e.g. Chang et al., 2000) suggest that the Bjerknes feedback mechanism
appears to operate mainly in summer and fall seasons. The second dynamic feedback
mechanism, sometimes referred to as the Ekman feedback, involves interactions between
upwelling induced by surface Ekman divergence associated with cross-equatorial wind
anomaly and the cross-equatorial SST gradient (Mitchell and Wallace, 1992; Chang and
Philander, 1994).
Imaging that a warm SST anomaly is introduced to the north of the
equator, a northward wind anomaly will then be developed near the equator as a result of
the low pressure anomaly over the warm SST region.
The cross-equatorial wind
anomaly will give rise to a surface Ekman divergence and upwelling to the south of the
equator, which will then cause surface cooling in the southern hemisphere and enhance
north-south temperature difference. This mechanism appears to be consistent with the
ocean general circulation model experiment by Philander and Pacanowski (1981).
However, the extent to which it may have an effect on TAV has not been explored.
The feedback mechanism that has received most attention and perhaps is also most
controversial is a thermodynamic feedback referred to as the Wind-Evaporation-SST
feedback (Xie and Philander, 1994; Carton et al., 1996; Chang et al., 1997). Similar to
the Ekman feedback, the response of cross-equatorial wind to the interhemispheric SST
gradient is a key element. What makes this feedback different from the Ekman feedback
is the effect of the anomalous winds on surface heat fluxes. Here, the anomalous
atmospheric circulation works in such a way that it weakens latent heat flux from the
ocean in the warm SST region while enhancing it in the cold SST region. The resultant
surface heat flux anomalies then cause initial SST perturbation to grow, giving rise to a
positive feedback.
Figure 2 gives a systematic illustration of the Wind-Evaporation-
SST feedback in the tropical Atlantic region. Whether or not such a feedback can operate
in a region depends on the relative importance of the wind-induced flux component and
the air-sea temperature induced flux component. The former acts as a positive feedback,
while the latter acts as a negative feedback (Chang et al. 2000). Although some evidence
supporting this feedback mechanism in the deep tropics of Atlantic has been presented
(Chang et al. 2000),
the validity of this feedback mechanism in reality remains
controversial. At issue is whether or not the effect of anomalous winds are strong enough
to produce a sizable wind-induced surface heat flux anomaly to sustain the positive
feedback. Given large uncertainties in the surface heat flux estimates, a rigorous test on
this hypothesis seems to be out of reach at the moment.
The other thermodynamic feedback of potential relevance to TAV is cloud-SST feedback
(Philander et al., 1996). Off the west coast of Africa where low level stratus clouds form
as a result of the increased static stability of the lower atmosphere due to cold surface
waters, solar radiation is blocked, causing a further cooling in surface waters. Colder
SSTs produce more clouds which block more solar radiation, …, and so on. The extent
to which this feedback can really affect TAV needs to a thorough investigation.
Finally, massive convective activities over the Amazon basin can potentially be an active
player in TAV. Although a specific feedback loop involving land-atmosphere-ocean has
not yet been identified, recent modeling study (Battisti, person communication) indicates
that in the absence of land feedback, the pattern of simulated TAV differs substantially
from that when land feedback is present, implying the importance of land processes in
TAV.
Interaction among land-atmosphere-ocean is an ongoing research area in TAV
studies. Much of progress in this area will reply on a better understanding of land surface
processes over the Amazon basin.
Predictability Issues
Evidence at hand suggests that “active” air-sea couplings are likely to an important
contributing factor to TAV. The next logical question is how these “active” couplings
can have an impact on the predictability of TAV.
To shed some light on this issue, a
simple coupled model is developed. The formulation of the model is based on a number
of simplifications that are justifiable based on either observations or general circulation
model simulations. First, zonal variation in SST anomaly is ignored, based on the
observation that the spatial pattern of SST anomalies in the tropical Atlantic consists
primarily banded structures with little zonal variation. Second, it assumes that a positive
air-sea feedback takes place in the tropical Atlantic ITCZ region, which is supported by
the atmospheric general circulation model studies of Chang et al (2000) and Saravanan
and Chang (2000). Finally, the change of upper ocean heat transport is assumed to be
regulated by the advection of anomalous temperatuers by the mean meridional current
and equatorial upwelling. This simplification is supported by ocean general circulation
model study (Chang et al. 2000 and Seager et al., 2000). These simplifications lead a
simple coupled model for tropical Atlantic SST variability,
T
T
 2T
 V ( y)
 Q( y, t )  D( y )T  
  ( y, t )
t
y
y 2
where T is the SST anomaly, the second term in the left hand side represents the
meridional advection of heat; Q(y,t)=S(y)T(yo,t) is the heat flux anomaly that is
influenced by T within the coupling zone centered at "yo"; D(y)T represents damping due
to mean upwelling; the third term in the right hand side is a diffusion term and (y,t)
stands for noise forcing.
A spatial discretization of the above equation leads to a multi-variable linear stochastic
system. The general predictability of the system then depends on the dynamical operator
which describes the deterministic part of the system and the spatial distribution of noise
forcing. The dominant coupled mode is described by the leading eigenmode of the
dynamical operator (Farrell and Ioannou, 1996) which is influenced by various processes
in the system, such as, the feedback and advective processes. In general, a strong
coupling will give rise to an oscillatory mode. For the system at hand, the leading
eigenmode shows a “dipole”-like structure with oscillation periods ranging from 6 years
to multidecadal time scale depending on the strength of the feedback. If TAV were
dominated by strong air-sea feedbacks, the variation of TAV would be largely governed
by an oscillating mode and the predictability of TAV would be determined by the
oscillating behavior of the coupled mode. In this case, one would expect to see a
pronounced spectral peak near the frequency of the dominant mode.
In reality, pronounced spectral peaks are not found in TAV. However, there are some
indications of a weak spectral structure at 11-13 years in the cross-equatorial SST
gradient and associated rainfall anomalies. This may be an indicative of a weak coupling
in Tropical Atlantic Variability. How does then a weak coupling have an effect on the
predictability of TAV ?
If we define a predictability measure as error variance growth, then it can be shown that
for a weakly coupled system where the dominant coupled mode is highly damped,
contribution to predictability of the system comes predominantly from non-normal
dynamics which allow transient energy growth of initial perturbation due to constructive
interference among non-orthogonal normal modes.
Various physical processes can
introduce non-normality into the system. For the simple coupled TAV system, it is found
that the non-local effect caused by the air-sea coupling provides an effective way of
introducing non-normal dynamics into the system. Figure 3 illustrates the effect of
coupling strength on the correlation skills of the system.
Even though the air-sea
feedback in the tropical Atlantic may be weak, it can still provide the system with
superior predictive skills than the persistence forecasts. Furthermore, the predictability
of the system depends also on the spatial structure of stochastic forcing. These results
suggest that to fully understand the predictability of coupled system, it is necessary to
examine not only the deterministic dynamics of the system, but also the stochastic
component of the system.
Concluding Remarks
The main points to be highlighted in this presentation are:
1) "Active" air-sea coupling does play a role in tropical Atlantic variability. The strongest
feedback occurs in the deep tropics of Atlantic Ocean where the atmosphere is sensitive
to SST changes. Outside the deep tropics the variability is largely driven by the internal
variability of the atmosphere.
2) The positive air-sea feedback in the tropical Atlantic is likely to be weak, so that the
coupled system is stable. Tropical Atlantic variability may be viewed as a response of a
stable coupled system driven by stochastic processes.
3) In addition to reducing damping time scales, air-sea coupling can increase the
"memory" of the system by introducing non-normality. As a result, a coupled system,
albeit a weak coupling, is likely to have a better predictability than an uncoupled system.
4) The predictability of a stochastically forced coupled system depends on not only the
coupled dynamics, but also the spatial structure of stochastic forcing.
D. References
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