Name of Lecturer: Rowan Sutton
Title:
Theory
and
modeling
of
low
frequency variability in the tropical Atlantic
ocean and atmosphere.
Date of Lecture: 28 of July
Notes by: Marcelo Barreiro
Emanuele Di Lorenzo
1. Introduction
Several studies have demonstrated the existence of interannual and decadal variability in the
rainfall of northeast Brazil and Sahel regions. This climate variability has been shown to be
linked to local tropical Atlantic sea surface temperature (SST) changes, as well as to remote SST
in the Pacific. (e.g. Hastenrath 1985, Moura and Shukla 1981). Also, SST variability in the
tropical Atlantic region may have lead to the observed long term variations in hurricane activity.
By comparison with the extratropics the tropical atmosphere is very sensitive to small changes in
SST. This fact may allow the existence of coupled ocean-atmosphere processes, which can
explain a significant portion of the regional climate variability and have global implications like
El Niño in the tropical Pacific ocean. However, the tropical Atlantic is not dominated by a single
process like the Pacific ocean, but its climate is the result of various competing processes, some
of which are highlighted in figure 1. Another possible ingredient having a key role in the region
is the thermohaline circulation (THC); it is believed that changes in the tropical ocean due to the
THC could further influence the tropical atmosphere.
In the following sections we will review the current understanding of the interannual and decadal
sources of variability of the upper ocean and atmosphere in the tropical Atlantic region.
2. Mean state of the tropical Atlantic ocean–atmosphere system
The tropical Atlantic region extends roughly from 300S - 300N and from 100E - 450W (about
6000 km). The ocean is forced by the atmosphere through the Trade winds, which generate
Ekman divergence on the Equator and downwelling to the south and north of it. On average the
main currents are the South Equatorial Current (SEC) flowing to the west between 30N - 80S, the
North Equatorial Counter Current (NECC) flowing to the east between 30N - 80N and the
Equatorial Undercurrent flowing to the east at the equator at a depth of 50 to 300 meters. In the
atmosphere the most important feature is the Intertropical Convergence Zone (ITCZ), a zone of
strong convective activity which moves seasonally from 30S in March to 100N in August.
3. Variability of the ocean in the tropical Atlantic region
The different processes acting on SST variability can be studied with a simple model of the heat
budget of the oceanic mixed layer. Separating a seasonally varying mean from the anomalies, the
evolution of large-scale anomalies can be described through anomalous advection due to Ekman
transport and geostrophic currents acting on the mean SST gradient as well as by mean currents
acting on the SST anomaly gradient. Further effects in the SST are caused by anomalous
entrainment and variations of mixed layer depth, diffusive processes and heat fluxes.
Many of these processes are affected by the wind stress. Changes in the windspeed can affect the
latent and sensible heat fluxes. Near the equator the windstress changes impact the Ekman
advection, Ekman pumping and equatorial/coastal upwelling. These changes in Ekman
pumping/upwelling affect the thermocline depth, Rossby/Kelvin waves patterns and the
geostrophic currents. Entrainment is also controlled by wind mixing energy. The relative
importance of these dynamical processes in SST variability is difficult to estimate from
observations. For this reason modeling studies have been carried out to isolate and quantify the
individual processes.
a. Model studies of low frequency variability in the tropical Atlantic ocean
The first modeling study presented is by Carton et al. (1996) on interannual variability in the
tropical Atlantic. In their study they conducted a GCM simulation forced with observed winds
and parameterised fluxes from 1960 to 1989. The goal of the study is to investigate the relative
importance of heat flux and momentum forcing. The model is able to capture the observed SST
variations. Two dominant timescales for variability of SST are identified: a decadal timescale
that is controlled by latent heat flux anomalies (induced by windspeed variations) and is
primarily responsible for SST anomalies off the equator, and an equatorial mode with a timescale
of 2-5 years that is dominated by dynamical processes (controlled by near-equator wind
changes). The interhemispheric gradient of anomalous SST (the so called SST "Atlantic Dipole")
is
primarily
linked
to
the
former
process
and
thus
results
from
the
gradual
strengthening/weakening of the trade wind system of the two hemispheres.
A second study presented is by Seager et al. (2000) on the role of the ocean in tropical Atlantic
decadal climate variability. They run a 40-year simulation (1958-98) with observed winds using
an atmosphere mixed layer model, focusing on the causes of decadal SST variability. Their
results show that off-equator SST anomalies are forced by surface fluxes (induced by windspeed
as in Carton et al., 1996) and damped by the ocean heat transport. The dominant contribution to
the heat transport is advection of the anomalous temperatures by the mean poleward Ekman
flow. On the equator they find that SST anomalies are forced by anomalous upwelling and
damped by the mean upwelling. Overall, their results suggest that the role of the ocean in tropical
Atlantic decadal SST variability is largely passive (forced by winds) and damping.
Huang and Shukla (1997) performed a Principal Oscillations Patterns analysis of the upper ocean
heat content output from a GCM simulation forced with observed winds and parameterized
fluxes (1964-87). They find that the interannual variations are associated with tropical oceanic
waves stimulated by the fluctuations of the equatorial easterlies. A decadal mode is also found,
which is associated with the ocean's adjustment in response to a basin-wide out-of-phase
fluctuation between the NE and SE trade winds. This dynamical process excited by cross-equator
winds can have subsequent influence on equatorial SST, but there is little evidence for an effect
on off-equator SST except near the coast.
b. Ocean teleconnections to the tropical Atlantic region
There are suggestions that changes of the Meridional Overturning Circulation (MOC) may be
driven from the high latitudes on decadal timescales. According to observations, the Labrador
Sea Water (LSW) thickness varies significantly on this time scale. Sea surface temperature (SST)
in the tropical Atlantic also exhibits considerable decadal changes, the so-called tropical Atlantic
SST Dipole. Yang (1999) proposes that the SST dipole and variations of LSW thickness are
linked through the Meridional Overturning Circulation (MOC). Southward transport of LSW
along the deep western boundary must be compensated by northward flow in the upper ocean. It
is suggested that the pressure signal is communicated from high latitudes to the tropics by
coastally trapped waves. The response of the tropical upper ocean generates a dipole pattern in
the SST field. The correlation between observed LSW thickness and SST with a lag of 5 years is
significant at 95% level.
Another possible teleconnection mechanism is associated to heat content propagation round the
subtropical gyres, which could enter the subtropics and possibly influence SST (Mehta 1998).
These mechanisms could be particularly important for low frequency variations and might offer
long-lead predictability.
c. Summary of low frequency variability in the upper tropical Atlantic ocean
1. Most of the low frequency variability is driven by the atmosphere .
2. Near equator SST variability is largely governed by wind-driven upwelling.
3. Off-equator SST variability is mainly driven by windspeed induced fluctuations in latent
heat flux, and damped by anomalous ocean heat transport.
4. Wind-driven upwelling is important along the coast of Africa.
5. Variability in wind stress curl drives variations in off-equator heat content but little
evidence of any subsequent influence on SST.
6. Oceanic teleconnections associated with changes in the meridional overturning
circulation or anomalous subduction in the subtropics may influence the tropical Atlantic,
but their impact on SST is yet unclear.
7. It has been shown that the seasonal and interannual variability in freshwater budget is
dominated by precipitation (Yoo and Carton 1990). Its importance on decadal timescales
is not clear yet, however. Much more work is needed in order to clarify the potential role
of freshwater budget on low frequency variability in SST.
4. Variability of the atmosphere in the tropical Atlantic region
The atmospheric variability in the tropical Atlantic region can be conceptualized as a
combination of internal variability, which is essentially unpredictable, and forced variability,
which is potentially predictable and arises mainly as a response to changes in SST.
a. Atmospheric response to SST.
The basic mechanisms of the steady response of the tropical atmosphere to SST anomalies can
be expressed by linear dissipative shallow water models, the solutions of which can often be
interpreted in terms of stationary Rossby and Kelvin waves. Two mechanism have been
proposed:
Matsuno/Gill mechanism (Gill 1980): positive SST anomalies generate an increase in
atmospheric convection which leads to an increase of the latent heat release in the middle
troposphere, which in turn generates anomalous ascent, pressure gradients and wind anomalies.
Lindzen/Nigam mechanism (Lindzen and Nigam, 1987): SST anomalies cause changes in
atmospheric boundary layer temperature creating anomalous surface pressure gradients, winds
and low level mass convergence which may influence convection.
b. Internal and Forced atmospheric variability
In order to separate the contributions of internal and forced variability to the total variance, an
ensemble of integrations using an atmospheric general circulation model (AGCM) forced with
SST boundary conditions may be used. Sutton et al. (2000) separated the internal and forced
variability of the tropical atmosphere in the Atlantic region using the signal-to-noise maximizing
Empirical Orthogonal Function (S/N-EOF) analysis (see colloquium notes; also Venzke et al,
1999, and Chang et al, 2000).
According to the results of Sutton et al (2000) the low frequency internal variability is dominated
by the equatorward extension of extratropical modes of variability (see fig. 2). Particularly
important is the southward extension of the North Atlantic Oscillation, which generates largest
fluctuations in the northeast (NE) trades during seasons December-February (DJF) and MarchMay (MAM).
In the deep tropics the internal variability is much lower than in off equatorial regions and the
atmospheric variability is mainly governed by boundary forcing, particularly by changes in SST.
Three particular SST patterns and associated atmospheric responses have been identified to play
a role in the dynamics of the tropical Atlantic region. A remote response to SST in the tropical
Pacific, which is characterized by weakening of the Atlantic NE trades during El Niño events,
particularly in DJF season (Curtis and Hastenrath 1995, Enfield and Mayer 1997).
Together with this remote influence are two competing responses to local SST patterns. A local
atmospheric response to fluctuations in the cross-equator SST gradient, which consists in a cross
equator flow, particularly during MAM season, directed toward the hemisphere in which SST is
anomalously high and shifting the ITCZ from its climatological position (Moura and Sukla
1981). Chang et al (1997) first proposed that this interplay between the ocean and the atmosphere
may be the manifestation of a coupled mode, which arises as consequence of a thermodynamic
feedback having an associated decadal time scale (the "Atlantic Dipole" mode). Further work,
using S/N-EOF analysis, showed that this "Dipole" appears as the dominant mode of forced
response to SST throughout the whole year (see fig 3). In agreement with Chang et al, 1997 there
is an indication of positive feedback between wind-induced surface heat flux and SST anomalies
within the deep tropics, particularly in the western tropical Atlantic warm pool region (fig.3).
Other studies (Sutton et al., 2000) did not find any evidence of a thermodynamic feedback and
propose that the Atlantic Dipole should be viewed simply as a sensitivity of the atmosphere to
variations in the cross-equator SST gradient. Furthermore, Saravanan and Chang (2000) reported
that ENSO signal contributes significantly to the "Dipole" mode by looking at correlations
between NNE Brazil rainfall and Atlantic SST (Moura and Shukla 1981). Saravanan and Chang
(2000) noted that in the absence of the ENSO signal these correlations become significantly
weaker north of the Equator, implying that the "Dipole" may not be a truly local mode of
variability.
The second local atmospheric response arises due to variations in central/eastern equatorial
Atlantic SST, and consists in zonal wind anomalies to the west of the SST maximum and in
convergence towards the SST maximum. Zebiack (1993) first suggested that associated with this
response is a coupled mode of interannual variability that is analogous to Pacific ENSO and has
its largest manifestation during June-August (JJA) and September-November (SON) seasons.
The existence of this coupled mode was supported by recent works, e.g, Chang et al. 2000,
Sutton et al. 2000.
Sutton et al. (2000) quantified the contribution of SST forced and internal variability to the total
variance of three chosen indices of atmospheric variability: NE trades, cross equatorial flow and
equatorial trades (see fig. 4). According to their results the NE trades are dominated by internal
variability, while the cross equatorial flow is dominated throughout the year by a Dipole SST
index which explains about 70% of total variance in MAM season. Finally, the equatorial trades
variability is mainly forced and dominated by the Dipole and equatorial SST indices. These
results show that there is high intrinsic potential predictability (maximum in MAM season) for
fluctuations of winds near the Equator, but it falls of rapidly with increasing latitude where
internal variability is large.
5. Variability of rainfall in North Africa and land-surface processes.
The location of North Africa with coasts to the eastern tropical north Atlantic makes its climate
sensitive to SST forced and internal variability as well as to land-surface processes.
As found above the influence of SST forcing due to fluxes of heat on climate variability is
greatest at low latitudes. Therefore SST forced variability is expected to have larger impact than
internal variability over that region. However, land-surface processes, not considered yet, may
also provide an additional means to explain a fraction of the total variability.
A key surface variable for land-surface processes is the albedo, which affects the local and global
radiative balance. Also, variations in soil moisture influence evaporation, heat capacity of land
(memory) and may feedback to affect the albedo. A third important ingredient in land surface
processes is the vegetation cover and type. Its changes may modify not only the local albedo and
evaporation, but also water retention and surface roughness.
Possible land-surface feedback mechanisms are summarized in Rowell et al, 1995:
1) A diminution of soil moisture during periods of drought lessens the evaporation necessary to
help convection, which in turn leads to negative rainfall anomalies increasing drought.
2) Reduced vegetation increases surface albedo causing anomalous local subsidence, which
leads to reduced in rainfall.
3) Reduced vegetative cover may lower values of surface roughness to a degree which changes
precipitation patterns through variations in low-level wind convergence.
4) Drought and loss of vegetation may increase the generation of atmospheric dust affecting
radiation budget, which may also lead to climate effects.
These feedback mechanisms have often been suggested to explain part of the decadal variability
in North Africa. However, a problem with some of these ideas is the lack of a convincing
explanation about how the memory arises from one year to the next.
Rowell et al (1995) investigated the relative importance of land-surface processes, SST forced
and internal variability in the variability of rainfall in the rainy season (JAS) over tropical North
Africa. They performed three sets of experiments with an AGCM including "bucket" hydrology
in order to isolate the relative importance of each source of variability.
Their results showed that the variability in seasonal rainfall over tropical North Africa is mainly
controlled by global SST, with only a small influence from internal variability. Consequently,
the intrinsic potential predictability is very large, greater than 80%. Also, different regions of
SST may be important on different time scales. The land surface moisture feedback was found to
play an important role during particular years, but in general SST forcing was still the dominant
process affecting low frequency rainfall variability.
References
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Chang, P., L. Ji, and H. Li, 1997: A decadal climate variation in the tropical Atlantic ocean
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Figure 1.
Figure 2. Dominant empirical orthogonal function of internal variability in tropical Atlantic
surface wind streess for different seasons. The vectors are scaled so as to indicate the typical
(one standard deviation) magnitude (in Nm-2) of fluctuations in wind stress associated with this
mode (after Sutton et al, 2000).
Figure 3. The leading signal-to-noise maximizing EOFs from the (left column) GOGA, (middle
column) TOGA, and (right column) TAGA experiment. (top) SST anomalies regressed onto the
(bottom) time series of the dominant forced responses. (c)–(e) and (f)–(h). Spatial patterns of
surface heat flux and surface wind stress vectors of the dominant forced response. (bottom) The
time series of the dominant forced responses (solid) and the cross-equatorial SST gradient index
(dashed). Correlations between the two time series are indicated in the lower left corner of each
panel (after Chang et al. 2000).
Figure 4. Contributions to the variance in three indices of surface wind stress variability: a) NE
trades index (zonal wind stress averaged over the region 100-200N; 500-100W); b) cross-equator
flow index (meridional wind stress averaged over the region 40S-40N; 500-200W; c) equatorial
trades index (zonal wind stress averaged over the region 40S-40N; 400-200W). The upper panels
show the total interannual variance (solid lines) and the internal variance (dotted lines) of the
index as a function of season. (Units are 10-5 (Nm-2)2.) The difference between these two lines is
the SST-forced variance. The lower panels show the fraction of the SST-forced variance that can
be accounted for by linear relationship to one of the SST indices: ENSO SST index (solid lines);
Dipole SST index (dotted lines); ATL3 SST index (dashed lines) (after Sutton et al.2000).