Download Regional Atmospheric Circulation and Rainfall Variability in South

15 JANUARY 2015
DEZFULI ET AL.
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Regional Atmospheric Circulation and Rainfall Variability in South
Equatorial Africa
AMIN K. DEZFULI, BENJAMIN F. ZAITCHIK, AND ANAND GNANADESIKAN
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland
(Manuscript received 7 May 2014, in final form 17 October 2014)
ABSTRACT
This study examines daily precipitation data during December–March over south equatorial Africa (SEA) and
proposes a new zonal asymmetric pattern (ZAP) that explains the leading mode of weather-scale precipitation
variability in the region. The eastern and western components of the ZAP, separated at about 308E, appear to be
a consequence of an anomalous zonal atmospheric cell triggered by enhanced low-level westerly winds. The
enhanced westerlies are generated by a diagonal interhemispheric pressure gradient between the southwestern
Indian and north tropical Atlantic Oceans. In eastern SEA these winds hit the East African Plateau, producing
low-level convergence and convection that further intensifies the westerlies. In western SEA a subsiding branch
develops in response, closing the circulation cell. The system gradually dissipates as the pressure gradient
weakens. Through this mechanism, simultaneous changes in two hemispheres generate a regional zonally oriented circulation that relies on climatic communication between eastern and western equatorial Africa.
1. Introduction
South equatorial Africa (SEA) is a climatically diverse
region comprising the Congo basin to the west and the
East African Plateau (EAP) to the east. Because of the
lack of conventional weather data and a tendency for researchers to treat East (Latif et al. 1999; Mutai and Ward
2000; Williams and Funk 2011) and western (Balas et al.
2007; Dezfuli and Nicholson 2013; Hirst and Hastenrath
1983) Africa as separate regions, the meteorological
dynamics of SEA have received relatively little attention, particularly at subseasonal time scales. Climate
variability in the SEA is a result of interactions among
several features acting on local and global scales. The
eastern sector of this region is most strongly influenced
by the Indian Ocean (Black et al. 2003; Seidel et al. 2008;
Ummenhofer et al. 2009), whereas the western part is
controlled by tropical Atlantic variability (Hirst and
Hastenrath 1983; Pokam et al. 2012; Vigaud et al. 2007).
Denotes Open Access content.
Corresponding author address: Amin K. Dezfuli, Department of
Earth and Planetary Sciences, Johns Hopkins University, 301 Olin
Hall, 3400 North Charles St., Baltimore, MD 21218.
E-mail: [email protected]
DOI: 10.1175/JCLI-D-14-00333.1
Ó 2015 American Meteorological Society
Indian and Atlantic Ocean variability may in turn reflect
remote forcing from the tropical Pacific, which modulates the location and intensity of major zonal atmospheric cells (Alexander et al. 2002; Dezfuli and
Nicholson 2013; Giannini et al. 2008; Nicholson and
Dezfuli 2013). These large-scale drivers explain a significant fraction of precipitation variability in East Africa on
interannual and multidecadal (Tierney et al. 2013) time
scales. However, the drivers of precipitation variability
over the Congo River basin (Fig. 1a) are less well understood because of the lack of reliable gauge data and
a subsequent lack of research over this region. That research, which does exist, suggests that there is not
a consistent response to global drivers across the whole
basin. This has been explained in part by the fact that the
eastern and western portions of the basin respond differently to the large-scale features, with the central area
acting as a transition zone that is sometimes in phase
with the western sector and sometimes with the eastern
sector (Dezfuli and Nicholson 2013). Nevertheless, a full
understanding of the global drivers of the Congo basin
climate deserves further investigation. At synoptic
scales, migration of the intertropical convergence zone
(ITCZ) and strength and location of regional atmospheric jets have been associated with interannual variability in seasonal rainfall (Farnsworth et al. 2011;
Laing et al. 2011; Pokam et al. 2012).
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FIG. 1. (a) Topographic map of south equatorial Africa (SEA) and long-term mean (1998–
2011) horizontal wind vectors at 850 hPa during December–March. The annual cycle of precipitation (mm) using TRMM data for the period 1998–2011, averaged over 12.58S to equator
for (b) western south equatorial Africa (108–308E) and (c) eastern south equatorial Africa (308–
408E). Red bars show the months used in this study.(d) Meridionally averaged (12.58S–08) cross
section of long-term mean vertical wind (shading) and zonal wind (contours, m s21) fields
during December–March, using NCEP–NCAR Reanalysis 2 data. Negative (positive) values of
the vertical wind represent upward (downward) motion.
However, there is significant heterogeneity in rainfall
variability within the region that cannot be explained by
large-scale dynamics alone (Balas et al. 2007; Dezfuli
2011; Dezfuli and Nicholson 2013). The internal geography of SEA, particularly topography, has been identified as a major driver of precipitation variability.
Topography influences the distribution of precipitation
within the EAP (Hession and Moore 2011; Oettli and
Camberlin 2005), and the abrupt contrast between the
lowland Congo rain forest and the EAP highlands has
been implicated in the initiation of convectively coupled
Kelvin waves and associated mesoscale convective systems (MCS; Farnsworth et al. 2011; Nguyen and Duvel
2008), which are particularly strong in western SEA
(Collier and Hughes 2011; Mohr and Zipser 1996). The
relative importance of regional versus large-scale dynamics to precipitation climatology and variability is an
area of active investigation (Nguyen and Duvel 2008).
Disentangling the influence of local and large-scale
drivers of precipitation variability in SEA has been
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FIG. 2. (left) Spatial pattern of the first EOF of 3-day mean rainfall of (a) December, (c) January, (e) February, and (g) March using
TRMM data. (right) Composite of extreme (positive 2 negative) phase of rainfall corresponding to the first EOF of (b) December,
(d) January, (f) February, and (h) March. A positive phase is defined when the eastern SEA (308–408E) and western SEA (108–308E)
simultaneously experience a wet and dry condition, respectively. The reverse condition is defined as the negative phase.
particularly challenging because of the limited amount
of high-quality, daily meteorological data over much
of the region. However, recent advances in satellite
observations and reanalysis data have improved the
quality and resolution of meteorological records and
consequently facilitated climate research on this historically data-limited region, though some concerns for
using these datasets persist due to the lack of gauge
records for validation in the Congo basin. Here, we use
daily satellite-derived precipitation estimates in combination with meteorological reanalysis to understand
the mechanisms that control rainfall variability across
SEA on short time scales. We focus on austral summer
(December–March), when SEA is under the influence
of the winter phase of the Indian monsoon and when it
receives approximately half of its total annual average
precipitation (Figs. 1b,c). This period that has been
poorly studied relative to other seasons in SEA (spring,
summer, and fall), when the Greater Horn of Africa
receives most of its precipitation (e.g., Camberlin 1997;
Williams et al. 2012). In contrast to previous studies, we
consider the SEA as a whole, rather than focusing exclusively on the EAP or the Congo basin. This approach allows us to examine how these neighboring
sectors may communicate. Of particular interest is the
influence that the plateau has on dynamic and thermodynamic aspects of the circulation affecting both
eastern and western portions of SEA.
2. Data
The rain gauge network in SEA, particularly over the
western part, is very sparse, temporally sporadic, and
mainly available at a monthly time scale. This study,
however, requires a rainfall data, which is spatially complete, continuous in time, and available at a daily time
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scale. For this reason, we use daily precipitation data
from the Tropical Rainfall Measuring Mission (TRMM)
Multisatellite Precipitation Analysis (TMPA) product
3B42v7 (Huffman et al. 2010), which is available at
0.258 3 0.258. All atmospheric variables are obtained from
the National Centers for Environmental Prediction–
National Center for Atmospheric Research (NCEP–
NCAR) Reanalysis 2 datasets at 2.58 3 2.58 resolution
(Kistler et al. 2001). Previous research (Washington et al.
2013) has identified large discrepancies between various
model and satellite datasets, and therefore other satellitederived rainfall estimates and reanalysis products may show
different results. For this reason, we repeated our analysis
using the ERA-Interim reanalysis product. Results were
very similar, so we do not include the ERA-Interim figures
in this paper. The period for this study is 1998–2011, determined by the availability of TRMM data. The study
area is SEA, which extends coast to coast from the Atlantic
to Indian Ocean between 12.58S and the equator.
3. Empirical orthogonal function (EOF) analysis of
rainfall
In all four months of the analysis period—December
through March—the first EOF of the 3-day mean rainfall exhibits a similar zonal asymmetric pattern (ZAP)
(Fig. 2). This pattern is most evident for 3-day composites, but it is also the dominant mode of variability for
precipitation composited on any period from 2 to 5 days
(we tested EOF for averaging periods ranging from 1 to
15 days). In all four months this first principal component (PC) of variability is almost perfectly correlated
with east-minus-west regional rainfall. For each calendar month, therefore, we select five extreme positive and
negative cases out of the 14-yr record on the basis of
east-minus-west rainfall difference. The positive and
negative composites are then created by compiling these
extreme cases for all four months. EOF analysis is performed separately for each month in order to weight all
four months equally in the analysis. This approach also
allows us to avoid the impact of the seasonal cycle in
EOF analysis, although it is negligible for these four
months (Figs. 1b,c). It is worth mentioning that the latitudinal extent of the study area was determined by first
performing an EOF analysis over a region that spanned
208S–108N (not shown). The areas with zero or nearzero rainfall values in that broader analysis region were
excluded to reduce any artifacts introduced by regions
that are in their dry season and receive minimal precipitation
during the period of analysis, as were latitudes with no
significant structure in EOF loadings. When the EOF
analysis is repeated for other months (April–November)
the characteristic ZAP pattern is not observed.
FIG. 3. (a) Meridionally averaged (12.58S–08) composite of (positive 2
negative) phase of the vertical wind (shading) and zonal wind (contours, m s21) using NCEP–NCAR Reanalysis 2 data. The dark brown
shading shows the mean vertical cross section of topography. The
brown (black) thick dashed lines indicate the areas where the difference between positive and negative phases of the vertical wind
(zonal wind) is significant at the 5% level, using a Mann–Whitney
U test. (b),(c) As in (a), but for the composites of positive and negative phases, respectively.
4. Dynamical analysis of the ZAP
The December–March season is coincident with the
winter Indian monsoon, during which low-level northeasterly winds flow from the Indian Ocean toward
equatorial Africa and extend to about 308E. The wind
over the western half of SEA is weak westerly (Fig. 1a),
partly driven by the cyclonic flow associated with a heat
low over land in Southern Hemisphere Africa due to
heating over subtropical deserts and highlands. Upperlevel winds are easterly, and may be generated or
intensified by four different components: upper-level
anticyclonic flow associated with the near-surface heat
low, a Walker-like circulation in the equatorial Indian
Ocean, a regional thermodynamically driven circulation
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FIG. 4. Composite of (positive 2 negative) phase of horizontal
wind vectors and MFD (shading), using NCEP–NCAR Reanalysis
2 data. The winds are vertically averaged over 850–700-hPa levels
and MFD is averaged over 850–300 hPa. The negative (positive)
values of MFD represent flux convergence (divergence). The green
dashed box shows the study area. The brown thick dashed lines
indicate the areas where the difference between positive and
negative phases of MFD is significant at 5% level, using a Mann–
Whitney U test. The wind vectors are shown only if both zonal and
meridional components of the difference between positive and
negative phases are significant.
in SEA, and the northward branch of the outflow associated with convective activity in southwest Indian
Ocean that is deflected westward by the Coriolis effect
(Nicholson and Grist 2003). A distinct feature specific to
the December–March season is a rising cell covering the
entire SEA (Fig. 1d). This cell is confined between two
sinking branches in the eastern Atlantic and western
813
Indian Oceans; the latter is relatively weak and narrow.
This pattern can be distinguished from that observed
during the summer phase of the Indian monsoon, when
the subsiding cell extends over the entire SEA and the
adjacent oceans. Another characteristic of this season is
that the region of maximum rainfall in tropical Africa
appears in the Southern Hemisphere, consistent with the
ITCZ migration.
For every month in the December–March study period, analysis of daily satellite-derived precipitation records reveals that the first EOF of the 3-day mean
precipitation, explaining about 14% of the total variance, is an east–west asymmetric pattern between the
Congo basin and the EAP (Fig. 2). This pattern, the
ZAP, is apparent during December–March, and is weak
or absent in the other months. The separating longitude
of the eastern and western components of the ZAP runs
along the western edge of the East African Plateau. We
define a positive phase when the eastern and western
sectors experience simultaneously a wet and dry condition, respectively. The reverse condition is defined as the
negative phase. The composite of extreme positiveminus-negative phase of rainfall is similar to the first
EOF pattern (Fig. 2). Considering the vertical wind
(Fig. 3) as well, it is clear that the positive and negative
phases of the ZAP correspond to a true reversal in the
zonal location of rising motion and precipitation in the
region. Such a reversal is meaningful from a meteorological and hydrological perspective, motivating exploration of its controlling factors.
FIG. 5. Time evolution of horizontal pattern of geopotential height at 850 hPa (shading) and wind vector (averaged over 850–700 hPa)
anomalies using NCEP–NCAR Reanalysis 2 data. The panels show the composite of (positive 2 negative) phase during (a)–(f) days 6–1
before the composites onset, (g)–(i) days 1–3 of composites, and (j)–(l) days 1–3 after the last day of the composites. The green dashed box
shows the study area. The brown thick dashed lines indicate the areas where the difference between positive and negative phases of
geopotential height is significant at the 5% level, using a Mann–Whitney U test. The wind vectors are shown only if both zonal and
meridional components of the difference between positive and negative phases are significant.
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FIG. 6. Longitude–time (day) Hovmöller diagrams of geopotential height anomalies at 850 hPa using NCEP–NCAR Reanalysis 2 data.
The anomalies are taken along (a) 22.58S and (b) 58N for the positive; and (c) 22.58S and (d) 58N for the negative composites. (e) Longterm mean (1998–2011) of geopotential height at 850 hPa (contours). The horizontal red lines show the longitudes along which the
Hovmöller diagrams in (a)–(d) are produced. The line AA0 corresponds to (a),(c); and BB0 corresponds to (b),(d). The green dashed box
shows the location of SEA. In (a)–(d), day 0 on the y axis is the onset of the composites, so that the 12-day time sequence is the same as that
used in Fig. 5. Because the mean geopotential height in the southwest Indian Ocean and north equatorial Africa is not the same, for the
sake of presentation, two different arbitrary values are subtracted from the heights of these two regions, so that the anomalies would fall
within a comparable range. Note that this does not affect the magnitude of the diagonal pressure gradient.
To unravel the driving factors of the ZAP, several
atmospheric variables are examined during positive and
negative phases. Results show that during the positive
phase, wind is predominantly westerly within the
moisture-abundant lower troposphere and easterly at
upper levels (Fig. 3). The westerlies carry local moisture
from the Congo basin toward the eastern highlands.
Moisture availability over the western section, however,
does not seem to play a role in rainfall variability, as
there are negligible changes in this field between positive and negative phases (not shown). The spatial pattern of the moisture flux divergence (MFD), on the
other hand, demonstrates a sharp contrast in the zonal
direction (Fig. 4). The separating longitude between flux
convergence in the east and divergence in the west is
consistent with the ZAP. In addition to MFD, the longitudinal movement of the rising cell over the region
determines the location of the precipitation maximum.
The core of the maximum upward motion appears at
about 158E in the negative phase and moves some 158
eastward during the positive phase (Figs. 3b,c). This
variation is coincident with spatial changes of the MFD
and precipitation fields. The configuration of vertical
motion along with the zonal wind in the lower and upper
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FIG. 7. Schematic illustration of key components of the zonal asymmetric pattern (ZAP) during a positive phase.
The yellow arrows show the zonal atmospheric circulation. The red (blue) regions present positive (negative)
pressure anomalies. The low-level green arrow shows low-level winds driven by the pressure gradient force and
deflected by the Coriolis force. The upper-level green arrow depicts the outflow produced by the convective cell in
the southern Indian Ocean, deflected by the Coriolis force.
troposphere forms an anomalous counterclockwise circulation in the east–west direction. The surface and
eastern branches of the circulation are stronger than the
upper-level and western components.
The geographic contrasts in MFD and the wind
anomalies associated with the ZAP (Fig. 4) are spatially
coincident with the topographic contrast between the
Congo basin and the eastern highlands (Fig. 1a). This
suggests that interaction between near-surface atmospheric features and the east–west topographic contrast
within SEA plays a role in triggering the ZAP. For this
reason, we examine variations in lower-troposphere
winds and geopotential heights associated with the
ZAP (Fig. 5). To begin to understand the causality of the
system, the evolution of the circulation is examined from
6 days before to 3 days after the precipitation composites. This analysis was performed on a larger domain in
order to detect the potential forcing from adjacent regions including the Indian and Atlantic Oceans.
Several days before the onset of the ZAP, the winds
and geopotential heights at 850 hPa show nearly no
contrast between the positive and negative phases
(Fig. 5a). As time progresses, an area of low pressure
anomaly develops in the southwestern Indian Ocean. At
the same time, a high pressure anomaly forms to the
northwest of the study area (Figs. 5b–d). This region of
high pressure is centered in the low latitudes of the
Northern Hemisphere, with an influence that extends
across the equator. These two regions of opposite
pressure anomalies produce a diagonal pressure gradient. This gives rise to a northwest–southeast-oriented
flow north of the equator that is deflected by the Coriolis
force as it crosses into SEA, yielding anomalous westerly winds. These westerlies are responsible for transporting the moist convective cell from the western to the
eastern sector and for initiating low-level convergence
as they hit the mountains. This convergence and topographically forced convection feeds into the rising cell
and further intensifies it.
The diagonal pressure gradient and the corresponding
westerlies strengthen and reach their maximum one day
before the ZAP is at its most intense (Figs. 5e,f). The
well-developed upward motion over the highlands generates an upper-level outflow (not shown), the easterly
component of which travels toward the western part of
the SEA and contributes to the upper branch of the circulation cell. The subsiding branch, in the western sector,
is weaker and delayed relative to rising motion in the east.
It is a product of surface divergence associated with nearsurface westerlies and, to a lesser degree, weak upperlevel convergence. The circulation gradually dissipates as
the diagonal pressure gradient weakens (Figs. 5i–l).
Variability of the pressure anomaly in the southwestern
Indian Ocean is controlled by changes in intensity and
longitudinal extent of the subtropical high or Mascarene
high. During the positive phase, the western edge of the
Mascarene high moves away from the southwestern Indian Ocean while the center remains relatively fixed,
allowing the low pressure region to grow (Fig. 6a). This
expansion of the low pressure region starts about 6 days
prior to the positive phase and expands eastward, reaching
maximum extent during the mature positive phase. Simultaneously, the region of high pressure anomaly, located to the northwest of the study area, develops and
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FIG. 8. Composites of (positive 2 negative) phase anomalies of OLR (shading) and horizontal wind vectors averaged over 850–700 hPa during (a) the three days of the composites, and
(b) three days prior to the composites onset, using NCEP–NCAR Reanalysis 2 data. The green
dashed box shows the location of SEA. The red box shows the region where tropical-temperate
troughs (TTTs) are most active and associated with rainfall behavior in southern Africa. The
OLR field is used to represent the ITCZ and the cloud bands associated with the TTTs. The
brown dashed lines indicate the areas where the difference between positive and negative
phases of the OLR is significant at 5% level, using a Mann–Whitney U test. The wind vectors
are shown only if both zonal and meridional components of the difference between positive and
negative phases are significant.
expands eastward at about the same rate (Fig. 6b). These
concurrent changes in pressure anomalies provide favorable conditions for initiation of the zonal circulation.
During the negative phase, the Mascarene high expands
westward over the southwestern Indian Ocean, and the
pressure anomalies of the Northern Hemisphere component decrease, resulting in a weakened or reversed diagonal pressure gradient (Figs. 6c,d).
5. Discussion
Key elements of the circulation associated with a positive phase of the ZAP are presented schematically in
Fig. 7. The ZAP develops in the presence of a crossequatorial pressure gradient and the zonal topographic
contrast within SEA. The circulation initiates when simultaneous changes in pressure anomalies are favorable
in both hemispheres. As a consequence of the specific
configuration of pressure gradient and topography,
the surface and eastern components of the circulation
develop earlier and are stronger than the western and
upper-level components. Upper-level easterlies are primarily driven by outflow from EAP convection. The
upper-level southerly flow produced by a convective cell
in the southwestern Indian Ocean partially contributes to
the ZAP by inducing cold advection from the east, which
enhances the static instability over the eastern sector. In
the negative phase, this convective cell is suppressed. The
Southern Hemisphere heat low over land shows little
variability between positive and negative phases.
The ZAP is reasonably independent from the driving factors of interannual variability and from remote
phenomena acting on seasonal time scales such as
ENSO, as extreme positive and negative phases may
appear in the same month and year and the ZAP does
not extend far into the oceans surrounding SEA. The
ZAP is also independent from the ITCZ, as the ITCZ
passes through the region in November and March but
is located to the south of it in December–February,
and from tropical-temperate troughs (TTTs), which
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DEZFULI ET AL.
significantly impact summer synoptic systems in
southern Africa and the south Indian Ocean convergence zone (SICZ; Cook 2000; Fauchereau et al. 2009;
Manhique et al. 2011) but show no evidence of influence on the ZAP. This is apparent in Fig. 8, which
shows that the maximum contrast in anomalies of the
horizontal winds and outgoing longwave radiation (OLR)
are confined within the latitudes 08–158S, and are negligible
to the south of SEA where TTTs are most active. This also
implies that the Angola low that impacts the TTTs initiation
and modulates the moisture transport from the Indian
Ocean into southern Africa just to the south of SEA (e.g.,
Cook et al. 2004; Reason and Jagadheesha 2005; Macron
et al. 2014) may not be relevant to the variability of the
ZAP. However, changes in the Mascarene high intensity,
which can alter the location of the SICZ (Cook 2000; Todd
and Washington 1999), may provide a link between the
SICZ and the eastern extension of the ZAP.
The precipitation pattern characterized in this study
provides a mechanism of atmospheric communication
between eastern and western equatorial Africa—two
regions that are generally treated as climatically separate units. In the datasets studied here, this pattern is the
dominant source of weather-scale variability in precipitation for SEA. Under climate change, the position
and intensity of subtropical highs are projected to
change due to widening of the tropics (Li et al. 2012;
Seidel et al. 2008). Additionally, the intensity of precipitation over equatorial Africa is projected to change
(Cook and Vizy 2013; Hulme et al. 2001; James et al.
2013). Both of these trends have implications for the
intensity and frequency of the ZAP and the occurrence of
extreme wet and dry spells in the region. Also, further
analysis is needed to explore the controls on pressure
anomalies in both hemispheres that contribute to generation of the ZAP, as well as the role of the equatorial
Kelvin waves that have been shown to interact with the
MCSs over SEA (Nguyen and Duvel 2008).
Acknowledgments. We thank the two anonymous reviewers for their constructive comments. The first author
would also like to acknowledge the Cormack Postdoctoral
Fellowship of the Earth and Planetary Sciences Department
at Johns Hopkins University that partly supported his
work on this project. This study was also supported in
part by NASA Applied Sciences Grant NNX09AT61G.
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