1. No category

Global Journal of Agricultural Research and Reviews
ISSN: 2437-1858 Vol. 3 (2), pp. 133-145, May, 2015.
© Global Science Research Journals
http://www.globalscienceresearchjournals.org/
Full Length Research Paper
Tropical Atlantic variability impacts on the Sub-middle
São Francisco Valley, a Brazilian wine-producing area
Dóris Veleda1,2*, Raul Montagne2,3, Moacyr Araujo1,2, Giuliano Pereira4, Pedro Tyaquiçã1,2,
Carlos Noriega1,2, Francis Lacerda5
1
Departamento de Oceanografia, Universidade Federal de Pernambuco (UFPE), Recife, Pernambuco, Brazil.
Centro de Estudos e Ensaios em Risco e Modelagem Ambiental, Universidade Federal de Pernambuco (UFPE),
Recife, Pernambuco, Brazil.
3
Departamento de Física, Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil.
4
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Uva e Vinho - Semiárido, Petrolina, Pernambuco, Brazil.
5
Instituto Agronômico de Pernambuco, Recife, Pernambuco, Brazil.
2
Accepted 23 April, 2015
Abstract
The Sub-middle São Francisco Valley (SMSF), Northeast Brazil, is known for its great potential for
viticulture, as it is one of the few regions in the world with two to three harvests per year. Results
presented here establish the influence of remote Sea Surface Temperature (SST) on the rainfall
variability and wine chemical compounds in the SMSF. Cross-correlation analyses show that the rainfall
in the SMSF is negatively correlated with the SST at the North Tropical Atlantic, and positively
correlated with the SST at Southwestern Atlantic. Cross-wavelet analyses identify that, in addition to
the annual signal, intermittent signals in rainfall at the SMSF respond to remote influences of the SST
with 3-4 months of periodicity at interannual timescales (~36-month band). Coherence analyses identify
that the SST in Southwestern Atlantic affects the rainfall in the SMSF at the 3-4-month periodicity band.
Principal Component Analysis indicated different influences between wet and dry meteorological
seasons on the chemical wine samples produced at SMSF. Extreme rainfalls observed during 2009, are
linked to northward displacement of the South Atlantic Convergence Zone, when strong rainfall and
high relative humidity at SMSF contributed to reduce Free Sulfur Dioxide (FSO2) in grapes. During dry
seasons the northward displacement of Intertropical Convergence Zone (ITCZ) is correlated to lower
rainfall and relative humidity (RH) in SMSF region, resulting in higher Sulfur Dioxide (TSO2, FSO2),
alcohol and higher acidity (VOL. AC) wines.
Keywords: Wine chemical compounds, rainfall, sea surface temperature, Sub-middle São Francisco Valley,
tropical Atlantic Ocean, principal component analysis, cross-wavelet analysis.
INTRODUCTION
The local climatic condition is one of the key factors
influencing the grapevine phenology (Caprio & Quamme
2002; Jones et al. 2005; Soar et al. 2008; Hall & Jones
2008; Ramos et al. 2008; Caffarra & Eccel 2010, 2011).
*Corresponding Author. E-mail: doris.veleda@ufpe.br
Weather conditions control the growth and the grape
physiology, such as the tannin, acid and sugar content,
i.e. the main ingredients entering in the phenolic
composition of grapes (Spayd et al. 2002) and thus
driving the wine quality (Montes 2010). Furthermore,
seasonal and interannual climate variability, sometimes
coming from remote regions, may have a direct influence
on the quantity and quality of agricultural production
Glob. J. Agric. Res. Rev.
134
(Salinger et al. 2005; Chevet et al. 2011). For instance,
the indirect influence of the oceanic regions on different
cultures of vineyards has been analyzed by authors, such
as Bonnardot et al. (2005) Grifoni et al. (2006) and
Montes (2010).
Of the climatic factors that come into play, the most
important are air temperature, sunshine hours and
rainfall. Consequently, viticulture and wine industries are
both particularly affected by climatic variations (Chevet et
al. 2011). If the air temperature and the sunshine duration
follow mainly the regional conditions, the rainfall
variability can often resulting from remote forcing, as for
instance humidity coming from adjacent oceanic regions.
The behavior of grapevines involves adequate weather
conditions, which control the tannin, acid and sugar
content that are key components influencing wine quality
(Montes 2010), such as the phenolic composition of
grapes (Spayd et al. 2002). While daily extreme weather
conditions can influence grape physiology and growth,
seasonal and interannual climate variability, such as
drought and other climate extremes events, have a direct
influence on the quantity and quality of agricultural
production (Salinger et al. 2005). This scenario is verified
in the Sub-middle São Francisco River Valley (hereafter
referred to as SMSF region), one of the most important
wine-producing areas in Brazil, which is often subjected
to periods of prolonged drought or, conversely, to heavy
rainfall episodes (Rao et al. 1993).
Located around the latitude of 8°30 S, in a region
presenting a tropical semiarid climate, the vineyards of
SMSF are considered on all the planet as the closest to
Equator. In spite of these dry weather conditions, the big
advantage of the SMSF region comes from plentiful water
resources for the irrigation due to the proximity of the São
Francisco River, the biggest Brazilian river which rises
inside Brazil and pours in the Atlantic Ocean along the
Brazilian coast. That allows to give to the wine growers
the complete control of the hydric contributions (SECTI
2008). After the grape harvest, it is enough to stop
irrigating so that the vineyard undergoes a stress which
brings down its leaves and lets in dormancy, just like the
winter makes it in temperate regions. But unlike these,
instead of waiting for the spring, it is enough, one couple
of weeks later, to reopen the water gates, so that the
vineyard begins a new vegetative cycle, what allows to
obtain two, even three harvests a year.
The vineyards of São Francisco count approximately
10,500 ha, among which 500 for the production of wine,
the rest being intended for the production of dessert
grapes. The most used vines are the Syrah, the
Cabernet-Sauvignon and the Cabernet Ruby (crossing of
Cabernet-Sauvignon and Carignan), for red wines, White
Chenin and Canelli Muscatel for the whites wines. Since
the SMSF’s wine boom of the 1980s, its production has
received heavy funding, as the weather in this region
allows the harvesting of grapes and other tropical fruits at
any time of the year (Rabobank 2005). The SMSF is
actually the second largest fine wine-producing region of
Brazil, where the wine industry employs directly and
indirectly approximately 6,000 people, contributing in turn
to the cities of the region, mainly Petrolina-PE and
Juazeiro-BA, the two most important industrial hubs of
the São Francisco Valley.
During the dry season, i.e. between June and
December, irrigation techniques associated with optimal
sunny and warm conditions, allow harvesting of grapes
with high enological potential. Conversely, during the
main rainy season, from January to May, it may happen
that the excess of precipitation can lower the quality of
sugar and acids, which in turn can reduce the flavors of
the wine grapes and damage the harvests.
The enological research in SMSF has a focus on
developing different agronomical practices, developing
new products, explaining the characteristics wines
produced under tropical climate in the Northeast of Brazil
and attempting to find chemical markers distinctive of this
region.
This paper aimed to investigate the links between SST
in the tropical Atlantic and rainfall variability in the SMSF
events, with a focus on the remote influence of the ocean
temperature on the interannual variability of extreme
rainfall events at the SMSF, as well as the influence of
these extreme events on the chemical compounds of
wines produced in this region. It is expected that the
analyses presented herein give climatological support for
a more effective adaptation of techniques for water
resource management and for the agriculture activities in
this important Brazilian viticulture region.
MATERIAL AND METHODS
Site Selection and Description
The SMSF region makes part of the Northeast of Brazil
(NEB) which spreads between 1°-18°S and 35°-47°W
(Rao et al. 1993). The NEB is roughly divided into three
different climatic areas, the Northern, the Eastern, and
the South-Central parts (Moura & Shukla 1981; Rao et al.
1993; Chaves & Cavalcanti 2001). The SMSF region is
centered on the geographical division between the
Northern and the South-Central parts, and it is located
between the parallels 8°S and 9°S, around 40°W. The
mean altitude is 330 m (Figure 1). The mean temperature
is 27°C, with a small annual range below to 2°C. The
cumulated yearly precipitation varies generally from 350
to 600 mm, but with the rainy season only concentrated
between January and April (Marsden et al. 1996; Tonietto
& Carbonneau 2004). During the rest of the year, the long
dry season from May to December, the relative air
humidity is always close to 50%.
As for most of the Northeast of Brazil (NEB), the
distribution of rainfall in the SMSF region is not of local
origin, and the moisture source is attributed to the tropical
Veleda et al.
135
Figure 1: Key meteorological systems acting on the Sub-middle São Francisco Valley (in yellow),
São Francisco River Basin, Northeast Brazil: Seasonal positions of the ITCZ (September and
March), the SACZ intrusion and the representation of the HLTCV. The locations of the PIRATA
buoys 15°N38°W and 14°S32°W in tropical Atlantic are also indicated.
Atlantic Ocean (e.g. Ramos 1975). In fact, it was proved
that the sea surface temperature (SST) variability in the
tropical Atlantic has a strong impact on the regional
climates over the Americas and Africa. The SST
modulates the rainfall anomaly patterns that cause
severe droughts and floods over tropical areas such as
the NEB (Hastenrath & Heller 1977; Moura & Shukla
1981; Nobre & Shukla 1996; Nobre et al. 2004) and the
African Sahel (Palmer 1986; Lough 1986; Parker et al.
1988). In particular, the rainfall variability in the Northern
and central semiarid regions of the NEB is associated
with variations of the inter-hemispheric gradient of SST
anomalies in the tropical Atlantic along with the northsouth migrations of the Inter Tropical Convergence Zone
(ITCZ) (Hastenrath & Greischar 1993; Moscati & Gan
2007; Hastenrath 2011).
The NEB presents pronounced time and space
variability in the rainfall distribution as result of different
atmospheric forcings (Rao et al. 1993; Chaves &
Cavalcanti 2001; Hastenrath 2011). The amount and
frequency of precipitation can fluctuate enormously from
one year to the next. This interannual variability in rainfall
is mainly controlled by the sea-surface temperatures of
the tropical Pacific and Atlantic and is associated with El
Niño/La
Niña
events,
which
cause
extreme
drought/rainfall over this region. Other meteorological
systems are observed during the austral spring and
summer, bringing rainfall to the southern, western and
central regions of the NEB. One can distinguish the
following key meteorological systems: The Inter Tropical
Convergence Zone (ITCZ), which causes a maximum
rainfall in the SMSF from February to April as a
consequence of its southernmost displacement (Kousky
& Chu 1978; Rodrigues et al. 2011). The latitudinal
location of the ITCZ is determined, among other
phenomena, by the meridional peak of the SST.
The South Atlantic Convergence Zone (SACZ), which
brings rainfall to the semiarid region of the NEB during
austral spring and summer (Ferreira et al. 2001; Paegle &
Mo 2002). The SACZ is characterized by an organized
Convective Cloud-Band (CCB) that generally extends
from the Amazon to the Atlantic Ocean in a northwestsoutheast axis (Kousky 1988; Lenters & Cook 1995;
Robertson & Mechoso 2000; Barreiro et al. 2002). This
system is typically observed during the summer months
(November to March), when the tropical convection is
stronger, contributing to the generation and maintenance
of the phenomenon (Kodama 1992). The third
meteorological system influencing the time variability of
the rainfall over the NEB is the High-Level Troposphere
Cyclonic Vortices (HLTCVs), which also occur more
frequently in January and February (Ferreira et al. 2001).
These cyclonic vortices are formed and/or intensified
downstream from strongly amplifying mid-latitude frontal
systems, which can penetrate fairly deeply into the South
Atlantic subtropic. HLTCVs are maintained by a direct
thermal circulation, with cold air sinking in the center and
relatively warm air rising on the periphery (Frank 1970;
Kousky & Gan 1981).
Sea Surface Temperature, Meteorological Data and
Wine Chemical Compounds
The Sea Surface Temperature (SST) data are collected
from the ATLAS buoy moored at 15°N38°W (February
1998 to April 2011) and 14°S32°W (September 2005 to
April 2011) as part of the Prediction and Research
moored Array in the Tropical Atlantic Project (PIRATA)
Glob. J. Agric. Res. Rev.
136
Table 1: Wines from Ouro Verde Farm, Casa Nova-BA, SMSF, for the harvests 2008 to
2010, and the respective acronyms used in the Principal Component Analysis.
WINE
HARVEST
ACRONYM
WWYY_HY(Wine,Year,Halfyear)
Chenin Blanc
VerdejoT1
Viognier
Sauvignon Blanc
Grenache
Cabernet Sauvignon
Tempranillo
Syrah
Petit Verdot
Tempranillo2
Syrah
Verdejo
Chenin Blanc T4
Chenin Blanc T2
Tempranillo
VerdejoT2
VerdejoT3
Chenin BlancT1
Sauvignon Blanc
CheninT3
VerdejoT4
Syrah192
Syrah193
Syrah194
Syrah rótulo
Petit Verdot
Sauvignon Blanc liqueur
Viognier liqueur
Scarlitta 72hmac
Grenache Rosé
Scarlitta s.mac
Grenache liqueur
Grenache
Cabernet Sauvignon
Cabernet Sauvignon T1
Cabernet SauvignonT2
Cabernet SauvignonT3
Viognier
Sauvignon Blanc
SyrahT1
SyrahT2
SyrahT3
TempranilleT1
TempranilleT2
TempranilleT3
MarianaT1
MarianaT2
2/2008
2/2008
2/2008
2/2008
2/2008
2/2008
2/2008
2/2008
2/2008
2/2008
2/2008
1/2009
1/2009
1/2009
1/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
2/2009
1/2010
1/2010
1/2010
1/2010
1/2010
1/2010
1/2010
1/2010
1/2010
1/2010
CB08_2
Ve08_2
Vi08_2
SB08_2
Gr08_2
CS08_2
Te08_2
Sy08_2
PV08_2
Te08_2
Sy08_2
Ve09_1
CB09_1
CB09_1
Te09_1
Ve09_2
Ve09_2
Ve09_2
CB09_2
SB09_2
Ch09_2
Ve09_2
Sy09_2
Sy09_2
Sy09_2
Sy09_2
PV09_2
SBL09_2
VL09_2
Sc09_2
GR09_2
Sc09_2
Gr09_2
Gr09_2
CS09_2
CS09_2
CS09_2
Vi10_1
SB10_1
Sy10_1
Sy10_1
Sy10_1
Te10_1
Te10_1
Te10_1
Ma10_1
Ma10_1
Veleda et al.
137
Table 1 cont’d
MarianaT3
MarianaT4
MarianaT5
Grenache Rosé1
Grenache Rosé2
Grenache Red
SyrahT1
SyrahT2
SyrahT3
SyrahT4
SyrahT5
SyrahT6
SyrahT7
SyrahT8
1/2010
1/2010
1/2010
2/2010
2/2010
2/2010
2/2010
2/2010
2/2010
2/2010
2/2010
2/2010
2/2010
2/2010
(Servain et al. 1998; Bourlès et al. 2008). The primary
objective of the PIRATA Project is to understand the
seasonal and interannual variability of the coupled oceanatmosphere interactions over the tropical Atlantic as a
scientific way to improve the climate forecast (Bourlès et
al. 2008; McPhaden et al. 2010). The Autonomous
Temperature Line Acquisition System - ATLAS buoys in
the original array of the PIRATA Project were launched in
1997 (Servain et al. 1998). Since then, daily
transmissions of ocean temperature have been collected
and transmitted via the Argos satellite system and made
available on the web since their validation
(http://www.pmel.noaa.gov/pirata/).
The PIRATA site 15°N38°W was chosen as an
indicator of the variability of the ITCZ position (Hastenrath
& Greischar 1993; Uvo et al. 1998; Hastenrath 2011),
while 14°S32°W buoy sensors receive the influence of
the SACZ (Lenters & Cook 1995; Robertson & Mechoso
2000; Paegle & Mo 2002; Barreiro et al. 2002).
To ensure the long-term SST information were
compatible with the rainfall time series, we used a series
of monthly composites of 4-km Pathfinder V5 Advanced
Very High Resolution Radiometer (AVHRR) to fill the
gaps during the time period of October 2004 to July 2005
for the buoy at 15°N38°W and to complete the series for
the buoy at 14°S32°W, from February 1998 to August
2005.
The meteorological monthly data in the SMSF were
obtained from the Meteorological Station Bebedouro,
Petrolina-PE, located at 09°09'S-40°22'W. These data
are delivered by the EMBRAPA - Empresa Brasileira de
Pesquisa Agropecuária (www.cpatsa.embrapa.br). In a
first analysis we used monthly mean rainfall and SST
series (January 1998 to April 2011) in order to identify
possible correlations between extreme events and
seawater temperature. After that, monthly meteorological
parameters as relative humidity (RH), maximum air
temperature (MAXT), minimum air temperature (MINT),
mean air temperature (MEANT) and insolation (INS),
Ma10_1
Ma10_1
Ma10_1
GR10_2
GR10_2
GT10_2
Sy10_2
Sy10_2
Sy10_2
Sy10_2
Sy10_2
Sy10_2
Sy10_2
Sy10_2
were used to detect probable impacts of extreme rainfall
events on chemical compounds of different wines
produced in the commercial vineyard Ouro Verde Farm
(Casa Nova-BA, SMSF) (harvests 2008 to 2010), as
presented in Table 1. These compounds are: pH,
Alcohol, Total Acidity (TOT.AC.), Volatile Acidity
(VOL.AC.), Free Sulfur Dioxide (FSO2) and Total Sulfur
Dioxide (TSO2).
Cross-correlation Function,
Coherence Analysis
Cross-Wavelet
and
The correlations between SST and rainfall time series
were analyzed using cross-correlation function, crosswavelet and coherence analyses. The cross-correlation
functions are commonly used in geophysical time series
analysis, and they indicate how closely two recorded data
are related in the time domain (Emery & Thompson
2001).
The signals with higher variance were pointed out using
wavelet analyses. This approach also identifies the
frequency of occurrence of the most important signals
and its location in the time-frequency domain. The
concept of the wavelet transform was formalized in the
early 1980s in a series of papers published by Morlet
(1981). Since its introduction, wavelet analysis has been
applied to numerous studies in the atmospheric and
oceanic sciences and in physics, biology and economics,
among others. A complete description, mainly focused on
the geophysical applications of wavelet analysis, can be
found in (Kumar & Foufoula-Georgiou 1997); a theoretical
treatment of wavelet analysis is given in (Daubechies
1992, 1999); and a broad perspective on the principles
and applications of transient signal processing with
wavelets is presented in (Mallat 1998; Torrence & Compo
1998). Liu, among others, investigated atmospheric
and/or oceanic variability using wavelet analysis (Liu
1994; Gu & Philander 1995; Setoh et al. 1999; Veleda et
al. 2012). Here, we use the Morlet wavelet as a mother
Glob. J. Agric. Res. Rev.
138
function, as it proved to be the most adequate for
geophysical applications (Gu & Philander 1995; Setoh &
Imawaki 1999; Lau & Weng 1999). The wavelet spectrum
is defined as the square root of the wavelet transform
module, and it provides a measure of the variance in
each series and time scale.
Besides the analysis of a time series itself, it is often
desirable to examine two time series together that are
expected to be linked in some way. Specifically, it may be
of interest to investigate whether non–stationary
frequencies, with a large common power, have a
consistent phase relationship, and therefore, if they
indicate causality between the time series. The Cross–
Wavelet Transform (XWT) is the recommended tool for
this purpose (Setoh & Imawaki 1999; Lau & Weng 1999;
Veleda et al. 2012). From two Continuous Wavelet
Transforms (CWTs), we can also construct the XWT,
which indicates at what specific time a particular
frequency is more relevant for SST and rainfall time
series simultaneously.
The coherence analysis is another useful tool to identify
relationships between two time series at a particular
frequency (Schelter et al. 2007). This analysis was used
here to identify the intensity of the correlation between
SST and rainfall signals in specific frequency bands.
phenomena, varying from intraseasonal to interannual.
The time series of SST anomalies at 15°N38°W and
14°S32°W and rainfall anomalies at Bebedouro
Meteorological Station are plotted in Figures 2 and 5.
Figures 3 and 6 show the cross-correlation between the
SST and rainfall time series, while Figures 4 and 7
represent the cross-wavelet spectra between time series,
from monthly to interannual scales. The cross-wavelet
spectra are represented using a color scale that
corresponds to the intensity of the correlation, with the
correlation increasing from blue to red. The adopted
confidence limit of 95% is represented by black lines
covering the region above the confidence limit; it is also
represented by a black line on the white background, the
so-called Cone of Influence (CI) (Torrence & Compo,
1998). The values below this line are not valid and are
subject to great influence of statistical errors.
Principal Component Analysis (PCA)
The chemical data of the SMSF wines are available from
the second harvest of 2008 to the second harvest of
2010. A Principal Component Analysis (PCA) is
performed to better understand how the variability of the
atmosphere-ocean interaction affects the chemical
compounds of wines in the SMSF. The PCA allows
quantifying relationships between meteo-oceanographic
parameters and chemical compounds of wine. For the
PCA, the meteorological data were averaged for each six
months corresponding to the respective periods of
harvest data.
The meteo-oceanographic parameters used in the PCA
analysis are: RH, rainfall, MAXT, MINT, MEANT, INS,
SST14S (SST at the buoy 14°S32°W) and SST15N (SST
at the buoy 15°N38°W). The PCA chemical compounds
are: pH, Alcohol, TOT.AC., VOL.AC., FSO2 and TSO2
for the wines described in Table 1.
RESULTS AND DISCUSSION
Correlations among meteo-oceanographic data
We applied the cross-correlation, cross-wavelet and
coherence analyses to the SST time series of the
PIRATA buoys (15°N38°W and 14°S32°W) and the
rainfall data. The cross-wavelet analyses presented in
Figures 2 to 7 involve time series of monthly averaged
SST and rainfall. We find different time scales for
Figure 2. Anomalies of SST at 15°N38°W (continuous line) and
rainfall at the Bebedouro Meteorological Station (dotted line), Submiddle São Francisco Valley.
The cross-correlation between SST at the north tropical
Atlantic (15°N38°W) and rainfall at the SMSF exhibits
simultaneous negative correlations (cc = - 047) at zero
lag (Figure 3), which corresponds to the Atlantic Dipole
pattern (Moura & Shukla 1981; Nobre & Shukla 1996).
Our results also agree with the findings of Uvo et al.
(1998), among others, indicating that the rainfall in
Northeast Brazil is positively correlated with SST in the
south tropical Atlantic and negatively correlated with SST
in the north tropical Atlantic. The XWT analysis shows a
dominant annual signal as well as intermittent signals of
common power in 2002 and 2004 (Figure 4). It is
important to emphasize that the interaction of
simultaneous meteorological systems registered in
January 2004 were responsible for this anomaly. During
this period, the southward ITCZ displacement, the action
of HLTCV, the incursion of cold fronts toward the southcentral NEB and the establishment of the SACZ north of
its climatological position, increased the persistence of
rain at the north-central NEB (Alves et al. 2006).
The time series of the SST anomalies at 14°S32°W and
the rainfall anomalies at Bebedouro Meteorological
Station are plotted in Figure 5. The cross-correlation
between SST and rainfall (Figure 6) exhibits a positive
Veleda et al.
Figure 3: Cross-correlation between monthly averaged time series of SST at 15ºN38ºW (north tropical Atlantic) and
Rainfall in Petrolina–PE, Sub-middle São Francisco Valley.
Figure 4: Cross-Wavelet Transform (XWT) between monthly averaged time series of SST at 15ºN38ºW (north
tropical Atlantic) and Rainfall in Petrolina–PE, Sub-middle São Francisco Valley. Coherence increases from blue
to red.
Figure 5: Anomalies of SST at 14°S32°W (continuous line) and rainfall at the Bebedouro Meteorological
Station (dotted line), Sub-middle São Francisco Valley.
Figure 6. Cross-correlation between monthly averaged time series of SST at 14ºS32ºW (south tropical
Atlantic) and rainfall in Petrolina–PE, Sub-middle São Francisco Valley.
139
Glob. J. Agric. Res. Rev.
140
Figure 7: Cross-Wavelet Transform (XWT) between monthly averaged time series of SST at 14ºS32ºW (south
tropical Atlantic) and Rainfall in Petrolina–PE, Sub-middle São Francisco Valley. Coherence increases from
blue to red.
Figure 8. Coherence and Phase spectra between SST at 15°N38°W (Left Panels), 14°S32°W (Right Panels) and Rainfall
time series in Petrolina–PE, Sub-middle São Francisco Valley. The coherence is considered significant above the
threshold/significance level indicated by the dashed lines.
correlation (cc = 0.60) between the SST at the 14°S32°W
buoy and the rainfall at the SMSF at zero lag. For shorter
timescales, the cross-wavelet transform shows welldefined intermittent signals with common power at a 3-4month band (Figure 7). Additionally, the interannual
occurrences of common powers in Figure 7 appear in the
years 2001, 2002, 2004 and 2009.
In fact, some works have confirmed the impacts of
interannual rainfall events on grapevines in the SMSF.
Leão & da Silva (2005), for example, stated that due to
rainfall events, the grapevine cultivar Italia was harvested
before reaching its maturity during the production cycle
between September 2001 and January 2002 in the
SMSF. This excess of rainfall damaged the fruit quality by
promoting the reduction of the sugar content in the
grapes (averaging 12.4º Brix), with an overall high mean
total acidity (1.06%), resulting in a very low ratio between
soluble solids and total acidity. Dantas et al. (2007) also
studied the climatic impacts on the levels of insoluble and
soluble sugars in leaves of Syrah in the SMSF during the
phenological cycle in the first semester of 2004.
According to these authors, high values of rainfall (above
the climatological average) were verified 33 to 70 days
after pruning in January 2004. These anomalies in rainfall
were associated with lower values of mean and
maximum temperatures, radiation and insolation when
compared with the climatological data. Due to these
abnormal changes, the phenological phase between the
onset and early ripening of fruits was longer than usual,
and foliar concentrations of reducing and soluble sugars
as such starch were lower compared with other cycles.
Furthermore, Araujo et al. (2009) indicated that the
harvest of the cultivar Tempranillo in the SMSF during the
first half of 2009 showed high levels of acidity compared
with the same crop of 2008, once more due to the high
rainfall in the first half of 2009, resulting in increased
acidity.
As previously discussed, the SMSF is affected by
different meteorological systems operating during roughly
the same period of the year, i.e., austral summer. The
results presented in Figures 2 to 7 emphasize the
intraseasonal response of the rainfall at SMSF to the SST
Veleda et al.
141
Table 2: Factor loadings of chemical analysis of wines and meteorological
parameters for 2008 to 2010 harvests at Sub-middle São Francisco Valley.
PH
TOT.AC.
VOL.AC.
ALCOHOL
FSO2
TSO2
RH
RAINFALL
MINT
MAXT
MEANT
INS.
SST14S
SST15N
F1
-0.051
-0.081
-0.260
-0.381
0.214
0.230
0.775
0.947
0.984
0.706
0.854
-0.880
0.986
-0.842
F2
0.197
0.034
-0.457
0.483
0.836
0.850
-0.486
-0.204
0.136
0.273
0.313
0.012
0.061
0.439
variability associated with meteorological phenomena
coming from the North, as in the ITCZ displacement, or
from the southern hemisphere, as in the SACZ intrusion.
We then used the coherence analysis between the SSTs
at 15°N38°W and 14°S32°W and the rainfall, to discern
the influence of both processes over the SMSF (Figure 8).
The spectra (coherence-squared) between the SST
and the rainfall time series are shown in Figures 8 (a) and
(b). The corresponding spectral phases are plotted in
Figures 8 (c) and (d). The coherence between the SST at
15°N38°W and the rainfall shows an annual dominant
signal (Figure 8a) with a negative phase, which means
that SST lags rainfall. For the SST at 14°S32°W and the
rainfall, the coherence analysis also presents a dominant
annual signal (Figure 8b) with a positive phase, indicating
that the SST is forcing the atmosphere.
Besides the annual forcing, results show coherent
interannual signals (north and south) centered on the 2436-month band. At higher frequencies, the coherence
between the SST at 15°N38°W and rainfall is confined to
a narrow band of frequencies (Figure 8a), while a broader
band at 3-4 months is verified for the 14°S32°W SST and
rainfall (Figure 8b). This relatively broader band of
coherence found for the 14°S32°W data is a clear
indication that the contribution of the SST in the South
Atlantic to the intraseasonal rainfall variability at the
SMSF is stronger.
Climate Effect on Chemical Compounds
Table 2 shows the factor loadings of variables and
observations, which gives the proportion of the variance
explained by each component.
PC1 and PC2 together accounted for about ~64% of
the original variance. The first component (PC1)
explained 46.22% of total variance, being affected by
MINT (+0.98), RAINFALL (+0.95), MEANT (+0.85), RH
F3
0.610
0.367
0.695
-0.491
0.149
0.058
-0.096
-0.128
0.023
0.361
0.170
0.274
0.052
0.033
F4
0.701
-0.699
0.164
0.242
0.177
-0.059
0.124
0.116
-0.028
-0.247
-0.133
-0.160
-0.045
-0.120
(+0.78), MAXT (+0.71), which are positively correlated
with SST14S (+0.99) and negatively with INS (-0.88) and
SST15N (-0.84). Moreover, the PC2 explained 18.34% of
total variance, being influenced by the TSO2 (+0.85), and
FSO2 (+0.83) (see Table 2 and Figure 9a,b). The PC3
and PC4 components showed lower contributions to the
variance (10.8 and 8.9%, respectively) (Table 2 and
Figure 9a, b).
The PCA/Biplot (Figure 9a,b) indicates that chemical
compounds samples of the second wine harvests (dry
seasons) of 2008, 2009 and 2010 were positioned in the
negative side of PC1 axis, while the samples obtained
during the first harvests (wet seasons) of 2009 and 2010
fell onto the positive side of PC1 axis. The varieties,
which fell on the right side of PC1, were the ones with a
very strong interaction with the SST, rainfall, humidity and
air temperature.
In order to evaluate the influence of the meteooceanographic parameters on the harvests during the
wet and dry seasons, a PCA was separately applied to
the data corresponding to first and second harvests.
Figure 10 shows the results for the first harvests in 2009
and 2010, which corresponds to wet seasons at the
SMSF. Results indicate that the PC1 explained 65.23%
of total variance, illustrating that MINT, MAXT, MEANT,
INS are positively correlated with SST14S, SST15N and
FSO2, and negatively correlated with RH and rainfall.
According to Figure 10, more important contributions of
the meteo-oceanographic parameters are associated to
FSO2 in PC1.
Probably the most striking result is Figure 10 that is the
negative correlation found between SST14S and rainfall
during two wet harvests, once it is in opposition to
previous long-term cross-correlation analysis, as showed
in Figs. 6 and 7. This result can be explained by the
negative ocean-atmosphere feedback as identified by
Chaves & Nobre (2004) and de Almeida et al. (2007). In fact,
Glob. J. Agric. Res. Rev.
142
(a)
(b)
Figure 9: Principal Component Analysis (a) and Biplot (b) of climate and chemical compounds
data for the wine harvests 2008-2010, Sub-middle São Francisco Valley.
these authors showed that warm SST anomalies are able
to intensify the SACZ through the low-level convergence
of moisture, shifting it northward. The associated
increase of cloudiness, in turn, causes the appearance of
cold SST anomalies or the weakening of pre-existing
warm SST anomalies through the reduction of the
incident shortwave solar radiation. Hence, the intensified
SACZ in 2009 could be associated to lower SST in the
South Atlantic and higher rainfall in the SMSF.
The PC1 in Figure 10 also indicates that the strong
rainfall and RH contribute to a reduction of FSO2. The
FSO2 plays an important role in preserving wine from
oxidation and from some micro-organisms, so a reduction
of the FSO2 could contribute to reduce the wine quality.
The PC2 explained 15.92% of the total variance during
wet harvests, in which the more important contribution is
due to the VOL. AC. followed by TSO2 and TOT.AC. The
parameters such as PH and ALCOHOL (negatively
correlated) are represented in PC3 by 9.7% of the total
variance. The factor loadings in Figure 10 also point out a
direct correlation between rainfall and RH with all wine
samples of the first harvest of 2009 and an anticorrelation with the all wine samples of the first harvest of
2010. This discrepancy between the wet season of 2009
and 2010 is due the occurrence of the extreme rainfall
event in 2009.
Veleda et al.
143
Figure 10: Principal Component Analysis/Biplot of climate and chemical compounds data for the wet
wine harvests of 2008-2010, Sub-middle São Francisco Valley.
Figure 11: Principal Component Analysis/Biplot of climate and chemical compounds data for the dry
wine harvests of 2008-2010, Sub-middle São Francisco Valley.
Figure 11 shows the PCA for the dry seasons periods of
2008, 2009 and 2010. The PC1 explained ~39.4% of total
variance. The most important contribution of the
meteorological parameters in PC1 is the RH, which is
directly correlated with RAINFALL, followed by MAXT
and VOL. AC. and, negatively correlated with SST15N,
TSO2, ALCOHOL and FSO2. The factor loadings in the
PC1 point out that a lower MAXT associated with a lower
RH have more influence on the higher TSO2, FSO2,
alcohol and VOL. AC., during the dry seasons. The PC2
explained ~35.5% of total variance, in which the most
important contributions come from MEANT and INS
positively correlated with the second harvest of 2008.
However, this harvest was negatively correlated with
SST14S and MINT.
(Figure 11)
CONCLUSIONS
The Sub-middle São Francisco Valley (SMSF) is an
important grapevine production area in the semiarid
region of Northeast Brazil. As far as we know this study
represents a first step in focusing the remote influence of
Glob. J. Agric. Res. Rev.
144
the ocean-atmosphere mechanisms on the variability of
rainfall in the SMSF, a region marked by the alternation
of extreme rainfall events and droughts.
Results show that the intensity of rainfall in the SMSF is
linked to the Sea Surface Temperature (SST) variability
in the tropical Atlantic. The in situ SST measurements
obtained from two buoys of the Prediction and Research
moored Array in the Tropical Atlantic (PIRATA) Project
are correlated to the rainfall at the SMSF. Those buoys
are located in places that capture the variability of two
most important meteorological phenomena acting on
climatic changes in the area of study: the displacement of
the Intertropical Convergence Zone - ITCZ (15°N38°W
PIRATA buoy) and the intrusion of the South Atlantic
Convergence Zone - SACZ (14°S32°W PIRATA buoy).
We found, for example, that the interannual changes in
the rainfall intensity in SMSF are negatively correlated (cc
= - 0.47) with the SST in the North tropical Atlantic and
are positively correlated (cc = +0.60) with the SST at the
South tropical Atlantic. A strong variance in rainfall
responds to the interannual SST variability at 14°S32°W.
Furthermore, a broader band of coherence between the
sea measurements and rainfall exists at the 3-4 month
band, indicating that the intraseasonal response of the
rainfall in the SMSF is mainly driven by the SST
variability in the South tropical Atlantic. The Principal
Component Analysis (PCA) stressed differences between
wet and dry meteorological seasons on the chemical
wine samples produced at Sub-middle São Francisco
Valley. For the wet season, PCA indicate different
influences for 2009 and 2010 due to extreme rainfall
event occurrence in the year of 2009 linked to south
Atlantic via SACZ. During this period strong rainfall and
high relative humidity at SMSF contributed to reduce
Free Sulfur Dioxide (FSO2) in grapes. Otherwise, the dry
seasons analysis evidence correlations between high
SST in North Atlantic (northward ITCZ displacement),
lower rainfall and relative humidity (RH) in SMSF region,
resulting in higher Sulfur Dioxide (TSO2, FSO2), alcohol
and higher Volatile Acidity (VOL. AC) wines.
Results presented here show the connections between
remote ocean-atmosphere forcing, rainfall variability and
wine quality in the Sub-middle São Francisco Valley. It
that sense, real-time ocean PIRATA data appear as a
first order indicator for rainfall prediction and wine quality
in this important Brazilian wine-producing region.
ACKNOWLEDGEMENTS
The INCTAmbTropic – Brazilian National Institute of
Science and Technology for Tropical Marine
Environments, CNPq/FAPESB Grants, supported this study:
565054/2010-4 and 8936/2011. The first author acknowledges
support from CNPq Process 520271/2006-8. R. M.
acknowledges support from FACEPE and CNPq Process
303718/2010-2. The authors would like to thank the wavelet
software provided by C. Torrence and G. Compo and is
available at URL: http://atoc.colorado.edu/research/wavelets/.
The
authors
thank
the
PIRATA
Project,
http://www.pmel.noaa.gov/pirata/,
for
their
valuable
measurements.
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