EIDENAR: Ejemplar 7 / Enero - Diciembre 2008
VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN
Octubre 10 2008
Octubre 31 2008
|| Jose Daniel
Departament of Goegraphy, National Universty of
| Jennifer Dorado
posgraduate Program in Meteorology , Departament of Geosciences,
National University of Colombia
decadal (amounts for each ten days) precipitation data
meteorological stations situated in Northern South America and
Caribbean region, a decadal precipitation index (DPI) was calculated in
order to study the intraseasonal variability (ISV) of regional
rainfall. The spectral analysis of DPI allows to identify signals with
20-25, 30, 40 and 50-60 days period. According to the analysis of their
spatial distribution these signals are well defined over the Caribbean
island and coastal sector such as in some sectors of the Andean region;
the 60-days signal is presented only over Caribbean region and in some
places in the Pacific sector; in the eastern lowlands of Orinoco and
Amazon basin these signals are not clearly expressed.
relationship between regional ISV and Madden-Julian
Oscillation correlation analysis was made. Due to the presence of
signals different of 30-60 days, the correlation coefficients were very
low. Considering this situation, high frequency smoothing was applied
to DPI time series; after that, a relative correlation was detected
between smoothed DPI and Madden-Julian Index (MJI).
Intraseasonal Variability, Madden-Julian Oscillation, Rainfall.
Con base en registros de precipitación media de cada diez
días (DPI), tomados de estaciones de Norte América, Sur
América y el Caribe, se calculo la variabilidad de la
precipitación inter-estacional (ISV) de la región. El
análisis espectral del DPI, permitió identificar periodos
entre 20-25, 30, 40 y 50-60. De acuerdo al análisis de esas
distribuciones espaciales, estas se encuentran bien definidas en las
islas del caribe y la región caribe y en algunos lugares de los
andes, las series de 60 días se presentan solamente en la
región caribe y en algunos lugares del pacífico: en las
cuencas de la orinoquía y el amazonas las señales de
estas distribuciones no se encuentran claramente definidas.
Se realizó un análisis de correlación la
oscilación entre el ISV y la oscilación
Madden-Julián. El coeficiente de correlación encontrado
fue muy bajo debido a la presencia de señales entre 30-60
días. Considerando esta situación se utilizó una
frecuencia mas leve con las series DPI, encontrando una
correlación relativa entre periodos depurados de DPI y el
indicador Madden´Julián (MJI).
Variabilidad Inter.-estacional, Oscilación Madden-Julián,
Extreme phases of climate variability bring to different regions warm
or cold periods, rainy (more precipitation than normal or more frequent
heavy rainfall events) or dry conditions, and so. This variability
impacts in several ways ecosystems and economic systems of the
countries around the world, producing in some cases disasters. In the
climate system many processes generate this variability. For example,
the tropical Pacific phenomena El Niño (warm condition) and La
Niña (cold conditions) are the cause of 2-7 years time scale
oscillations of climatic variables known as ENSO cycle
(Philander, 1990; Hastenrath, 1996; see also ENSO bibliography in
COAPS, 2006). In addition to the ENSO cycle, signals such as
quasi-biennial component (Ropelewski et al., 1992; Meehl, 1997; Baldwin
et al., 2001), and fluctuations in the period interval of 20-90 days
called intraseasonal oscillations (Knutson & Weickman, 1987;
Bantzer & Wallace, 1996; Nogués-Paegle et al., 2000;
Krishnamurti & Shukla, 2000; Goswami & Mohan, 2001; Bond &
Vecchi, 2003; Krishnamurti & Shukla, 2007) have been identified.
Today the most studied signal of climate variability is that caused by
ENSO. There are many works related to the effects of ENSO in monthly
precipitation of different regions in the world (Ropelewski et al.,
1986; Ropelewski & Halpert, 1987; Pabón & Montealegre,
1992; Peel et al., 2002; Poveda, 2004; and many others).
Currently, seasonal climate prediction schemes are based on the
knowledge about particularities of ENSO cycle in a given region,
however, because they do not include other modes of climate
variability, prediction fails frequently, especially in month-to-month
range and less (see for example Hendon et al., 2000; Jones &
Schemm, 2000; Jones et al., 2004c). A source of fails in prediction in
month-to-month range is associated to the no inclusion of intraseasonal
variations in the schemes. In fact, the phases of intraseasonal
fluctuations activate and deactivate rainfall for periods of a couple
of weeks lasting or forwarding the beginning or end of rainy season, or
breaking it. The rainy phase of intraseasonal variability also
activates heavy precipitation events and related to them disasters
(flashfloods, landslides, etc). Due to practical value to improve
subseasonal predictability (Waliser et al. 2003; Webster & Hoyos,
2004), the interest on intraseasonal modes of climate variability has
been increasing in last decade and many efforts have been doing to
study this variability especially the
Figure A. Estaciones Colombianas
Madden-Julian Oscillation (Madden & Julian, 1994), the dominant
mode in intraseasonal climate variability.
Several authors have been studied the intraseasonal variability
(hereafter ISV) in precipitation for different geographical regions of
the world. Krishnamurti & Shukla, (2000, 2007), for example, found
modes with 45 and 20 days period in precipitation in India. Wang et al.
(1996) explored ISV of precipitation in China finding 12,
21 and 43
days period. Analysis was made also for Africa (Janicot & Sultan,
2001; ; Mathews, 2004) and signals over 10-25 and 25-60 days period
were found in convection and precipitation in the western region
(Sultan et al., 2003; Mounier & Janicot, 2004); satatistically
significant spectral peaks over 15 and 40 days period were found for
Sahel precipitation (Janicot & Sultan 2001). Jones et al. (2004a)
using outgoing long wave radiation data developed a climatology for
tropical intraseasonal convective anomalies. Also, Ye & Cho (2001),
analyzed precipitation data for United States, and found 24 and 37 days
signals. ISV of convection and precipitation for different
regions of South America has been studied by Garreaud (2000), Petersen
et al. (2002), Misra (2005).
Exploring the causes of ISV of precipitation many researchers have been
paying special attention to its relationship to Madden-Julian
Oscillation (MJO), because the MJO is the dominant mode of tropical
ISV. Thus, Bantzer & Wallace (1996) analyzed temperature and
precipitation data using satellite data and found a 40-50 days
component, close to MJO period. Liebman et al. (1994)
investigated the relationship between tropical cyclones of the Indian
and western Pacific oceans and the MJO and found that cyclones
preferentially occur during the convective phase of the oscillation;
but they noted, however, that the increase in cyclone activity during
active periods of convection is not restricted to MJO activity and
concluded that the last does not influence tropical cyclones in a
unique fashion (this situation may be due to the existence of other
modes of ISV). A similar analysis was done by Maloney & Hartman
(2000a,b) for hurricanes of eastern north Pacific and Gulf of Mexico
(information on Caribbean is also included). Kayano & Kousky
(1999) studied the MJO in the global tropics using pentad-means for the
1979–1995 period computed for 200- and 850-hPa zonal winds,
200-hPa velocity potential, 500-hPa geopotential height and pressure
vertical velocity, 925-hPa temperature and specific humidity, SLP and
total precipitable water (PW); they found in all variables an eastward
traveling large-scale oscillatory regime with a period of approximately
45 days. In the other hand, Jones et al. (2004b) using pentadal
precipitation data based on Global Precipitation Climatology Project
(GPCP) confirmed that over Indian Ocean, Indonesia, Western Pacific,
Eastern South America, Western North America, northeast Africa, the
Middle East, and Eastern China, extremes precipitation events increases
with the presence of active (convective) phase of MJO. Barlow et al.
(2005) analyzing daily precipitation for Southwest Asia found that this
variable is modulated by MJO activity in the eastern Indian Ocean, with
strength comparable to the interannual variability. Bond &
Vechi (2003) found a relationship between MJO and precipitation of
Oregon and Washington states. ISV was detected in convective processes
over Amazon region by Petersen et al. (2002).
The climate variability for northern South America and Caribbean region
has been studied mainly in interannual scale (Hastenrath, 1976;
Pabón & Montealegre, 1992; Enfield, 1996; Alfaro et al.,
1998; Enfield & Alfaro, 1999; Montealegre & Pabon, 2000;
Giannini et al., 2000; Chang & Stephenson, 2000; Chang &
Taylor, 2002; Taylor et al., 2002; Poveda, 2004, Nobre et al., 2006),
especially the associated to ENSO, with purpose to improve
seasonal-interannual climate prediction. The ISV of precipitation have
been less studied, however some attempts have been carried out
by Poveda et al. (2002), who analyzed the daily cycle of
precipitation of Colombian Andes and found a significant relationship
between MJO and daily precipitation, such as between MJO and amplitude
of daily cycle. Pabón (2007), explored ISV using decadal
(ten days amounts) precipitation data for different regions of Colombia
and found 20-25 and 50-70 days periodical components; searching the
relationship of the analyzed times series with MJO it was found a low
correspondence because the presence of other mode of ISV different to
MJO mode. These works show the evidences of ISV modes in climate of the
Taking in account the state of knowledge about the ISV and the regional
importance for improving climate prediction and to strength the
disaster prevention systems, especially in the component related to
heavy rainfall, this paper try to analyze in more detail the
characteristics of ISV of precipitation in northern South America
and Caribbean region.
Northern South America and Caribbean region and distribution of
meteorological stations used for analysis
Table 1. The number corresponds to station listed in .
2. Data and
analysis in the current study as basic data was used daily
precipitation for the 1978-2004 period from meteorological stations
distributed over northern South America and Caribbean region as showed
in Figure 1 (the 75 are listed in Table 1). Selection of the
meteorological stations was done considering criteria
as representativeness of a given region, length of record period at
least 20 years and minimal gaps in data series. Considering the complex
topography over Colombian territory that generates a rich climate
diversity, it was necessary to include a relatively high density of the
network for this region.
An initial check was carried out in order to test the quality of data.
After this quality control a decadal (amounts for each ten days period)
precipitation series were organized and an index (DPI) was calculated
using the equation:
Figure 3. Spectral density for DPI of stations located on the islands
(left) and coastal sector (right) of Caribbean region
Caribbean coastal zone, mountainous
(Andean) region, eastern lowlands of Orinoco-Amazon basin, and Pacific
sector. To identify signals of ISV of precipitation spectral analysis
(Wilks, 1995) was applied to time series of DPI using commercial
software that calculates the spectral density.
Considering that Madden-Julian Oscillation induces the most outstanding
signal of ISV in the tropics, an attempt to associate the regional ISV
of precipitation with MJO was done; therefore the Madden-Julian Index
(MJI) for 120 and 40°W was compared with DPI series. For that, DPI
was smoothed using moving averages to filtrate high frequency modes.
MJI data was taken from NOAA/NCEP/CPC Web page (see
Finally, correlation coefficients for MJI and original and smoothed DPI
(or simply, the z-score of decadal precipitation) where: P –
decadal precipitation; multianual precipitation average for
respective decade; standard deviation for the series of a
given decade (time sequences of first decades, or second decades of the
year and so on).
To facilitate the presentation of analysis and results the report was
organized for five sectors of the region: islands in Caribbean Sea
region, continental plain lowland of the
4. Same as Figure 3, but for the stations located on Andean (left) and
eastern plain (right) zone region
Analysis and discussion
Figure 2 shows the 3-points (30 days) moving averages of DPI for five
stations (one for each delimited sector) in the region; this
presentation visualizes the intraseasonal fluctuations of DPI. The MJI
for 120 and 40°W is also presented to compare with DPI series.
The lines that correspond to MJI over both 120 and 40°W
has similar fluctuations with a noticeable delay caused by the eastward
propagation of MJO, however in 1997-
1998 (during strong El Niño event) this concordance was
disrupted. It is possible to observe also that during El Niño
events (1997-1998 and 2002-2003 in the analyzed period) the MJI tends
to have the lowest values, while during cold events La Niña
(1996, 1999-2000, and 2003-2004) the highest values are presented.
At first glance, in the Figure 2 it is possible to observe too, The
analysis of Figure 3 (left side) shows that for East and Central
Caribbean region the graphics are similar: all spectra have peaks at
20, 30, 45 and 60 days period. In the Western sector (Aepto El Embrujo
– Providence Island, and Aepto Sesquicentenario – San Andres Island)
regions analyzed in this paper (due to limitation of space, is not
possible to present the spectra for all 78 stations). Also, it is
necessary to consider that in these graphics the periods less than 2
decades (20 days) are not observed, because 2 decades coincide with 0.5
frequency (Niquist frequency), under which is not possible to represent
these peaks are not marked and just the 20, 30 and 60 days period are
slightly noticeable. In several spectra peaks are presented even over
80-90-days period, however this interval approaches to seasonal
scale. The spectra over Caribbean coastal sector (Figure 3,
right) show also peaks at 20, 30, and 45-days period, but the signal
over 60-days period is very weak or is missing.
In the mountainous sector of the region (Figure 4) there
Figure 5. Same as in Figure 3, but for the
stations of Pacific zone.
are some places were spectra did not presented an outstanding signal or
the signals are weak (Airport La Nubia, Airport A Nariño and
Obonuco in Pasto, Colombia), however many of them (Airport Camilo Daza
– Cúcuta, UPTC and Eldorado, also in Colombia) have the 20, 30,
and 45-days periods are well defined
The Figure 4 (right side) shows spectra for meteorological stations of
the eastern lowlands of Orinoco and Amazon basin. These spectra in the
sector of intraseasonal frequencies are very noisy and it is difficult
to identify clearly defined peaks at a given period.
The same situation occurs in several places in the Pacific sector
(Figure 5), however there are points like Tocumen (Panama),
Panamericana and Bonanza (Colombia) and Manta (Ecuador) where peaks
outstand over 20-25, 30 and 45-days period. Also, a marked peak appears
over the interval of 50-60-days period in Panamericana and Buenaventura
(Colombia) such as in Manta (Ecuador).
Spatial distribution of spectral density for 20-, 30-, 45- (top, form
left to right), 55-, 60- and 90-days (bottom) region
Summarizing in a whole the region, the
signals 20-25, 30- and 45-days period are the most noticeable of the
ISV of precipitation. A 50-60-days signal appears clearly in the
Caribbean region, but is not important in others regions.
The maps presented in Figure 6 show the spatial distribution of
spectral density of 20-, 30-, and 45-days signals. It is possible to
conclude that in spite the 20-days signal is observed in all region,
the major spectral density is observed over Western Caribbean and
southwestern sector (Ecuador and Pacific ocean). For 30- and 45-days
signals there is a similar distribution.
The relationship between DPI and MJI was explored calculating the
correlation coefficients. These coefficients are very low when the
correlation is calculated for original (not smoothed) DPI time series,
and increase as the high frequency modes are smoothing by moving
averages. This fact suggests that the high frequency of ISV of regional
precipitation is controlled by processes different from MJO. The
spatial distribution of correlation coefficients are presented in
Spatial distribution of correlation coefficients between MJI over
120°W (top) and 40°W (bottom) and the DPI original series
(left), its 3-points smoothed DPI (center), and 5-points smoothed DPI
The analysis made above shows that in the ISV of precipitation over
Northern South America and Caribbean region there are signals with 20,
30, 45 and 60-days period. The three first are persistently observed in
all the zones of the analyzed region, while the last is observed only
in both the Caribbean islands and some places of Pacific sector.
Searching the relationship between intraseasonal variability of
regional precipitation and Madden-Julian Oscillation it did not find a
defined association pattern and even the correlation coefficients
between MJI and DPI were very low; however, was established that the
heavy rainfall events are associated with low values of MJI.
The low values of correlation coefficients and their increasing with
smoothing of DPI suggest that the regional ISV is controlled not only
by MJO. It is necessary to explore the nature of high frequency
(20-days, for example) modes
Cid, L. & Enfield, D. (1998). Relaciones entre el inicio y el
término de la estación lluviosa en Centroamérica y
los Océanos Pacífico y Atlántico Tropical. Revista
de Investigaciones Marinas 26, 59-69.
Baldwin M.P., Gray L.J.,
Dunkerton T. J., Hamilton
K., Haynes P. H., Randel W. J., Holton J. R., Alexander M. J.,
Hirota I., T. Horinouchi T., Jones D. B. A.,
Kinnersley J. S., Marquardt C., Sato K., Takahashi M., 2001: The
Quasi-Biennial Oscillation. Reviews of Geophysicis, 39,2, pp. 179-229
Bantzer C H., Wallace J M., 1996:
Intraseasonal Variability in
Tropical Mean Temperature and Precipitation and their Relation to
the Tropical 40-50 Day Oscillation. J. of Atmos. Sc., v.
53, No. 21 (Nov ), pp. 3032-3045.
Barlow M., Wheeler M., Lyon B.,
Cullen H., 2005: Modulation of
Daily Precipitation over Southwest Asia by the Madden–Julian
Oscillation Monthly Wea. Review, 133 pp3579-3594.
Bond N.A., Vecchi G.A., 2003: The
Influence of the Madden-Julian
oscillation on precipitation in Oregon and Washington. Wea.
Forecasting, v. 18, pp. 600-613.
COAPS, 2006: A Comprehensive
Bibliography On The El Nino
Phenomena, Center for Ocean-Atmospheric Prediction Studies
(COAPS), . Florida State University, . URL:
at december 2006).
Chen A.A., Stephenson T.S., 2000:
Analyzing and Understanding Climate
Variability in the Caribbean Islands. University of the West Indies,
Kingston 7, Jamaica.
Chen, A. A., and M.Taylor, 2002:
Investigating the link between early
season Caribbean rainfall and the El Niño+1 year. Int. J.
Climatology, v. 22 (1), pp.87-106.
Enfield, D. B., 1996: Relationships of
inter-American rainfall to
tropical Atlantic and Pacific SST variability. Geophys. Res. Letters.
This paper partially reports the results
of the research project
“Analysis of colombian climate variability generated by processes
different from El Niño-La Niña-Southern Oscillation
cycle” supported by both COLCIENCIAS (Colombian research support
agency; grant No. 1118-05-16900, RC 178-2004) and Research
Division of National University of Colombia (project numbers
DIB-20100004448 (805253) and DIB-20101004929).
Enfield, D. & Alfaro, E. (1999). The
dependence of Caribbean
rainfall on the interaction of the tropical Atlantic and Pacific
Oceans. Journal of Climate 12, 2093-2103.
Garreaud R.D., 2000: Intraseasonal
Variability of Moisture and Rainfall
over the South American Altiplano. Monthly Wea. Rev., 128, pp. 337-3346.
Giannini, A., Y. Kushnir Y., Cane M.A.,
2000: Interannual Variability
of Caribbean Rainfall, ENSO, and the Atlantic Ocean.
Journal of Climate, 13, pp. 297–311.
Goswami, B. N., and R. S. A. Mohan,
2001: Intraseasonal oscillations
and interannual variability of the Indian summer monsoon. J.Climate,
Hastenrath, S., 1976: Variations in
low-latitude circulation and
extreme climatic events in the tropical Americas. J. Atmos. Sci., 33,
Hastenrath S.,1996: Climate
Dynamics of the Tropics. Updated
Edition from Climate and Circulation of the Tropics. Atmospheric
Sciences Library. Kluwer Academic Publishers. Dordrecht,
Netherlands, 488 p.
Hendon H.H., Liebmann B., Newman M.,
Glick J.D., 2000: Mediu-range
Forecast Errors Associated with Actives Episodes of the Madden-Julian
Oscillation. Mon. Wea. Review, v. 128, pp. 69-86.
Janicot S., Sultan B., 2001:
Intra-seasonal modulation of convection in
the West African monsoon. Geophys. Res. Lett., 28, 523–526.
Jones C., Carvalho L.M.V., Higgins R.W.,
Waliser D.E., Schem J.K.E.,
2004a: Climatology of Tropical Intraseasonal Convective Anomalies:
1979-2002. J. of Climate, v. 17, pp. 523-539.
Jones, C., and Schemm J.-K. E., 2000:
The influence of intraseasonal
variations on medium-range weather forecasts over South America. Mon.
Wea. Rev., 128, 486–494.
Jones C., Waliser D.E., Lau K.M., Stern
W., 2004b: Global Occurrence of
Extreme Precipitation and Madden-Julian Oscillation: Observations and
Predictability. J. of Climate, v. 7, pp. 4575-4589.
Jones C., Waliser D.E., Lau K.M., Stern
W., 2004c: The Madden–Julian
Oscillation and Its Impact on Northern Hemisphere Weather
Predictability. Mon. Wea. Rev., 115, pp. 1462–1471.
Kayano M.T., Kousky V.E., 1999:
Intraseasonal (30–60 day)
variability in the global tropics: principal modes and their evolution.
Tellus, 51A, pp. 373-376.
Knutson, T. R., and K. M. Weickmann,
1987: 30–60 day atmospheric
oscillations: Composite life cycles of convection and circulation
anomalies. Mon. Wea. Rev., 115, pp. 1407–1436.
Krishnamurti V., Shukla J., 2000:
Intraseasonal and interanual
variability of rainfall over India. J. of Climate, v. 13, pp.
Krishnamurti V., Shukla J., 2007:
Intraseasonal and Seasonally
Persisting Patterns of Indian Monsoon Rainfall. J. of
Climate, v. 20, pp. 3-20.
Liebmann B., H. H. Hendon, and J.
D. Glick, 1994: The
relationship between tropical cyclones of the western Pacific and
Indian Oceans and the Madden–Julian oscillation. J. Meteor. Soc. Japan,
Madden R.A., Julian P.R., 1994:
Observations of the 40-50-day
tropical oscillation — a review. Mon. Weather Rev., v. 122, No.
5, pp. 814-837.
Maloney E.D., Hartmann D.L., 2000a:
Modulation of hurricane activity in
the Gulf of Mexico by the Madden–Julian oscilllation. Science, 287,
Issue 5460, pp. 2002–2004.
Maloney E.D., Hartmann D.L., 2000b:
Modulation of Eastern North Pacific
Hurricanes by the Madden-Julian Oscillation. J. of Climate,
v. 13, No. 9, pp. 1451-1460.
Meehl G.A., 1997: The South Asian
monsoon and the tropospheric biennial
oscillation (TBO). J. Climate, 10, p. 1921–1943.
Misra V., 2005: Simulation of the
Intraseasonal Variance of the South
American Summer Monsoon., Monthly Wea. Rev., 133, pp. 663-676.
Montealegre J.E., Pabón J.D.,
2000: La variabilidad
climática interanual asociada al ciclo El Niño-La
Niña-Oscilación del Sur y su efecto en el patrón
pluviométrico de Colombia. Meteorología Colombiana,
Nº 2, pp. 7-21.
Mounier, F., and S. Janicot (2004),
Evidence of two independent modes
of convection at intraseasonal timescale in the West African summer
monsoon, Geophys. Res. Lett., 31, L16116, doi:10.1029/2004GL020665.
Nobre P., Marengo J.A., Calvacanti
I.F.A., Obregón G., Barros
V., Camilloni I., Campos N., Ferreira A.G., 2006: Seasonal-to-Decadal
Predictability and Prediction of South American Climate. J.
of Climate, 19, pp. 5988-6004.
Nogués-Paegle, J., L. A. Byerle,
and K. Mo, 2000:
Intraseasonal modulation of South American summer
precipitation. Mon. Wea. Rev., 128, 837–850.
Pabón J.D., Montealegre
Interrelación entre el ENOS y la precipitación en el
noroccidente de Suramérica. Boletín
ERFEN, No. 31, p. 12.
Pabón J.D., 2007: Improving
Climate Prediction Schemes With
Intraseasonal Variability: A Key Tool Toward
Hydrometeorological Disasters Reduction in Tropical America. In
Proceedings of International Roundtable Meeting on Lessons from Natural
Disasters Policy Issues and Mitigation Strategies (Vellore, India, 8 –
12 January 2007), 10 p.
Peel M.C., McMahon ., T.A., Finlayson
B.L, 2002: Variability of Annual
Precipitation and Its Relationship to El Niño-Southern
Oscillation. J. of Climate, 15 (6), pp. 545-551.
\Petersen W.A., Nesbitt S.W., Blaskeslee
R.J., Cifelli R., Hein P.,
Rutledge S.A., 2002: TRMM Observations of Intraseasonal Variability in
Convective Regimes over the Amazon. J. of Climate, 15,
Philander S.G.H., 1990: El
Niño, La Niña and
Southern Oscillation. Academic Press, 291p
Poveda G., Mesa O., Agudelo P., Alvarez
J., Arias P., Moreno H.,
Salazar L., Toro V., Vieira S., 2002: Ibfluencia de ENSO,
oscilación Madden-Julian, ondas del Este, huracanes y fases de
la Luna en el ciclo diruno de la precipitación en los Andes
Tropicales de Colombia. Meteorología Colombiana. No. 5, pp.3-12.
Poveda G., 2004: La
hidroclimatología de Colombia: una
síntesis desde la escala inter-decadal hasta la escala diurna.
Rev. Acad. Colomb. Cien., 28 (107): 201-222.
Ropelewski, C. F., and M. S. Halpert,
1986: North American
precipitation and temperature patterns associated with the El
Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114, 2352–2362.
Ropelewski C.F., Halpert M.S.,
1987: Global and regional scale
precipitation patterns associated with the El Niño/Southern
Oscillation. Mon. Wea. Rev., 115, pp. 1606-1626.
Ropelewski C.F., Halpert M.S., X. Wang
1992: Observed Tropospheric
Biennial Variability and its Relationship to the Southern Oscillation.
J. of Climate, 5 (6), pp. 594-614.
Sultan B., Janicot S., Diedhiou A.,
2003: The West African monsoon
dynamics. Part I: Documentation of intraseasonal variability. J.
of Climate, 16, pp. 3389-3406.
Taylor M.A., Enfield D.B., Chen A.A:,
2002: Influence of the tropical
Atlantic versus the tropical Pacific on Caribbean rainfall. J.
of Geoph. Res., v. 107, No. C9, 3127, doi:10.1029/2001JC001097,
Walisser D.E., Stern W.,Schubert S., Lau
K.M., 2003: Dynamic
predictability of intraseasonal variability associated with the Asian
summer monsoon. Q. J. R. Meteorol. Soc., 129, pp. 2897–2925.
Wang X.L., Corte-Real J., Zhang X.,
1996: Intraseasonal oscillations
and associated spatial-temporal structures of precipitation over China.
J. of Geophysicar Research, v. 101, No. D14, pp. 19035-19042.
Webster P.J., Hoyos C., 2004: Prediction
of monsoon rainfall and river
discharge on 15-30-day time scales. Bull. of the American Meteo.
Soc., v. 85, pp. 1745-1765.
Wilks D.s., 1995: Statistical Methods in
Atmospheric Sciences. Academic
Press. San Diego. 467 p.
Cho H.-R., 2001: Spatial and temporal characteristics of
intraseasonal oscillations of precipitation over the United States.
Theoretical and Applied Climatology. v.68, pp. 51-66.
Ingeniería - Escuela de Ingeniería de los Recursos
Naturales y del Ambiente EIDENAR
Telefax: +57 2 3212153 - +57 2 3212159
Edificio 344 - Ciudadela Universitaria Meléndez
Universidad del Valle
©2009 - Universidad del Valle -Luis Eduardo González