ENERO%2031-GEOCIENCIAS-VOL%2012-2%20DIC-EN%20BAJA[1].pdf EARTH SCIENCES RESEARCH JOURNAL Earth Sci. Res. J. Vol. 12, No. 2 (December 2008): 194-212 INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION J.D. Pabón1 and J. Dorado2 1 Oficina 317, Edificio 212 (Aulas de Ciencias Humanas). Department of Geography, National University of Colombia. E-mail address: jdpabonc@unal.edu.co 2 Posgraduate Program in Meteorology, Department of Geosciences, National University of Colombia ABSTRACT Basedondecadal (amounts foreach tendays)precipitationdata from meteorologicalstationssituated inNorth- ernSouthAmericaandCaribbeanregion,adecadalprecipitationindex(DPI)wascalculatedinorder tostudythe intraseasonal variability (ISV) of regional rainfall. The spectral analysis of DPI allows to identify signals with 20-25,30,40and50-60daysperiod.According to theanalysisof their spatialdistribution thesesignalsarewell definedover theCaribbean islandandcoastal sector such as in somesectors of theAndean region; the60-days signal is presented onlyover Caribbean region and in someplaces in thePacific sector; in the eastern lowlands of Orinoco and Amazon basin these signals are not clearly expressed. Exploring the relationshipbetween regional ISVandMadden-JulianOscillationcorrelation analysiswasmade. Due to the presence of signals different of 30-60 days, the correlation coefficients were very low. Considering this situation,high frequencysmoothingwasapplied toDPI timeseries; after that, a relativecorrelationwasde- tected between smoothed DPI and Madden-Julian Index (MJI). Keywords: Intraseasonal Variability, Madden-Julian Oscillation, Rainfall. RESUMEN Con base en datos de precipitación decadal (acumulados de diez días) provenientes de estaciones meteorológicas localizadas en el norte de Suramérica y en el Caribe, se calculó un Índice de Precipitación De- cadal (IPD) para estudiar la variabilidad Intraestacional (VIS) de la precipitación de ésta región. El análisis espectral del IPD muestra señales con períodos de 20-25, 30, 40 y 50-60 días. De acuerdo con el análisis de la 194 Manuscript received: October 10th, 2008. Accepted for publication: November 22th, 2008. distribuciónespacial, estas señalesestánbiendefinidas sobre las islasdelCaribey lazonacostera, así comoen algunos sectores de la región andina; la señal de 60 días se presenta únicamente en el Caribe y sobre algunos lugares del Pacífico; en las tierras bajas de las cuencas del Orinoco y Amazonas las señales no se expresan claramente. Seanaliza tambiénlarelaciónentre laVISregionalde laprecipitaciónylaOscilacióndeMadden-Julian.Debido a la presencia de señales diferentes a las de 30-60 días en la precipitación, los coeficientes de correlación obtenidos son muy bajos. Por esto, se realizó la suavización de las altas frecuencias en las series del IPD y se calcularon nuevamente los coeficientes de correlación del IPDcon el Índice Madden-Julian, después de lo cual hubo un notorio aumento de los coeficientes de correlación. Palabras clave: Variabilidad intrestacional, Oscilación Maden-Julian, Lluvia. 1. Introduction Extremephases of climatevariabilitybring todiffer- ent regionswarmorcoldperiods, rainy(moreprecip- itation than normal or more frequent heavy rainfall events)ordryconditions, andso.Thisvariability im- pacts in several ways ecosystems and economic sys- temsof the countries around the world, producing in somecasesdisasters. In theclimatesystemmanypro- cesses generate this variability. For example, the tropical Pacific phenomena El Niño (warm condi- tion) and La Niña (cold conditions) are the cause of 2-7years timescaleoscillationsof climaticvariables 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 vari- ability is that causedbyENSO.Therearemanyworks relatedto theeffectsofENSO inmonthlyprecipitation 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 pre- diction schemes are based on the knowledge about particularities of ENSO cycle in a given region, however, because theydonot includeothermodesof climate variability, prediction fails frequently, espe- cially in month-to-month range and less (see for ex- ampleHendon et al., 2000; Jones& Schemm,2000; Jones et al., 2004c). Asourceof fails inprediction in month-to-month range is associated to the no inclu- sion 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 intraseasonalvariabilityalsoactivatesheavyprecipi- tation 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 manyefforts have been doing to study this variability especially the associ- ated toMadden-JulianOscillation(Madden&Julian, 1994), the dominant mode in intraseasonal climate variability. Several authors have been studied the intraseasonal variability (hereafter ISV) in precipi- tation 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) ex- plored ISV of precipitation in China finding 12, 21 and 43daysperiod.Analysiswasmadealso forAf- INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 195 rica (Janicot & Sultan, 2001; Mathews, 2004) and signals over 10-25 and 25-60 days period were found inconvectionandprecipitation in thewestern region (Sultan et al., 2003; Mounier & Janicot, 2004); satatistically significant spectral peaks over 15and40daysperiodwere found forSahel precipi- tation (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 pre- cipitation data for United States, and found 24 and 37 days signals. ISVof 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 theMJO is thedominantmodeof trop- ical ISV. Thus, Bantzer & Wallace (1996) analyzed temperatureandprecipitationdatausingsatellitedata and found a40-50 days component, close to MJO pe- riod. Liebman et al. (1994) investigated the relation- ship between tropical cyclones of the Indian and western Pacific oceans and the MJO and found that cyclones preferentially occur during the convective phaseof theoscillation;but theynoted,however, that the increase in cyclone activityduring activeperiods of convection is not restricted to MJO activity and concluded that the lastdoesnot influence tropicalcy- clones in a unique fashion (this situation maybe due to the existence of other modes of ISV). A similar analysiswasdonebyMaloney&Hartman (2000a,b) for hurricanes of eastern north Pacific and Gulf of Mexico (informationon Caribbean is also included). Kayano & Kousky (1999) studied the MJO in the global tropicsusingpentad-means for the1979-1995 period computed for 200- and 850-hPa zonal winds, 200-hPa velocity potential, 500-hPa geopotential height and pressure vertical velocity, 925-hPa tem- perature and specific humidity, SLP and total precipitablewater (PW); theyfound inallvariablesan eastward traveling large-scale oscillatory regime with a period of approximately45 days. In the other hand, Jones et al. (2004b) using pentadal precipita- tion data based on Global Precipitation Climatology Project (GPCP) confirmed thatover IndianOcean, In- donesia, Western Pacific, Eastern South America, Western North America, northeast Africa, the Mid- dle East, and Eastern China, extremes precipitation events increaseswith thepresenceof active (convec- tive) phase of MJO. Barlow et al. (2005) analyzing dailyprecipitation forSouthwestAsia 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 precipitationof Ore- gon and Washington states. ISVwas detected in con- vectiveprocessesoverAmazonregionbyPetersen et al. (2002). Theclimatevariability fornorthernSouthAmer- ica 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 & 196 J.D. PABÓN AND J. DORADO Figure 1. Northern South America and Caribbean region anddistributionofmeteorological stationsusedforanalysis (The number corresponds to station listed in Table 1). INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 197 Table 1. List of meteorological stations used in the study. ID Station name Latitud Longitude Altitude (m.s.n.m.) Country 1 Montego Bay 18,50 –77,91 1 Jamaica 2 3 Las Américas Aeropuerto San Juan Intl 18,43 18,41 –69,66 –66,00 18 19 Dominican Rep. Puerto Rico 4 5 Barahona Kingston 18,20 17,93 –71,10 –76,78 3 3 Dominican Rep. Jamaica 6 Aeropuerto El Embrujo 13,37 –81,35 1 Colombia 7 Aeropuerto Sesquicentenario 12,58 –81,72 1 Colombia 8 Aeropuerto Queen Beatrix 12,50 –70,01 18 Aruba 9 10 Aeropuerto Hato Aeropuerto Flamingo 12,20 12,15 –68,96 –68,28 9 6 Netherl Antills Netherl Antills 11 12 Manaure Coro 11,77 11,41 –72,45 –69,66 1 16 Colombia Venezuela 13 14 Matitas Esc Agr Carraipia 11,25 11,22 –73,05 –72,37 20 118 Colombia Colombia 15 Apto. Simón Bolívar 11,13 –74,23 4 Colombia 16 San Lorenzo 11,07 –74,03 4 Colombia 17 Aeropuerto Ernesto Cortissoz 10,88 –75,30 14 Colombia 18 19 Maiquetía Apto. S. Bolívar Aeropuerto Rafael Núñez 10,60 10,45 –66,98 –75,52 43 2 Venezuela Colombia 20 21 22 23 Maturín San Bernardo D. Aeropuerto Rafael Barvo Lorica 9,75 9,37 9,33 9,27 –63,18 –75,97 –75,26 –75,82 65 22 166 30 Venezuela Colombia Colombia Colombia 24 Aeropuerto Las Flores 9,07 –73,98 34 Colombia 25 Tocumen 9,05 –79,36 45 Panamá 26 Guanare 9,01 –69,73 163 Venezuela 27 28 Marcos A. Gelabert El Salado 8,98 8,92 –79,55 –75,58 13 40 Panamá Colombia 29 30 31 32 David Aeropuerto Camilo Daza San Fernando de Apore Aeropuerto Arauca 8,40 7,93 7,90 7,06 –82,41 –72,52 –67,41 –71,22 29 250 47 128 Panamá Colombia Venezuela Colombia 33 Aeropuerto Otu 7,03 –75,20 630 Colombia 34 Aeropuerto Yariguíes 7,02 –73,80 126 Colombia 198 J.D. PABÓN AND J. DORADO ID Station name Latitud Longitude Altitude (m.s.n.m.) Country 35 El Cucharo 6,87 –73,37 975 Colombia 36 Tulio Ospina 6,53 –75,92 1438 Colombia 37 Aeropuerto Olaya Herrera 6,37 –75,97 1490 Colombia 38 Panamericana 6,22 –77,40 4 Colombia 39 Aeropuerto Puerto Carreño 6,18 –67,48 50 Colombia 40 41 Tuparros Boca Torno Surbata Bonza 6,05 5,82 –67,52 –73,07 250 2485 Colombia Colombia 42 43 U.P.T.C. Isla del Santuario 5,57 5,47 –73,22 –73,73 2690 2580 Colombia Colombia 44 Aeropuerto Matecaña 5,37 –77,08 53 Colombia 45 Aeropuerto El Carano 5,12 –77,08 53 Colombia 46 La Camelia 5,08 –76,45 1670 Colombia 47 48 Aeropuerto La Nubia Aeropuerto El Edén 5,03 4,78 –75,47 –76,28 20,80 1204 Colombia Colombia 49 50 Tibaitatá Las Gaviotas 4,70 4,55 –74,22 –70,93 2543 171 Colombia Colombia 51 52 Centro Ad. La Unión El Salto 4,53 4,47 –76,05 –74,77 920 450 Colombia Colombia 53 Aeropuerto Santiago Villa 4,28 –75,33 266 Colombia 54 Aeropuerto El Dorado 4,12 –74,15 2547 Colombia 55 Aeropuerto Farfán 4,02 –76,37 955 Colombia 56 57 Palmira ICA Aeropuerto Buenaventura 3,87 3,85 –76,53 –76,97 975 14 Colombia Colombia 58 59 60 61 Aeropuerto Benito Salas Aeropuerto Alfonso Bonilla Vista Hermosa Bonanza 3,62 3,55 3,03 2,95 –75,50 –76,38 –73,73 –77,88 439 961 325 10 Colombia Colombia Colombia Colombia 62 Aeropuerto G. L. Valencia 2,43 –76,58 1730 Colombia 63 La Sierra 2,37 –77,37 1870 Colombia 64 La Macarena 2,30 –73,13 350 Colombia 65 66 Paleterá Aeropuerto Antonio Nariño 2,08 1,67 –76,05 –77,47 2900 1796 Colombia Colombia 67 68 69 70 Aeropuerto San Luis Aeropuerto Artunduaga Obonuco Valparaiso 1,42 1,36 1,30 1,15 –78,13 –75,32 –77,05 –75,42 2961 244 2710 270 Colombia Colombia Colombia Colombia Pabon, 2000; Giannini et al., 2000; Chang & Stephenson, 2000; Chang & Taylor, 2002; Taylor et al., 2002; Poveda, 2004, Nobre et al., 2006), espe- cially the associated to ENSO, with purpose to im- prove seasonal-interannual climate prediction. The ISV of precipitation have been less studied, however someattemptshavebeencarriedout by Povedaetal. (2002), whoanalyzed thedaily cycleof precipitation ofColombianAndesandfoundasignificant relation- shipbetweenMJOanddailyprecipitation, suchasbe- tween MJO and amplitude of daily cycle. Pabón (2007), explored ISV using decadal (ten days amounts) precipitation data for different regions of Colombiaandfound20-25and50-70daysperiodical components; searching the relationship of the ana- lyzed timesserieswithMJO itwas founda lowcorre- spondencebecause thepresenceofothermodeof ISV different to MJO mode. These works show the evi- dences of ISV modes in climate of the region. Taking in account the state of knowledge about the ISV and the regional importance for improving climate prediction and to strength the disaster pre- vention systems, especially in the component related toheavyrainfall, thispaper try toanalyze inmorede- tail the characteristics of ISV of precipitation in northern South America and Caribbean region. 2. Data and methodology For the analysis in the current study as basic data was used daily precipitation for the 1978-2004 pe- riod from meteorological stations distributed over northern South America and Caribbean region as showed inFigure1 (the75are listed inTable1).Se- lection of the meteorological stations was done considering criteria as representativeness of agiven region, length of record period at least 20 years and minimal gaps in data series. Considering the com- plex topography over Colombian territory that gen- erates 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 thequalityofdata.After thisqualitycontrol adecad- al (amounts for each ten days period) precipitation series were organized and an index (DPI) was calcu- lated using the equation: DPI P P P = - s (1) (or simply, the z-score of decadal precipitation) where:P–decadalprecipitation; multianualprecipi- tationaverage for respectivedecade; standarddevia- tion for the series of agiven decade (timesequences of firstdecades,or seconddecadesof theyear andso on). To facilitate the presentation of analysis and re- sults the report was organized for five sectors of the region: islands in Caribbean Sea region, continental plain lowland of the Caribbean coastal zone, moun- tainous (Andean) region, eastern lowlands of Orinoco-Amazon basin, and Pacific sector. To identify signals of ISV of precipitation spec- tral analysis (Wilks, 1995) was applied to timeseries INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 199 ID Station name Latitud Longitude Altitude (m.s.n.m.) Country 71 Villagarzón 1,05 –76,60 440 Colombia 72 Aeropuerto Mariscal Sucre –0,13 –78,48 28,11 Ecuador 73 Manta –0,95 –80,68 13 Ecuador 74 Aeropuerto Gral. M. Serrano –3,25 –69,93 84 Colombia 75 Aeropuerto Vásquez Cobo –4,16 –69,93 84 Colombia 200 J.D. PABÓN AND J. DORADO Estación San Juan (Puerto Rico) -2 -1 0 1 2 YEARS (In decadal resolution) Estación Manaure (Colombia) -2 -1 0 1 2 d Estación Apto La Nubia (Colombia) -2 -1 0 1 2 Estación Maturin (Venezuela) -2 -1 0 1 2 2 d Estación Apto Buenaventura (Colombia) -2 -1 0 1 2 d 2 0 0 4 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 1 9 9 6 1 9 9 7 1 9 9 9 1 9 9 8 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 D e c a d a l p re c ip it a ti o n in d e x YEARS (In decadal resolution) D e c a d a l p re c ip it a ti o n in d e x 2 0 0 4 1 9 9 6 1 9 9 7 1 9 9 9 1 9 9 8 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 YEARS (In decadal resolution) YEARS (In decadal resolution) YEARS (In decadal resolution) D e c a d a l p re c ip it a ti o n in d e x D e c a d a l p re c ip it a ti o n in d e x D e c a d a l p re c ip it a ti o n in d e x 2 0 0 4 1 9 9 6 1 9 9 7 1 9 9 9 1 9 9 8 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 1 9 9 6 1 9 9 7 1 9 9 8 2 0 0 0 2 0 0 1 2 0 0 2 Figure 2: Intraseasonal fluctuations of DPI. INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 201 Figure 3. Spectral density for DPI of stations located on the islands (left) and coastal sector (right) of Caribbean region. 202 J.D. PABÓN AND J. DORADO Cont. Figure 3. of DPI using commercial software that calculates the spectral density. Considering that Madden-Julian Oscillation in- duces the most outstanding signal of ISV in the trop- ics, an attempt to associate the regional ISV of precipitation with MJO was done; therefore the Mad- den-Julian Index (MJI) for 120 and 40°W was com- pared 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 http://www.cpc.ncep.noaa.gov/prod- ucts/precip/CWlink/daily_mjo_index/details.shtml) Finally, correlationcoefficients for MJIandorig- inal and smoothed DPI were calculated. 3. Analysis and discussion Figure 2 shows the 3-points (30 days) moving aver- ages 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 se- ries. 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 in1997-1998 (during strongElNiñoevent) this concordance was disrupted. It is possible to ob- servealso thatduringElNiñoevents (1997-1998and 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 firstglance, in theFigure2 it ispossible toob- serve too, that some extreme events of DPI are synchronic indifferent regions in spite theydiffer by theirmagnitude.Comparing DPIand MJI evolution is not possible to identify anypatternof correlation be- cause insomeperiods theyare in thesamephase, and in timesare in counter phase. However, a correspon- dence may be detected for extreme rainfall events: maximums values of DPI are associated with mini- mum of MJI; at least the most outstanding rainfall (heavyrainfall)eventsareassociatedto lowvaluesof MJI. The figures 3-5 present spectra of several sta- tions of the 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 thesegraphics theperiods less than 2decades (20days)arenotobserved,because2de- cades coincide with 0.5 frequency (Niquist fre- quency), under which is not possible to represent the spectrum. TheanalysisofFigure3(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 ( Aeropuerto El Embrujo–Providence Island, and Aeropuerto Sesquicentenario – San Andres Island) these peaks arenotmarkedand just the20,30and60daysperiod are slightly noticeable. In several spectra peaks are presentedevenover80-90-daysperiod,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 sig- nal over 60-days period is very weak or is missing. In the mountainous sector of the region (Figure 4) there are some places were spectra did not pre- sented an outstanding signal or the signals are weak (AirportLaNubia,AirportANariñoandObonuco 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 TheFigure4 (right side) showsspectra formete- orological stations of the eastern lowlands of OrinocoandAmazonbasin.These spectra in the sec- tor of intraseasonal frequencies are very noisy and it isdifficult to identifyclearlydefinedpeaksatagiven period. Thesamesituationoccurs inseveralplaces in the Pacific sector (Figure 5), however there are points likeTocumen(Panama),PanamericanaandBonanza (Colombia) and Manta (Ecuador) where peaks outstand over 20-25, 30 and 45-days period. Also, a INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 203 204 J.D. PABÓN AND J. DORADO Figure 4. Same as Figure 3, but for the stations located on Andean (left) and eastern plain (right) zone region. INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 205 Cont. Figure 4. markedpeak appears over the interval of 50-60-days period in Panamericana and Buenaventura (Colom- bia) such as in Manta (Ecuador). Summarizing in awhole the region, the signals 20-25, 30- and 45-days period are the most notice- able of the ISV of precipitation. A 50-60-days sig- 206 J.D. PABÓN AND J. DORADO Figure 5. Same as in Figure 3, but for the stations of Pacific zone. INTRASEASONAL VARIABILITY OF RAINFALL OVER NORTHERN SOUTH AMERICA AND CARIBBEAN REGION 207 F ig u r a 6 . S p at ia l di st ri b u ti o n of sp ec tr al de n si ty fo r 2 0 -, 3 0 -, 4 5 - (t o p , fo rm le ft to ri g h t) , 5 5 -, 6 0 - an d 9 0 -d ay s (b o tt o m ) . 208 J.D. PABÓN AND J. DORADO F ig u r e 7 .S p at ia l di st ri b u ti o n of co rr el at io n co ef fi ci en ts b et w ee n M JI ov er 12 0° W (t op ) an d 40 °W (b ot to m ) an d th e D P I o ri g in al se ri es (l ef t) ,i ts 3 -p o in ts sm o o th ed D P I (c en te r) , an d 5- p o in ts sm o o th ed D P I (r ig h t) . nal appears clearly in the Caribbean region, but is not important in others regions. The mapspresented in Figure 6 show the spatial distribution of spectral density of 20-, 30-, and 45-dayssignals. It ispossible toconclude that inspite the20-dayssignal isobserved inall region, themajor spectral density isobservedoverWesternCaribbean and southwestern sector (Ecuador and Pacific ocean). For 30- and45-days signals there is a similar distribution. The relationship between DPI and MJI was ex- plored calculating the correlation coefficients. These coefficients are very low when the correla- tion 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 re- gional precipitation is controlled by processes dif- ferent from MJO. The spatial distribution of correlation coefficients are presented in Figure 7. 4. Conclusions 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 Carib- bean islands and some places of Pacific sector. Searching therelationshipbetweenintraseasonal variability of regional precipitation and Mad- den-JulianOscillationitdidnot findadefinedassoci- ation pattern and even the correlation coefficients betweenMJIandDPIwereverylow;however,wases- tablished that theheavy rainfall events areassociated 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. Acknowledgments 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 Re- search Division of National University of Colombia (project numbers DIB-20100004448 (805253) and DIB-20101004929). 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