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  • View in gallery

    (a) Provinces and Cordillera in Panama with topography (shaded, m). (b) Distribution of precipitation gauge stations (red squares) and the eight stations used in this study (enclosed by blue circles). See also Table 3.

  • View in gallery

    Geographical distribution of climatological annual precipitation (mm day−1) in Panama determined with the (a) MRI, (b) CRU, (c) GPCP, and (d) TRMM datasets.

  • View in gallery

    Seasonal variations of climatological pentad precipitation (solid line) ± 1 standard deviation (gray shading). The horizontal green line denotes the threshold value of 3 mm day−1. See Fig. 1b and Table 3 for detailed information on each station location.

  • View in gallery

    Geographical distributions of the (a) onset and (b) withdrawal dates of the rainy season in Panama. The onset and withdrawal dates have been defined with the uniform threshold value method and a threshold value of 3 mm day−1. Areas with no dry season have pentad 1 for the onset date and pentad 73 for the withdrawal date. See also Fig. 5 for the areas.

  • View in gallery

    Geographical distributions of the length of the rainy season in Panama (pentads). Areas with no dry season have 73-pentad rainy seasons.

  • View in gallery

    Climatological monthly means of mesoscale column total water vapor fluxes (vector; kg kg−1 m s−1) and their convergences (shading; 10−5 kg kg−1 m s−1 m−1). The abbreviation P. in the top right of each panel denotes pentad.

  • View in gallery

    As in Fig. 6, but for SST (°C).

  • View in gallery

    (a) Minimum of the actual number of acceptable observations in each grid in the 73 pentads during the 40 years. A value of 20 indicates that 50% of the records available out of 40 years were acceptable, and a value greater than 40 indicates that there were multiple stations in the same grid. (b) Normalized minima by dividing the numbers in Fig. 8a by the 73-pentad mean number of acceptable observations in that grid.

  • View in gallery

    Comparison of the geographical distributions of the (left) onset and (right) withdrawal dates with different threshold values: (a),(b) 3, (c),(d) 4, (e),(f) 5, and (g),(h) 6 mm day−1. Note that (a) and (b) are the same as in Figs. 4a and 4b, but with a different color scale.

  • View in gallery

    As in Fig. 4, but calculated with the distributed threshold value method. The distributed threshold values are seen in Fig. 2a.

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Investigation of Climatological Onset and Withdrawal of the Rainy Season in Panama Based on a Daily Gridded Precipitation Dataset with a High Horizontal Resolution

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  • 1 Climate Research Department, Meteorological Research Institute, Tsukuba, Japan
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Abstract

The present study investigated the onset and withdrawal dates of the rainy season in Panama by using newly developed, gridded, daily precipitation datasets with a high horizontal resolution of 0.05° based on ground precipitation observations. The onset and withdrawal dates showed very complicated geographical features, although the country of Panama is oriented parallel to latitude lines, and the geographical patterns of the onset and withdrawal dates could simply reflect the latitudinal migration of the intertropical convergence zone, as seen in other regions and countries. An absolute threshold value of 3 mm day−1 (pentad mean precipitation) was used to determine the onset and withdrawal dates. The onset and withdrawal dates obtained from the gridded daily precipitation dataset clearly depicted the migration of the rainy season. The rainy season starts suddenly in pentad 21 (11–15 April) in most of eastern Panama and in pentad 22 (16–20 April) in most of western Panama. The termination of the rainy season begins in Los Santos Province during pentad 67 (27 November–1 December) and expands to both the eastern and western surrounding areas. There is no dry season in the western part of the Caribbean coastal zone. Water vapor fluxes and topography suggest dynamical causes, such as a topographically induced upward mass flux accompanied by high humidity, for the complicated geographical features of the onset and withdrawal dates. An assessment was made of uncertainties in the timing of the onset and withdrawal associated with the definition of these terms.

Corresponding author address: T. Nakaegawa, Climate Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tnakaega@mri-jma.go.jp

Abstract

The present study investigated the onset and withdrawal dates of the rainy season in Panama by using newly developed, gridded, daily precipitation datasets with a high horizontal resolution of 0.05° based on ground precipitation observations. The onset and withdrawal dates showed very complicated geographical features, although the country of Panama is oriented parallel to latitude lines, and the geographical patterns of the onset and withdrawal dates could simply reflect the latitudinal migration of the intertropical convergence zone, as seen in other regions and countries. An absolute threshold value of 3 mm day−1 (pentad mean precipitation) was used to determine the onset and withdrawal dates. The onset and withdrawal dates obtained from the gridded daily precipitation dataset clearly depicted the migration of the rainy season. The rainy season starts suddenly in pentad 21 (11–15 April) in most of eastern Panama and in pentad 22 (16–20 April) in most of western Panama. The termination of the rainy season begins in Los Santos Province during pentad 67 (27 November–1 December) and expands to both the eastern and western surrounding areas. There is no dry season in the western part of the Caribbean coastal zone. Water vapor fluxes and topography suggest dynamical causes, such as a topographically induced upward mass flux accompanied by high humidity, for the complicated geographical features of the onset and withdrawal dates. An assessment was made of uncertainties in the timing of the onset and withdrawal associated with the definition of these terms.

Corresponding author address: T. Nakaegawa, Climate Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tnakaega@mri-jma.go.jp

1. Introduction

The country of Panama is located in the tropics between latitudes and longitudes of 7°–10°N and 77–83°W, respectively. It is bordered on the south by the Pacific Ocean and on the north by the Caribbean Sea (Fig. 1a). The climate of Panama is affected by its proximity to the Atlantic Ocean and the fact that it is bordered by the Caribbean Sea and the Pacific Ocean. Together these bodies of water create a maritime atmospheric environment characterized by high humidity that is typical of much of Central America and the Caribbean region (Hastenrath 1978; Ropelewski and Halpert 1987; Nakaegawa et al. 2014b). The northeast trade winds, which contribute to the Caribbean low-level jet (CLLJ; Amador 1998, 2008), are directed over the country by the North Atlantic subtropical high (Taylor and Alfaro 2005). The climates of Panama fall into two main groups based on the Köppen climate classification system: group A includes tropical climates and group C includes mild, temperate climates (UNESCO 2008). The climate of more than 90% of Panama is tropical: the climate of the coastal zone of west Panama is a tropical rain forest climate (Af), the climate of the provinces of Chiriquí and Veraguas and the coastal zone of east Panama is a tropical monsoon climate, and the climate of the coastal zone surrounding the Gulf of Panama is a tropical wet and dry climate (Aw). Mild, temperate climates are confined to the Cordillera de Talamanca and Cordillera Central Mountains. Both of these climates are characterized by high humidity with much precipitation.

Fig. 1.
Fig. 1.

(a) Provinces and Cordillera in Panama with topography (shaded, m). (b) Distribution of precipitation gauge stations (red squares) and the eight stations used in this study (enclosed by blue circles). See also Table 3.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

In most of Panama, except for a small part of the Caribbean side, there are two seasons, namely a dry season and a rainy season (Taylor and Alfaro 2005; UNESCO 2008). This seasonality is primarily associated with the migration of the intertropical convergence zone (ITCZ), although the latitudinal migration of the ITCZ is minimal near Panama, and other regional phenomena affect the seasonality (e.g., Magaña et al. 1999; Alfaro 2002; Wang and Enfield 2003; Amador et al. 2006). The dry season typically occurs from December to April, when the ITCZ is moving south from Panama toward the equator, and the rainy season occurs from May to November, when the ITCZ is passing over Panama toward the Caribbean Sea, just to the north of Panama (Mitchell and Wallace 1992). The rainy season can be divided into three phases: rainfall is heavy during the onset period from late April to early May, when the ITCZ is passing northward over Panama; after the ITCZ has moved over the Caribbean Sea, rainfall weakens slightly (Magaña et al. 1999); and as the ITCZ is moving back toward the south over Panama, the heaviest rainfall of the rainy season occurs in some regions, including the Panama Canal, before the withdrawal period (e.g., Murphy et al. 2014).

The climatological onset and withdrawal of the rainy season have been investigated in many regions: India (IMD 1943); the Indochina Peninsula (Matsumoto 1997); Japan (JMA 2014); the Asia–Pacific region (Wang and Linho 2002); the tropical Americas, including Panama (Gramzow and Henry 1972; Horel et al. 1989; Alfaro 2002); the Amazon region (Marengo et al. 2001); and South America (Liebmann et al. 2007). These transitions are of great interest because they determine the timing of agricultural practices (Yavitt et al. 2004), and the length of the dry season determines the type of ecosystem in a region (Sombroek 2001). These studies have documented distinct spatial and temporal differences in the onset and withdrawal dates of the rainy season because the target areas have been relatively large. For such large areas, datasets obtained from sparsely located gauge stations or satellite-based datasets with a low horizontal resolution are insufficient for an informed analysis.

The area of Panama is 75 416 km2, only about 0.05% of Earth’s land area. The onset or withdrawal of the rainy season can occur simultaneously over the entire country, because the area of Panama is smaller than that of the synoptic-scale phenomena that usually trigger the onset and withdrawal. Alternatively, the onset can proceed from the Pacific to the Caribbean coast with the northward migration of the ITCZ, and the withdrawal can proceed from the Caribbean to the Pacific coast as the ITCZ moves south. Analysis of such a small area requires a precipitation dataset with high horizontal resolution to delineate the detailed spatial features of the onset and withdrawal. Enfield and Alfaro (1999) computed annual onset and withdrawal dates based on data from fewer than 20 gauge stations in the western part of Panama to elucidate the effect of the tropical North Atlantic and El Niño–Southern Oscillation on the interannual variability of the dates and showed that the variability of the sea surface temperature (SST) anomaly in the tropical Atlantic influences rainfall over the Caribbean and Central America more than the variability of the SST in the tropical eastern Pacific. They did not, however, provide climatological onset and withdrawal dates. No comprehensive investigations of the climatological onset and withdrawal dates of the rainy season have been carried out for Panama. In the present study we used a gridded precipitation dataset with high horizontal resolution developed from gauge-station observations to investigate the climatological onset and withdrawal dates of the rainy season in Panama.

2. Data

a. Gridded data production

Records of precipitation have been maintained by the Panama Canal Authority (ACP) for the Panama Canal region and by the Empresa de Transmisión Eléctrica, S.A. (ETESA) for the remainder of Panama. Both datasets have been archived by the ETESA. There are officially 151 meteorological gauge stations in Panama (ETESA 2014). However, we used the data from the 118 gauge stations that were available in digital form (Fig. 1b).

First, all the time series of daily precipitation data were plotted for visual analysis and manually checked. We found several types of doubtful data: artificial repetition of the same values (seven gauge stations), unnatural time series (four instances), and values that were integer multiples of 10 (three instances). Extremely large values (more than 10 instances) were found and were regarded as missing values; omission of these large values may have excluded actual extremes. These data are under review at the ETESA. Second, we applied the automated, objective quality control used in the Asian Precipitation–Highly Resolved Observational Data Integration toward Evaluation of Water Resources (APHRODITE) high-resolution, daily precipitation product (Kamiguchi et al. 2010; Yatagai et al. 2012) to the daily precipitation data. The nation and the elevation of stations in the metadata were compared with the digital maps. Time series of daily precipitation data from both single stations and multiple stations were subjected to this quality control. A total of 104 gauge stations were used for the final analysis. The density of gauge stations was nine stations per 104 km2; the corresponding average area per station was 1109 km2. This density of stations is comparable to the highest density of rain gauge stations in the world. For comparison, in Japan there are 34 stations per 104 km2, the corresponding area per station being 290 km2 (JMA 2013). Table 1 lists gauge stations used in this analysis at which fewer than 80% of the total observations were acceptable after the quality control. There were 12 such stations, corresponding to 11.5% of the total gauge stations. The distance of each of these stations from the nearest station at which more than 80% of the total observations were acceptable (Table 1) was less than 20 km in all cases and less than 8 km in six cases. Therefore, the low percentage of acceptable observations at these 12 stations did not substantially affect the gridded precipitation dataset.

Table 1.

Stations with less than 80% of acceptable observations used in this analysis. Distance is the distance to the nearest stations where more than 80% of the observations are acceptable. (Note that the distances denote not the one between the exact two station locations but the one between the two grid points closest to the observation stations for each.)

Table 1.

To improve the interpolation near the borders with Costa Rica and Colombia, we also used two additional datasets: the Global Historical Climatology Network-Daily (Klein Tank et al. 2002) and the Global Surface Summary of the Day (GSOD; http://www.ncdc.noaa.gov/cgi-bin/res40.pl). As the base climatological monthly precipitation for the spatial interpolation, we adopted WorldClim, which is a dataset of interpolated climate surfaces at a spatial resolution of 30 arc s (Hijmans et al. 2005). These data were obtained in 2010. We confirmed that the data in these two datasets were independent of the ETESA dataset used for Panama.

We estimated daily precipitation using the same statistical methods used in APHRODITE. The analysis period was 40 years, from 1 January 1970 to 31 December 2009. The temporal resolution was 1 day, and the horizontal resolution was 0.05°, or 3 min.

Daily climatology was produced as follows. We computed monthly climatologies from the ETESA dataset. We used the Mountain Mapper method (Schaake 2004) to compute the ratio of the monthly climatology between WorldClim and the ETESA dataset at each gauge station. The Sheremap scheme (Willmott et al. 1985), an angular-distance-weighting method, was used to spatially interpolate the ratios in Panama. We obtained a new monthly climatology by multiplying the WorldClim climatology by these ratios at a horizontal resolution of 0.05°. The new monthly climatology at each observation station was consistent with the climatology obtained from the ETESA dataset. We constructed the daily climatology at each grid by adding the first six Fourier components of the annual cycle of the new monthly climatology. This construction assumes that the daily climatology changes smoothly with time when sufficient observations are available. We produced high-frequency variability in the following way.

Daily gridded precipitation data were produced as follows. We spatially interpolated the ratios of daily precipitation between observations and climatology with the Sheremap scheme. The ratio used for this interpolation was the daily precipitation divided by the sum of the climatology and 1 mm day−1 to avoid unnatural behavior of the interpolated ratio. In this interpolation, we applied a small weight to a target grid on the opposite side of a high ridge and a large weight to a target grid on the same side of a high ridge. We refer to this newly developed, daily gridded precipitation dataset as the Meteorological Research Institute (MRI) dataset. The current version of this dataset should not be used for meteorological analysis because extreme precipitation events may have been excluded during the quality control, as mentioned in section 2a. However, this dataset can be used for a climate analysis such as the present study.

b. Observations

Four other observation-based precipitation datasets were used to compare the climatological annual precipitation for the entire country of Panama against the MRI dataset. A United Nations Educational, Scientific and Cultural Organization (UNESCO) dataset was developed by applying a simple spatial interpolation scheme to the climatological annual mean of surface gauge station data in the ETESA (UNESCO 2008). The climatological annual mean was the only available value in the ETESA, and the geographical distribution was in the form of a paper map. The three other datasets contained gridded data from different periods of time and with different temporal and horizontal resolutions: the Climate Research Unit dataset (CRU; Harris et al. 2014), the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset (Huffman et al. 2007), and the Global Precipitation Climatological Project 1-day and 1-degree dataset (GPCP1DD; Huffman et al. 2001). The characteristics of these datasets are summarized in Table 2.

Table 2.

Climatological annual precipitation averaged over the entire land area of Panama estimated with five different precipitation datasets.

Table 2.

We used the Japanese 55-yr Reanalysis produced by the JMA (JRA-55; Kobayashi et al. 2015) to depict the mesoscale circulation of water vapor fluxes and their convergences. JRA-55 has a horizontal resolution of about 55 km and covers the period of time from 1958 to the present. Climatological values were computed for the 40 years from 1970 to 2009. A high-resolution blended analysis of daily SST [the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Daily Sea Surface Temperature (OISST) version 2; Reynolds et al. (2007); http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html] was also used to depict the geographical distribution of SST surrounding Panama. The dataset has a horizontal resolution of 0.25° and covers the period of time from September 1981 to the present. Climatological values were computed for the 28 years from 1982 to 2009.

3. Definition of onset and withdrawal

Many methods of determining the onset and withdrawal dates of the rainy season have been proposed. Two methods are widely used: the uniform threshold method and the distributed threshold method. The uniform threshold method defines the onset to be the first pentad when the mean pentad precipitation exceeds a certain uniform value over a target domain (e.g., Gramzow and Henry 1972; Ogallo 1989; Marengo et al. 2001; Alfaro 2002). Zhang and Wang (2008) modified the definition for global monsoon rainfall. They defined the uniform value to be 3 mm day−1 during the rainy season, the interval of time when greater than 55% of the annual precipitation occurs. The Indian Meteorological Department (IMD) uses the distributed threshold method and defines the onset date to be the first pentad when the mean pentad precipitation exceeds the climatological annual mean precipitation in at least three consecutive pentads (IMD 1943). We calculated the threshold value of the climatological annual mean precipitation throughout the target domain (Fig. 2). Almost the same methods have been used in many studies (e.g., Matsumoto 1997; Liebmann et al. 2007). Wang and LinHo (2002) have modified this definition for Asian monsoon rainfall by equating the onset to the first pentad when the maximum pentad precipitation exceeds the mean precipitation for January by 5 mm day−1. In a similar manner, the distributed threshold method defines the withdrawal date to be the last pentad when the mean pentad precipitation is less than the climatological annual mean in at least three consecutive pentads. In contrast, the uniform threshold method defines the withdrawal date to be the last pentad when the mean pentad precipitation is less than a certain value. We determined the onset and withdrawal dates primarily with the uniform threshold method. We used the distributed threshold method for comparative purposes to address the uncertainty in the estimates due to definitional differences.

Fig. 2.
Fig. 2.

Geographical distribution of climatological annual precipitation (mm day−1) in Panama determined with the (a) MRI, (b) CRU, (c) GPCP, and (d) TRMM datasets.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

The threshold value in the present study was set to 3 mm day−1 based on Zhang and Wang (2008). The threshold value of Wang and LinHo (2002) is also similar to this threshold based on the definition of the threshold used in the present study. The threshold of 3 mm day−1 corresponds to 91 mm month−1 or 1095 mm yr−1. In general, a period of time with precipitation greater than 3 mm day−1 cannot be regarded as a dry season. Most wheat is harvested in areas with annual precipitation of 500–1000 mm yr−1, and most rice is harvested in areas with annual precipitation greater than 1000 mm yr−1 (MAFF 2003). Hence this threshold value is useful from an agricultural standpoint. Previous studies have used 5 mm day−1 (e.g., Gramzow and Henry 1972; Alfaro 2002) for the definition of the rainy season, but in a slightly different context such as clear identification of the rainy pentad.

4. Comparison of rainfall datasets

Table 2 compares the average climatological annual precipitation over the entire land area of Panama estimated with different datasets. The climatological average of the MRI datasets was 2578 mm yr−1 for the period 1971–2009 and varied from 2545 to 2715 m yr−1, depending on the time interval. The values determined from the CRU and MRI datasets were closest for the time interval 1971–2002, and the averages determined from the GPCP and MRI datasets were closest for the time interval 1998–2009. The largest and smallest values were derived from the UNESCO and TRMM datasets, respectively; those values differed by about 17%, which is comparable to the differences between different Japanese rainfall datasets (Utsumi et al. 2008).

Figure 2 compares the spatial patterns of climatological annual mean precipitation among the four datasets. The spatial patterns derived from the MRI and TRMM datasets were similar (Figs. 2a,d): high precipitation in the Caribbean coastal zone and in the provinces of Chiriquí and Veraguas, and low precipitation in the western coastal zone of the Gulf of Panama from Los Santos Province to Panama Province. However, large differences were apparent in the eastern part of Panama, where the distribution of gauge stations is sparse (Fig. 1b). In addition, small-scale features of the spatial distribution were apparent only in the MRI dataset. The spatial patterns apparent from the CRU, GPCP, and TRMM datasets were similar, but the patterns of rainfall derived from the CRU and GPCP (Figs. 2b,c) were more uniform. The spatial pattern in the UNESCO dataset [Fig. 10 of UNESCO (2008)] resembles that of the MRI dataset but differs from the MRI dataset with respect to small-scale features.

The MRI dataset showed high precipitation rates (>10 mm day−1) in two areas, Chiriquí Province and the western part of Colón Province (Fig. 2a), but the TRMM dataset did not (Fig. 2d). The dominant vegetation in the coastal zone between the provinces of Coclé and Los Santos is dry forest (Condit et al. 2010), and in that region the MRI dataset indicates that the rate of precipitation is less than 4 mm day−1. The CRU and GPCP datasets indicate that precipitation is high (>10 mm day−1) only in the eastern coastal zone of Darién Province (Figs. 2b and 2c, respectively). In the UNESCO dataset [Fig. 10 of UNESCO (2008)], many local maxima are apparent. Such maxima are often artifacts of the use of a simple interpolation method. These comparisons indicate that the MRI dataset provides good estimates of the spatial patterns and mean values of climatological annual precipitation throughout the entire country of Panama. The MRI dataset should therefore be a reliable source of information for determining the onset and withdrawal of the rainy season in Panama.

5. Results

a. Onset and withdrawal during the seasonal cycle

We compared onset and withdrawal dates and the differences of the seasonal cycle of pentad precipitation at eight gauge stations in Panama (Fig. 1b). The eight stations consisted of four pairs of gauge stations that were located at a similar longitude on the Caribbean and Pacific sides of Panama. Table 3 provides information about the eight gauge stations.

Table 3.

Geographical information about the eight precipitation gauge stations. See Fig. 1b for the geographical locations in Panama.

Table 3.

Figure 3a depicts the time series of pentad precipitation at station David. The rainy season starts at pentad 22 and terminates at pentad 69 (7–11 December). A clear seasonal cycle of precipitation is apparent in Fig. 3a. A sharp peak of pentad precipitation occurs in late May, and a broad peak is evident in September and October. Similar seasonal cycles are apparent at stations Los Santos and Amador, but with sharp maxima in October and November (Figs. 3c,e). The break of the rainy season in July and August is a result of passage of the ITCZ to the north, a time of year referred to as “Veranito (de San Juan)” [UNESCO 2008; in English, “the mid-summer drought” (Magaña et al. 1999)]. The rainy season at Amador persists for 47 pentads, from pentad 23 to pentad 70 (21 April to 16 December). At Los Santos a late onset is apparent at pentad 27 (11–15 May), and withdrawal occurs at pentad 67. San Pedro has a similar seasonal cycle, but with a single sharp peak before withdrawal at pentad 2 (6–10 January) (Fig. 3f). Stations Garachine and Mulatupo have small seasonal variations of pentad precipitation and similar seasonal cycles (Figs. 3g,h). The onset and withdrawal dates at Garachine occur at pentad 23 (21–25 April) and pentad 71 (17–21 December), respectively. The onset and withdrawal dates at Mulatupo occur during pentads 23 and 70 (2–6 December), respectively.

Fig. 3.
Fig. 3.

Seasonal variations of climatological pentad precipitation (solid line) ± 1 standard deviation (gray shading). The horizontal green line denotes the threshold value of 3 mm day−1. See Fig. 1b and Table 3 for detailed information on each station location.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

At the two other gauge stations on the Caribbean side, Seiyic and Cocle del Norte, precipitation exceeds 3 mm day−1 even during the period from January to April (Figs. 3b,d), the indication being that there is no dry season at these two gauge stations. The intense rainy season at Seiyic continues from pentad 26 to pentad 71 (6 May to 21 December), during which time the rate of precipitation is about 9 mm day−1, with no distinct peak. At Coclé del Norte, the intense rainy season starts in pentad 23 and terminates during pentad 3 (11–15 January). There is a local minimum of pentad precipitation from pentad 47 to pentad 57 (19 August–12 October). Peak pentad precipitation occurs during pentad 65 (17–21 November).

The heaviest rainfall in the late rainy season occurs at Los Santos, Cocle del Norte, Amador, San Pedro, and Garachine (Murphy et al. 2014); a distinct peak of the rainfall during the rainy season is not seen at Seiyic and Mulatupo. Veranito, the midsummer drought (Magaña et al. 1999), is clearly apparent at David, Los Santos, and Amador in the Pacific coastal zone; three peaks of rainfall with two midsummer droughts are apparent at Cocle del Norte and San Pedro in the Caribbean coastal zone.

b. Geographical distribution of onset and withdrawal

Figure 4a shows the geographical distribution of the onset dates of the rainy season in Panama. There is no dry season in the Caribbean coastal zone from the province of Bocas del Toro to the province of Colon (Figs. 3b,d). The earliest onset of the rainy season in pentads less than 16 occurs on the Pacific side of the Cordillera de Talamanca Mountains, close to the national border with Costa Rica. The second earliest onset, in pentad 20 (6–10 April), occurs in Chiriquí Province, the southern part of Veraguas Province, and the eastern part of Colon Province and western part of San Blas Province. The rainy season starts suddenly in pentad 21 in most of the eastern part of Panama, including San Miguel Island, although it does so in a very confined area in the western part of Panama. The onset occurs at pentad 22 in Veraguas Province and the northwestern part of Darién Province. The latest onset, in pentad 26 (6–10 May), occurs in the coastal zone of the Gulf of Panama in Los Santos Province, following the onset in adjacent areas in pentads 23 to 25 (21 April–5 May). Even in a country as small as Panama, the onset varies from pentads 16 to 26, a time interval of 55 days or about 2 months.

Fig. 4.
Fig. 4.

Geographical distributions of the (a) onset and (b) withdrawal dates of the rainy season in Panama. The onset and withdrawal dates have been defined with the uniform threshold value method and a threshold value of 3 mm day−1. Areas with no dry season have pentad 1 for the onset date and pentad 73 for the withdrawal date. See also Fig. 5 for the areas.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

Figure 4b shows the geographical distribution of the withdrawal dates of the rainy season. As mentioned in the previous paragraph, there is no withdrawal date in the Caribbean coastal zone from the province of Bocas del Toro to Colon Province because there is no dry season. The earliest withdrawal of the rainy season occurs during pentad 67 in the northern coastal zone of the Gulf of Panama in Los Santos Province. The withdrawal extends to the surrounding areas and reaches Veraguas Province and the region from the mouth of the Panama Canal to the Gulf of Panamá in pentad 68 (2–6 December), when the withdrawal also occurs in Chiriquí Province. In the remainder of the western part of Panama, the rainy season terminates on the Pacific side during and after Pentad 69, except for the Cordillera de Talamanca mountain range near the national border with Costa Rica, where the latest withdrawal occurs in pentad 73 (27–31 December). In contrast, the withdrawal migrates from the Panama Canal to the national border with Colombia in pentads 70 and 71. In the southern part of Darién Province near the national border with Colombia, the latest withdrawal occurs in pentad 1 (1–5 January).

The Caribbean coastal zone from the province of Bocas del Toro to the province of Colon, except for the eastern part of Bocas del Toro Province and the northern part of Veraguas Province, has the longest rainy season, the entire year (73 pentads), because there is no dry season. Areas with rainy seasons longer than 60 pentads are confined to the Cordillera de Talamanca Mountains near the national border with Costa Rica (Fig. 5). In contrast, the shortest rainy season, less than 43 pentads, is found in the coastal zone of the Gulf of Panama in the provinces of Los Santos, Herrera, and Cocle. The rainy season persists for 43–46 pentads in adjacent areas. The remaining areas have rainy seasons that last 47–52 pentads.

Fig. 5.
Fig. 5.

Geographical distributions of the length of the rainy season in Panama (pentads). Areas with no dry season have 73-pentad rainy seasons.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

The geographic variability in the length of the rainy season is larger than the variability of the onset and withdrawal dates. The geographical distributions of the onset and withdrawal dates resemble each other (Fig. 4). The fact that the spatial correlation coefficient between the two is −0.89 suggests that early onset dates often correspond to late withdrawal dates, and vice versa. The difference between the onset and withdrawal dates, or the length of the rainy season, therefore shows the largest geographical variability.

c. Mesoscale circulations

In the dry season, January to March, water vapor fluxes are divergent over Panama (Figs. 6a,b). Weakly convergent water vapor fluxes cover Panama in April (Fig. 6c) and migrate to the north in May (Fig. 6d). At this time, the rainy season starts over all of Panama (Fig. 3). A northeasterly water vapor flux, accompanied by a branch of the CLLJ (e.g., Amador 1998; Nakaegawa et al. 2014a) or the Panama jet (Xie et al. 2005; Amador et al. 2006), prevails from January to April and weakens in May. The water vapor flux turns toward the east in June (Fig. 6e) as it blows toward the ITCZ north of Panama. These changes in the source of water vapor in Panama have been identified with a backward water-vapor-tracking analysis (Durán-Quesada et al. 2010). The convergence is intensified by the southern migration of the ITCZ in September (Fig. 6f). This convergent water vapor flux field is associated with SSTs greater than the threshold value of 26.5°C, which is a diagnostic for active convective development (Gray 1968). These high SSTs spread to the eastern Caribbean in May, cover the entire Caribbean by August, and extend to the entire tropical North Atlantic by October (Fig. 7) (Taylor et al. 2002; Wang and Enfield 2003). The seasonal cycles of the SSTs and the CLLJ are closely related. After the second precipitation peak with the passage of the ITCZ over Panama, the convergence field migrates to the south in November and diminishes in December. Most of Panama experiences a concurrent withdrawal of the rainy season in December (Fig. 4b).

Fig. 6.
Fig. 6.

Climatological monthly means of mesoscale column total water vapor fluxes (vector; kg kg−1 m s−1) and their convergences (shading; 10−5 kg kg−1 m s−1 m−1). The abbreviation P. in the top right of each panel denotes pentad.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for SST (°C).

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

These mesoscale circulation patterns can explain the onset and withdrawal of the rainy season in Panama on a country-wide scale, but they cannot account for the details of the spatial features depicted in Figs. 3 and 4. The earliest onset and the latest withdrawal should occur on the Pacific side based on the northward and southward migration, respectively, of the convergent water vapor fluxes; the second earliest onset occurs in Chiriquí Province and the southern part of Veraguas Province on the Pacific side, as well as in the eastern part of Colon Province and the western part of San Blas Province on the Caribbean side. This discontinuous pattern reflects the low horizontal resolution of the JRA-55, which cannot represent the detailed spatial features of topography and land–sea distributions that determine the onset and withdrawal. However, we can infer a possible cause of the detailed spatial features of the onset and withdrawal dates from the water vapor flux fields. For example, during January to March a topographically induced upward mass flux accompanied by a strong water vapor flux produces a local-scale convergence in the Caribbean coastal zone from the province of Bocas del Toro to the province of Colon (Taylor and Alfaro 2005), the result being the absence of a dry season (Figs. 6a,b). In addition, the coastal zone of the Gulf of Panama in Los Santos Province is surrounded by the Cordillera Central mountain range. The northeasterly water vapor fluxes tend to converge on the Caribbean side of the highlands because of the topographically induced upward mass flux, which prohibits humid air from reaching the coastal zone of the Gulf of Panama in Los Santos Province. The rainy season can therefore start only when cyclonic water vapor fluxes come from the south in May (Fig. 6d).

d. SST

Because of the temperature dependence of evaporation from the sea surface, the geographical distribution of SST may partly account for the timing of the onset and withdrawal dates. On the Pacific side, the northeasterly wind over the Gulf of Panama, the Panama jet, becomes strongest in March and April and induces a lowering of SST (Figs. 7b,c) only in the Gulf of Panama. Xie et al. (2005) have shown that the suppression of atmospheric convection to the south of 5°N produces a local band of minimal precipitation within the ITCZ. This cooling may delay the onset dates around the Gulf of Panama. In contrast, SSTs off the coastal zones of the Pacific coast of western Panama (Chiriqui and Veraguas) are warmer than the SSTs off the coastal zones of the Caribbean side of western Panama (Bocas del Toro and Veraguas), and the southeastern edge of the eastern North Pacific/western Hemisphere warm pool (Xie et al. 2005; Wang and Enfield 2001; Wang and Enfield 2003) may advance the onset of the rainy season. On the Caribbean Sea side, only San Blas Province has a dry season. The SST is lower in March off the coastal zone of San Blas than off Bocas del Toro, but this lower SST is unlikely to delay the onset. The SST off San Blas is higher than 26.5°C (Fig. 7b) and is therefore sufficiently high that the active convection and strong water vapor fluxes are similar in the two regions (Fig. 6b). The water vapor fluxes in JRA-55 show divergence fields over Panama mainly due to the low horizontal resolution and insufficient representation of the orography. The existence of dry season in San Blas may be the result of orographic effects on precipitation since the Cordillera de Talamanca and Cordillera Central Mountains in Bocas del Toro is much higher than the mountains in San Blas (Fig. 1a).

The SST contrast on the Pacific side (Fig. 7h) is not as distinct during the withdrawal season (December) as during the onset season because of the small upwelling of cold water by the Panama jet. Relatively warm SSTs off the coastal zone of the provinces of Chiriqui and Veraguas may be associated with withdrawal dates that are later than withdrawal dates in the coastal zone of the Gulf of Panama, where the SSTs are relatively cold. However, the fact that there is no SST contrast on the Caribbean Sea side suggests that the SST difference does not influence the withdrawal dates on the Caribbean Sea side, and other factors must therefore be responsible for the differences in withdrawal dates on the Caribbean Sea side.

6. Discussion

a. Spatiotemporal inhomogeneity in the availability of observation data

The availability of observation data for the gridded dataset varied both temporally and spatially. Systematic variations of data availability can affect the spatial distribution of the climatological onset and withdrawal dates. First, we counted the actual number of acceptable observations in each grid for each pentad in the 40 years and then determined the minimum of the 40-yr mean number of acceptable observations during the 73 pentads for each grid (Fig. 8a). This figure reflects the length of the observation period. If the observation period was short, the minimum of acceptable observations was small. In several grids the minimum was less than 4. These grids were randomly distributed and not clustered together; there was always another station with a large minimum number of acceptable observations within 20 km of the stations in these grids (Table 1). In some grids the minimum number of acceptable observations exceeded 40 because there was more than one station in the grid. Each number in Fig. 8a was normalized by dividing it by the 73-pentad mean number of acceptable observations in that grid. The normalized minima at most of the grids with low minima in Fig. 8a were about 1.0, except for three grids (Fig. 8b): two in the Caribbean coastal zone near the border with Costa Rica, Bocas del Toro and Changuinola 1, and one on the Pacific side of the Panama Canal, Howard Z.C. The incompleteness of the data record in these three grids was due to their stations being relatively new stations, to termination of the station with a record shorter than 40 years, and to missing data. Our gridding method weighted and interpolated the precipitation data based on the number of observations and was probably not affected by the inhomogeneity displayed in Fig. 8. A comparison of the geographical distribution of the onset and withdrawal dates in Fig. 4 with the spatiotemporal inhomogeneity of the available observation data in Fig. 8 does not reveal any particular geographical pattern in Fig. 4 that corresponds to the station locations with low minima in Fig. 8. We conclude that the spatiotemporal inhomogeneity in our gridded data did not influence the geographical distribution of the calculated climatological onset and withdrawal dates.

Fig. 8.
Fig. 8.

(a) Minimum of the actual number of acceptable observations in each grid in the 73 pentads during the 40 years. A value of 20 indicates that 50% of the records available out of 40 years were acceptable, and a value greater than 40 indicates that there were multiple stations in the same grid. (b) Normalized minima by dividing the numbers in Fig. 8a by the 73-pentad mean number of acceptable observations in that grid.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

The uncertainties in the onset and withdrawal dates are large in areas where the observation stations were sparsely distributed, such as Bocas del Toro and Darien, relative to areas with dense distributions of observation stations. In the spatial interpolation scheme described in section 2a, weighting depended on which side of high ridges the station was located. Mulaputo is the only station in San Blas, and the geographical distribution of the onset and withdrawal dates in San Blas was strongly influenced by the data from Mulaputo. The data from Garachine had a similar influence on estimates for Darien. Therefore, the actual geographical distribution of onset and withdrawal dates may be more inhomogeneous.

b. Uniform threshold value method with different values

There are logical reasons for varying the threshold value used to define the rainy season. For agricultural, hydrological, and/or ecological purposes, 4, 5, or 6 mm day−1 might work better as the threshold value. Enfield and Alfaro (1999), Alfaro and Cid (1999), and Alfaro (2002) used 5 mm day−1 for Costa Rica and the western part of Panama. Figure 9 shows the geographical distributions of onset and withdrawal dates as a function of threshold values. The qualitative features of the geographical distributions are unaffected by the choice of different threshold values. However, as the threshold value increases, the onset is delayed, and the withdrawal date advances over the entire country, especially in the coastal zones of the provinces of Los Santos and Herrera. This result can be understood by increasing the threshold value in Fig. 3. At the same time, areas with no dry season become smaller, because a large threshold value can produce a dry season. The geographical contrast in the onset dates seems to decrease because of the disappearance of areas with no dry season, whereas the differences of the withdrawal dates seems to increase because of the appearance of areas with withdrawal dates earlier than pentad 50 (3–7 September). Most of these features are apparent in the analysis by Marengo et al. (2001) of the sensitivity to the threshold value of the onset and withdrawal dates of the rainy season in the Brazilian Amazon basin. This similarity suggests that sensitivity to the threshold value is a general characteristic of the onset and withdrawal dates.

Fig. 9.
Fig. 9.

Comparison of the geographical distributions of the (left) onset and (right) withdrawal dates with different threshold values: (a),(b) 3, (c),(d) 4, (e),(f) 5, and (g),(h) 6 mm day−1. Note that (a) and (b) are the same as in Figs. 4a and 4b, but with a different color scale.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

The pioneering work of Gramzow and Henry (1972) concerning the onset and withdrawal of the rainy season in Central America is generally in good agreement with the results in Figs. 9e and 9f. They used observation data at 61 stations for all of Central America and a threshold value of 5 mm day−1 to depict the geographical distributions of the onset and withdrawal dates. On the one hand, features similar to our results are apparent in their results: early onset in the Caribbean coastal zone, eastward migration of the onset from the national border with Costa Rica to the Panama Canal, and late onset in the coastal zone of the Gulf of Panama in Los Santos Province. On the other hand, the onset migrates from the Panama Canal to the east in Gramzow and Henry (1972), whereas it occurs in mid-April in most of the eastern part of Panama in Fig. 9e. The withdrawal occurs earlier in the coastal zone of the Pacific than of the Caribbean in both results; the withdrawal migrates westward in Gramzow and Henry (1972) but occurs concurrently in Fig. 9f. In general, the onset (withdrawal) dates in Fig. 9e (Fig. 9f) are earlier (later) by about 15 days than those of Gramzow and Henry (1972). These differences are due to the small number of station data available in Panama and the subjective interpolation of the onset and withdrawal dates in Gramzow and Henry (1972).

The station-based work of Alfaro (2002) with a threshold value of 5 mm day−1 showed spatially discrete geographical distributions of the onset and withdrawal dates that were similar to those depicted in Figs. 9e and 9f, although the dates in Alfaro (2002) and the present study differ by one or two pentads. Because the Alfaro (2002) analysis involved only one station on the Caribbean side near the Panama Canal, that analysis indicated that all areas of Panama experience a dry season. Therefore, Figs. 9e and 9f are the first to demonstrate that an area with no dry season exists in the western part of the Caribbean coastal zone of Panama.

c. Distributed threshold value method

It would be interesting to know how sensitive the results are to the choice of the uniform or distributed threshold value methods. The choice between the two methods depends on the purpose of the study. The distributed threshold value method may be useful for qualitative comparative studies because it uses anomalous pentad precipitation data from climatological annual mean precipitation values. Therefore, onset and withdrawal dates always exist in most places.

Based on the distributed threshold method, the rainy season starts in the eastern part of Panama during pentad 22 from the eastern part of Colon Province and the eastern inland part of Darien Province. The rainy season expands to the surrounding areas during pentad 23 and occurs concurrently in the eastern part of the provinces of San Blas and Darien. The remaining eastern part of Panama experiences the onset during pentad 24 (26–30 April; Fig. 10a). In the western part of Panama, the rainy season starts from the Caribbean coastal zone near the western part of Colon Province during pentad 22, extends to the south, and then stalls. In the western part of Chiriquí Province on the Pacific side, the rainy season starts during pentad 24 and extends to the north on the Caribbean side. The onset occurs simultaneously in most of the remaining areas during pentad 26. The latest onset occurs during pentad 27 in two remote areas: the Cordillera Central mountains in Veraguas Province and the coastal zone of the Gulf of Panama in Los Santos Province.

Fig. 10.
Fig. 10.

As in Fig. 4, but calculated with the distributed threshold value method. The distributed threshold values are seen in Fig. 2a.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00243.1

The onset dates estimated with the distributed threshold method are concentrated during pentads 22–27, whereas the onset dates based on the uniform threshold method occur during pentads 16–26. The earliest onset occurs in different areas: the Pacific side of the Cordillera Talamanca Mountains close to the national border with Costa Rica based on the uniform threshold value method and in Colón Province and the eastern inland part of Darién Province based on the distributed threshold value method. Both methods show that the latest onset occurs in the coastal zone of the Gulf of Panama in Los Santos Province, whereas only the distributed threshold value method shows that the latest onset occurs simultaneously in the Cordillera Central Mountains in Veraguas.

Based on the distributed threshold method, the earliest withdrawal occurs during pentad 63 (7–11 November) in two areas isolated from surrounding areas: the Cordillera de Talamanca Mountains close to the national border with Costa Rica in Chiriquí Province and the Cordillera Central Mountains in Veraguas Province (Fig. 10b). The withdrawal of the rainy season migrates from Costa Rica during pentad 65 to Los Santos Province and through the Pacific side of Panama during pentad 66 (22–26 November) to the coastal zone of the Gulf of Panama and West Panama Province. Three remote areas simultaneously experience withdrawal during pentad 68: the coastal zone of the Gulf of Panama in Los Santos Province, the provinces of Darién and San Blas, and the Caribbean coastal zone. The latest withdrawal occurs in the eastern part of Colon Province during pentad 69. Both of the areas with the earliest and latest withdrawals of the rainy season are found in the Caribbean coastal zone.

Table 4 compares the onset and withdrawal dates determined with the two different methods at eight gauge stations. Onset and withdrawal dates estimated with the distributed threshold value method were generally within the range of values estimated with the uniform threshold value method. In wet areas, the onset and withdrawal dates estimated with the distributed threshold value method were closer to those estimated by using the uniform threshold value method with a threshold value of 6 mm day−1; in dry areas, these dates were closer to those estimated by using the uniform threshold value method with a threshold value of 3 mm day−1. These comparisons reveal that the different methods provided different estimates of the onset and withdrawal dates, although the different methods retained the fundamental features of the onset and withdrawal dates. A comparison between the two different methods has not yet been made in other regions; determining whether these conclusions are general or not will require additional comparisons in different regions.

Table 4.

Comparison of the onset and withdrawal dates of the rainy season estimated with different methods.

Table 4.

d. Low-horizontal-resolution atmospheric dataset

The JRA-55, with a grid spacing of 55 km, does not have a horizontal resolution sufficient to capture the small-scale features of atmospheric circulation. However, its horizontal resolution is the highest of the global reanalysis datasets. A higher horizontal resolution dataset requires understanding the dynamical causes of the detailed features of the geographical distributions, such as we inferred from the water vapor fluxes. Conversely, the low horizontal resolution atmospheric datasets normally used for this type of analysis cannot reveal the detailed geographical features of the onset and withdrawal depicted in Figs. 4, 9, and 10.

We also examined the water vapor fluxes and convergence fields in the North American Regional Reanalysis, which has a high horizontal resolution of 32 km (Mesinger et al. 2006). However, we could not obtain reasonable seasonal cycles or migrations, perhaps because the regional lateral boundaries were too close to Panama.

e. Synoptic-scale circulations

The mesoscale circulations and the local SST distributions presented above are embedded in large regional-scale or synoptic-scale atmospheric circulation. The seasonal cycle of SST in the Western Hemisphere warm pool is a key to defining the climatological features of synoptic-scale atmospheric circulation in Central America and the Caribbean (Amador et al. 2006). The Atlantic warm pool is the eastern part of the Western Hemisphere warm pool and warms in the boreal summer after the eastern North Pacific warm pool does so in the boreal spring (Wang and Enfield 2003). In the spring, the fact that the warm pool in the eastern North Pacific (ENP) is warmer than the Atlantic warm pool intensifies the CLLJ, whereas cooler conditions in the ENP during the summer weakens it (Enfield and Alfaro 1999). The CLLJ is closely related to the seasonal SST contrast between the two pools through the activity of the North Atlantic subtropical high (Wang 2007). The eastern North Pacific warm pool influences midsummer droughts (Magaña et al. 1999) by changing the intensity of convective activity. Thus synoptic-scale atmospheric circulation influences the onset and withdrawal dates through its effect on mesoscale atmospheric circulation.

As mentioned above, the CLLJ is a unique feature of Central America and the Caribbean. The semiannual cycles of the CLLJ and accompanying water vapor fluxes evidence a local minimum during the onset period in April and May when the North Atlantic subtropical high is weak. The CLLJ and accompanying water vapor fluxes are intensified during the withdrawal period. These relationships suggest that the seasonal variations of the CLLJ influence at least the onset dates and perhaps weakly influence the withdrawal dates.

The eastern North Pacific warm pool reaches a maximum temperature of >29°C during the period from April to June and activates deep atmospheric convections that trigger the onset of the rainy season in Central America. When the temperature of the eastern North Pacific warm pool drops below 28°C, the midsummer drought occurs in the ITCZ, although 28°C is higher than the threshold for active atmospheric convection. This suggests that synoptic-scale circulation such as the convergence of water vapor fluxes maintains the deep convection of the ITCZ in addition to high SST (Magaña et al. 1999). These processes determine the timing on a monthly time scale (April to May) of the onset of the rainy season on a synoptic scale, but the detailed geographical features in Figs. 4, 9, and 10 suggest the importance of mesoscale processes. Subtropical cold surges or Nortes bring synoptic-scale stratiform precipitation to Central America during the withdrawal period corresponding to the boreal winter (Cortez 2000). This type of precipitation can delay the withdrawals until December and January, as seen in Figs. 4, 9, and 10. However, subtropical cold surges or Nortes almost never reach Panama and persist only a week or less (Garreaud 2001). Such phenomena can modulate the withdrawal dates on an interannual time-scale, but not on a climatological mean time scale.

7. Concluding summary

In the present study we investigated the onset and withdrawal dates of the rainy season in Panama. First, we used ground precipitation observations to develop gridded daily precipitation datasets with a high horizontal resolution of 0.05° to make it possible to analyze the geographical distributions of the onset and withdrawal dates.

The country of Panama is oriented parallel to latitude lines, and onset and withdrawal dates could therefore adhere to a simple geographical pattern, with the onset dates moving from south to north and the withdrawal dates from north to south concurrently with the migration of the ITCZ, a pattern evident in other regions and countries. However, the geographical distributions showed very complicated features in Panama.

The rainy season starts in pentads 22 and 23 at four of the eight gauge stations listed in Table 3 and terminates in pentads 69–71 at the same four gauge stations (Fig. 3). The first onset and latest withdrawal occur at San Pedro in pentads 20 and 5, respectively, whereas the latest onset and the first withdrawal occur at Los Santos in pentads 26 and 67, respectively. There is no dry season at the two gauge stations at Seiyic and Cocle del Norte in the Caribbean coastal zone if we use a uniform threshold value of 3 mm day−1. During the rainy season, a single peak of rainfall or bimodal peaks separated by Veranito are apparent at each gauge station.

The geographical distribution of the onset dates of the rainy season show a wide range of about 2 months from pentads 16 to 26 (Fig. 4a). The earliest onset of the rainy season occurs in the Cordillera de Talamanca Mountains in pentads less than 16. The second-earliest onset occurs during pentad 20 in Chiriquí Province, the southern part of Veraguas Province, the eastern part of Colon Province, and the western part of San Blas Province. The rainy season starts suddenly during pentad 21 in most of the eastern part of Panama and in pentad 22 in most of the western part. The onset moves toward and finally reaches Los Santos Province during pentad 26.

The rainy season terminates in Los Santos Province during pentad 67 (Fig. 4b). The withdrawal expands to the surrounding areas to the east and west and reaches Veraguas Province and the Panama Canal during pentad 68, when it occurs simultaneously in Chiriquí Province. The withdrawal occurs during and after pentad 69 in the remaining western part of Panama on the Pacific side, whereas it migrates from the Panama Canal to the national border with Colombia by pentad 71. The latest withdrawal occurs in the Cordillera de Talamanca Mountains during pentad 73 and in the southern part of Darien Province during pentad 1. The length of the rainy season varies from 40 pentads in Los Santos to 73 pentads in the western part of the Caribbean coastal zone, where there is no dry season (Fig. 5).

Although the low horizontal resolution of the JRA-55 cannot directly reveal the cause of the high variability of the onset and withdrawal dates and of the length of the rainy season, the water vapor fluxes (Fig. 6) and topography (Fig. 1) suggest dynamical causes, such as topographically induced upward mass fluxes accompanied by strong water vapor fluxes. Differences of SST on the Pacific side seem to be associated with the timing of the onset and withdrawal, but this is not the case on the Caribbean Sea side.

As we have discussed, the optimal choice of the threshold value for the uniform threshold value method depends on the purpose. A similar conclusion applies to the choice between the uniform threshold and distributed threshold methods. Figures 9 and 10 may facilitate that choice.

The uncertainty in precipitation datasets apparent in Fig. 2 creates uncertainty in the estimated onset and withdrawal dates. There is need for creation of more reliable gridded daily precipitation datasets by combining satellite and ground observations to cover areas where there are no ground observation data available, such as Darien Province and the Caribbean coastal zone. In this study, we were not able to clarify the causes of the high variability of the onset and withdrawal dates in Panama. A regional climate simulation with high horizontal resolution is needed to facilitate understanding of this variability with respect to atmospheric dynamics and local SST effects.

The present study focused on climatological onset and withdrawal dates. Atmospheric circulation over Central America and the Caribbean is modulated by the interannual variability of atmospheric circulation over the Pacific and Atlantic (Ropelewski and Halpert 1987; Enfield and Alfaro 1999). The CLLJ influences the interannual variability of wet and dry years (Méndez and Magaña 2010), and the water vapor flux accompanied by the CLLJ is projected to be intensified in the future climate (Nakaegawa et al. 2014a). This topic has been investigated in other regions (e.g., Marengo et al. 2001). The interannual variability of the onset and withdrawal dates of the rainy season in Panama is therefore an important research topic. Future changes in the onset and withdrawal dates are also important because of their potential effect on anthropogenic activities such as water resource and flood managements (e.g., Fábrega et al. 2013; Nakaegawa et al. 2014b,c). The present study has provided information that will facilitate these further studies.

Acknowledgments

The present study was initiated by the technical cooperation scheme of the Japan International Cooperation Agency. The precipitation dataset was provided by ETESA. The authors thank Professors Fábrega and Pinzón at the Technological University of Panama for their kind support during TN’s stay in Panama and Professor Nakayama of the Kitami Institute of Technology, who paved the way for this research. The authors wish to acknowledge Dr. Rosana Nieto-Ferreira as editor and three anonymous reviewers for their constructive and meaningful comments on this manuscript. This research was conducted as part of an MRI project entitled “Research on the mechanisms and the predictability of extreme climate events.” TN was financially supported by KAKENHI Grant 23226012 from the Japan Society for the Promotion of Science, and OA and KK by the SOUSEI program from the Ministry of Education, Culture, Sports, and Science, Japan.

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