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  • View in gallery

    (a) The Eta Model domain (hatched area), the area where diagnostics are performed (the large rectangle between 60°S–15°N and 87°–33°W), and the location of La Plata subbasins; (b) the second mode obtained from an EOF analysis of the Eta Model forecasts, representing the summer precipitation dipole pattern. The two regions employed as the basis for the composites (SAMS and SESA, see text) are represented by two dashed boxes.

  • View in gallery

    Time series of precipitation and the corresponding scatterplot for (a)–(c) SESA and (d)–(f) SAMS. The analysis is for the austral summer seasons November 2001–March 2002 and November 2002–March 2003. Observations are represented by a heavy line, and the Eta Model forecasts by a thinner line. Units are mm day−1. In (c) and (f) the line with slope 1:1 is depicted for reference.

  • View in gallery

    (a), (b), (d), (e) Histogram of observed precipitation and Eta Model forecasted precipitation for SESA and SAMS regions and (c), (f) histogram of vertically integrated (1000–500 hPa) moisture flux convergence for SESA and SAMS regions. Units are mm day−1. The histograms show the number of days in which precipitation (vertically integrated moisture flux convergence) falls into each interval for the combined summer seasons November 2001–March 2002 and November 2002–March 2003.

  • View in gallery

    (a) Seasonal average of Eta Model forecasted precipitation (mm day−1); (b) 200-hPa vector wind (m s−1); (c) 850-hPa vector wind (m s−1; values smaller than 2 m s−1 are not shown); and (d) vertically integrated moisture flux (kg m−1 s−1; values smaller than 50 kg m−1 s−1 are not shown) and moisture flux convergence (mm day−1). Positive values indicate convergence, while negative values (divergence of moisture flux) are not displayed to avoid overcrowding of the figure.

  • View in gallery

    (a) Difference between the SESA Eta Model forecast precipitation composites and the seasonal-averaged precipitation [mm (3 h)−1]; (b) corresponding runoff composite represented as a percentage of the mean values.

  • View in gallery

    Difference between SESA composites and the seasonal average for (a) 200-hPa vector wind (m s−1); (b) 850-hPa vector wind (m s−1); and (c) vertically integrated moisture flux and moisture flux convergence (mm day−1).

  • View in gallery

    Difference between SESA composites and the seasonal average for (a) precipitable water (mm); (b) equivalent potential temperature (K) at 925 hPa; and (c) CAPE (J kg−1); and (d) CIN (J kg−1).

  • View in gallery

    (a) Composite time evolution of the departures of precipitation, vertically integrated moisture flux convergence, CAPE, and CIN for SESA. Composite time–height section representing the evolution of (b) temperature (K), (c) meridional wind (m s−1), (d) moisture flux convergence, (e) specific humidity (g kg−1), and (f) wind divergence (s−1 × 10−6).

  • View in gallery

    As in Fig. 5 but for SAMS.

  • View in gallery

    As in Fig. 6 but for SAMS.

  • View in gallery

    As in Fig. 7 but for SAMS.

  • View in gallery

    As in Fig. 8 but for SAMS.

  • View in gallery

    Cross section of the (a) SESA and (b) SAMS composite of wind perpendicular to the line shown in the inset. Units are m s−1.

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Intense Rainfall Events Affecting the La Plata Basin

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  • 1 Department of Atmospheric and Oceanic Science/ESSIC, University of Maryland, College Park, College Park, Maryland
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Abstract

The circulation features associated with intense precipitation events over the La Plata Basin (LPB) during the austral summers of 2001/02 and 2002/03 are investigated using the Eta Model runs generated at the University of Maryland. Based on the main mode of variability over LPB, two regions were selected: (i) the region of Brazil that is at the core of the South American summer monsoon system (SAMS) and (ii) the central region of LPB in southeastern South America (SESA). First, a comparison between the 24-h total precipitation in the Eta Model and the 24-h observed precipitation was made. Results show that the Eta Model captures well the temporal variability of precipitation events in both regions, although a positive bias is noticed over SAMS. Likewise, the model reproduces the distribution of precipitation rate over SESA, but not over SAMS. Nevertheless, the distribution of the moisture flux convergence intensity, which represents the dynamical forcing, is closer in shape to the observed precipitation distribution, suggesting that the model can be a useful tool in identifying the forcing for heavy precipitation events over both regions.

Composites of atmospheric and surface variables were constructed for intense precipitation events during austral summer over both regions. Intense rainfall over the central La Plata Basin (SESA) is linked to an amplified upper-tropospheric midlatitude wave pattern in which rainfall occurs just east of an enhanced cyclonic circulation. Accompanying this circulation pattern, an enhanced low-level jet (LLJ) transports warm, moist air from the Amazon toward the region, contributing to an increase in the thermal contrast over SESA. The combined patterns of thermal and dynamical variables suggest that large-scale systems, like frontal systems, are important in producing intense rainfall events. The SAMS region events have a similar upper-level structure as in SESA, but they are longer lived. In this case, the moisture fluxes are determined by an eastward shift of the LLJ, but also directly from the Amazon Basin to the north. As expected, precipitation events produce large increases of simulated runoff. The largest impact is on the SESA region, affecting the streamflow of the Paraná, Paraguay, and Uruguay, the three main rivers of the LPB.

Corresponding author address: Ernesto Hugo Berbery, Department of Atmospheric and Oceanic Science/ESSIC, University of Maryland, College Park, 3427 Computer and Space Sciences Building, College Park, MD 20742-2425. Email: berbery@atmos.umd.edu

Abstract

The circulation features associated with intense precipitation events over the La Plata Basin (LPB) during the austral summers of 2001/02 and 2002/03 are investigated using the Eta Model runs generated at the University of Maryland. Based on the main mode of variability over LPB, two regions were selected: (i) the region of Brazil that is at the core of the South American summer monsoon system (SAMS) and (ii) the central region of LPB in southeastern South America (SESA). First, a comparison between the 24-h total precipitation in the Eta Model and the 24-h observed precipitation was made. Results show that the Eta Model captures well the temporal variability of precipitation events in both regions, although a positive bias is noticed over SAMS. Likewise, the model reproduces the distribution of precipitation rate over SESA, but not over SAMS. Nevertheless, the distribution of the moisture flux convergence intensity, which represents the dynamical forcing, is closer in shape to the observed precipitation distribution, suggesting that the model can be a useful tool in identifying the forcing for heavy precipitation events over both regions.

Composites of atmospheric and surface variables were constructed for intense precipitation events during austral summer over both regions. Intense rainfall over the central La Plata Basin (SESA) is linked to an amplified upper-tropospheric midlatitude wave pattern in which rainfall occurs just east of an enhanced cyclonic circulation. Accompanying this circulation pattern, an enhanced low-level jet (LLJ) transports warm, moist air from the Amazon toward the region, contributing to an increase in the thermal contrast over SESA. The combined patterns of thermal and dynamical variables suggest that large-scale systems, like frontal systems, are important in producing intense rainfall events. The SAMS region events have a similar upper-level structure as in SESA, but they are longer lived. In this case, the moisture fluxes are determined by an eastward shift of the LLJ, but also directly from the Amazon Basin to the north. As expected, precipitation events produce large increases of simulated runoff. The largest impact is on the SESA region, affecting the streamflow of the Paraná, Paraguay, and Uruguay, the three main rivers of the LPB.

Corresponding author address: Ernesto Hugo Berbery, Department of Atmospheric and Oceanic Science/ESSIC, University of Maryland, College Park, 3427 Computer and Space Sciences Building, College Park, MD 20742-2425. Email: berbery@atmos.umd.edu

1. Introduction

The La Plata Basin (LPB) has become the latest continental-scale experiment of the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorological Panel due to its particular characteristics in a region of great economic value for South America. The scientific communities of GEWEX and the Climate Variability and Prediction Panel (CLIVAR)1 have jointly prepared a document summarizing the current knowledge of the basin’s climate and hydrology, its economic importance, as well as defining areas of interest for future studies (Baethgen et al. 2001). The basin covers about 3.2 × 106 km2 and comprises three main rivers: the Paraná, Paraguay, and Uruguay (see their basins’ locations in Fig. 1a). Their joint discharges at the lower part of the basin are called the La Plata River.

The La Plata Basin has two geographical maxima in annual precipitation: one in the central part of the basin and the other one, although centered over the Brazilian Altiplano, affecting the northern portion of the basin (Berbery and Barros 2002). The central-basin rainfall shows little seasonal variation (Berbery and Barros 2002) and has been related in large part to mesoscale convective systems (MCSs; see, e.g., Velasco and Fritsch 1987; Mohr and Zipser 1996). Intense precipitation events in this region produce floods in the middle and lower portions of the Paraná River (Camilloni and Barros 2003) and in the Uruguay River (Barros et al. 2002). During austral summer, precipitation is a maximum over the southern Amazon Basin and the Brazilian highlands (approximately 13°–17°S, 46°–52°W), which make up the core region of what is known as the South American monsoon system (SAMS; Zhou and Lau 1998; Nogués-Paegle et al. 2002). The precipitation pattern associated with the monsoon system extends southeastward from the Amazon Basin to southeastern Brazil and into the Atlantic Ocean as a band of clouds and precipitation called the South Atlantic convergence zone (SACZ; Kodama 1993). According to Barros et al. (2004), the greatest peaks in the Paraguay River discharge are related to the monsoon precipitation in the upper portion of the Paraguay Basin. Interestingly, the monsoon precipitation, although also affecting the upper part of the Paraná River, does not contribute to the floods occurring in its middle and lower portions (Camilloni and Barros 2003). As a clarification, sometimes the name monsoon and SACZ are used indistinctly.

A noticeable feature affecting the two precipitation regions is the northwesterly low-level jet (LLJ; Virji 1981) that extends from the western Amazon Basin southeastward across eastern Bolivia and Paraguay. The flow associated with the LLJ transports moisture from the tropical Amazon to higher latitudes of South America (e.g., Berbery and Collini 2000; Gan et al. 2004; Marengo et al. 2004). Although this jet is present throughout the year (Berbery and Barros 2002), during summer it has a well-defined link to the monsoon (Zhou and Lau 1998; Rodwell and Hoskins 2001). The monsoon depends on other atmospheric and surface conditions, apart from the LLJ; see, for example, Fu et al. (1999). The LLJ has recently been the focus of a field program [the South American Low-Level Jet Experiment (SALLJEX)] with the endorsement of the Variability of the American Monsoon Systems Panel (VAMOS)/CLIVAR. The Scientific Prospectus and Implementation Plan for SALLJEX are presented in Paegle et al. (2001), and the results of the experiment and further information are available online at the “SALLJEX Data Management” page, see http://www.ofps.ucar.edu/salljex/.

The austral summer monsoon precipitation over the Brazilian Altiplano exhibits an out-of-phase relationship with precipitation over the central La Plata Basin so that, when it is wet over the SAMS/SACZ region, the central La Plata Basin is relatively dry, and vice versa (e.g., Casarin and Kousky 1986; Kousky and Cavalcanti 1988; Nogués-Paegle and Mo 1997; Liebmann et al. 2004). This dipole structure is very robust and has been found in several studies at intraseasonal to interannual time scales. Each phase of the mode has distinct moisture fluxes (Doyle and Barros 2002; Diaz and Aceituno 2003) that affect the atmospheric water budget (Herdies et al. 2002). Casarin and Kousky (1986) suggested that the increased precipitation might be related to the preferred phasing of synoptic waves due to variations of the Madden–Julian oscillation (MJO). Nogués-Paegle and Mo (1997) also found evidence that larger precipitation in the mode’s northern center (and suppressed to the south) is related to the convectively active phase of the MJO and that the increased precipitation in the mode’s southern center (and suppressed to the north) is related to a strong influx of moisture supplied by the LLJ. Thermodynamic and vorticity budgets were employed by Robertson and Mechoso (2000) to diagnose the vertical structure of the mode, which they associate with a stationary Rossby wave. The studies of Diaz and Aceituno (2003) and Liebmann et al. (2004) also associate the pattern to a wave train progressing from the South Pacific Ocean. These articles have different definitions of the mode, and some differences result in its structure, but they all agree on the importance of the low-level flow in the development of precipitation at each center of the dipole.

The role of advective processes is apparent in the above studies, but it is not clear yet whether there are other contributing local effects. The objective of this study is to diagnose the regional forcings associated with intense precipitation events over the La Plata Basin at synoptic time scales, and the resulting impacts in the model runoff. The analysis will focus on the two subregions of the basin that represent the centers of the dipole pattern: (i) southeastern South America (SESA), which includes portions of southern Brazil, northeastern Argentina, and Paraguay, and (ii) the center of the summertime SAMS.

The structure of this article is as follows: section 2 describes the dataset, model, and criteria used to create the composite analyses. Section 3 presents an evaluation of the Eta Model precipitation forecasts, and section 4 discusses the characteristic atmospheric features of intense rainfall events over SESA and SAMS. Section 5 contains a summary and discussion.

2. Methodology

a. Model description and datasets

The Eta Model employed in this article is the 2000 workstation version of the operational Eta Model used at the National Centers for Environmental Prediction (NCEP). This version was run routinely at the University of Maryland for forecast and research purposes until recently replaced by the 2003 version (http://www.atmos.umd.edu/~berbery/etasam). The grid spacing of the model is 80 km, and the domain covers all of South America and a portion of adjacent oceanic areas (see Fig. 1a). The model’s 38 vertical levels are unevenly distributed, providing a detailed description of the vertical structure, especially over lower terrain regions.

The model boundary layer formulation is presented in Janjić (1990, 1994), and the convective scheme is discussed in Betts and Miller (1986) with modifications by Janjić (1994). The atmospheric model is coupled with a land surface model, referred to as “Noah,” that solves the soil surface water and energy budgets when forced by near-surface variables (Ek et al. 2003, and references therein). The land surface model carries four soil uneven layers (10 cm, 30 cm, 60 cm, and 100 cm) with predicted states of soil moisture and temperature using soil moisture diffusion and heat conduction equations, respectively, along with intercepted canopy water (see Chen et al. 1996; Ek et al. 2003). Processes taken into account include bare soil evaporation, canopy evaporation–transpiration, frozen soil physics, and snow density/depth effects. The model also has an infiltration scheme for the subgrid variability of precipitation, runoff, and soil moisture (Schaake et al. 1996). Upgrades have been made to the Eta Model along the years, and its performance over North America has steadily increased (see Berbery et al. 2003). For a documentation of the changes to the operational Eta Model over North America the reader is referred to http://www.emc.ncep.noaa.gov/mmb/research/eta.log.html.

NCEP’s global model forecasts were used to initialize the Eta Model at 0000 UTC each day and to provide the boundary conditions. Initialization of the surface model is also done with the Global Forecast System (GFS) products of NCEP because the model (in this version) does not have an associated data assimilation system, as is the case for NCEP’s operational Eta Model, whose soil moisture states are in equilibrium due to their continuous cycling (Luo et al. 2005).

This study covers two warm seasons (November 2001–March 2002 and November 2002–March 2003) during which no model changes were done. The model forecasts are produced up to 72 h at 3-h intervals. Here, we use the 12–36-h forecasts as a proxy of regional analyses: to avoid potential spinup problems, the first 12 h are not included in the diagnostics; likewise, to avoid possible drifts of the model as time increases, we do not consider forecasts after 36 h. Therefore, a “day” is defined as the forecast from 12 to 36 h, and monthly values can be obtained after binning all 12–36-h forecasts. While 12 h may be enough to reduce the atmospheric and upper soil layer spinup, it is not sufficient for deep soil layer variables. Several studies have investigated the time it takes a model to achieve thermal and hydrologic equilibrium with the atmospheric forcing. For example, Yang et al. (1995) found that it may take years to reach equilibrium starting from dry or wet initial conditions, but also found a great disparity between models, and even between variables. Cosgrove et al. (2003), on the other hand, found that initialization with global reanalysis soil moisture is more advantageous than a dry or saturated initialization, with important reductions in spinup time. According to Rodell et al. (2005), spinup time is significantly reduced in humid regions, and, given that freezing temperature is one of its main controls, a quicker equilibrium is expected in subtropical South America. Our composites, which mostly reflect surface runoff, should not be much affected by the deep layer spinup. Nevertheless, recognizing that the composites of runoff in this article may be influenced by the lack of an equilibrium, it is suggested that those results be taken as qualitative.

To assess the ability of the Eta Model to forecast precipitation, a comparison was made between the forecast precipitation and a set of observed daily precipitation, available from the NCEP Climate Prediction Center (CPC). The analyses of observed precipitation are gridded at a horizontal resolution of 1° × 1° latitude–longitude over the domain (60°S–15°N, 110°–30°W). More details about the CPC dataset can be found in Shi et al. (2000).

b. Composite analyses

Composites of atmospheric and surface variables were prepared for the intense precipitation events during the austral summers of 2001/02 and 2002/03 at the regions that correspond to the two centers of action of the dipole pattern discussed in the introduction. The dipole pattern is rather ubiquitous, and, in fact, an empirical orthogonal function analysis of the Eta Model precipitation shows it as the second mode (Fig. 1b) after the tropical mode (not shown) that represents the shifts in the intertropical convergence zone (ITCZ). Notice in Fig. 1b the existence of a third center, in phase with SESA, straddling the equatorial region. The third center will not be discussed here, as it does not relate to the hydroclimate of the La Plata Basin. For practical purposes, the geographical location of SESA is defined as 30°–20°S, 60°–50°W while that of SAMS is 20°–10°S, 55°–45°W (dashed boxes in Fig. 1b).

A wet event is defined when P > PAVE + 1.5PSD, where all values are area averaged over the selected region, PAVE is the overall seasonal average, and PSD is the corresponding standard deviation. The statistical significance of the composites was calculated using a Student’s t test in which independent cases were assumed every 5 days (the number of degrees of freedom equals 60). All of the precipitation features discussed here are significant at the 95% level. Table 1 shows the number of cases (3-h wet periods) in each composite as well as the precipitation averages and standard deviations used to define the composite thresholds for each region. The seasonal average is defined as the mean of all available forecasts for the period from 1 November to 31 March (of 2001/02 and 2002/03). From here on, the averages and composites will refer to the two seasons combined. Despite the different number of cases for each year in SESA, composites for each year separately did not reveal significant differences (not shown). The increased number of wet periods in SESA during the summer 2002/03 with respect to the first summer may be related to increased storm activity associated with the 2002–03 El Niño. Wetter-than-average conditions in portions of SESA during El Niño have been discussed, for example, in Kousky et al. (1984), Ropelewski and Halpert (1987), Velasco and Fritsch (1987), and Grimm et al. (1998) among many others.

3. Model evaluation

Figure 2 shows the time series of 24-h accumulated Eta Model forecast precipitation and the 24-h observed precipitation for the two austral summer seasons 2001/02 and 2002/03. For the SESA region (Figs. 2a,b), there is good agreement in magnitude and day-to-day variability. The correlation between the two time series is 0.83, while the spread (Fig. 2c) does not show any distinguishable bias. The summer averages of the observed and model forecast precipitation are nearly identical, 5.1 and 5.2 mm day−1. On the other hand, for the SAMS region, Figs. 2d–f reveal a positive bias in the model forecast, and the corresponding seasonal averages are 6.5 mm day−1 for the observations and 9.4 mm day−1 for the model forecasts. Thus, the Eta Model overforecasts precipitation in the SAMS region by about a 50%. Despite this bias, the day-to-day variability is still well represented and the correlation, although not as high as in SESA, is 0.75.

The observed distribution of rainfall rate (Figs. 3a,d) shows more cases of intense precipitation over SAMS than over SESA. This is consistent with the study of Silva and Kousky (2001) based on outgoing longwave radiation (OLR) data, in which they showed a greater intensity of convection at lower latitudes. According to Figs. 3a and 3b, the histograms of observed and forecast precipitation are in good agreement over the SESA region, but not in the SAMS region (Figs. 3d,e) where the Eta Model has a flatter and broader distribution than the observations. This means that, compared to observations, the Eta Model has fewer days of relatively light precipitation and more days with heavy precipitation.

To understand better the differences noticed in the SAMS region, similar histograms were prepared for the vertically integrated moisture flux convergence (Figs. 3c,f). The Eta Model vertically integrated moisture flux convergence for SESA (Fig. 3c) shows a distribution that is slightly shifted toward positive values. In the SAMS region (Fig. 3f), there is a more marked shift of the distribution toward convergence, which is consistent with the higher observed and Eta Model seasonal-averaged precipitation rates in the SAMS region. Nevertheless, the shape of the histogram for moisture flux convergence (i.e., considering positive values only), although still with some differences, is closer to the shape of the observed precipitation rate histogram (Fig. 3d) than it is to the shape of the model forecast precipitation rate histogram (Fig. 3e). This suggests that there is an adequate representation of the large-scale precipitation forcing, in which case the failure to produce the right distribution of precipitation rate would result from either an inadequate parameterization of the convection or of the processes that trigger it (including indirect effects of the land surface conditions via surface temperature or surface evaporation).

4. Intense precipitation composites

As discussed in the introduction, the dominant mode of precipitation over nontropical South America takes the form of a dipole pattern. One center is located over the SACZ/monsoon region, while the other one is toward the south. This dipole pattern has also been called a seesaw pattern because, when it is wet over the SAMS region, the southern center is relatively dry, and vice versa. The articles of Nogués-Paegle and Mo (1997), Herdies et al. (2002), Doyle and Barros (2002), and Diaz and Aceituno (2003), among others, determined the basic structure of the pattern at different time scales, and showed that the active phase of its centers (when precipitation is increased) is associated with a stronger-than-normal influx of moisture carried by the LLJ, as estimated from global reanalysis. Our discussion in this section, apart of furthering the analysis of the moisture fluxes with finer-resolution analyses, investigates other regional aspects that either contribute to, or result from, the dipolar mode of precipitation.

a. Seasonal averages

We first present the model forecast averages that will be employed as the mean state to compute the composites anomalies. The seasonal mean precipitation pattern (Fig. 4a) is similar to the observed climatological patterns over South America (see, e.g., Kousky and Ropelewski 1997). The features in Fig. 4a include a precipitation maximum over the Amazon Basin with a southeastward extension over southeastern Brazil, which is the climatological position of the SACZ (Casarin and Kousky 1986; Kousky 1988; Kodama 1993). The ITCZ is found over the Pacific Ocean off the coast of Colombia and over the Atlantic Ocean east of Brazil.

Figure 4b shows that the upper levels have an anticyclonic circulation (Bolivian anticyclone) dominating over central South America, a ridge over the coastline of southern Brazil, and a well-defined subtropical jet stream located near 30°S. In lower levels (Fig. 4c), easterly winds flow into the Amazon Basin, turning south and southeast toward the western part of the basin. The pattern of vertically integrated moisture flux (Fig. 4d) shows a transport of moisture from the Amazon toward southern Brazil, consistent with previous climatologies based on global reanalyses like those presented in Labraga et al. (2000) and Berbery and Barros (2002; see also Marengo et al. 2004). The moisture flux convergence (shaded areas in Fig. 4d) is largest in the Amazon, and along the SACZ, where precipitation is maximum. Large values of moisture flux convergence are also found over southern Chile, again consistent with the precipitation maximum, and finally near the eastern side of the Andes Mountains around 30°S.

b. Wet period features in SESA

To highlight the features of the composites, we will show their deviations with respect to the corresponding two-season averages. The precipitation composites in SESA (Fig. 5a) depict positive precipitation anomalies with maximum values larger than 2.5 mm (3 h)−1 (equivalent to 20 mm day−1, but the units are more appropriate for discussing the intensity of these events); negative precipitation anomalies of smaller magnitude over central Brazil reflect the structure of the dipole pattern. Hints of the third center of the same sign as in SESA are noticed near the equator in the western Amazon Basin, as was also seen in the EOF analysis of Fig. 1b. The effect of the increased precipitation becomes evident in the corresponding runoff (including baseflow) composite (Fig. 5b): while absolute runoff values are small, the percent increase over SESA is up to 6 times the mean value with a reduction elsewhere, particularly over central and southern Argentina and central and northeastern Brazil. The increased runoff has important implications for the analysis of river discharge and floods: as stated earlier, the Uruguay River streamflow is sensitive to precipitation—therefore runoff—near its headwaters (Barros et al. 2002), while the Paraná River is sensitive to precipitation over the basin’s middle section (Camilloni and Barros 2003).

The upper-level circulation anomalies associated with the intense rainfall events over SESA show (Fig. 6a) (i) an enhanced subtropical jet stream over northern Argentina, Uruguay, southern Brazil, and the western Atlantic, with its left entrance region located near the region of largest precipitation; (ii) an enhanced anticyclonic circulation over the Atlantic Ocean near the coast of southern Brazil; and (iii) an enhanced cyclonic circulation over central Argentina and Chile. This pattern is very similar to that discussed by Jones and Carvalho (2002) and also resembles the results of Diaz and Aceituno (2003). According to Figs. 6b and 6c, the most relevant feature at low levels is an enhanced northwesterly flow (the LLJ). This enhanced low-level flow is associated with the presence of an anomalous cyclonic circulation centered over southern Brazil, therefore also showing a southerly flow anomaly over northern and central Argentina. Consequently, increased southeastward moisture flux from the southwestern Amazon Basin across eastern Bolivia and into southern Brazil is found. The moisture flux shows increased convergence over a large band, but with largest values toward the eastern part of the SESA region.

The enhanced northwesterly low-level flow contributes to increased precipitable water (Fig. 7a) and increased equivalent potential temperature (θe) over SESA (Fig. 7b). Here θe was computed following Bolton (1980), and its increase is a good indicator for the development of thunderstorms and MCSs (Velasco and Fritsch 1987). On the other hand, the southerly flow over central Argentina is associated with slightly negative θe anomalies to the west of SESA. The θe pattern indicates an enhanced thermal gradient in the vicinity of Paraguay and southern Brazil during SESA wet periods, suggesting the repeated occurrence of frontal systems in that region [Siqueira and Machado (2004) found that this is a typical mode during summer; see also Rickenbach et al. (2002)]. Figure 7c shows that convective available potential energy (CAPE) anomalies are negative in most of the precipitation region, but positive elsewhere; the picture is one of increased regional CAPE, except where precipitation develops, due to the associated cooling of the atmospheric column. This will be addressed later when discussing the time evolution of the composite. On the other hand, positive anomalies of convective inhibition (CIN) are found over SESA (Fig. 7d), which means that CIN is less negative and conditions are more favorable for convection. The circulation and thermodynamic features discussed above indicate that large-scale dynamics are important in organizing and enhancing convection over SESA during summer. The importance of the dynamic forcing on the diurnal cycle of precipitation, through an increased convergence of moisture flux associated with the LLJ, was already discussed by Berbery and Collini (2000).

To explore the composite time evolution, area-averaged values were constructed for selected variables at 1-day intervals from 3 days prior up to 3 days after each event. The 1-day intervals are chosen to avoid the signature of the diurnal cycle, which is noticeable in CAPE and CIN, and to a lesser extent in the precipitation (not shown). Figure 8a shows that the vertically integrated moisture flux convergence slowly increases in the preceding days and reaches a maximum of about 2 mm (3 h)−1 on the same day of the event, when the largest precipitation is of about the same magnitude. During the first hours after the heaviest precipitation, moisture flux convergence decreases and later remains close to zero. CAPE anomalies reach a maximum one day prior to the event and then decrease, reaching a minimum about one day after the maximum precipitation. These results support the concept that, in addition to the dynamic forcing, CAPE is contributing in the early stages to the development of precipitation, but, once precipitation starts, CAPE decreases, hence the lack of signal noticed in Fig. 7c.

It is also of interest to analyze the time–height evolution leading to the intense precipitation events. Figures 8b–e show that warmer temperatures, northerly low-level flow, and moisture flux convergence precede the event. The opposite signs are found past the composite events. Specific humidity changes, although small, increase leading up to the event and decrease after the event, consistent with the evolution of the low-level wind and moisture flux convergence. In all cases there is an asymmetry with respect to day 0 (the day on which the composite is based) so that absolute values before the precipitation maximum are larger than afterward. The vertical structures of temperature, meridional wind, moisture flux convergence, and specific humidity departures (Figs. 8b–e) support the idea that frontal systems may be important features contributing to heavy precipitation events over SESA (Rickenbach et al. 2002; Siqueira and Machado 2004). The vertical profile of wind divergence (low-level convergence and strong upper-level divergence, shown in Fig. 8f) implies upward vertical motion.

c. Wet period features in SAMS

The wet-period composite based in SAMS precipitation (Fig. 9a) bears a close resemblance to the mode presented in Fig. 1b, which in addition shows the SACZ extending over the Atlantic Ocean. Positive precipitation anomalies (Fig. 9a) of about 2.5 mm (3 h)−1 are found over central Brazil, and slightly negative anomalies spread over SESA as well as over the northern Amazon, southern Venezuela, and eastern Colombia. Despite that the precipitation anomaly is of the same magnitude as that found for the SESA composite, the associated runoff increases are much smaller comparatively, of the order of 150%. Over northeastern Argentina and southern Brazil the runoff decrease is of the order of 50%–75% (Fig. 9b). The reasons for the differences in amplification of the precipitation signal in the runoff for SAMS and SESA composites are difficult to assess: the land surface model defines how the incoming precipitation is partitioned into evapotranspiration, runoff, and storage. The parameterizations of these terms are complex (even in “simple” models); for example, evaporation consists of terms for direct evaporation from the surface, wet canopy evaporation, and canopy evapotranspiration (Chen et al. 1996; Ek et al. 2003). In turn, these parameterizations depend on soil and vegetation characteristics. Similarly, the physics governing the partition between interception, infiltration, and runoff are highly nonlinear (Schaake et al. 1996).

The 200-hPa wind has an enhanced cyclonic circulation anomaly (Fig. 10a) centered over southern Brazil and an anomalous northwesterly jet stream southeast of the SAMS region with its left entrance near the area of intense precipitation, as it was noticed for SESA as well. In this case, the pattern represents a northward shift of the jet. Figures 10b and 10c show an anomalous low-level cyclonic circulation over the northern La Plata Basin. An anomalous northwesterly flow (LLJ) now extends from the Amazon Basin southeastward toward the SAMS region increasing the transport of moisture into the SAMS region. Notice that the wind and moisture flux toward the SAMS region are not limited to the LLJ, but rather inflow is also apparent directly from the entire Amazon Basin located to the north.

These circulation features contribute to the positive precipitable water anomalies over the SAMS region depicted in Fig. 11a and slightly increased equivalent potential temperature over southeastern and eastern Brazil (Fig. 11b). The pattern of negative CAPE anomalies over the SAMS region and positive CAPE anomalies over other regions (Fig. 11c) probably reflects again temperature changes [cooler in the area of enhanced precipitation (SAMS region) and warmer in areas of drier conditions]. On the other hand, the CIN anomalies depict positive values over the central portion of the continent (Fig. 11d). The interplay between thermodynamic properties and moisture fluxes was discussed by Fu et al. (1999) who found an important effect of CIN reductions in the Amazon precipitation. These results are suggestive of large-scale mechanisms associated with the monsoon dynamics, which produce low-level convergence and rising motion throughout the troposphere (Zhou and Lau 1998), and agreeing with Silva and Kousky (2001) in their analysis of intense rainfall events over eastern Brazil.

The composite area-averaged time evolution for SAMS (Fig. 12a) reflects the longer duration of events. Precipitation increases slowly from day −3 until day 0, and then decays as slowly as it began. The moisture flux convergence is positive throughout the period (day −3 to day +3) with a maximum close to the precipitation maximum at day 0. CAPE anomalies slowly decrease as precipitation increases; as in the SESA case this is probably due to the cooling associated with precipitation. CIN, on the other hand, has a slight increase that could favor the development of precipitation in the early stages. Unlike in the SESA composite evolution, here all variables show a more symmetric evolution with respect to day 0.

The vertical structures of temperature, zonal wind, moisture flux convergence, and specific humidity departures (Figs. 12b–e) appear to be more uniform in the vertical, in agreement with the barotropic structure of a stationary Rossby wave discussed by Robertson and Mechoso (2000) and Liebmann et al. (2004). The basic features of the vertical structure show (i) warmer temperatures 3 days prior to the events and cooler temperatures from day −1 to day +2; (ii) increased westerly low-level flow during the events, which is consistent with Rickenbach et al. (2002) and Gan et al. (2004), who showed that rainfall events over central Brazil are associated with westerly low-level winds; and (iii) increased moisture flux convergence and specific humidity during the events. The vertical structure of the wind divergence (Fig. 12f) is somewhat weaker in upper levels than in the SESA composite but it tends to last longer, as expected from the more persistent evolution just discussed.

d. Comparison of the SESA and SAMS composites

The anomalous circulation patterns relative to the SESA and SAMS regions broadly show similar features: a subtropical jet stream with its left entrance region located near the band of largest precipitation, a northerly flow transporting moisture from the Amazon toward the region of intense rainfall events, and a southerly flow over northern and central Argentina. However, the time evolution in each region has its own particular characteristics. In the case of SESA, the evolution resembles the occurrence of frontal systems, squall lines, or propagating MCSs. In the case of SAMS, there seems to be a buildup period that leads to the precipitation event, therefore resulting in longer-lasting events.

One particular aspect of interest is whether the structure of the LLJ differs for each composite. The composites of the wind normal to a cross section running from 17°S, 73°W to 5°S, 34°W (approximately perpendicular to the flow) for wet periods in SESA and SAMS are shown in Fig. 13. Both composites show a low-level jet structure, with the main differences being that (i) the LLJ for the SESA composite is closer to the Andes (over eastern Bolivia) than in the SAMS composite, when the LLJ shifts to western Brazil, and (ii) the LLJ for the SESA composite is narrower and at lower levels than in the SAMS composite. This figure, as well as Figs. 6c and 10c, depicts lateral shifts of the LLJ so that moisture flux convergence increases in its exit region, favoring precipitation either in SESA or SAMS depending on the direction of the LLJ. The above changes are in response to the larger-scale circulation.

Although in the two cases in which the LLJ structure is found, the transport of moisture for wet periods in the SESA region is directly from the western Amazon (northwesterly wind), while for wet periods in the SAMS region, in addition to the LLJ contribution, the transport of moisture comes from the entire Amazon Basin. These results are consistent with those found by Rickenbach et al. (2002) for “westerly” and “easterly” regimes during the field campaign of the Tropical Rainfall Measuring Mission Large-Scale Biosphere Atmosphere (TRMM-LBA).

The vertical structures of temperature, moisture flux convergence, and specific humidity departures for SAMS and SESA show different characteristics as well. Over SESA dynamically organized systems appear responsible for intense rainfall events over that area. In contrast, frontal systems are not identified in heavy rainfall events in the SAMS region. However, the atmospheric circulation changes in the SAMS region associated with heavy rainfall events extend throughout the troposphere as in the SESA composite, suggesting that large-scale dynamical forcing is probably contributing to the enhanced precipitation. Additionally, the slower evolution of the events is suggestive of the proposed Rossby wave pattern as discussed, for example, by Liebmann et al. (2004).

5. Summary and discussion

This study has, first, examined the performance of the workstation version of NCEP’s Eta Model over South America and, second, presented a diagnosis of intense precipitation events associated with the La Plata Basin’s main mode of variability. Based on this mode of dipolar structure over the basin, two regions were examined with the objective of documenting their circulation features: 1) the region of Brazil that is at the heart of the South American summer monsoon system (SAMS), which includes the headwaters for rivers flowing from the northern La Plata Basin, and 2) southeastern South America (SESA), which covers the major rivers in the central-eastern portion of the La Plata Basin.

a. Model evaluation

A comparison between daily observed precipitation and the Eta Model precipitation forecasts shows that the model captures well the temporal variability of rainfall events over the central La Plata Basin (SESA) and the Brazilian Altiplano (SAMS). The model accurately predicts the amplitude of events in the SESA region, while overforecasting events at lower latitudes (SAMS region). The model rainfall rate distribution over SAMS does not follow the observed rainfall rate distribution. Nevertheless, the moisture flux convergence histogram is in closer agreement with the observed precipitation, suggesting that (i) the model identifies well the dynamical forcing features for heavy precipitation events over both regions and (ii) the convective parameterization scheme does not trigger properly the precipitation events over SAMS. It should be noted that currently a new version of the workstation Eta Model is being employed, and we are looking particularly to its performance over the SAMS region.

b. SAMS and SESA composites

Composite circulation patterns were made for intense precipitation events during austral summer for the SAMS and SESA regions. The precipitation bias was removed from the composites by considering deviations with respect to a mean state. The departures from the seasonally averaged circulation were then studied to determine the more prominent features. Intense rainfall over the central-eastern La Plata Basin (SESA) is linked to an amplified upper-tropospheric midlatitude wave pattern in which rainfall occurs just east of an enhanced cyclonic circulation. Accompanying this circulation pattern, a LLJ transports warm, moist air from the Amazon toward SESA, which helps increase the thermal contrast over the region. The pattern of equivalent potential temperature differences (positive over SESA and negative to the south) is consistent with the presence of frontal system structures.

The SAMS region reveals a slower time evolution and also shows signs of dynamical forcing, but not a frontal system structure. In this case, it is likely that the pattern is responding to the presence of a stationary Rossby wave, as suggested by Robertson and Mechoso (2000) and Liebmann et al. (2004). Thus, all of these features are consistent with the idea that the large-scale circulation pattern organizes convection, leading to intense rainfall events and increased runoff over both regions.

c. Combined analysis

The tendency for an out-of-phase behavior is a characteristic feature in the precipitation patterns for the SAMS and SESA composites. The Eta Model forecasts show that, when it is wet over SESA, it is dry over SAMS and vice versa. This dipole pattern has already been found in a broad range of time scales, from synoptic to interanual, and our results show that at synoptic time scales it is well represented by the Eta Model forecasts. Many aspects of the SESA and SAMS composites are similar, albeit with a translation of the center of action. However, the moisture fluxes and corresponding LLJ structure suggest that moisture for SESA precipitation is primarily supplied by the LLJ from the western part of the Amazon Basin, while in the case of SAMS, the moisture is supplied not only by the eastward-shifted LLJ but also by widespread northerly flow coming directly from the Amazon Basin. The seesaw pattern is noticeable not only in precipitation but in other variables, more remarkably in runoff. According to the model, parameterized runoff is more sensitive to the SESA precipitation than to the SAMS precipitation. However, runoff fields in the model forecasts may still be influenced by the spinup of the surface variables; thus they are of lesser confidence than the other fields discussed here. In future work we will focus on how changes in runoff affect the river discharge of the Paraguay, Paraná, and Uruguay Rivers.

Acknowledgments

The authors are grateful to Brant Liebmann and Michael Ek for their valuable comments. Also acknowledged is Estela Collini for her support with the model runs at Maryland, and Matthew Pyle from NCEP’s Environmental Modeling Center for providing the workstation version of the Eta Model. The comments of two anonymous reviewers are appreciated; they helped clarify different aspects of the paper. The model is available from http://www.emc.ncep.noaa.gov/mmb/wrkstn_eta/. This research was supported by NOAA Grants NA76GP0479 (PACS) and NA16GP1479 (GAPP).

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Fig. 1.
Fig. 1.

(a) The Eta Model domain (hatched area), the area where diagnostics are performed (the large rectangle between 60°S–15°N and 87°–33°W), and the location of La Plata subbasins; (b) the second mode obtained from an EOF analysis of the Eta Model forecasts, representing the summer precipitation dipole pattern. The two regions employed as the basis for the composites (SAMS and SESA, see text) are represented by two dashed boxes.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 2.
Fig. 2.

Time series of precipitation and the corresponding scatterplot for (a)–(c) SESA and (d)–(f) SAMS. The analysis is for the austral summer seasons November 2001–March 2002 and November 2002–March 2003. Observations are represented by a heavy line, and the Eta Model forecasts by a thinner line. Units are mm day−1. In (c) and (f) the line with slope 1:1 is depicted for reference.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 3.
Fig. 3.

(a), (b), (d), (e) Histogram of observed precipitation and Eta Model forecasted precipitation for SESA and SAMS regions and (c), (f) histogram of vertically integrated (1000–500 hPa) moisture flux convergence for SESA and SAMS regions. Units are mm day−1. The histograms show the number of days in which precipitation (vertically integrated moisture flux convergence) falls into each interval for the combined summer seasons November 2001–March 2002 and November 2002–March 2003.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 4.
Fig. 4.

(a) Seasonal average of Eta Model forecasted precipitation (mm day−1); (b) 200-hPa vector wind (m s−1); (c) 850-hPa vector wind (m s−1; values smaller than 2 m s−1 are not shown); and (d) vertically integrated moisture flux (kg m−1 s−1; values smaller than 50 kg m−1 s−1 are not shown) and moisture flux convergence (mm day−1). Positive values indicate convergence, while negative values (divergence of moisture flux) are not displayed to avoid overcrowding of the figure.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 5.
Fig. 5.

(a) Difference between the SESA Eta Model forecast precipitation composites and the seasonal-averaged precipitation [mm (3 h)−1]; (b) corresponding runoff composite represented as a percentage of the mean values.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 6.
Fig. 6.

Difference between SESA composites and the seasonal average for (a) 200-hPa vector wind (m s−1); (b) 850-hPa vector wind (m s−1); and (c) vertically integrated moisture flux and moisture flux convergence (mm day−1).

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 7.
Fig. 7.

Difference between SESA composites and the seasonal average for (a) precipitable water (mm); (b) equivalent potential temperature (K) at 925 hPa; and (c) CAPE (J kg−1); and (d) CIN (J kg−1).

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 8.
Fig. 8.

(a) Composite time evolution of the departures of precipitation, vertically integrated moisture flux convergence, CAPE, and CIN for SESA. Composite time–height section representing the evolution of (b) temperature (K), (c) meridional wind (m s−1), (d) moisture flux convergence, (e) specific humidity (g kg−1), and (f) wind divergence (s−1 × 10−6).

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 9.
Fig. 9.

As in Fig. 5 but for SAMS.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 10.
Fig. 10.

As in Fig. 6 but for SAMS.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 11.
Fig. 11.

As in Fig. 7 but for SAMS.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 12.
Fig. 12.

As in Fig. 8 but for SAMS.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Fig. 13.
Fig. 13.

Cross section of the (a) SESA and (b) SAMS composite of wind perpendicular to the line shown in the inset. Units are m s−1.

Citation: Journal of Hydrometeorology 7, 4; 10.1175/JHM520.1

Table 1.

Characteristics of the composites for the SAMS region and SESA regions.

Table 1.

1

The CLIVAR and GEWEX panels belong to the World Climate Research Programme of the World Meteorological Organization.

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