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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, G00B11, doi:10.1029/2007JG000596, 2008

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Spatial and temporal rainfall variability near the Amazon-Tapajo´s confluence David R. Fitzjarrald,1 Ricardo K. Sakai,1 Osvaldo L. L. Moraes,2 Raimundo Cosme de Oliveira,3 Ota´vio C. Acevedo,2 Matthew J. Czikowsky,1 and Troy Beldini4 Received 17 September 2007; revised 31 July 2008; accepted 27 October 2008; published 31 December 2008.

[1] Do the influences of river breezes or other mesoscale effects lead to a systematic river

proximity bias in Amazon rainfall data? We analyzed rainfall for a network of 38 rain gauges located near the confluence of the Tapajo´s and Amazon rivers in the eastern Amazon Basin. Tipping bucket rain gauges worked adequately in the Amazon rainfall regime, but careful field calibration and comparison with collocated conventional rain gauges were essential to incorporate daily totals from our array into regional maps. Stations very near the large rivers miss the afternoon convective rain, as expected if a river breeze promotes subsidence over the river, but paradoxically, this deficiency is more than compensated by additional nocturnal rainfall at these locations. The NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH) passive infrared inferred rainfall data do an adequate job of describing medium scale variability in this region, but some localized breeze effects are not resolved at 0.25° resolution. For areas inland from the rivers, nocturnal rainfall contributes less than half of total precipitation. A large-scale rainfall increase just to the west of Santare´m manifests itself locally as a ‘tongue’ of enhanced rain from along the wide area of open water at the Tapajo´s-Amazon confluence. The Amazon River breeze circulation affects rainfall more than does the Tapajo´s breeze, which moves contrary to the predominant wind. East of the riverbank, the effects of the Tapajo´s breeze extend only a few kilometers inland. Rainfall increases to the north of the Amazon, possibly the result of uplift over elevated terrain. Dry season rainfall increases by up to 30% going away from the Amazon River, as would be expected given breeze-induced subsidence over the river. Citation: Fitzjarrald, D. R., R. K. Sakai, O. L. L. Moraes, R. Cosme de Oliveira, O. C. Acevedo, M. J. Czikowsky, and T. Beldini (2008), Spatial and temporal rainfall variability near the Amazon-Tapajo´s confluence, J. Geophys. Res., 113, G00B11, doi:10.1029/2007JG000596.

1. Introduction 1.1. Background [2] Attempts to determine the hydrological balance observationally for the Amazon Basin as a whole, the world’s largest rain forest ecosystem, have been hampered by uncertain estimates in each of the components of the hydrological balance: precipitation, interception, runoff, evaporation and advection. Moisture inflow from model reanalysis fails to account for as much as 50% of the estimated precipitation [Marengo, 2006]. Even in regions

1 Atmospheric Sciences Research Center, State University of New York at Albany, Albany, New York, USA. 2 Departamento de Fı´sica, Universidade Federal de Santa Maria, Santa Maria, Brazil. 3 EMBRAPA Amazoˆnia Oriental, Santare´m, Brazil. 4 Projeto LBA, Escrito´rio e Laborato´rio de Apoio em Santare´m, Santare´m, Brazil.

with a dense observational network as in the United States, the observational water balance fails to account for a large amount of water substance [e.g., Roads et al., 2002]. Precipitation is the most commonly measured component of the hydrological budget. Because of the patchy nature of convective rainfall, assembling regionally representative precipitation measurements remains a challenge. A significant systematic error in Amazon rainfall estimates could limit the utility of using the climate station record in assessing results of regional climate models, alter the forcing of ecosystem models [e.g., Botta et al., 2002], limit understanding of the role of drought in forcing ecosystem change, and impair assessments of the agricultural viability of the region. [3] There is a clear systematic bias in the location of precipitation observing stations in the Amazon Basin. Since there are few roads in this region (Figure 1a), climate stations are near settlements along the rivers’ banks (Figure 1b). Local mesoscale circulations near the 5 – 20 km wide rivers

Copyright 2008 by the American Geophysical Union. 0148-0227/08/2007JG000596$09.00

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Figure 1a. Study area in South America. Santare´m marked with the letter S. of the Amazon include the river breeze [Oliveira and Fitzjarrald, 1993, 1994; Silva Dias et al., 2004] and the ‘vegetation breeze’, more frequently modeled than observed [D’Almeida et al., 2007]. The vegetation breeze, believed to result from circulations initiated by thermal contrasts at land cover change boundaries [Silva Dias et al., 2005; Ramos da Silva and Avissar, 2006] may promote cloudiness over cleared areas [Cutrim et al., 1995]. However, in areas where both effects are present, the much stronger water-land temperature contrasts mean that river breeze circulations should dominate any vegetation breeze. [4] Daytime subsidence-induced clearing over the river with cloudiness inland is commonly observed in satellite images (Figure 1c) [Molion, 1987]. Molion and Dallarosa [1990] examined data from four stations near Manaus for the period 1978 – 1988 and found that stations 100 km inland reported rainfall totals 20% higher than that observed at an island on the Negro River. They claimed similar rainfall depression observed at four stations near the Trombetas River near 56°W also resulted from breeze-induced subsidence, but in this case the explanation is not as convincing (see below). Other studies confirm that afternoon convective precipitation more typical of continental areas is observed inland from the river, with rainfall suppressed near the river [Ribeiro and Adis, 1984; Lloyd, 1990; Garstang and Fitzjarrald, 1999, p. 290; Cutrim et al., 2000]. The roughness difference between the river surfaces and the surrounding terrain causes the boundary layer wind to channel along the river course. We do not yet know whether proximity to the river influences the strength of nocturnal instability lines, a major source of precipitation in this region. Anecdotal evidence (e.g., Figure 1d) indicates that convection can even be enhanced along the Amazon River channel. [5] For at least 45 years, it has been recognized that there is a ‘transverse dry zone’ between Bele´m at the coast and Manaus [Reinke, 1962] (map reproduced as Figure 2 in the

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work of Haffer [1969]) [Pires-O’Brien, 1997] in both the dry (July– December) and rainy (January – June) seasons. Recent rainfall estimates based on observations obtained using the satellite-based CMORPH technique [Joyce et al., 2004] confirm a sharp precipitation increase just to the west of 55°W (Figures 2a and 2b). The transverse dry zone just to the east of this longitude is clearly shown, and is particularly prominent at night during the rainy season. The pronounced rainy and dry seasons in the eastern Amazon Basin [Sombroek, 2001; Mahli and Wright, 2004] make its ecosystems particularly sensitive to perturbation by prolonged drought and fire [Nepstad et al., 2004]. In the face of extensive drought, some natural forests in the Amazon may be converted to savanna [Sternberg, 2001; Oyama and Nobre, 2003], a process that could be accelerated by deforestation associated with increasing intensive agriculture in recent years [Brown et al., 2005]. The presence of the El Nin˜o reduces precipitation in the eastern Basin. The correlation between the Southern Oscillation index [Trenberth, 1984] and rainfall anomaly is largest near 55°W, the longitude of Santare´m [Zeng, 1999; Liebmann and Marengo, 2001], but the correlation coefficient (0.6) is so small that other factors are needed to explain the bulk of the interannual variance. South Atlantic sea surface temperatures may also play a role [e.g., Ronchail et al., 2002; Marengo et al., 2008]. [6] Amazon rainfall reflects contributions both from convective systems stimulated by local forcing and from organized instability lines (referred to here as ‘squall lines’) that move inland from the coast [Molion, 1987; Garstang et al., 1994; Cohen et al., 1995]. The tendency toward nocturnal wet season rainfall at the longitude of Santare´m, evident in Figure 2b, has often been noted [Cutrim et al., 2000; Angelis et al., 2004; Moraes et al., 2005]. The evening precipitation preference at Santare´m led Nechet [1993] to describe the rainfall regime as ‘coastal’, distinct from a typical inland pattern of afternoon rainfall [e.g., Lloyd, 1990]. [7] Squall lines arrive predominantly at night in Santare´m, out of phase with afternoon heating. Molion [1987] postulated that this explains the transverse dry zone between

Figure 1b. Selected Brazilian Meteorological Service (INMET) climate stations in the eastern Amazon Basin for the rectangle shown in Figure 1a. T, stations along the Tapajo´s River; A, stations along the main channel of the Amazon River; other stations are shown with crosses. Manaus, Macapa´, Bele´m, and Santare´m stations are also shown. A solid rectangle shows the approximate location of Figures 1b, 1c, 3a, and 10. A dashed rectangle indicates the area detailed in Figures 3b, 11, and 12 (river locations from http://www.ngdc.noaa.gov/mgg coastline).

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Figure 1c. May 2001 average GOES visible low cloudiness near the Tapajo´s-Amazonas confluence. The horizontal dark line shows the relative clearing over the Amazon River channel; the dark area to the south is the Tapajo´s channel. Large dots indicate the locations of several LBA-ECO weather stations (see Figures 3a and 3c) (adapted from Moore et al. [2001] using a GoogleTM overlay). Bele´m and Manaus. Recent analyses of satellite-based rainfall sensors confirm that the instability lines propagating inland from the coast reach the longitude of Santare´m at 03– 04 UT [Negri et al., 2000; Kousky et al., 2005]. We

can probably associate much of the nocturnal precipitation near 55°W with the nocturnal arrival of the squall lines and afternoon precipitation with local convective forcing. The squall lines arrive in Manaus (60°W) in the afternoon,

Figure 1d. Space Shuttle photo of clouds near the Tapajo´s-Amazonas confluence on 21 July 2003, 20:55 UT (16:55 local time). Locations of selected stations are shown. Original image ID ISS007-E10748, available at http://ntrs.nasa.gov. 3 of 17

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Figure 2a. November (minimum precipitation month) average CMORPH precipitation estimates (mm/d, scale at right) in the eastern Amazon Basin 2003– 2006 averaged for (top) 12 UT – 0 UT (daytime) and for (bottom) 0 UT –12 UT (nighttime). Locations of Manaus (MAO), Santare´m (STM), Macapa´ (MAC), and Bele´m (BEL) are shown. in time to be in phase with afternoon locally forced convection [Garstang et al., 1994; Lloyd, 1990; Cutrim et al., 2000]. [8] The need to assess model output and calibrate remotely sensed data has led to gridded precipitation data sets based on rain gauge records [Liebmann and Allured, 2005; Xie and Arkin, 1996]. Because of their accessibility, such processed precipitation data are widely used for comparison with model output and used to assess (‘‘validate’’) remotely sensed data products. The considerable range of annual total Amazon Basin precipitation estimates [e.g., Costa and Foley, 1998] reflects inputs from differing data sources and the use of alternate methods to grid data [e.g., Willmott and Johnson, 2005]. The gridding techniques proposed to date do not account for the singular nature of the station proximity to the great rivers of the region. In addition, many approaches use reanalysis data, which is known to underestimate both moisture inflow and precipitation in the region, in part owing to the limited sounding network in the region [Marengo, 2006]. [9] The aims of this work are (1) to introduce a new detailed precipitation data set for the region of the eastern

Amazon Basin near the Tapajo´s-Amazon river confluence, an important location near a strong regional precipitation gradient; and (2) to identify interannual, seasonal, diurnal, and spatial patterns in regional precipitation. Documenting rainfall patterns sets the stage to determine the extent to which river breezes or other mesoscale circulations may introduce a bias in the regional rainfall climate record. If river proximity effects are systematic and properly documented, more physically reasonable explanations of precipitation patterns, spatial correlations, and interpolation procedures can be adopted. Both instrument and sampling issues must be addressed. It seems unlikely that any bias would change the conclusions of the large-scale correlation studies, but it might be critical in issues that require quantitative accuracy, such as observationally closing the hydrological balance [Marengo, 2006]. [10] Do the influences of river breezes or other mesoscale effects lead to a systematic river proximity bias in Amazon rainfall data? We document the temporal and spatial patterns of precipitation in one region likely to be affected by river proximity. We constructed a mesoscale weather station network near Santare´ m, Para´ Brazil

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Figure 2b. March (maximum precipitation month) average CMORPH precipitation estimates (mm/d, scale at right) in the eastern Amazon Basin 2003– 2007 averaged for (top) 12 UT –0 UT (daytime) and for (bottom) 0 UT – 12 UT (nighttime). Locations of Manaus (MAO), Santare´m (STM), Macapa´ (MAC), and Bele´m (BEL) are shown. (2°2504800S, 54°4301200W) as part of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) Ecological Component (LBA-ECO) [Keller et al., 2004]. The geometry of rivers’ confluence and the fact that ‘synoptic’ and ‘diurnal’ precipitation contributions can be easily distinguished according to the hour of occurrence make this region singularly useful to assess mesoscale landscape influences on precipitation. We analyze rainfall observed in this network collected during the period 1998 – 2006, and supplement the network with data from regional operational rain gauge networks for the same period and with satellite-based microwave sensor rain estimates. Analysis of errors associated with rain gauge sensors and their siting is given in section 2. At two sites, a long-term comparison of tipping bucket with conventional wedge gauges is used to assess measurement accuracy. Analysis of precipitation results for interannual, seasonal, and diurnal scales for the period 1998 – 2006 is presented in section 3. Conclusions and suggestions for continuing work (section 4) complete the paper. A study of other aspects of this study region with emphasis on observing the forcing mechanisms of the river breeze and its effect on other aspects of mesoclimate will appear in a companion paper.

1.2. Qualitative Predictions [11] If river breezes generated by river-land temperature contrasts exert a dominant influence, stations near the river should be in a subsident region and lack an afternoon convective rainfall peak. One expects breezes to be more important during the dry season. The Amazon River is roughly parallel to the predominant easterlies, while the Tapajo´s is approximately normal. Mean low cloudiness is indeed suppressed over the rivers near the Tapajo´s-Amazon river confluence (Figure 1b). Naı¨ve expectation is that the Tapajo´s breeze might produce the largest effect on precipitation since it lies roughly normal to the prevailing easterly winds, promoting enhanced convergence east of the river. This expectation was supported by model studies [Silva Dias et al., 2004; Lu et al., 2005], which correctly simulated enhanced cloudiness on the east bank of the Tapajo´s River for selected cases. [12] Large instability lines propagate inland from the coast, arriving at Santare´m (about 55° W) predominantly at night [Cohen et al., 1995; Kousky et al., 2005]. The nocturnal arrival time facilitates objective partition of rainfall into events of ‘basin scale’ associated with these lines and those of local convective origin, a task previously done subjectively [e.g., Garstang et al., 1994]. One expects rainfall at all stations subject to predominantly squall

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Figure 3a. LBA-ECO rain gauge locations and regional topography. Station 15 is the Santare´m airport. Stations 1 and 18 are adjacent in Belterra. Scale shown is terrain altitude above mean sea level (m). rainfall should exhibit a nocturnal maximum. This has been taken to be the norm at the longitude of Santare´m [e.g., Nechet, 1993; Angelis et al., 2004]. Since near-river stations would lack the afternoon convective component, one might expect a negative precipitation bias overall at stations near the rivers. Topographic effects would be expected to lead to increased rainfall along slopes oriented normal to the predominantly easterly regional flow. No simple qualitative prediction can be made from consideration of the channeling of the airflow over the rivers. Clear evidence of channeling exists for the mouth of the Amazon [e.g., Cohen et al., 2006]. We hypothesize that there is enhanced rainfall in convergent regions where the channel narrows after attaining a long trajectory over a wider water area, such as just to the west of the Amazon-Tapajo´s confluence.

2. Instrumentation and Observations 2.1. LBA-ECO Network [13] In 1998, we began deployment of what grew to be a nine-station mesoscale weather station network (Figure 3a and Table 1). This network began with two stations acquired from the U.S. Forest Service. Later, three stations from the Brazilian agricultural research corporation EMBRAPA and four obtained using LBA-ECO resources were added. To promote data uniformity, we upgraded each station to include a GPS receiver (to ensure compatible timing), a barometer, and soil temperature and moisture sensors. Data were recorded and processed in real time using similar Campbell Scientific data loggers running identical programs. Data were retrieved manually at weekly intervals. [14] Additional LBA-ECO tipping bucket data were available for limited periods from the project office in

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Santare´m (station 15, Table 1). Rainfall data was also collected at three flux towers at sites known by their approximate location along the BR-163 highway. These were an agricultural site (km77, station 10 [Sakai et al., 2004]), an old-growth forest site (km67, station 9 [Saleska et al., 2003; Hutyra et al., 2005]), and a forested site where selective logging was done (km83, station 11 [Miller et al., 2004; da Rocha et al., 2004]). The recording rain gauges used were Texas Electronics TE-525 tipping buckets that report 0.1 mm per tip except at the Cacoal Grande, Sudam, and Vila Franca sites where English unit rain gauges reported 0.254 mm tips. The tipping bucket mechanism is identical in all gauges, but the ones denominated in English units have an appropriately larger bucket diameter. Data were sampled at 5-s intervals and accumulated to halfhourly totals. We recorded data at 5-s intervals at the km77 site. The sensors were installed approximately 1 m above the surface, except at the forest sites where gauges were placed on towers just above or near the 45 m average canopy top. Sensors were inspected and cleared of obstructions each time data were retrieved. Windshields were not installed at stations in this network. Hanna [1995] notes that their utility for rain measurement is not certain. Daily totals from wedge rain gauges in forest clearings were available at two sites (Table 1 and Figure 3a), one approximately 500 m (Casa da Onc¸a, CO) and the other 6.4 km (Terra Rica, TR) away from the km67 study site [Nepstad et al., 2004]. We acquired daily total precipitation data from these sites (except for weekends and holidays) from the beginning of 1999 to the present. Limited in situ calibrations were performed at each tipping bucket station annually. 2.2. Operational Network Data [15] Precipitation data from the additional stations in the operational network (Figure 3b) complement our analysis. Stations of the Brazilian Hidro network [Angelis et al., 2004] (hidroweb.ana.gov.br) are equipped with Vaisala 55 ES 13S tipping buckets that report hourly rainfall totals. A larger number of stations equipped with conventional gauges were read manually for daily rainfall total reports (Table 1). The data period considered in this paper for these and all other stations is shown in Figure 3c. 2.3. Gridded Data Products [16] Three regional data products were obtained: (1) Rainfall data from several of the operational stations has been gridded to 1° resolution [Liebmann and Allured, 2005]; (2) output from the Brazilian Numerical Weather Prediction Center (CPTEC) 40-km resolution Eta model reanalysis data [Chou et al., 2005]; and (3) the NOAA Climate Prediction Center Morphing (CMORPH) 0.25° resolution data product [Joyce et al., 2004] for the period December 2002 to May 2007. The CMORPH data (Figure 2), used here as a guide to regional mean rainfall gradients, are based on the twice-daily polar orbiting satellite-based passive microwave precipitation estimates, with the time resolution improved by tracking identified cloud elements between microwave estimates following geostationary satellite infrared cloud images. This data is particularly valuable for understanding the mean horizontal precipitation gradients for our region of interest. Recently Tian and Peters-Lidard [2007] argued that the CMORPH

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Table 1. Precipitation Stations Station

Name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Belterra Cacoal Grande Vila Franca Guarana Jamaraqua km117 Mojui Sudam (Curua Una) km67 km77 km83 above canopy km83 below canopy CPTEC Liebmann LBAoffice Santare´m Terra Rica Casa da Onc¸a Belterra INMET Arapari Maicuru Boca do Inferno Nova Esperanca Obidos Oriximina˜ Santare´m Vila Conceica˜o Prainha Arapari Inferno Alenquer Santarem Barragem Curuauna Curuai Arua Sa˜o Jose Juruti Nhamunda Sa˜o Pedro Carvalho Mutum

Frequency 30 min 30 min 30 min 30 min 30 min 30 min 30 min 30 min 1 hour 30 min 1 hour 1 hour 1 hour 1 day 30 min 1 day 1 day 1 day 1 hour 1 hour 1 hour 1 hour 1 hour 1 hour 1 hour 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day 1 day

precipitation estimates are too high near large water bodies. However, they identified these positive anomalies occurring with light rain rates ( 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days Dry tr (trjtr > 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days a

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2003

2004

2005

1.02 3.03 24 3 1

0.85 2.35 15 0 0

0.87 2.31 46 2 0

3.28 6.78 16 7 2

5.92 8.55 20 10 7

2.91 10.87 15 7 4

Units are in days. Locations are given in Figure 3 and Table 1.

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Table 2b. Rainfall Recurrence Interval and Frequency by Location, Year, and Season: River Bank West of the Confluence of the Tapajo´s and Amazon Rivers Vila Franca (Station 3)

2003

Wet tr (trjtr > 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days Dry tr (trjtr > 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days

2004

2005

0.87 2.57 45 4 1

0.79 2.45 30 0 0

0.51 2.2 38 1 0

4.6 6.59 25 13 7

3.88 8.81 19 7 4

3.24 6.85 23 9 5

more inland stations the presence of more convective precipitation means that such long stretches of dry days do not occur. At Belterra, for example, the dry season is more intense, there is a longer interval between rains, than that experienced further inland at km117, especially during the 2001 – 2004 dry seasons. At inland stations km77 (station 10) and km117 (station 6), there were between 35 and 40 cases of one-day rain-free periods in the wet season. The mean rainfall interval is 12 h, again reflecting the presence of the bimodal diurnal pattern of rainfall.

4. Conclusions [39] We found that the near-river stations do indeed miss the afternoon convective rain as would be expected if the river breeze influence dominates, but paradoxically this deficiency is more than compensated by additional nocturnal rainfall. This effect is local; for areas only a few kilometers inland from the rivers, nocturnal squall lines contribute less than half of total precipitation. [40] Describing the proper mixture of types of precipitation should be a concern for those assessing model sensitivity, especially since the reanalysis rainfall data are currently flawed [Marengo, 2006], and for those applying isotope studies to infer moisture recycling in this region [Henderson-Sellers et al., 2002]. It is clear that models and remote sensing products should be evaluated not only comparing rainfall daily totals, but also by the ability to reproduce the diurnal precipitation pattern. At a larger scale,

Table 2c. Rainfall Recurrence Interval and Frequency by Location, Year, and Season: 4.5 km East of the Tapajo´s River Belterra (Station 1) Wet tr (trjtr > 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days Dry tr (trjtr > 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days

1999

2000

2001

2002

2003

2004

2005

0.5375 1.8 40 0 0

0.41 1.5 35 0 0

0.46 1.5 39 0 0

0.48 1.9 39 1 0

0.62 2.7 16 2 1

0.47 1.9 38 0 0

0.58 2.1 34 1 0

1.54 3.49 26 5 0

1.46 3.35 36 6 1

3.09 5.82 26 10 5

2.88 5.42 30 10 4

2.10 4.84 28 7 3

2.49 6.22 28 8 4

2.46 5.21 19 5 4

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Table 2d. Rainfall Recurrence Interval and Frequency by Location, Year, and Season: Inland South of the Amazon, East of the Tapajo´s km77 (Station 10)

2001

2002

2003

2004

2005

0.39 1.45 48 0 0

0.44 1.94 40 1 0

0.50 1.64 50 0 0

0.49 2.03 43 3 0

0.51 2.05 39 2 0

2.19 4.26 29 10 6

1.88 5.02 27 7 5

1.23 3.19 41 4 2

0.84 4.39 30 8 2

1.86 3.91 27 8 1

Wet tr (trjtr > 1 day) tr > 1 day tr > 5 day tr > 10 day Dry tr (tr > 1 day) (day) tr > 1 day tr > 5 day tr > 10 day

the rainfall totals increase just to the west of the Tapajo´sAmazon confluence. [41] In general, the breeze circulations associated with the Amazon River (with a wind component approximately normal to the mean flow) affect rainfall more than does the Tapajo´s breeze (which approximately opposes the prevailing wind). Evidence for the importance of the Tapajo´s breeze on precipitation is sketchy, but it appears that the breeze influence extends only a few kilometers inland. There is a distinct increase in the top three extreme rainfall events comparing Jamaraqua, (station 5 in Figure 3a; 96, 80, 76 mm, Figure 8) as compared to that measured at the km67 site in the nearby forest on the plateau just a few km inland (station 16; 150, 150, 150 mm). Increased rainfall north of the Amazon is possibly the result of orographic uplift, but further studies are needed to confirm this. [42] We found that tipping bucket rain gauges work adequately in the Amazon rainfall regime, but careful field calibration and comparison with collocated conventional rain gauges is essential to incorporate daily totals from operational array in area-wide maps. The 0.25°CMORPH data product does an adequate job of describing medium scale variability in this region, but very localized breeze effects are not resolved. [43] Nechet [1993] first hypothesized that local mesoscale circulations, perhaps related to the large lake-like expanse of water at the confluence, are responsible for the nocturnal precipitation preference. As squall lines approach this

Table 2e. Rainfall Recurrence Interval and Frequency by Location, Year, and Season: Further Inland From Table 2d km117 (Station 6) Wet tr (days) (trjtr > 1 day) trjtr > 1 day trjtr > 5 days tr j tr > 10 days Dry tr (days) (trjtr > 1 day) trjtr > 1 day trjtr > 5 days trjtr > 10 days a

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1999

2000

2001

2002

2003

2004a

2005

0.41 1.62 38 0 0

0.35 1.39 35 0 0

0.38 1.54 23 0 0

0.50 2.20 28 2 0

0.53 2.09 36 2 0

1.12 4.11 4 1 1

0.52 2.22 39 3 1

1.22 3.27 28 4 1

1.19 3.65 34 7 4

1.18 4.74 23 6 3

1.96 5.16 30 8 4

1.25 3.46 34 5 3

1.86 4.12 27 6 2

1.26 4.58 31 9 3

Partial year data record.

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region, enhanced moist inflow is possible, perhaps augmented by southerly channeling up the Tapajo´s channel as the storm approaches, a feature that has been observed elsewhere (e.g., LaPenta et al. [2005] in the Hudson Valley of New York). No simple isolated breeze or topographic enhancement hypothesis is adequate to explain these data. The relative importance of the nocturnal squall lines to overall precipitation is exaggerated if one relies solely on data from stations along the Amazon River channel near Santare´m. Larger rainfall totals further west could plausibly result from convergence promoted by air following the narrowing channel. We plan to address this issue using mesoscale modeling case studies of well-documented individual squall line passages. Complementary case studies of fair weather days will be conducted to understand the important of orographic effects on the apparently enhanced rainfall just to the north of the Amazon River in this region. [44] To improve our analysis, we plan to analyze higher resolution 8-km CMORPH rainfall data. We will acquire longer data sets and investigate the statistical distribution of extreme rainfall events. With colleagues, we will conduct mesoscale model studies to estimate bias at other points in the Amazon Basin, with the express aim to understand how the river orientation relative to prevailing easterly winds affects the nature of the rain biases. River proximity bias in other climate variables will be discussed in a companion paper that emphasizes extracting wind, radiative flux, and diurnal pressure gradients and exploring how these vary seasonally. [45] Acknowledgments. This work is part of the LBA-ECO project, supported by the NASA Terrestrial Ecology Branch under grants NCC5 – 283 and NNG-06GE09A to the University at Albany. The UFSM authors also acknowledge support by CNPq, the Brazilian Science Agency. Michael Keller got the network started by facilitating the transfer of the original two weather stations to the region, whose installation at Belterra and km117 was guided by expert technician Jorge de Melo of CPTEC before the rest of the LBA-ECO project began in earnest. Teams from Harvard University (S. C. Wofsy and L. Hutyra) and University of California, Irvine (M. Goulden and S. Miller), generously allowed us access to data from the Tapajo´s National Forest flux towers. Daniel Nepstad and his team also kindly made available their wedge gauge data from the Tapajo´s National Forest. Carlos F. Angelis of CPTEC provided data from the Hidro network. We acknowledge help in the field by Kathleen Moore, Dwayne Spiess, Ralf Staebler, Alex Tsoyref, Eleazar Brait, and Valdelı´rio Miranda. Staff at the LBA-ECO office in Santare´m for logistics, especially Bethany Reed, provided additional support. Rainfall data from the LBA office, part of research efforts led by Paulo Artaxo, were provided by Rodrigo da Silva (UFPa, Santare´m). Crucial daily total rainfall data for the Belterra INMET station were provided through the kind offices of Alaor Moacyr Dall’Antonia Junior and Divino Moura of INMET. We acknowledge the work of the CPC CMORPH team and especially R. Joyce at NOAA who have made those data available to the community. The manuscript was greatly improved by suggestions from J. W. Snow, MIT Lincoln Laboratories, the Editor, and three anonymous reviewers.

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O. C. Acevedo and O. L. L. Moraes, Departamento de Fı´sica, Universidade Federal de Santa Maria, Santa Maria, RS 97105-900, Brazil. T. Beldini, Projeto LBA, Escrito´rio e Laborato´rio de Apoio em Santare´m, Santare´m, PA, Brazil. R. Cosme de Oliveira, EMBRAPA Amazoˆnia Oriental, Santare´m, PA, Brazil. M. J. Czikowsky, D. R. Fitzjarrald, and R. K. Sakai, Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY 12203, USA. ([email protected])

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