Observational analysis of the daily cycle of the planetary boundary layer in the central Amazon during a non-El Niño year and El Niño year (GoAmazon project 2014/5)

Carneiro, Rayonil Gomes; Fisch, Gilberto

doi:10.5194/acp-20-5547-2020

Cited by 22 publications

(45 citation statements)

References 34 publications

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“…The results found by the simulation were similar to those obtained by the ceilometer during the same time interval, with an overestimation of~50 m. For a typical day of IOP 2 (Figure 4B), during this interval, the height of the NBL was more stable at approximately 250 m. It was also observed that in the first hours of simulation (between 02 and 04 LT), the PALM model outputs underestimated (~100 m) the results found in relation to the RS and the ceilometer, and after this interval, the simulation was in agreement with the observed data. The results found for the heights of the NBL on the typical days of IOPs 1 and 2 are in agreement with the average of these IOPs, which were observed by Carneiro and Fisch [18] for the same region, verifying higher and lower fluctuations in the height of the NBL for IOPs 1 and 2, respectively. In addition, these results are similar to those reported by Neves and Fisch [26] for another site in southwestern Amazonia.…”

Section: Characteristics Of Iop 1 and Iopsupporting

confidence: 90%

“…The T3 site was located to the north of the Municipality of Manacapuru in the State of Amazonas (Figure 1), the central region of the Amazon, about 80 km from the city of Manaus. The region's altitude is approximately 50-60 m [2,18]. The data collection site, hereafter referred to as T3, is an area of pasture vegetation in a 2.5 × 2.0 km 2 clearing, 2 km north of the nearest road (AM-070) that connects Manaus with Manacapuru.…”

Section: Description Of the Experimental Areamentioning

confidence: 99%

“…The IOPs were planned to capture the peak of the rainy (February/March) and dry (September/October) seasons in the region for the two years: 2014, a typical year, and 2015, a year under the influence of ENSO. The 2015 ENSO event started in May and lasted until November [18].…”

Section: Description Of the Experimental Areamentioning

confidence: 99%

“…However, the frequency of soundings is limited, usually in synoptic times, which is not sufficient to conduct studies of long-term trends in the height of the PBL [14,15]. Recently, some remote sensing instruments, such as ceilometers, light detection and ranging (LIDAR), and sound detection and ranging (SODAR) have become available and have been used to calculate the height of the PBL [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Nocturnal Boundary Layer Erosion Analysis in the Amazon Using Large-Eddy Simulation during GoAmazon Project 2014/5

et al. 2021

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This study investigated the erosion of the nocturnal boundary layer (NBL) over the central Amazon using a high-resolution model of large-eddy simulation (LES) named PArallel Les Model (PALM) and observational data from Green Ocean Amazon (GoAmazon) project 2014/5. This data set was collected during four intense observation periods (IOPs) in the dry and rainy seasons in the years 2014 (considered a typical year) and 2015, during which an El Niño–Southern Oscillation (ENSO) event predominated and provoked an intense dry season. The outputs from the PALM simulations represented reasonably well the NBL erosion, and the results showed that it has different characteristics between the seasons. During the rainy season, the IOPs exhibited slow surface heating and less intense convection, which resulted in a longer erosion period, typically about 3 h after sunrise (that occurs at 06:00 local time). In contrast, dry IOPs showed more intensive surface warming with stronger convection, resulting in faster NBL erosion, about 2 h after sunrise. A conceptual model was derived to investigate the complete erosion during sunrise hours when there is a very shallow mixed layer formed close to the surface and a stable layer above. The kinematic heat flux for heating this layer during the erosion period showed that for the rainy season, the energy emitted from the surface and the entrainment was not enough to fully heat the NBL layer and erode it. Approximately 30% of additional energy was used in the system, which could come from the release of energy from biomass. The dry period of 2014 showed stronger heating, but it was also not enough, requiring approximately 6% of additional energy. However, for the 2015 dry period, which was under the influence of the ENSO event, it was shown that the released surface fluxes were sufficient to fully heat the layer. The erosion time of the NBL probably influenced the development of the convective boundary layer (CBL), wherein greater vertical development was observed in the dry season IOPs (~1500 m), while the rainy season IOPs had a shallower layer (~1200 m).

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Section: Characteristics Of Iop 1 and Iopsupporting

confidence: 90%

Section: Description Of the Experimental Areamentioning

confidence: 99%

Section: Description Of the Experimental Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nocturnal Boundary Layer Erosion Analysis in the Amazon Using Large-Eddy Simulation during GoAmazon Project 2014/5

et al. 2021

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“…The choice of 1.5 km is motivated by previous studies that have quantified PBL depth in nearby regions. For example, Carneiro and Fisch (2020) analysed radiosonde and remote sensing data from the GoAmazon project (Martin et al, 2016) and show that the typical minimum PBL height is 250 m and the deepest PBLs occur during daytime in the dry season and are 1.5 km deep on average. The global study by von Engeln and Teixeira (2013), based on reanalysis data, shows that PBL heights are somewhat deeper near CHC than in the Amazon but PBL heights typically range between 500 m and 1.5 km.…”

Section: Additional Diagnostics and Datamentioning

confidence: 99%

Identifying source regions of air masses sampled at the tropical high-altitude site of Chacaltaya using WRF-FLEXPART and cluster analysis

Aliaga¹,

Sinclair²,

Andrade³

et al. 2021

Preprint

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Abstract. Observations of aerosol and trace gases in the remote troposphere are vital to quantify background concentrations and identify long term trends in atmospheric composition on large spatial scales. Measurements made at high altitude are often used to study free tropospheric air however such high-altitude sites can be influenced by boundary layer air masses. Thus, accurate information on air mass origin and transport pathways to high altitude sites is required. Here we present a new method, based on the source-receptor relationship (SRR) obtained from backwards WRF-FLEXPART simulations and a k-means clustering approach, to identify source regions of air masses arriving at measurement sites. Our method is tailored to areas of complex terrain and to stations influenced by both local and long-range sources. We have applied this method to the Chacaltaya (CHC) GAW station (5240 m a.s.l.,16.35° S, 68.13° W) for the 6-month duration of the “Southern hemisphere high altitude experiment on particle nucleation and growth” (SALTENA) to identify where sampled air masses originate and to quantify the influence of the boundary layer and the free troposphere. A key aspect of our method is that it is probabilistic and for each observation time, more than one air mass (cluster) can influence the station and the percentage influence of each air mass can be quantified. This is in contrast to binary methods, which label each observation time as influenced either by boundary layer or free troposphere air masses. We find that on average, 9% of the air sampled at CHC, at any given observation time, has been in contact with the surface within 4 days prior to arriving at CHC, 24% of the air was located below 1.5 km above ground level and consequently, 76% of the measured air masses at CHC represent free tropospheric air. However, pure free-tropospheric influences are rare and often samples are concurrently influenced by both boundary-layer and free-tropospheric air masses. A clear diurnal cycle is present with very few air masses that have been in contact with the surface being detected at night. The 6-month analysis also shows that the most dominant air mass (cluster) originates in the Amazon and is responsible for 29% of the sampled air. Furthermore, short-range clusters (origins within 100 km of CHC) have high temporal frequency modulated by local meteorology driven by the diurnal cycle whereas the mid- and long-range clusters’ (>200 km) variability occurs on timescales governed by synoptic-scale dynamics. To verify the reliability of our method, in-situ sulfate observations from CHC are combined with the SRR clusters to correctly identify the (pre-known) source of the sulfate: the Sabancaya volcano located 400 km northwest from the station.

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Rainfall partitioning in Amazon Forest: Implications of reduced impact logging for hydrological processes

Lima

Tonello

2023

Agricultural and Forest Meteorology

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Observational analysis of the daily cycle of the planetary boundary layer in the central Amazon during a non-El Niño year and El Niño year (GoAmazon project 2014/5)

Cited by 22 publications

References 34 publications

Nocturnal Boundary Layer Erosion Analysis in the Amazon Using Large-Eddy Simulation during GoAmazon Project 2014/5

Nocturnal Boundary Layer Erosion Analysis in the Amazon Using Large-Eddy Simulation during GoAmazon Project 2014/5

Identifying source regions of air masses sampled at the tropical high-altitude site of Chacaltaya using WRF-FLEXPART and cluster analysis

Rainfall partitioning in Amazon Forest: Implications of reduced impact logging for hydrological processes

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