A influência da topografia da região sul de Minas Gerais nas variáveis atmosféricas  simuladas com o RegCM4 (Topography influence of the Southern Region of Minas Gerais State on the atmospheric variables simulated by RegCM4)

Oliveira, Debora Martins; Reboita, Michelle Simões; Rocha, Rosmeri Porfírio da

doi:10.26848/rbgf.v11.3.p758-772

Cited by 3 publications

(7 citation statements)

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“…Portanto, esse é o período mais propício a eventos extremos de precipitação. Além disso, devido à topografia complexa de MG, com as serras do Espinhaço e da Mantiqueira, há a formação de circulações locais que influenciam o clima local (CAMPOS et al, 2016;OLIVEIRA et al, 2018). Estudos para MG têm avaliado a tendência da temperatura do ar e precipitação em séries históricas, bem como a tendência de eventos extremos dessas variáveis, e também utilizado modelos climáticos globais para projetar o futuro climático do Estado.…”

Section: Introductionunclassified

Cenários De Mudanças Climáticas Projetados Para O Estado De Minas Gerais

Reboita

Marrafon

Llopart

et al. 2018

ABClima

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O estado de Minas Gerais está inserido numa região de clima de monção, o que significa que mais da metade do total anual da precipitação ocorre entre a primavera e o outono do ano seguinte. Portanto, essa época é a mais propícia à ocorrência de eventos extremos de precipitação. Uma vez que episódios de chuva intensa ou grandes sequências de dias com chuva, ou sem, podem causar danos a vários setores da economia como, por exemplo, agrícola, energético, turístico etc., o objetivo do presente estudo é analisar projeções futuras de precipitação e temperatura do ar no estado de Minas Gerais, com base em três simulações climáticas realizadas com o Regional Climate Model version 4 (RegCM4). O período histórico corresponde aos anos de 1980-2005 e o futuro aos de 2070-2095. Para esse último, é considerado o cenário de mudanças climáticas Representative Concentration Pathways 8.5 (RCP8.5) do Painel Intergovernamental de Mudanças Climáticas (IPCC). Para a precipitação, foram analisados índices climáticos relacionados a valores médios e extremos. Já para a temperatura do ar apenas analisaram-se os valores médios. Entre os resultados têm-se que a temperatura do ar poderá aumentar em até 5 oC no estado, enquanto que a precipitação mostra uma tendência de aumento no verão e redução no inverno. No futuro, as projeções climáticas indicam que a sequência de dias consecutivos úmidos tende a reduzir, enquanto a de dias secos a aumentar.

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Section: Introductionunclassified

Cenários De Mudanças Climáticas Projetados Para O Estado De Minas Gerais

Reboita

Marrafon

Llopart

et al. 2018

ABClima

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“…The main meteorological systems over SEB are SASA (Oliveira and Silva Quaresma, 2018; Gilliland and Keim, 2018b; Pantoja et al ., 2019), the South American Monsoon Systems (SAMS) (Reboita et al ., 2010; Nielsen et al ., 2016), FS (Reboita et al ., 2010; Campos et al ., 2016; Oliveira et al ., 2018), and the SACZ (Reboita et al ., 2010; Nielsen et al ., 2016; Nielsen et al ., 2019). Weather over Brazilian coastal regions, including over SEB, is strongly influenced by SASA, which changes air pressure at sea level causing significant variability in wind patterns from March to July (Oliveira and Silva Quaresma, 2018; Pantoja et al ., 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The Ouro Branco/MG (ID 28) station was the one with the highest altitude in G2 (1,061 m), located over Serra do Espinhaço mountain range. Orography is important over northeast SP state and west RJ state, associated with the wind channelling between the mountain ranges of Serra do Mar and Mantiqueira, over the Paraíba River valley (Paraíba Valley) (Reboita et al ., 2014; Oliveira et al ., 2018).…”

Section: Resultsmentioning

confidence: 99%

“…SEB covers 924,511.3 km 2 ; the region is the most populated in the country with 80,364,410 inhabitants and a population density of 87 people per km 2 . Topography over SEB is characterized by plains, plateaus, lowlands, and mountain ranges such as the Serra do Mar and Mantiqueira (Reboita et al ., 2014; Campos et al ., 2016; Oliveira et al ., 2018).…”

Section: Methodsmentioning

confidence: 99%

“…The friction with Earth's surface changes the wind's intensity and direction (Stull, 1988; Crosman and Horel, 2010; Assireu et al ., 2011). Physiography influences the wind regime, especially over mountain ranges and coastal regions (Crosman and Horel, 2010; Assireu et al ., 2011; Reboita et al ., 2014; Oliveira et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

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The wind regime over the Brazilian Southeast: Spatial and temporal characterization using multivariate analysis

Filho

Souza

Júnior

et al. 2021

Intl Journal of Climatology

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The characterization of spatial and temporal patterns of wind is essential to several sectors, including energy, urban climate, and applied meteorology. However, few studies describe the regional characteristics of the wind regime over the Brazilian Southeast (SEB), the most developed and populated part of the country. The objectives of the current work were (a) to assess the spatial patterns of the wind regime using cluster analysis (CA) and (b) to apply principal components analysis (PCA) to investigate which meteorological systems influence the spatial and temporal patterns of the wind regime. The dataset consisted of wind speed and direction from 70 automatic weather stations with records from 2008 to 2019. According to the CA method, four groups of homogeneous wind speed (G1–G4) were identified; G4 presented the highest magnitudes of wind speed (wind speed >5 m·s−1, with maxima of 7.2 m·s−1). Seasonal well‐defined minima (March–June) and maxima (July–October) were observed only for G2 and G3. The systems South Atlantic Subtropical Anticyclone (SASA), Frontal Systems (FS), and the South Atlantic Convergence Zone (SACZ) influenced these groups. In addition, the mesoscale meteorological systems likely influence other groups such as breezes from land/sea (G1, G3, and G4), lakes (G2 and G3), and valley/mountain (G2 and G3). The topography had a strong influence on G3, mainly due to the mountain ranges of Mantiqueira and Serra do Mar and Paraíba River valley. Dominant wind directions were E, N/NE, and NW, associated with SASA, in addition to S and SE/SW, influenced by FS and SACZ. The wind speed range of 1.4–3.9 m·s−1 was dominant in all groups, except for G4 (range of 2.4–5.5 m·s−1). According to PCA analysis, two PCs are enough to explain the wind pattern in the SEB with 69.9% of the total variance explained. The surface roughness accounted for 29% of the total variance explained in PC1. The latitude (37.6%) and distance to the coast (30.8%) are the most important variables for PC2. Region's physiography and meteorological systems strongly influenced the wind regime over the Brazilian Southeast. The assessment of wind regime variability presented in this work is expected to support public policies on renewable energy applications, air pollution, and climate change mitigation.

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Spatiotemporal distribution of precipitation and its characteristics under tropical atmospheric systems of Brazil: Insights from a large sub‐hourly database

Lemos,

Coelho,

Freitas

et al. 2023

Hydrological Processes

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The study of rainfall properties at various spatiotemporal scales is deeply important for understanding a large range of socio‐environmental processes and variables (e.g., water resources, agriculture, socio‐ecosystemic services, natural risk assessment). However, such studies on rainfall characteristics, especially at sub‐hourly resolutions, are scarce in large areas of South America due to the lack of a high‐resolution temporal database. The Brazilian National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) has gradually implemented, starting in 2013, a sub‐hourly monitoring network composed of approximately 3500 automated rain gauges distributed across Brazil, enabling access to new hydrological studies in this vast tropical country featuring a range of biomes. This study analysed the characteristics of rainfall events for the whole of Brazil on sub‐daily and sub‐hourly timescales, using 7 years of data (from 2014 to 2021) provided by CEMADEN. Rainfall events were defined by the minimum inter‐event time (MIT) and the minimum depth (1 mm). Seven MITs (30, 60, 120, 180, 360, 720, and 1440 min) were considered to evaluate the behaviour of rainfall event characteristics and their relationships. The Gaussian mixture model method was applied to identify regions with similar rainfall patterns according to the MIT. Six groups with homogeneous characteristics were identified, evidencing the climatic diversity of Brazil. The results show that the MIT strongly influences precipitation properties (especially the dry time and the number of events). The highest number of events occurred in the North‐east Coast region, which exceeded 200 events per year (MIT < 60 min), while the lowest number of events was observed in the Semiarid region, which reached only 38 events per year with an MIT of 1440 min. Moreover, the events with the highest rainfall intensities were found in the Central region. The results found in this study provide a better understanding of precipitation and its characteristics in Brazil, highlighting the climatological diversity of this country.

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A influência da topografia da região sul de Minas Gerais nas variáveis atmosféricas simuladas com o RegCM4 (Topography influence of the Southern Region of Minas Gerais State on the atmospheric variables simulated by RegCM4)

Cited by 3 publications

References 24 publications

Cenários De Mudanças Climáticas Projetados Para O Estado De Minas Gerais

Cenários De Mudanças Climáticas Projetados Para O Estado De Minas Gerais

The wind regime over the Brazilian Southeast: Spatial and temporal characterization using multivariate analysis

Spatiotemporal distribution of precipitation and its characteristics under tropical atmospheric systems of Brazil: Insights from a large sub‐hourly database

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