Projections of Climate Change in the Coastal Area of Santos

Chou, Sin Chan; Marengo, José A.; Silva, Adan J.; Lyra, André; Tavares, Priscila; Souza, Célia Regina de Gouveia; Harari, Joseph; Nunes, Lucí Hidalgo; Greco, Roberto; Hosokawa, Eduardo Kimoto; Aragão, Luiz E. O. C.; Alves, Lincoln M.

doi:10.1007/978-3-319-96535-2_4

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Pattern 3 presented no change in the storm surge frequencies in the future, but the winds tended to be weaker. Chou et al [8] analyzed the surface pressure on the São Paulo coast and found a trend towards weakening of low-pressure systems in the future climate. Armani et al [9] calculated the projections of extreme precipitation indices for the São Paulo state.…”

Section: Of 19mentioning

confidence: 99%

Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

Chou,

Sondermann,

Chagas

et al. 2024

JMSE

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The coast of São Paulo, Brazil, is exposed to storm surges that can cause damage and floods. These storm surges are produced by slowly traveling cyclone–anticyclone systems. The motivation behind this work was the need to evaluate high-resolution forecasts of the mean sea-level pressure and 10 m winds, which are the major drivers of the wave model. This work is part of the activity in devising an early warning system for São Paulo coastal storm surges. For the evaluation, four case studies that had a major impact on the coast of São Paulo in 2020 were selected. Because storm surges that reach the coast may cause coastal flooding, precipitation forecasts were also evaluated. The mesoscale Eta model produces forecasts with a 5 km resolution for up to an 84 h lead time. The model was set up in a region that covers part of southeast and south Brazil. The ERA5 reanalysis was used to describe the large-scale synoptic conditions and to evaluate the weather forecasts. The cases showed a region in common between 35° S, 40° S and 35° W, 45° W where the low-pressure center deepened rapidly on the day before the highest waves reached the coast of São Paulo, with a mostly eastward, rather than northeastward, displacement of the associated surface cyclone and minimal or no tilt with height. The winds on the coast were the strongest on the day before the surge reached the coast of São Paulo, and then the winds weakened on the day of the maximum wave height. The pattern of the mean sea-level pressure and 10 m wind in the 36 h, 60 h, and 84 h forecasts agreed with the ERA5 reanalysis, but the pressure was slightly underestimated. In contrast, the winds along the coast were slightly overestimated. The 24 h accumulated precipitation pattern was also captured by the forecast, but was overestimated, especially at high precipitation rates. The 36 h forecasts showed the smallest error, but the growth in the error for longer lead times was small, which made the 84 h forecasts useful for driving wave models and other local applications, such as an early warning system.

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Section: Of 19mentioning

confidence: 99%

Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

Chou,

Sondermann,

Chagas

et al. 2024

JMSE

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“…The Eta model (Mesinger & Veljovic 2017;Mesinger et al 2012) has been used by the National Institute for Space Research (INPE) to produce forecasts for South America in different time ranges, for example, for weather forecasting (Seluchi et al 2011;Seluchi & Chou 2009;Siqueira et al 2016), subseasonal and seasonal forecasts (Chou et al 2005;Chou et al 2018;Chou et al 2019;Ferreira & Chou 2019;Pilotto et al 2012;Rezende & Chou 2014), projections of climate change (Chou et al 2012;Dereczynski et al 2020;Lyra et al 2018;Marengo et al 2012), and for paleoclimate simulations (Marchi et al 2022).…”

Section: The Eta Modelmentioning

confidence: 99%

Evaluation of Precipitation Simulations at the Subseasonal Range in the Sao Francisco River Basin, Brazil

Santos,

Resende Ferreira,

Dereczynski

et al. 2023

Anu. Inst. Geociênc.

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The objective of this work is to evaluate the subseasonal precipitation simulations of the Eta model during the onset of the rainy season in the Sao Francisco River Basin (SFB). Accumulated precipitation every 20 days in September and October (2011 to 2015) was evaluated based on subseasonal simulations (integration period of up to 60 days) of the Eta-20-km model, nested within the Climate Forecast System Reanalysis (CFSR), here called Eta-CFSR. MERGE precipitation data were used to evaluate Eta-CFSR simulated precipitation. A statistical method was employed to evaluate the model’s ability to simulate the onset of the rainy season in the Upper SFB. The main results are: i) The Eta-CFSR accurately represents the seasonality of the precipitation pattern in the SFB and the seasonal and interannual variation of the accumulated precipitation in all of the chosen three consecutive 20-day periods of years 2011 to 2015; ii) The model can represent the transition pattern from the dry to the rainy season, showing better results in 2011 and 2015, which were marked, respectively, by La Niña and El Niño, and by anomalies of sea surface temperature favorable and unfavorable for the occurrence of precipitation; iii) Although the model underestimates the amount of precipitation, it effectively captures the seasonal patterns and the onset of the rainy season, with a maximum advancement of two pentads in wetter years and delaying it in drier years.

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“…Recentemente, Almagro et al (2020) fizeram uma análise similar a esta sobre o elemento climático precipitação para o bioma característico de Floresta Atlântica, que tem vegetação e topografia similar ao Rio de Janeiro. Além do mais, considerando-se que toda a técnica de regionalização ou downscaling contém erros derivados do modelo global, forçando o modelo regional, era de se esperar maior dif iculdade do modelo em representar espacialmente a precipitação regional (Kharin et al, 2005;Ambrizzi et al, 2007;Ambrizzi et al, 2019;Chou et al, 2019).…”

Section: Precipitaçãounclassified

“…Alves et al (2004), ao avaliarem o desempenho e a evolução das previsões sazonais de precipitação previstas pelo Eta sobre o Brasil, verificaram que para a região Sudeste, o modelo global também superestima os valores de precipitação em praticamente toda a série, e levantaram a expectativa de que o modelo regional possa prever razoavelmente a distribuição temporal e espacial de dias com ou sem chuvas durante o mês. Chou et al (2019) No estado do Rio de Janeiro, especificamente, Silva et al (2014Silva et al ( , 2020 realizaram pesquisas acerca das mudanças de temperatura do ar e precipitação entre as condições do clima atual e aqueles projetados para até o final do século XXI no estado do Rio de Janeiro ou na região da Floresta Atlântica por meio do modelo regional Eta-HadCM3, com resolução de 40 km. Os resultados mostraram que as projeções futuras para os períodos de 2011-2040, 2041-2070 e 2071-2100 diferem apenas em relação às magnitudes das diferenças dos indicadores comparadas ao clima atual , sempre mantendo o mesmo sinal de aumento ou diminuição.…”

Section: Precipitaçãounclassified

“…Além disso, os autores encontraram que as distribuições de frequência da temperatura do ar nas simulações do Eta e do MCGA contêm mais valores extremos do que os dados observados para o clima presente. Resultado similar foi obtido para a região Sudeste do Brasil por Chou et al (2019) usando o Eta com grade de 5 km e iniciado com o HadGEM2-ES.…”

Section: Temperatura Do Arunclassified

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Precipitação e temperatura do ar simuladas pelo modelo ETA/CPTEC - HADCM3 para o estado do Rio de Janeiro

Pinheiro

Santos

Lyra

et al. 2020

Rev. Bras. Geog. Fis.

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O trabalho avaliou simulações de precipitação e temperatura do ar do modelo Eta-CPTEC para o Rio de Janeiro de 1961-1990. Nas simulações, considerou-se resolução espacial de 40 km para uma grade que compreendeu a América do Sul, com o Eta-CPTEC inicializado com o modelo HadCM3. As séries climáticas observadas das variáveis estudadas foram obtidas de estações meteorológicas do INMET distribuídas no estado do Rio de Janeiro. Estas séries foram comparadas com aquelas extraídas dos pontos de grade do modelo mais próximas das estações. Nas avaliações considerou-se o coeficiente de determinação (r²) da regressão linear simples entre dados observados e simulados; o índice de concordância de Willmott (d), o índice de desempenho (c) e a Raiz do Quadrado Médio do Erro (RQME) e seus componentes sistemático (RQMEs) e não sistemático (RQMEu). As simulações de precipitação apresentaram r² menores do que 0,32, o que indicou baixa precisão, enquanto que a exatidão (d) foi superior a 0,50, com exceção de Bangu (0,16). A baixa precisão comprometeu o desempenho (c) das simulações, com 0,07 <= c <= 0,42, classificados entre “péssimo” e “ruim”. A RQME variou entre 76,2 e 133,4 mm, que correspondeu a um erro de 78,1 e 115,5% em relação à precipitação média. As simulações de temperatura do ar mostraram desempenho melhor do que a precipitação, com maior precisão (0,39 <= r² <= 0,53), exatidão (0,50 <= d <= 0,79) e desempenho (0,36 <= c <= 0,52). A RQME ficou entre 1,9 e 5,7oC, representando 9 e 26% da média da temperatura do ar. Na maior parte das estações, o RQMEs se sobressaiu em relação ao RQMEu, indicando que as simulações podem ser corrigidas usando técnicas estatísticas. Precipitation and air temperature numerical simulations through ETA/CPTEC - HADCM3 model in Rio de Janeiro A B S T R A C TThe present study evaluated the precipitation and air temperature simulations of the Eta-CPTEC model for Rio de Janeiro state in 1961-1990. In the simulations, a spatial resolution of 40 km was considered for a grid that comprised South America, with Eta-CPTEC initialized with HadCM3 model. The observed climatic series of the studied variables were obtained from INMET meteorological stations distributed at Rio de Janeiro state. These series were compared with those extracted from the grid points of the model near to the stations. The coefficient of determination (r²) of the simple linear regression between observed and simulated, Willmott's index of agreement (d), performance index (c), Root Mean Square Error (RQME) and their systematic (RQMEs) and unsystematic (RQMEu) components were considered in the evaluations. The precipitation simulations showed r² less than 0.32, which indicated low precision, while the accuracy (Willmott's d) was greater than 0.50, except for Bangu station. The low precision compromised the performance (index “c”) of the simulations, with 0,07 £ c £ 0,42, classified as "terrible" and "bad". The RQME varied between 76.2 and 133.4 mm, which corresponded error of 78.1 and 115.5% in relation to mean precipitation. The simulations of air temperature showed better performance than precipitation, with greater precision (0.39 £ r² £ 0.53), accuracy (0.50 £ d £ 0.79) and performance (0.36 £ c £ 0.52). The RQME was between 1.9 and 5.7oC, which represented respectively 9 and 26% for average of air temperature. In most stations, RQMEs were higher than the RQMEu, which indicated that simulators can be fitted using statistical techniques.Keywords: climate model, meteorological dataset, downscaling

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Projections of Climate Change in the Coastal Area of Santos

Cited by 9 publications

References 15 publications

Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

Evaluation of Precipitation Simulations at the Subseasonal Range in the Sao Francisco River Basin, Brazil

Precipitação e temperatura do ar simuladas pelo modelo ETA/CPTEC - HADCM3 para o estado do Rio de Janeiro

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