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@PhDThesis{Santos:2015:TrUmAm,
               author = "Santos, Jos{\'e} Guilherme Martins dos",
                title = "Transporte de umidade na Amaz{\^o}nia e sua rela{\c{c}}{\~a}o 
                         com a temperatura da superf{\'{\i}}cie do mar dos oceanos 
                         adjacentes utilizando as simula{\c{c}}{\~o}es do CMIP5",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2015",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2015-05-12",
             keywords = "bacia Amaz{\^o}nica, balan{\c{c}}o de umidade, modelos 
                         num{\'e}ricos, precipita{\c{c}}{\~a}o, rean{\'a}lises, Amazon 
                         basin, moisture balance, numerical models, precipitation, 
                         reanalysis.",
             abstract = "Os estudos sobre modelagem num{\'e}rica na Amaz{\^o}nia mostram 
                         que os modelos n{\~a}o conseguem capturar aspectos importantes da 
                         variabilidade do clima nesta regi{\~a}o, por isso, {\'e} 
                         importante entender as raz{\~o}es que causam essa dificuldade. 
                         Neste trabalho s{\~a}o utilizados os resultados de Modelos de 
                         Circula{\c{c}}{\~a}o Geral do Coupled Model Intercomparison 
                         Project Phase 5 (CMIP5) com o objetivo de avaliar as 
                         interrela{\c{c}}{\~o}es entre a precipita{\c{c}}{\~a}o 
                         regional, a converg{\^e}ncia de umidade e a Temperatura da 
                         Superf{\'{\i}}cie do Mar (TSM) nos oceanos adjacentes, para 
                         estudar como a falha na representa{\c{c}}{\~a}o por parte dos 
                         modelos pode traduzir-se em bias na precipita{\c{c}}{\~a}o 
                         simulada na Amaz{\^o}nia. Utilizando dados de rean{\'a}lise 
                         (GPCP, CMAP, TSM, ERAI e evapotranspira{\c{c}}{\~a}o) e 21 
                         simula{\c{c}}{\~o}es num{\'e}ricas do CMIP5 durante o clima 
                         atual (1979-2005), em junho, julho e agosto (JJA) e dezembro, 
                         janeiro e fevereiro (DJF), respectivamente, para representar as 
                         caracter{\'{\i}}sticas das esta{\c{c}}{\~o}es seca e chuvosa, 
                         s{\~a}o avaliados como os modelos simulam a 
                         precipita{\c{c}}{\~a}o, o transporte e a converg{\^e}ncia de 
                         umidade, e a velocidade vertical em diferentes regi{\~o}es da 
                         Amaz{\^o}nia. Assim, {\'e} poss{\'{\i}}vel identificar 
                         {\'a}reas que s{\~a}o mais ou menos influenciadas pela TSM dos 
                         oceanos adjacentes. Os resultados mostraram que a maioria dos 
                         modelos do CMIP5 tiveram dificuldade em representar adequadamente 
                         alguns aspectos observados. As an{\'a}lises regionais de 
                         precipita{\c{c}}{\~a}o mostraram que na m{\'e}dia a 
                         subestimativa e o erro padr{\~a}o da m{\'e}dia (SEM) no 
                         per{\'{\i}}odo seco (JJA) foram maiores do que no 
                         per{\'{\i}}odo chuvoso. Verificou-se que a TSM do Atl{\^a}ntico 
                         e do Pac{\'{\i}}fico tropical modularam o setor norte da 
                         Amaz{\^o}nia durante JJA devido a influ{\^e}ncia do gradiente 
                         inter-hermisf{\'e}rico, enquanto em DJF a TSM do 
                         Pac{\'{\i}}fico influenciou somente a parte leste desta 
                         regi{\~a}o devido a influ{\^e}ncia do El 
                         Niņo-Oscila{\c{c}}{\~a}o Sul (ENOS). A an{\'a}lise do 
                         transporte de umidade em JJA mostrou que ela preferencialmente 
                         entra na Amaz{\^o}nia atrav{\'e}s da borda leste pela Alta 
                         Subtropical do Atl{\^a}ntico Sul (ASAS). Por outro lado, em DJF a 
                         entrada ocorreu tanto pela borda norte quanto pela leste via 
                         ventos al{\'{\i}}sios e ASAS. O balan{\c{c}}o de umidade anual 
                         foi positivo e a floresta funcionou como sumidouro 
                         (converg{\^e}ncia) de umidade atmosf{\'e}rica na 
                         esta{\c{c}}{\~a}o chuvosa e fonte (diverg{\^e}ncia) na 
                         esta{\c{c}}{\~a}o seca. Na m{\'e}dia anual, a Amaz{\^o}nia 
                         atuou como sumidouro (converg{\^e}ncia) de umidade 
                         atmosf{\'e}rica e fonte para as regi{\~o}es Sul e Sudeste do 
                         Brasil e norte da Argentina sendo a umidade transportada pelo Jato 
                         de Baixos N{\'{\i}}veis. Al{\'e}m disso, os resultados 
                         mostraram que durante DJF as simula{\c{c}}{\~o}es no setor 
                         nordeste da Amaz{\^o}nia apresentaram um bias na 
                         precipita{\c{c}}{\~a}o e subestimativa da converg{\^e}ncia de 
                         umidade devido a influ{\^e}ncia do bias na TSM do 
                         Pac{\'{\i}}fico. Durante JJA, um bias na 
                         precipita{\c{c}}{\~a}o foi observado no setor sudoeste associado 
                         tamb{\'e}m com um bias negativo de converg{\^e}ncia de umidade, 
                         mas com menor influ{\^e}ncia da TSM dos oceanos adjacentes. A 
                         dificuldade em representar os mecanismos produtores de 
                         precipita{\c{c}}{\~a}o na Amaz{\^o}nia por parte dos modelos e 
                         em simular adequadamente a variabilidade da TSM nos oceanos 
                         Pac{\'{\i}}fico e Atl{\^a}ntico podem ser respons{\'a}veis por 
                         essas subestimativas na Amaz{\^o}nia. Algumas 
                         limita{\c{c}}{\~o}es associadas aos modelos foram apresentadas, 
                         como por exemplo, dificuldade em simular a intensidade do 
                         padr{\~a}o de circula{\c{c}}{\~a}o zonal e a 
                         representa{\c{c}}{\~a}o da ZCIT mais intensa nos oceanos 
                         adjacentes que contribuiu para condi{\c{c}}{\~o}es secas na 
                         Amaz{\^o}nia. Com base na avalia{\c{c}}{\~a}o das 
                         vari{\'a}veis precipita{\c{c}}{\~a}o, TSM e 
                         circula{\c{c}}{\~a}o foram selecionados os melhores (ACCESS1-0, 
                         BCC-CSM1.1, CNRM-CM5, HADGEM2-CC, HADGEM2-ES, MIROC5 e MIROC-ESM) 
                         e os piores modelos (CSIRO-MK3-6-0, FGOALS-G2, GISS-E2-R, INM-CM4, 
                         MRI-CGCM3). Alguns pontos foram sugeridos como os fatores 
                         respons{\'a}veis para que um modelo seja melhor do que o outro em 
                         representar a precipita{\c{c}}{\~a}o, isto {\'e}, mecanismos 
                         produtores de precipita{\c{c}}{\~a}o, representa{\c{c}}{\~a}o 
                         satisfat{\'o}ria do ciclo anual de precipita{\c{c}}{\~a}o e a 
                         variabilidade da TSM dos oceanos adjacentes. ABSTRACT: Studies on 
                         numerical modeling in Amazonia show that the models fail to 
                         capture important aspects of climate variability in this region 
                         and it is important to understand the reasons for this drawback. 
                         This work used the general circulation models of the Coupled Model 
                         Intercomparison Project Phase 5 (CMIP5) results to evaluate the 
                         inter- relations between regional precipitation, moisture 
                         convergence and Sea Surface Temperature (SST) in the adjacent 
                         oceans, to assess how flaws in the representation of these 
                         processes can translate into biases in simulated rainfall in 
                         Amazonia. Using reanalysis (GPCP, CMAP, ERSST.v3, ERAI and 
                         evapotranspiration) and 21 numerical simulations from CMIP5 during 
                         the present climate (1979-2005) in June, July and August (JJA) and 
                         December, January and February (DJF), respectively, to represent 
                         dry and wet season characteristics, are evaluate how the models 
                         simulate precipitation, moisture transport and convergence, and 
                         vertical velocity in different regions of Amazonia. Thus, it is 
                         possible to identify areas of Amazonia that are more or less 
                         influenced by adjacent ocean SSTs. The results showed that most of 
                         the CMIP5 models have poor skill in adequately representing some 
                         aspects observed. The regional rainfall analysis showed that on 
                         average the underestimation in the dry season (JJA) were higher 
                         than in the rainy season. It was found that Atlantic and Pacific 
                         SSTs modulate the northern sector of Amazonia during JJA due to 
                         the influence of the inter-hermispheric gradient, while in DJF 
                         Pacific SST only influences the eastern sector of the region due 
                         to the influence of the El Niņo-Southern Oscillation (ENSO). The 
                         analysis of moisture transport in JJA showed that moisture 
                         preferentially enters the Amazon through the eastern edge by 
                         Atlantic Subtropical High (ASH). On the other hand, in the DJF 
                         entry was either from northern edge or the east via trade winds 
                         and ASH. The anual moisture balance was positive and the forest 
                         was considered as a sink (convergence) of atmospheric moisture 
                         during the rainy season and a source (divergence) in the dry 
                         season. In the annual average, the Amazon was considered as a sink 
                         (convergence) of local moisture to the atmosphere and a source for 
                         the South and Southeast regions of Brazil and northern Argentina 
                         transported by Low Level Jet. Additionally, the results showed 
                         that during DJF the simulations in northeast sector of Amazonia 
                         showed a bias in precipitation and an underestimation of moisture 
                         convergence due to the influence of biases in the Pacific SST. On 
                         the other hand, during JJA, a strong precipitation bias was 
                         observed in the southwest sector associated, also with a negative 
                         bias of moisture convergence, but with weaker influence of SSTs of 
                         adjacent oceans. The poor representation of precipitation 
                         mechanisms in Amazonia by the models and the difficulty of 
                         adequately representing the variability of SSTs in the Pacific and 
                         Atlantic oceans may be responsible for these underestimates in 
                         Amazonia. Some limitations associated with the models were 
                         presented as for example, difficulty in simulating the intensity 
                         of zonal circulation pattern and the representation of more 
                         intense ITCZ adjacent oceans that contributed to dry conditions in 
                         the Amazon. Based on the assessment of rainfall, SST and 
                         circulation were selected the best (ACCESS1-0, BCC-CSM1.1, 
                         CNRM-CM5, HadGEM2-CC, HadGEM2-ES, MIROC5 and MIROC-ESM) and the 
                         worst models (CSIRO-MK3-6-0, FGOALS-G2, GISS-E2-R, INM-CM4, 
                         MRI-CGCM3). Some items have been suggested as factors responsible 
                         for such a model is better than another represent precipitation, 
                         i.e., precipitation mechanisms producers, satisfactory 
                         representation of the annual cycle ofprecipitation and the 
                         variability of SST adjacent oceans. Some points have been 
                         suggested as factors responsible for one model is better than the 
                         other to represent the precipitation, thats is, precipitation 
                         mechanisms, satisfactory representation of the annual cycle of 
                         precipitation and variability of SST adjacent oceans.",
            committee = "Herdies, Dirceu Luis (presidente) and Randow, Celso Von 
                         (orientador) and Oliveira, Gilvan Sampaio de (orientador) and 
                         Satyamurty, Prakki and Calheiros, S{\^a}mia Regina Garcia",
           copyholder = "SID/SCD",
         englishtitle = "Moisture transport in Amazon and its relationship with the sea 
                         surface temperature of adjacent oceans using the CMIP5 
                         simulations",
             language = "pt",
                pages = "130",
                  ibi = "8JMKD3MGP8W/3J3GFRL",
                  url = "http://urlib.net/rep/8JMKD3MGP8W/3J3GFRL",
           targetfile = "publicacao.pdf",
        urlaccessdate = "27 nov. 2020"
}


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