Análise da variabilidade da precipitação sobre o Brasil tropical via um índice intrassazonal multivariado

• Ano de defesa: 2015
• Autor(a) principal: Barreto, Naurinete de Jesus da Costa
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS CLIMÁTICAS
• Palavras-chave em Português: Intrassazonal; Mudanças climáticas; CMIP5; CNPQ::CIENCIAS EXATAS E DA TERRA: CIÊNCIAS CLIMÁTICAS
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/20409

The intraseasonal variability is an important component of Earth’s climate system, shows interaction with various meteorological phenomena, being a link between weather and climate systems, making it an essential tool for forecasting and climate projection. The aim of this study is to evaluate the behavior of intraseasonal precipitation over Brazil Tropical and possible changes caused in climate simulation scenarios, Historical that represents the current climate (1979 -2005) and Representative Concentration Pathways (RCP8.5) representing projections of climate change with increasing radioactive forcing of air at 8.5W/m2 for the period 2070 to 2100. Among the results are: the first step to the establishment of a intraseasonal multivariate index for Brazil Tropical, by applying the maximum covariance analysis, associated with the projection ofthedominantmodesinorthogonalaxes.Thusitispossibletocharacterizetheresulting patterns in eight phases, whose compositions represent the evolution of intrassazonalidade on the study region. In the second step was carried out an assessment of the sensitivity of the models Coupled Model Intercomparison Project Phase 5 (CMIP5) theweeklyvariabilityofrainfallduringthemonthsofsummerandfallAustal,thesixteen models evaluated, it was observed that only six were able to represent significantly the pattern of rainfall, and of these MRI-CGCM3 model was the one that obtained the best result. The third and final step was the application of the methodology used in step 1 in the model that best represented the rainfall pattern, found in Step 2, ie in MRI-CGCM3 in a general context it was noted that this model is able to represent well the pattern of spatial variability and evolutionary cycle, however the regional point of view, there is still need for improvement in the representation of systems.