IPCC-AR4 coupled models and the meridional gradient of sea surface temperature in the Tropical Atlantic: relationship with the precipitation over the norther northeast of Brazil

This article shows how three coupled models of the Intergovernmental Panel on Climate Change-Fourth Assessment Report (IPCC-AR4), the FGOALS1.0G - LASG of the Institute of Atmospheric Physics of China, the GISSER of the National Aeronautics and Space Administration (NASA) and the GFDL_CM2 of the National Oceanic and Atmospheric Administration (NOAA) have simulated the meridional gradient variability of the Sea Surface Temperature (SST) in the tropical Atlantic during February to May months for the period from 1901 to 1999. The precipitation during the rainy season (February-May) in the northern sector of Northeast Brazil (NEB) was also analyzed by those three models and compared with observations. The GISSER and FGOALS1.0G models showed better performance at signal simulation of signal of the meridional SST gradient over the tropical Atlantic for the period 1901-1999. It is noteworthy that the coupled models have performed better in simulating the decadal trend, explaining 50% to 80% of gradient variability, being the southern sector SST better simulated.Este artigo mostra como três modelos acoplados do Intergovernmental on Panel Climate Change - (IPCC-AR4), o FGOALS1.0G - LASG do Institute of Atmospheric Physics of China, o GISSER da National Aeronautics Space Admnistration (NASA) e o GFDL_CM2 da National Oceanic and Atmospheric Administration (NOAA), simularam a variabilidade do gradiente meridional de Temperatura da Superfície do Mar (TSM), entre os meses de fevereiro a maio, no Atlântico Tropical (1901-1999). A precipitação durante a estação chuvosa (fevereiro a maio) no setor norte do Nordeste do Brasil (NEB) foi também analisada pelos três modelos e comparada com as observações. Os modelos GISSER e FGOALS1.0G mostraram melhor desempenho na simulação do sinal do gradiente meridional de TSM no Atlântico Tropical para o período de 1901 a 1999. Destaca-se que os modelos apresentaram um melhor desempenho na simulação da tendência decadal, conseguindo explicar entre 50% a 80% da variabilidade do gradiente, com a TSM do setor sul sendo melhor simulada