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Enver Ramirez, Pedro L. da Silva Dias, and Carlos F. M. Raupp


In the present study a simplified multiscale atmosphere–ocean coupled model for the tropical interactions among synoptic, intraseasonal, and interannual scales is developed. Two nonlinear equatorial β-plane shallow-water equations are considered: one for the ocean and the other for the atmosphere. The nonlinear terms are the intrinsic advective nonlinearity and the air–sea coupling fluxes. To mimic the main differences between the fast atmosphere and the slow ocean, suitable anisotropic multispace/multitime scalings are applied, yielding a balanced synoptic–intraseasonal–interannual–El Niño (SInEN) regime. In this distinguished balanced regime, the synoptic scale is the fastest atmospheric time scale, the intraseasonal scale is the intermediate air–sea coupling time scale (common to both fluid flows), and El Niño refers to the slowest interannual ocean time scale. The asymptotic SInEN equations reveal that the slow wave amplitude evolution depends on both types of nonlinearities. Analytic solutions of the reduced SInEN equations for a single atmosphere–ocean resonant triad illustrate the potential of the model to understand slow-frequency variability in the tropics. The resonant nonlinear wind stress allows a mechanism for the synoptic-scale atmospheric waves to force intraseasonal variability in the ocean. The intraseasonal ocean temperature anomaly coupled with the atmosphere through evaporation forces synoptic and intraseasonal atmospheric variability. The wave–convection coupling provides another source for higher-order atmospheric variability. Nonlinear interactions of intraseasonal ocean perturbations may also force interannual oceanic variability. The constrains that determine the establishment of the atmosphere–ocean resonant coupling can be viewed as selection rules for the excitation of intraseasonal variability (MJO) or even slower interannual variability (El Niño).

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Silvio N. Figueroa, José P. Bonatti, Paulo Y. Kubota, Georg A. Grell, Hugh Morrison, Saulo R. M. Barros, Julio P. R. Fernandez, Enver Ramirez, Leo Siqueira, Graziela Luzia, Josiane Silva, Juliana R. Silva, Jayant Pendharkar, Vinicius B. Capistrano, Débora S. Alvim, Diego P. Enoré, Fábio L. R. Diniz, Praki Satyamurti, Iracema F. A. Cavalcanti, Paulo Nobre, Henrique M. J. Barbosa, Celso L. Mendes, and Jairo Panetta


This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

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Ariane Frassoni, Dayana Castilho, Michel Rixen, Enver Ramirez, João Gerd Z. de Mattos, Paulo Kubota, Alan James Peixoto Calheiros, Kevin A. Reed, Maria Assunção F. da Silva Dias, Pedro L. da Silva Dias, Haroldo Fraga de Campos Velho, Stephan R. de Roode, Francisco Doblas-Reyes, Denis Eiras, Michael Ek, Silvio N. Figueroa, Richard Forbes, Saulo R. Freitas, Georg A. Grell, Dirceu L. Herdies, Peter H. Lauritzen, Luiz Augusto T. Machado, Antonio O. Manzi, Guilherme Martins, Gilvan S. Oliveira, Nilton E. Rosário, Domingo C. Sales, Nils Wedi, and Bárbara Yamada
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