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Tatiana A. Tarasova
and
Iracema F. A. Cavalcanti

Abstract

The incident solar radiative fluxes, simulated by an atmospheric general circulation model over South America for the period 1986–88, are compared with the satellite-derived surface fluxes provided by the Surface Radiation Budget (SRB) datasets. The comparison shows that the model systematically overestimates both all-sky and clear-sky SRB fluxes while representing well their latitudinal variations. In order to analyze the reasons for the bias, the shortwave radiation code employed in the model is tested with more comprehensive techniques in a stand-alone mode. The results of testing demonstrate that the code underestimates solar radiation absorption in the clear-sky atmosphere due to trace gases and aerosols. The underestimation of the absorption due to aerosols contributes noticeably to the surface flux bias. The impact of clouds on the surface fluxes is estimated by calculating cloud radiative forcing, defined as the difference between the net surface fluxes in all-sky and clear-sky conditions. The comparison of model-simulated and satellite-derived values of cloud radiative forcing over South America demonstrates that the model simulates fairly well its latitudinal variations and annual cycles as compared with SRB data. However, the model overestimates the SRB surface cloud radiative forcing over the tropical region of South America and underestimates it over the extratropical region in both January and July. The comparisons of the incident surface fluxes simulated by the model at the grid points with those measured at three Amazonian observational sites show good agreement at one site and large discrepancies at the other two sites.

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Iracema F. A. Cavalcanti
,
Chris K. Folland
, and
Andrew W. Colman
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Iracema F. A. Cavalcanti
,
José A. Marengo
,
Prakki Satyamurty
,
Carlos A. Nobre
,
Igor Trosnikov
,
José Paulo Bonatti
,
Antonio Ocimar Manzi
,
Tatiana Tarasova
,
Luciano P. Pezzi
,
Cassiano D'Almeida
,
Gilvan Sampaio
,
Christopher C. Castro
,
Marcos B. Sanches
, and
Hélio Camargo

Abstract

The Center for Weather Forecasting and Climate Studies–Center for Ocean–Land–Atmosphere Studies (CPTEC–COLA) atmospheric general circulation model (AGCM) is integrated with nine initial conditions for 10 yr to obtain the model climate in an ensemble mode. The global climatological characteristics simulated by the model are compared with observational data, and emphasis is given to the Southern Hemisphere and South America. Evaluation of the model's performance is presented by showing systematic errors of several variables, and anomaly correlation and reproducibility are applied to precipitation. The model is able to simulate the main features of the global climate, and the results are consistent with analyses of other AGCMs. The seasonal cycle is reproduced well in all analyzed variables, and systematic errors occur at the same regions in different seasons. The Southern Hemisphere convergence zones are simulated reasonably well, although the model overestimates precipitation in the southern portions and underestimates it in the northern portions of these systems. The high- and low-level main circulation features such as the subtropical highs, subtropical jet streams, and storm tracks are depicted well by the model, albeit with different intensities from the reanalysis. The stationary waves of the Northern and Southern Hemispheres are weaker in the model; however, the dominant wavenumbers are similar to the observations. The energy budget analysis shows values of some radiative fluxes that are close to observations, but the unbalanced fluxes in the atmosphere and at the surface indicate that the radiation and cloud scheme parameterizations need to be improved. Besides these improvements, changes in the convection scheme and higher horizontal resolution to represent orographic effects better are being planned to improve the model's performance.

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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

Abstract

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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