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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.

This paper presents an evaluation of climate simulations produced by the Brazilian Global AtmosphericModel version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975-2017 period at two spatial resolutions, corresponding to ~180 and ~100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model´s ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Niño-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model.Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features.

Abstract. The performance of the coupled ocean–atmosphere component of the Brazilian Earth System Model version 2.5 (BESM-OA2.5) was evaluated in simulating the historical period 1850–2005. After a climate model validation procedure in which the main atmospheric and oceanic variabilities were evaluated against observed and reanalysis datasets, the evaluation specifically focused on the mean climate state and the most important large-scale climate variability patterns simulated in the historical run, which was forced by the observed greenhouse gas concentration. The most significant upgrades in the model's components are also briefly presented here. BESM-OA2.5 could reproduce the most important large-scale variabilities, particularly over the Atlantic Ocean (e.g., the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Atlantic Meridional Overturning Circulation), and the extratropical modes that occur in both hemispheres. The model's ability to simulate such large-scale variabilities supports its usefulness for seasonal climate prediction and in climate change studies.

Skillful and reliable predictions of week-to-week rainfall variations in South America, two to three weeks ahead, are essential to protect lives, livelihoods, and ecosystems. We evaluate forecast performance for weekly rainfall in extended austral summer (November–March) in four contemporary subseasonal systems, including a new Brazilian model, at 1–5-week leads for 1999–2010. We measure performance by the correlation coefficient (in time) between predicted and observed rainfall; we measure skill by the Brier skill score for rainfall terciles against a climatological reference forecast. We assess unconditional performance (i.e., regardless of initial condition) and conditional performance based on the initial phase of the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). All models display substantial mean rainfall biases, including dry biases in Amazonia and wet biases near the Andes, which are established by week 1 and vary little thereafter. Unconditional performance extends to week 2 in all regions except for Amazonia and the Andes, but to week 3 only over northern, northeastern, and southeastern South America. Skill for upper- and lower-tercile rainfall extends only to week 1. Conditional performance is not systematically or significantly higher than unconditional performance; ENSO and MJO events provide limited “windows of opportunity” for improved S2S predictions that are region and model dependent. Conditional performance may be degraded by errors in predicted ENSO and MJO teleconnections to regional rainfall, even at short lead times.