The current literature provides compelling evidence suggesting that an eddy-resolving (as opposed to eddy-permitting or eddy-parameterized) ocean component model will significantly impact the simulation of the large-scale climate, although this has not been fully tested to date in multi-decadal global coupled climate simulations. The purpose of this paper is to document how increased ocean model resolution impacts the simulation of large-scale climate variability. The model used for this study is the NCAR Community Climate System Model version 3.5 (CCSM3.5) -the forerunner to CCSM4. Two experiments are reported here. The first experiment (i.e., control) is a 155-year present-day climate simulation using a 0.5º atmosphere component (zonal resolution 0.625º meridional resolution 0.5º) coupled to ocean and sea-ice components with zonal resolution of 1.2º and meridional resolution varying from 0.27º at the equator to 0.54º in the mid-latitudes. The second simulation uses the same atmospheric model coupled to 0.1º ocean and sea-ice component models. The simulations are compared in terms of how the representation of smaller scale features in the time mean ocean circulation and ocean eddies impact the mean and variable climate. In terms of the global mean surface temperature, the enhanced ocean resolution leads to a ubiquitous surface warming of about 0.2 o C. The warming is largest in the Arctic and regions of strong ocean fronts and ocean eddy activity (i.e., Southern Ocean, western boundary currents). The Arctic warming is associated with significant losses of sea-ice in the high-resolution simulation. The SST gradients in the North Atlantic, in particular, are better resolved in the high-resolution model leading to significantly sharper temperature gradients and associated large-scale shifts in the rainfall. In the extra-tropics, the interannual sea surface temperature anomaly (SSTA) variability is increased with the resolved eddies, but decreases in the deep tropics (i.e., the variance of El Niño and the Southern Oscillation is reduced). Changes in global SSTA teleconnections and local air-sea feedback are also documented and show large changes in ocean-atmosphere coupling.3

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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.

There is a continually increasing demand for near‐term (i.e., lead times up to a couple of decades) climate information. This demand is partly driven by the need to have robust forecasts and is partly driven by the need to assess how much of the ongoing climate change is due to natural variability and how much is due to anthropogenic increases in greenhouse gases or other external factors. Here we discuss results from a set of state‐of‐the‐art climate model experiments in comparison with observational estimates that show that an assessment of predictability requires models that capture the variability of major oceanic fronts, which are, at best, poorly resolved and may even be absent in the near‐term prediction of Intergovernmental Panel on Climate Change class models. This is the first time that air‐sea interactions associated with resolved Gulf Stream sea surface temperature have been identified in the context of a state‐of‐the‐art global coupled climate model with inferred near‐term predictability.

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