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Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Here we respond to Jaramillo et al.’s recent critique of condensation-induced atmospheric dynamics (CIAD). We show that CIAD is consistent with Newton’s laws while Jaramillo et al.’s analysis is invalid. To address implied objections, we explain our different formulations of “evaporative force.” The essential concept of CIAD is condensation’s role in powering atmospheric circulation. We briefly highlight why this concept is necessary and useful.

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.

The response of the global climate system to atmospheric CO2 concentration increase in time is scrutinized employing the Brazilian Earth System Model Ocean–Atmosphere version 2.3 (BESM-OA2.3). Through the achievement of over 2000 yr of coupled model integrations in ensemble mode, it is shown that the model simulates the signal of recent changes of global climate trends, depicting a steady atmospheric and oceanic temperature increase and corresponding marine ice retreat. The model simulations encompass the time period from 1960 to 2105, following the phase 5 of the Coupled Model Intercomparison Project (CMIP5) protocol. Notwithstanding the accurate reproduction of large-scale ocean–atmosphere coupled phenomena, like the ENSO phenomena over the equatorial Pacific and the interhemispheric gradient mode over the tropical Atlantic, the BESM-OA2.3 coupled model shows systematic errors on sea surface temperature and precipitation that resemble those of other global coupled climate models. Yet, the simulations demonstrate the model’s potential to contribute to the international efforts on global climate change research, sparking interest in global climate change research within the Brazilian climate modeling community, constituting a building block of the Brazilian Framework for Global Climate Change Research.

The impact of ocean–atmosphere interactions on summer rainfall over the South Atlantic Ocean is explored through the use of coupled ocean–atmosphere models. The Brazilian Center for Weather Forecast and Climate Studies (CPTEC) coupled ocean–atmosphere general circulation model (CGCM) and its atmospheric general circulation model (AGCM) are used to gauge the role of coupled modes of variability of the climate system over the South Atlantic at seasonal time scales. Twenty-six years of summer [December–February (DJF)] simulations were done with the CGCM in ensemble mode and the AGCM forced with both observed sea surface temperature (SST) and SST generated by the CGCM forecasts to investigate the dynamics/thermodynamics of the two major convergence zones in the tropical Atlantic: the intertropical convergence zone (ITCZ) and the South Atlantic convergence zone (SACZ). The results present both numerical model and observational evidence supporting the hypothesis that the ITCZ is a thermally direct, SST-driven atmospheric circulation, while the SACZ is a thermally indirect atmospheric circulation controlling SST variability underneath—a consequence of ocean–atmosphere interactions not captured by the atmospheric model forced by prescribed ocean temperatures. Six CGCM model results of the Ensemble-based Predictions of Climate Changes and their Impacts (ENSEMBLES) project, NCEP–NCAR reanalysis data, and oceanic and atmospheric data from buoys of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) Project over the tropical Atlantic are used to validate CPTEC’s coupled and uncoupled model simulations.

The effects of Amazon deforestation on climate change are investigated using twin numerical experiments of an atmospheric general circulation model (AGCM) with prescribed global sea surface temperature and the same AGCM coupled to an ocean GCM (CGCM) over the global tropics. An ensemble approach is adopted, with 10-member ensemble averages of a control simulation compared with perturbed simulations for three scenarios of Amazon deforestation. The latest 20 yr of simulation from each experiment are analyzed. Local surface warming and rainfall reduction are simulated by both models over the Amazon basin. The coupled model presented a rainfall reduction that is nearly 60% larger compared to its control run than those obtained by the AGCM. The results also indicated that both the fraction of the deforested area and the spatial continuity of the vegetated area might be important for modulating global climate variability and change. Additionally, significant remote atmospheric responses to Amazon deforestation scenarios are detected for the coupled simulations, which revealed global ocean and atmosphere circulation changes conducive to enhanced ocean–atmosphere variability over the Pacific Ocean. This, in turn, is interpreted as a manifestation of enhanced El Niño–Southern Oscillation (ENSO) activity over the Pacific and a positive feedback contributing to the extra rainfall reduction over the Amazon on the coupled simulations.

The dynamical basis for seasonal to decadal climate predictions and predictability over South America is reviewed. It is shown that, while global tropical SSTs affect both predictability and predictions over South America, the current lack of SST predictability over the tropical Atlantic represents a limiting factor to seasonal climate predictions over some parts of the continent. The model’s skill varies with the continental region: the highest skill is found in the “Nordeste” region and the lowest skill over southeastern Brazil. It is also suggested that current two-tier approaches to predict seasonal climate variations might represent a major limitation to forecast coupled ocean–atmosphere phenomena like the South Atlantic convergence zone. Also discussed are the possible effects of global climate change on regional predictability of seasonal climate.

This study presents an evaluation of a seasonal climate forecast done with the International Research Institute for Climate Prediction (IRI) dynamical forecast system (regional model nested into a general circulation model) over northern South America for January–April 1999, encompassing the rainy season over Brazil's Nordeste. The one-way nesting is one in two tiers: first the NCEP's Regional Spectral Model (RSM) runs with an 80-km grid mesh forced by the ECHAM3 atmospheric general circulation model (AGCM) outputs; then the RSM runs with a finer grid mesh (20 km) forced by the forecasts generated by the RSM-80. An ensemble of three realizations is done. Lower boundary conditions over the oceans for both ECHAM and RSM model runs are sea surface temperature forecasts over the tropical oceans. Soil moisture is initialized by ECHAM's inputs. The rainfall forecasts generated by the regional model are compared with those of the AGCM and observations. It is shown that the regional model at 80-km resolution impr...

Empirical orthogonal functions (E0Fs) and composite analyses are used to investigate the development of sea surface temperature (SST) anomaly patterns over the tropical Atlantic. The evolution of large-scale rainfall anomaly patterns over the equatorial Atlantic and South America are also investigated. The EOF analyses revealed that a pattern of anomalous SST and wind stress asymmetric relative to the equator is the dominant mode of interannual and longer variability over the tropical Atlantic. The most important findings of this study are as follows. Atmospheric circulation anomalies precede the development of basinwide anomalous SST patterns over the tropical Atlantic. Anomalous SST originate off the African coast simultaneously with atmospheric circulation anomalies and expand westward afterward. The time lag between wind stress relaxation (strengthening) and maximum SST warming (cooling) is about two months. Anomalous atmospheric circulation patterns over northern tropical Atlantic are phase ...