Offshore oil spills in Brazil: An extensive review and further development.

The present study offers an extensive overview on the evolution and current state of marine oil spill research in Brazil and then discusses further directions. Given the historical and current relevance of this issue, this paper also aims to summarize the exploration, geological background, design of oil spills timeline and assessment of the most important of them. Moreover, it includes a critical comparison of Brazilian oil spill models in terms of their simulation abilities, real-time field data assimilation, space and time forecasts and uncertainty evaluation. This study also presents the perspectives of the Multi-User System for Detection, Prediction, and Monitoring of Oil Spills at Sea (SisMOM) the largest and most important Brazilian project to face the offshore oil spills.

Oil reaching the coast: Is Brazil on the route of international oceanic dumping?

After the oil spill disaster occurred in 2019, various events of tar balls reaching the Brazilian coast and archipelagos have been reported. The hypothesis here is that the oil/waste dumped in international waters by ships on-route to Cape of Good Hope is reaching the Brazilian coast. On that account, 30-year probabilistic simulations were used to estimate the probability of dumped oil residue reaching the Brazilian coast. The simulations considered three Zones following the South Atlantic route. The results have shown that up to 28.5 % of large ships could dump oil on-route. Inside the Brazilian Exclusive Economic Zone, the probability of dumped oil/waste reaching the coastline is about 62 % and quickly decreases for Dumping Zones 2 and 3. Equatorial and Northeast shores of Brazil are the most vulnerable to oceanic dumping when compared to other regions.

Mysterious oil spill on the Brazilian coast - Part 2: A probabilistic approach to fill gaps of uncertainties.

Over 5000 tons of spilled oil reached the northeast coast of Brazil in 2019. The Laboratory for Computational Methods in Engineering (LAMCE/COPPE/UFRJ) employed time-reverse modeling and identify multiple potential source areas. As time-reverse modeling has many uncertainties, this article carried out a methodology study to mitigate them. A probabilistic modeling using Monte Carlo approach was developed to test these source areas with the Spill, Transport, and Fate Model (STFM) and a scenario tree methodology was used to select possible spill scenarios. To estimate the performance of Lagrangian models, two new model performance evaluations were added to Chang and Hanna (2004). The combination of probabilistic simulations, scenario tree analysis, and model performance evaluation proved to be a powerful tool for mitigating the uncertainties of time-reverse modeling, yielding good results and simple implementation.

A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil.

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil.

Impacts of teleconnection patterns on South America climate.

Oceanic heat sources disturb the atmosphere, which, to come back to its initial state, disperses waves. These waves affect the climate in remote regions, characterizing the teleconnection patterns. In this study, we describe eight teleconnection patterns that affect South America climate: the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Tropical Atlantic Dipole (TAD), the South Atlantic Dipole (SAD), the Southern Annular Mode (SAM), the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). Precipitation and winds at 850-hPa anomalies, considering these teleconnection patterns in ENSO neutral periods, are also presented. Overall, southeastern South America and the north sector of the North and Northeast regions of Brazil are the most affected areas by the teleconnection patterns. In general, there is a precipitation dipole pattern between these regions during each teleconnection pattern.

Subtropical cyclones over the oceanic basins: a review.

Subtropical cyclones are hybrid systems presenting a warm core at low levels, as tropical cyclones, and a cold core at upper levels, as extratropical cyclones. Their genesis can be as proper subtropical system or from the transitions (extratropical to subtropical or tropical to subtropical). Subtropical cyclones occur mostly over the sea and generate intense near surface winds with great impacts on human activities and ecosystems. We present a review of the main features, as obtained from observations and numerical simulations, for subtropical cyclone development over the major oceanic basins.

The state of the art and fundamental aspects of regional climate modeling in South America.

Regional climate models have been used since 1989 in order to improve climate simulation in regions where mesoscale forcings modulate the regional climate. These models are driven by time-dependent lateral boundary conditions from global climate models or reanalysis, and this process is called dynamical downscaling. Here, we review the evolution of regional climate modeling, as well as present the studies developed for South America.

Are reanalysis data useful for calculating climate indices over South America?

Precipitation and temperature climate indices are calculated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and validated against observational data from some stations over Brazil and other data sources. The spatial patterns of the climate indices trends are analyzed for the period 1961-1990 over South America. In addition, the correlation and linear regression coefficients for some specific stations were also obtained in order to compare with the reanalysis data. In general, the results suggest that NCEP/NCAR reanalysis can provide useful information about minimum temperature and consecutive dry days indices at individual grid cells in Brazil. However, some regional differences in the climate indices trends are observed when different data sets are compared. For instance, the NCEP/NCAR reanalysis shows a reversal signal for all rainfall annual indices and the cold night index over Argentina. Despite these differences, maps of the trends for most of the annual climate indices obtained from the NCEP/NCAR reanalysis and BRANT analysis are generally in good agreement with other available data sources and previous findings in the literature for large areas of southern South America. The pattern of trends for the precipitation annual indices over the 30 years analyzed indicates a change to wetter conditions over southern and southeastern parts of Brazil, Paraguay, Uruguay, central and northern Argentina, and parts of Chile and a decrease over southwestern South America. All over South America, the climate indices related to the minimum temperature (warm or cold nights) have clearly shown a warming tendency; however, no consistent changes in maximum temperature extremes (warm and cold days) have been observed. Therefore, one must be careful before suggesting any trends for warm or cold days.