After a series of storms, wind gusts, and other intense weather events recorded in various areas of Brazil recently, the question arose as to whether El Niño is responsible for these occurrences. Experts consulted by Olhar Digital indicate that the climate phenomenon has already begun to affect the country, however, it is not the only explanatory factor.
Researchers at UNIASSELVI developed a study that uses artificial intelligence and automation to compile climate data and assess the potential impacts of a Super El Niño event in Brazilian territory.
The investigation indicates a 97% probability of the phenomenon remaining active until 2027 and an 81% chance of reaching a very strong intensity, whose effects may vary depending on the location within the country.
This study uses artificial intelligence to cross-reference data from various meteorological institutions, presenting projections on the dynamics of El Niño in a single analysis. The work was conducted by Fábio Henrique Moisés and Matheus Fernando Cunha, Software Engineering students at UNIASSELVI, under the supervision of Professor Pedro Sidnei Zanchett.
Additionally, the research is linked to Zanchett's RADIAN doctoral thesis, which focuses on decision support systems for natural disaster management, using generative AI, cloud computing, and automation.
It is important to note that despite the use of sophisticated technology, the study does not generate an autonomous climate forecast through artificial intelligence; its function is to organize and compare projections issued by both national and international meteorological centers. The novelty lies in the integration of AI, automation, and scientific knowledge to support decision-making in risk scenarios, given that climate change demands new ways of interpreting data.
Pedro Sidnei Zanchett, a professor at UNIASSELVI and advisor for the project, commented on the topic.
To build this analysis, the researchers collected information from entities such as NOAA, NASA, INPE, INMET, CEMADEN, and IRI. The purpose was to confront different climate models and identify similarities between the forecasts. According to Moisés, the technology facilitates the transformation of existing data into more structured analyses.
The researcher stated: 'Artificial intelligence allows consolidating thousands of pieces of information produced by different institutions and transforming them into analyses capable of supporting faster and more informed decisions. The challenge is not to produce more data, but to connect the knowledge that already exists.'
Among the central aspects examined by the study are: the possibility of a high-intensity El Niño until 2027; the combination of data from Brazilian and foreign climate agencies; the use of AI and automation in organizing information; and support for planning against possible extreme events. Although the term Super El Niño is not an official technical nomenclature, it is frequently used to describe episodes of great magnitude of the phenomenon, caused by the unusual warming of the equatorial Pacific waters.
The predicted effects will not be uniform across the entire country. While the North and Northeast regions may experience periods of greater drought, the Center-West and parts of the Southeast may record heatwaves and decreased precipitation. On the other hand, the South Region is pointed out as having the greatest potential for intense rainfall and flooding.
In the agricultural sector, the research signals possible losses to crops such as rice, wheat, soy, corn, and coffee. Furthermore, the combination of heat and altered rainfall patterns can increase thermal stress on livestock.
The study also highlights risks related to the expansion of fires in the Amazon, Cerrado, and Pantanal, coupled with the deterioration of air quality and the increase in respiratory and cardiovascular problems associated with heat peaks. Cunha reinforced that the research does not replace the work done by meteorological centers, stating: 'The consensus among the main international climate models is an important sign that the country must prepare itself. Our research organizes this evidence and demonstrates how intelligent technologies can contribute to increasing the response capacity in the face of extreme events.'
Even with technological advances, artificial intelligence remains a support tool. The results are conditioned by the quality of the analyzed data and may undergo modifications as the climate evolves, demonstrating how technology helps specialists organize information and anticipate potential risks, without replacing conventional scientific analysis.
The Brazilian government has announced an action plan aimed at mitigating the consequences of climate change caused by the El Niño phenomenon. According to a statement published in Palácio do Planalto, the allocated resources will be directed towards a number of measures.
These funds are intended to support food supply assurance, strengthen food security, improve healthcare, monitor hydrological conditions, conduct environmental supervision, and prevent and combat forest fires.
The El Niño phenomenon, which arises from rising ocean water temperatures, predicts various weather conditions across different regions of Brazil. The northern part of the country is expected to experience the most severe drought and heatwaves. In the central part, including the southeast and northwest, a delay in precipitation is forecast. The southern part of the country is expected to have intense rainfall with the potential for strong winds.
In the event of El Niño, the Brazilian government assesses an increased probability of fires in the northern and central regions, where the Amazon and Pantanal biomes are located. Furthermore, a decrease in river levels is expected in these areas. The drop in water levels negatively affects electricity generation, shipping, and, in some cases, water supply, which could lead to inflation.
In the southern region, intense rainfall may trigger floods and landslides. Such events have already been observed in the state of Rio Grande do Sul for more than a week.
