Southern Africa and Southern South America have experienced recent extremes in dry and wet rainy seasons which have caused severe socio-economic damages. Selected past extreme events are here studied, to estimate how human activity has changed the risk of the occurrence of such events, by applying an event attribution approach (Stott et al., 2004)comprising global climate models of Coupled Model Intercomparison Project 5 (CMIP5). Our assessment shows that models' representation of mean precipitation variability over Southern South America is not adequate to make a robust attribution statement about seasonal rainfall extremes in this region. Over Southern Africa, we show that unusually dry austral summers as occurred during 2002/2003 have become more likely, whereas unusually wet austral summers like that of 1999/2000 have become less likely due to anthropogenic climate change. There is some tentative evidence that the risk of extreme high 5-day precipitation totals (as observed in 1999/2000) have increased in the region. These results are consistent with CMIP5 models projecting a general drying trend over SAF during December–January–February (DJF) but also an increase in atmospheric moisture availability to feed heavy rainfall events when they do occur. Bootstrapping the confidence intervals of the fraction of attributable risk has demonstrated estimates of attributable risk are very uncertain, if the events are very rare. The study highlights some of the challenges in making an event attribution study for precipitation using seasonal precipitation and extreme 5-day precipitation totals and considering natural drivers such as ENSO in coupled ocean–atmosphere models

Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions.

This paper examines the distributions of anomalies of sea surface temperature (SST) and of the moisture sources in the South Atlantic Ocean during extreme dry events in southeastern Brazil in the austral autumn, winter and spring for the period 1982–2009. The extreme dry events were identified based on a combination in which consecutive dry days and variable percentiles were considered in five homogeneous regions in terms of precipitation in southeastern Brazil, as defined through cluster analysis. Composites of anomalies of SST and moisture sources for the dry events selected for the different homogeneous regions show specific characteristics for each region, but some similarities were apparent for the southern and northern parts of southeast Brazil. During spring in all regions, and during autumn and winter in the southern regions, a tripole pattern of SST anomalies was found (negative, positive and negative), together with an anomalous anticyclonic circulation in the Atlantic Ocean transporting moisture to southern Brazil associated with positive SST anomalies. A decrease in the climatological moisture sources in the south of Brazil then ensues, and dry conditions prevail in the regions of interest. In winter and autumn in the northern regions, the same tripole pattern of SST anomalies was also found, but shifted northwards. An anomalous cyclone is associated with the negative SST anomalies, and the climatological moisture sources to the northeast of Brazil reduce their contribution to the region of interest, where negative precipitation anomalies are registered. It seems that the events selected show the results of reductions both in terms of the availability of moisture and in atmospheric instability.

In the year 2023, the Earth experienced the highest near‐surface temperature anomalies ever recorded until then. In addition, several extreme weather and climate events occurred around the world, including in Brazil. In this context, the primary objective of this study is to analyze the anomalous temperature and precipitation patterns observed in Brazil during 2023, along with the most significant extreme events. Different datasets and methodologies were applied. The north coast of the state of São Paulo had the highest accumulation of rainfall recorded in Brazil in a single day. September, October, and November 2023 experienced the large precipitation deficits over the Amazon region, leading to a very intense and prolonged drought. The south of Brazil was affected by a large amount of precipitation in a short time, associated with cyclones, resulting in fatalities and economic losses. Southeast and Central‐West Brazil experienced two intense heatwaves in the austral spring, breaking daily temperature records in major cities like São Paulo and Rio de Janeiro. Overall, this study describes the main physical processes responsible for these extremes, along with the socioenvironmental impacts caused by most of them.

A tracking of the frontal systems over South America is performed by the Climate Studies Group at the University of São Paulo (GrEC/USP) since January 2014. This analysis used an objective methodology for tracking the frontal systems that consists in analyze daily data from I NCEP / NCAR Reanalysis I and verify the occurrence of decrease in temperature at 925hPa, simultaneous to a shift in the meridional wind in 925hPa and sea level pressure increase one day to another. This monitoring is done through maps with the number and anomalies of frontal systems and also analyzing the occurrence of these systems in grid points on the coast, inland and center of the continent.