Data Mining for Flooding Episode in the States of Alagoas and Pernambuco—Brazil

The increasing volume of data in the area of environmental sciences needs analysis and interpretation. Among the challenges generated by this “data deluge”, the development of efficient strategies for the knowledge discovery is an important issue. Here, statistical and tools from computational intelligence are applied to analyze large data sets from meteorology and climate sciences. Our approach allows a geographical mapping of the statistical property to be easily interpreted by meteorologists. Our data analysis comprises two main steps of knowledge extraction, applied successively in order to reduce the complexity from the original data set. The goal is to identify a much smaller subset of climatic variables that might still be able to describe or even predict the probability of occurrence of an extreme event. The first step applies a class comparison technique: p-value estimation. The second step consists of a decision tree (DT) configured from the data available and the p-value analysis. The DT is used as a predictive model, identifying the most statistically significant climate variables of the precipitation intensity. The methodology is employed to the study the climatic causes of an extreme precipitation events occurred in Alagoas and Pernambuco States (Brazil) at June/2010.

Climate Change Index: A Proposed Methodology for Assessing Susceptibility to Future Climatic Extremes

A Climate Change Index (CCI) was designed to assess the degree of susceptibility to the climatic extremes projected for the future. Climate projections for the period 2041-2070 are extracted from the numerical integrations of INPE’s Eta-HadCM3 model, using the SRES A1B emissions scenario. Five indicators were chosen to represent the climatic extremes: Total annual precipitation, precipitation on the days of heavy rain, the maximum number of consecutive dry days in the year and the annual mean maximum and mean minimum temperatures. The methodology was applied to the state of Paraná. The results point to a very strong warming in 99% of the municipalities, with temperature increases between 6 and 8 times greater than the variance observed in the present climate. On the other hand, projections of precipitation do not indicate major changes in relation to present behavior.

Future Changes in Temperature and Precipitation Extremes in the State of Rio de Janeiro (Brazil)

In this study, we document the air temperature and precipitation changes between present-day conditions and those projected for the period 2041-2070 in the state of Rio de Janeiro (Brazil) by means of Eta driven by HadCM3 climate model output, considering the variation among its four ensemble members. The main purpose is to support studies of vulnerability and adaptation policy to climate change. In relation to future projections of temperature extremes, the model indicates an increase in average minimum (maximum) temperature of between +1.1°C and +1.4°C (+1.0°C and +1.5°C) in the state by 2070, and it could reach maximum values of between +2.0°C and +3.5°C (+2.5°C and +4.5°C). The model projections also indicate that cold nights and days will be much less frequent in Rio de Janeiro by 2070, while there will be significant increases in warm nights and days. With respect to annual total rainfall, the Northern Region of Rio de Janeiro displays the greatest variation among members, indicating changes ranging from a decrease of -350 mm to an increase of +300 mm during the 21st century. The southern portion of the state has the largest increase in annual total rainfall occurring due to heavy rains, ranging from +50 to +300 mm in the period 2041-2070. Consecutive dry days will increase, which indicates poorly time distributed rainfall, with increased rainfall concentrated over shorter time periods.

Spatiotemporal Variability of Methane over the Amazon from Satellite Observations

The spatiotemporal variability of the greenhouse gas methane （CH4） in the atmosphere over the Amazon is studied using data from the space-borne measurements of the Atmospheric Infrared Sounder on board NASA＇s AQUA satellite for the period 2003-12. The results show a pronounced variability of this gas over the Amazon Basin lowlands region, where wetland areas occur. CH4 has a well-defined seasonal behavior, with a progressive increase of its concentration during the dry season, followed by a decrease during the wet season. Concerning this variability, the present study indicates the important role of ENSO in modulating the variability of CH4 emissions over the northern Amazon, where this association seems to be mostly linked to changes in flooded areas in response to ENSO-related precipitation changes. In this region, a CH4 decrease （increase） is due to the E1 Nifio-related （La Nifia-related） dryness （wetness）. On the other hand, an increase （decrease） in the biomass burning over the southeastern Amazon during very dry （wet） years explains the increase （decrease） in CH4 emissions in this region. The present analysis identifies the two main areas of the Amazon, its northern and southeastern sectors, with remarkable interannual variations of CH4. This result might be useful for future monitoring of the variations in the concentration of CH4, the second-most important greenhouse gas, in this area.