Subsistence Agriculture Productivity and Climate Extreme Events

The occurrence of rainfall extreme events leads to several environmental, social, cultural, and economic consequences, heavily impacting agriculture. The analysis of climate extreme indices at the municipal level is of the uttermost importance to the overall study of climate variability and regional food security. Corn, bean, and cassava are among the most cultivated temporary subsistence crops. Thus, the objective of this study was to analyze the relationship between subsistence agriculture productivity and the behavior of rainfall extreme indices in the Rio Grande do Norte state in the period from 1980 to 2013. We used the dataset provided by Xavier (2016) and the climate extreme indices obtained through the Expert Team on Climate Change Detection and Indices. Crop productivity data were retrieved from the Municipal Agriculture Survey from the Brazilian Institute of Geography and Statistics system. The methodology evaluated the behavior and the relationship between agricultural productivity time series and extreme precipitation indicators. We applied the following statistical techniques: descriptive analysis, time series trend analysis by the Mann-Kendall test, cluster analysis, and analysis of variance to check for equal means between identified groups. Cluster analysis was considered an adequate tool for the comprehension of data spatial distribution, allowing the identification of five homogenous subregions with different precipitation patterns. Rainfall extreme indices allowed the analysis of regional conditions regarding consecutive dry days, annual precipitation in wet days, and heavy rainfall. Trends were identified in these indices and they were significantly correlated with dryland crops productivity, indicating a direct relationship between water availability and regional agroclimatic stress.

Cross–Time Scale Interactions and Rainfall Extreme Events in Southeastern South America for the Austral Summer. Part I: Potential Predictors

The physical mechanisms and predictability associated with extreme daily rainfall in southeastern South America (SESA) are investigated for the December–February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This represents a basic set of daily circulation regimes related to the continental and oceanic phases of the South Atlantic convergence zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere polar jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different mesoscale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th and 99th percentiles) is associated with two of these weather types (WTs), which are characterized by moisture advection intrusions from lower latitudes and the Pacific Ocean; another three WTs, characterized by above-normal moisture advection toward lower latitudes or the Andes, are associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross–time scale interaction between the different climate drivers improves the predictive skill of extreme precipitation in the region.