Rainfall and climate variability: long-term trends in the Metropolitan Area of São Paulo in the 20th century

• Published in: Climate research 2014-09, Vol.61 (2), p.93-107
• Main Authors: Obregón, Guillermo O., Marengo, José A., Nobre, Carlos A.
• Format: Article
• Language: English
• Subjects: Marine
• ISSN: 0936-577X; 1616-1572
• DOI: 10.3354/cr01241

We investigated spatial−temporal variability and long-term trends of rainfall over the Metropolitan Area of São Paulo (MASP). Due to a lack of continuous and consistent rainfall observations in the MASP, a database for the spatial analysis was created from a composite of 94 stations, with daily rainfall data for 1973−1997, plus data from the University of São Paulo's Institute of Geophysics and Astronomy (IAG/USP) station for 1933−2011, as the base of the long-term variability analysis. It is shown that the seasonal and interannual rainfall variability result from underlying local influences and remote large-scale atmospheric dynamics. These effects appear to be more complex during the austral spring and fall seasons, and seem to determine the duration of the rainy period over the MASP. The intraseasonal (30−60 d) oscillation of rainfall does not exhibit any long-term modulation; interannual rainfall variability for 1975−1990 appears modulated by the El Niño−Southern Oscillation (ENSO) scale (2−8 yr). Nonetheless, there exists statistical evidence that the rainfall climate over the MASP had an abrupt change at the end of the 1950s, but the global climatic change that occurred in the middle of the 1970s is not evident. The progressive increase in the frequency of rainy days and total daily rainfall throughout the period of analysis are prominent factors in the trend of annual and seasonal rainfall. Furthermore, rainfall variability over the MASP may be modulated by the Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and ENSO at seasonal scales, altering the normal progression of the seasonal rainfall cycle, while at interannual scales, each one of these patterns drives the rainfall variability, mainly when each pattern reaches its extreme value.