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Trends and variability in extremes of precipitation in Curitiba – Southern Brazil
Author(s) -
Pedron Isabel Tamara,
Silva Dias Maria A. F.,
de Paula Dias Sandra,
Carvalho Leila M. V.,
Freitas Edmilson D.
Publication year - 2016
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4773
Subject(s) - climatology , precipitation , environmental science , percentile , geography , meteorology , geology , mathematics , statistics
ABSTRACT Based on the daily rainfall data since 1889 in Curitiba, one of the largest cities in southern Brazil, a trend towards increased precipitation and more intense rainfall can be seen. The annual and seasonal volume of rainfall has increased, amounts greater than 10, 20 and 40 mm being observed more often, but with a reduction in the number of rainy days and the number of days with rainfall below 10 mm. Seasonal 95th percentile series have increased in summer, fall and winter. In addition, several indices of climate extremes presented significant increasing trends: monthly maximum 1‐day precipitation, annual total precipitation greater than 95th and 99th percentiles, number of consecutive dry days and the daily intensity index. Generalized extreme value ( GEV ) distribution function parameters also indicated higher occurrence of extremes detected by the increase in both the scale parameter σ and the location parameter μ in summer, fall and winter. The return time for severe rainfall declined in the second half of the period compared to the first, indicating more frequent occurrence of future extreme events. The main climate indices affecting the 95th percentile series were sea surface temperature ( SST ), South Atlantic Convergence Zone ( SACZ ) and Southern Oscillation Index (SOI) during spring, and Atlantic Multi‐decadal Oscillation ( AMO ), Large‐Scale Index for South America Monsoon ( LISAM ) and SOI during the summer, which explained variability of the extremes at around 20 and 13%, respectively in each season. Regarding the variability of summer, fall and spring total rainfall, they presented values around 20% for the explained variance due to climate indices. Other factors should be investigated to explain the variability such as urbanization, air pollution and local circulations. Dominant oscillation periods in the time series constructed with one monthly extreme appeared at 3 to 8‐year (inter‐annual) cycles, with 12 years (decadal) and around 30–64 years on the inter‐decadal scale. These oscillations have resonance with SOI , SACZ and Southern Annular Mode ( SAM ) indices (high frequencies), and Pacific Decadal Oscillation ( PDO ) and AMO (low frequency).