CHANGES IN EXTREME DAILY RAINFALL FOR SÃO PAULO, BRAZIL. São Paulo, SP, Brasil,

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1 CHANGES IN EXTREME DAILY RAINFALL FOR SÃO PAULO, BRAZIL Maria A. F. Silva Dias, 1 Juliana Dias 2, Leila M. V. Carvalho, 3 EdmilsonD. Freitas 1, Pedro L. Silva Dias 1,4 1 Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosféricas - São Paulo, SP, Brasil, 2 Earth System Research Laboratory, NOAA, United States 3 University of California at Santa Barbara, United States 4 Laboratório Nacional de Computação Científica, Petrópolis, RJ, Brasil RESUMO: A evolução dos extremos na precipitação diária na cidade de São Paulo ao longo do período de 1931 a 2010 é analisada encontrando-se tendências de aumento positivas. Os índices climáticos explicam 85% da variância da frequência dos extremos de chuva diária na estação seca. Na estação chuvosa há maior correlação com a temperatura da superfície do mar nas proximidades da costa do estado de São Paulo porém essa temperatura e os índices climáticos explicam menor fração da variância dos extremos, quando comparado com a estação seca, indicando que outros fatores como a ilha de calor urbana e a poluição do ar devam ser considerados como possíveis causas das tendências observadas ao longo de quase oito décadas. ABSTRACT: The evolution of daily rainfall extremes in the city of São Paulo in the period from 1931 to 2010 is analyzed and significant positive trends have been found. The climatic indices explain 85% of frequency of extremes during the dry season. During the wet season there is correlation with the local sea surface temperature close to the coast of the state of Sao Paulo but this temperature and the climatic indices explain a smaller fraction of the variance when compared to the dry season indicating that other factors such as the urban heat island and air pollution need to be taken into account to explain the observed trends over eight decades. INTRODUCTION Global and regional climate have evolved during the last century due to natural and/or anthropogenic influences with associated changes in rainfall amounts and rainfall patterns. Haylock et al (2006), among others, discuss the change in rainfall over South America from 1960 to 2000 relating the observed changes to sea surface temperature (SST) anomalies like the ENSO cycles. The effect of large urban areas on rainfall is also a subject that has been extensively studied. Perhaps the more comprehensive research was the one performed in the St Louis area by the METROMEX (Changnon et al, 1991) where rainfall was seen to increase between 5 and 25 % downwind from the city area. More recently, Shepherd et al (2002) using the TRMM satellite precipitation radar data looked into several cities in the USA and confirmed

2 the same tendency of rainfall augmentation in the city and downwind from it not only in accumulated values but also an increase in the maximum rainfall rate. This paper examined a time series of daily rainfall for a climatological station in the São Paulo Metropolitan Area, SPMA, in Brazil. The SPMA population grew from less than 2 million inhabitants in 1940 to almost 20 million by 2010, a 10 fold increase in population that makes it one of the 5 largest urban conglomerates in the world. According to the state of São Paulo Official site the urban area of the SPMA increased from 200 km 2 in 1930 to 2,407 km 2 in 2002, a factor of 12 in seven decades. Another feature of the SPMA is the proximity to the Atlantic Ocean coast line. The sea-breeze coupled to the valley to mountain circulation penetrates from Southeast to the SPMA plateau (Freitas et al, 2007) and may interact with the urban heat island and in this way trigger convective storms. DATA AND METHODOLOGY The rainfall data from 1933 to 2010 were obtained from the climatological station located in the center of a state park (Parque do Estado) at latitude 23 39'S, longitude 46 37'W and altitude of 799 m above sea level. The station is operated by the Institute of Astronomy, Geophysics and Atmospheric Sciences of the University of São Paulo (IAG/USP). The park was originally in a rural environment where an Astronomy Observatory was installed in Time series for climate indices have been obtained from The indices used are: PDO -Pacific Decadal Oscillation Index, NAO North Atlantic Oscillation, AMC - Atlantic Meridional Circulation Index - ENSO - Multivariate ENSO The sea surface temperature SST, close to the coast line has been obtained from the NCEP reanalysis (Kalnay et al 1996) and averaged over the area delimited by 24 o S, 25 o S and 45 o W, 46 o W. A linear fit to SST has been used to obtain the residual ressst. The annual average of each of the above climatic indices has been obtained and a five year running average was performed. The Large-scale Index for South America Monsoon (LISAM) has been obtained according to Silva and Carvalho (2007). The original LISAM index has been normalized by removing the long term average and dividing by the standard deviation. RESULTS The probability density function (PDF) for the daily rainfall has changed from the beginning of the series to the present. The PDF of the first and last 20 years of the daily rainfall,removing the days with zero precipitation are shown in Figure 1. There is an overall increase in the frequency of daily rainfall events, except for a reduction of events with daily rainfall less than 4 mm. Xavier et al (1995) examined data from the same station ( ) and noticed a decreasing trend in the number of days with rainfall less than 5 mm and an increasing trend in the number of days with rainfall in excess of 30 mm in some months. Figure 2 has been constructed using

3 as a reference the daily rainfall value, hereafter called threshold, defining the percentiles of 80, 95 and 99% of the PDF, using a running window of 20 years starting from The label indicates the middle year of the corresponding 20 year period. The plots include all year daily rainfall threshold values as well as the wet season (ONDJFM) and dry season (AMJJAS). For the 80% threshold (Fig 2a) the value increases sharply up to middle 1950 s then shows a gentle decrease up to early 1980s and then increases sharply again for about 10 years leveling off after that until present. For the drier half of the year, the 95% threshold shows similar tendencies (Fig. 2b), except for the last couple of decades and for the dry season. The 99% threshold (Fig. 2c) shows about the same general behavior as the 95% threshold although with a more variable temporal evolution, likely due to the low frequency of the events. The Mann-Kendall trend has been computed for all year, wet and dry seasons. All trends have p-value less than 10-5 being significant at the 0.01% level. The largest trend is for the dry season and 99% threshold, with 2.5 mm/decade increase. The 95% threshold has a trend of 1.1 mm/(day.decade) for both the dry and wet seasons. For the wet and dry seasons the trend shows an increase from the lower thresholds to the higher indicating that there is a larger increase in the frequency of the most extreme cases. The number of days that the rainfall exceeds 30mm/day (roughly the 95% threshold) per decade obtained with a moving window of 10 years starting every 5 years and calculated separately for the wet and dry seasons is shown in Fig. 3a. A multi-decadal modulation exists in the number of days with rainfall exceeding 30 mm/day, with a tendency to increase over the whole period for the wet season. For the dry season there is a variability that shows that the last decade has values similar to the ones observed in different periods in the past. The behavior of the number of days when rainfall exceeds 60 and 80 mm/day for each season is shown in Figure 3b and c. Here, besides the decadal modulation seen in Figure 3a, the winter (J-J-A) had no cases of daily precipitation exceeding 60 mm/day up to the mid 1970s (Fig. 3b). For the 80 mm/day (Fig. 3c) there was only one case per decade in the first decades and after the early 1970s the number of cases increases reaching 9 cases in the last decade. To examine the possible relationship between the extreme daily precipitation thresholds and climatic indices, the Pearson linear correlation between different pairs has been calculated and results are shown in Table 1. Only values significant at the 0.05 level have been included in the table and the shaded values are significant at the 0.01 level. PDO shows a correlation coefficient of with the 95% threshold and with the 99% threshold both in the dry season and both with P<0.01. LISAM and SST also correlate well with all daily rainfall thresholds. For the wet season the higher correlation of the 95 and 99% threshold is with the local SST indicating a possible impact on the local circulation. The climatic indices have been used to generate a multiple linear regression equation (not shown). The combined indices linearly explain more than 85% of the variance of the 95 and 99% thresholds in the dry season. In the wet season for the 95% threshold they explain 67% of the variance and for the 99% threshold

4 they explain 37% of the variance. CONCLUSIONS The evolution of extreme daily rainfall events shows trends over the period that are larger for the more extreme events. The climatic indices explain most of the variance of frequency of extreme events in the dry season. In the wet season the increased frequency of daily rainfall events is influenced mainly by the local SST close to the coast and when combined with the climatic indices a smaller portion of the variance is explained than in the dry season. This indicates that urban processes related to the heat island and eventually to air pollution may partly explain the remaining observed variance of daily rainfall extremes. ACKNOWLEDGMENTS: The research is funded by FAPESP and CNPq. REFERENCES Changnon, S.A., R. T. Shealy, R. W. Scott, Precipitation Changes in Fall, Winter, and Spring Caused by St. Louis. J. Applied Meteor., 30, Freitas, E. D.,Rozoff, C. M. ; Cotton, W. R. ; Silva Dias, P. L., Interactions of an urban heat island and sea breeze circulations during winter over the Metropolitan Area of São Paulo - Brazil. Boundary - Layer Meteorology, 122, Haylock, M. R. et al Trends in Total and Extreme South American Rainfall in and Links with Sea Surface Temperature. J. Climate, 19, Kalnay, E. and Coauthors, 1996: The NCEP/NCAR Reanalysis 40-year Project. Bull. Amer. Meteor. Soc., 77, Shepherd, J. M, H.Pierce, A. J. Negri, 2002: Rainfall Modification by Major Urban Areas: Observations from Spaceborne Rain Radar on the TRMM Satellite. J. Applied Meteor., 41, Silva, A. E., L. M. V. Carvalho, 2007: Large-scale index for South America Monsoon (LISAM). Atmos. Sci. Let. 8: DOI: /asl.150 Xavier, T.M.S., A.F.S. Xavier, M. A. F. Silva Dias, 1994: Evolução da precipitação diária num ambiente urbano: o caso da cidade de São Paulo. Rev.. Bras. Meteor., 9, Table 1 Pearson correlation coefficient between variables. Only correlation coefficients with P < 0.05 are included. Shaded cells have P < PDO NAO AMC ENSO LISAM local SST Residual SST 95% ALL % WET % DRY % ALL

5 99% WET % DRY Figure 1 - Probability density function of daily rainfall for 20 years running window for , and for the % difference between the two periods. Figure 2 - Evolution of the daily rainfall values defining the percentiles of 80, 95 and 99% for 20 years running for all year, wet season (ONDJFM) and dry season (AMJJAS). Figure 3 (a) Number of days the daily rainfall exceeds 30 mm/day in 10 years for the wet (ONDJFM) and dry (AMJJAS) halfs of the year; the 10 year interval starts every 5 years. Evolution of the number of days that the rainfall (b) 60 mm/day (c) 80 mm/day in 10 years for the seasons.

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