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Problems in diagnosing Precipitation Trends in South America

Problems in diagnosing Precipitation Trends in South America. Brant Liebmann Vicente Barros Carolina S. Vera Juli á n B á ez Leila M.V. Carvalho Anji Seth In é s Camilloni Gil Compo Marty P. Hoerling Jos é A. Marengo Prashant Sardeshmukh Dave Allured

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Problems in diagnosing Precipitation Trends in South America

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  1. Problems in diagnosing Precipitation Trends in South America Brant Liebmann Vicente Barros Carolina S. Vera Julián Báez Leila M.V. Carvalho Anji Seth Inés Camilloni Gil Compo Marty P. Hoerling José A. Marengo Prashant Sardeshmukh Dave Allured Mario Bidegain

  2. Data We wish to thank the following agencies for providing the data used in this study: Agência Nacional de Águas (Brasil) Agência Nacional Energia Elétrica (Brasil) U.T.E. Uruguay C.T.M. Salto Grande Servicio Meteorologico Nacional (Argentina, Paraguay, Uruguay) FUNCEME (Ceará, Brasil) IAPAR - SIMEPAR (Paraná, Brasil) DAEE (São Paulo, Brasil) Minesterio del Ambiente y los Recursos Naturales (Venezuela) Meteorogische Dienst Suriname METEO-France ASANA (Bolivia)

  3. Observed Precipitation Trend mm/season

  4. Statistical Relevance of Observed Trends

  5. SACZ Southern Brazil 1948-1999 Southern Brazil (2.5 mm/yr; 21% variance explained by trend) SACZ (0.1 mm/yr; 0% variance explained by trend)

  6. Correlation of JFM precipitation with JFM with Missing days (noted on abcissa) 27 seasons at 49W, 15.3S Correlation between complete and incomplete record Days missing from 90 day record

  7. Trends calculated from missing seasons Trend per 24 years Trend explains 18% of variance over 24 years

  8. Station pair correlation with distance Each pair correlation average Daily, including annual cycle

  9. Stations with fewer than 22 years Stations with at least 22 years (larger dots)

  10. Stations with at least 22 years data Stations with fewer than 22 years 19 seasons required 24 seasons required

  11. Previous work: Barros, Castaneda, Doyle (2000): Increase in Annual total precipitation over most of Argentina from 1956-1991. Castaneda and Barros (1994): Humid Pampa (Argentina) increase in rain, mainly after 1960. Robertson and Mechoso (1998): Decadal variability and non-linear upward trend in southeast South America rivers. Marengo (2004): Northern Amazon Basin decrease related to El Nino?

  12. Observed Precipitation Trend mm/season

  13. Argentina – 36 year segments At least 34 years of 328 days per year

  14. Argentina – 24 year segments At least 22 years of 328 days per year

  15. Argentina – 15 year segments At least 14 years of 328 days per year

  16. Observations versus AMIP runs January – March 1976-1999 mm/season

  17. From Compo and Sardeshmukh (2004)

  18. Observations: University of Delaware monthly Models: ARPEGE – 8 runs CAM2 - 15 runs NSIPP - (14 + 9) 23 runs ECHAM4 - 24 runs ECHAM3 - 10 runs

  19. mm/season

  20. Observed Trend mm/season

  21. mm/season

  22. mm/season

  23. Running trends – 24 year segments July – September Central Africa index Observations 5 model average

  24. Running trends – 15 year segments July – September Central Africa index Observations 5 model average

  25. Central African Rainfall Anomaly July - September observed 5 model average

  26. Complete records versus dense coverage Problems if values are missing All these problems are relatively minor Compared to choosing appropriate period For practical purposes, what constitutes a ‘trend?’ Should I be more careful?

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