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Carolina Vera (1) , Gabriel Silvestri (1) , Brant Liebmann (2) , and Paula Gonzalez (1)

Dominant large-scale patterns influencing the interannual variability of precipitation in South America as depicted by IPCC-AR4 Models. Carolina Vera (1) , Gabriel Silvestri (1) , Brant Liebmann (2) , and Paula Gonzalez (1) CIMA-DCAyO/UBA-CONICET, Buenos Aires, Argentina

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Carolina Vera (1) , Gabriel Silvestri (1) , Brant Liebmann (2) , and Paula Gonzalez (1)

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  1. Dominant large-scale patterns influencing the interannual variability ofprecipitation in South America as depicted by IPCC-AR4 Models Carolina Vera (1), Gabriel Silvestri (1), Brant Liebmann (2), and Paula Gonzalez (1) CIMA-DCAyO/UBA-CONICET, Buenos Aires, Argentina NOAA/CDC, Boulder, Colorado, USA

  2. Objectives To describe the relative contributions of the leading modes of variability of the atmospheric circulation in the SH to the precipitation variance over southeastern South America (SESA) in present climate (from reanalyses). Main conclusions presented in 2004: AAO influences SESA precipitation during winter and spring, PSA1 does it during spring and summer, while PSA2 does it during summer and fall. To assess the ability of the IPCC-AR4 models in reproducing the precipitation variability in South America in present climate. To investigate the ability of IPCC-AR4 models in reproducing the main features of SH leading modes and their impact on South America precipitation. To diagnose variations of the activity of the leading modes of atmospheric circulation on climate change scenarios. to assess climate change scenarios of precipitation over South America based on such variations.

  3. Data and Methodology • IPCC-AR4 20c3m runs were used for the period 1970-1999 • Anomalies were defined removing the seasonal cycle and the long-term trend. • EOFs, correlation and regression maps were based on monthly mean anomalies and calculatedd over the whole year. • They were computed per individual run and then the results were averaged over all the runs available for each model.

  4. How well do IPCC-AR4 models represent basic precipitation features in South America?

  5. MPI GFDL GISS IPSL Climatological means for precipitation over South America JFM OBS UKMO MIROC MRI CNRM

  6. MPI GFDL GISS IPSL Climatological mean Standard Dev. for precipitation over South America JFM OBS UKMO MIROC MRI CNRM

  7. MPI GFDL GISS IPSL Climatological means for precipitation over South America JAS OBS UKMO MIROC MRI CNRM

  8. MPI GFDL GISS IPSL Climatological mean Standard Dev. for precipitation over South America JAS OBS UKMO MIROC MRI CNRM

  9. How well do IPCC-AR4 models represent the leading patterns on interannual variability of the circulation in the SH?

  10. GFDL OBS MPI Leading Patterns of 500-hPa geop. height anomalies. Mode 1 (AAO) IPSL GISS UKMO CNRM MIROC MRI

  11. GFDL OBS MPI Leading Pattern 1 (AAO) & SST anomalies IPSL UKMO GISS MRI MIROC CNRM

  12. Leading Patterns of 500-hPa geop. height anomalies. Mode 2 (PSA1) OBS MPI GFDL IPSL GISS UKMO CNRM MIROC MRI

  13. MPI GFDL OBS Leading Pattern 2 (PSA1) & SST anomalies IPSL UKMO GISS MRI MIROC CNRM

  14. MPI GFDL OBS Leading Patterns of 500-hPa geop. height anomalies. Mode 3 (PSA2) IPSL GISS UKMO MRI CNRM MIROC

  15. OBS MPI GFDL Leading Pattern 3 (PSA2) & SST anomalies GISS IPSL UKMO MIROC MRI CNRM

  16. How well do IPCC-AR4 models represent precipitation variability in Southeastern South America?

  17. Southeastern South America (SESA) (52ºW-65ºW ; 24ºS-31ºS)

  18. Correlation Maps between SESA Precipitation and SST anomalies GFDL OBS MPI IPSL UKMO GISS MRI MIROC CNRM

  19. SESA Precipitation anomalies & 500-hPa geop. height anomalies MPI GFDL OBS IPSL UKMO GISS MRI MIROC CNRM

  20. Model are able to reproduce some of the features of the leading modes of SH circulation interannual variability (particularly those associated with the AAO). Although the simulated anomalies exhibit different amplitude and are somewhat misplaced than those observed. • The ability of the models in representing the 2nd and 3rd (PSA) SH leading modes is related with their ability in reproducing ENSO features and the circulation along the subpolar regions of the SH influence. • Although some improvements are observed, models still have some deficiencies in representing the right amounts of precipitation and its interannual variability over the Amazon basin, SACZ, and la Plata Basin. Preliminary conclusions (1)

  21. Most of the models are able to reproduce in someway the cyclone-anticyclone circulation anomalies observed over South America in association with interannual precipitation variability in SESA. Nevertheless, just a few of them are able to represent the main features of the associated circulation anomalies in the SH (annular mode and wave-3 like patterns). • UKMO, GFDL and MPI are the models that better depict the climatological mean and standard deviations of precipitation anomalies in South America, as well as the main features of the SH circulation anomalies associated with precipitation variability in SESA. Preliminary conclusions (2)

  22. Climatological seasonal means of precipitation over South America Interannual Variability SESA-BOX (52ºW-65ºW ; 24ºS-31ºS) Seasonal Cycle Interannual Variability (ENSO removed)

  23. How do IPCC models represent the ENSO signal in the Southern Hemisphere?

  24. GFDL OBS MPI Correlation between EN3.4 & SST anomalies IPSL UKMO GISS MRI MIROC CNRM

  25. MPI GFDL OBS EN3.4 Index & 500-hPa geopotential height anomalies IPSL UKMO GISS MRI MIROC CNRM

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