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) (1)CIMA-DCAyO/UBA-CONICET, Buenos Aires, Argentina (2)NOAA/CDC, Boulder, Colorado, USA

2. 1.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. 2.To assess the ability of the IPCC-AR4 models in reproducing the precipitation variability in South America in present climate. 3.To investigate the ability of IPCC-AR4 models in reproducing the main features of SH leading modes and their impact on South America precipitation. 4.To diagnose variations of the activity of the leading modes of atmospheric circulation on climate change scenarios. 5.to assess climate change scenarios of precipitation over South America based on such variations. Objectives

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. Acronym Model NameN° of Runs OBS NCEP Reanalysis CMAP precipitation - CNRM Meteo France CNRM1 GFDL NOAA Geophysical Fluid Dynamics Laboratory, CM2.0 3 GISS NASA/GODDARD Institute for Space Studies, ModelE20/HYCOM 5 IPSL Institute Pierre Simon Laplace CM41 MIROC CSSR/NIES/FRGC, JAPAN, MIROC3.2 Medium resolution 3 MPI Max Planck Institute –ECHAM53 MRI Meteorological Research Institute Japan, CGM2.3.2a 5 UKMO UK Meteorological Office-HADCM32 Total Number of simulations 23

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

5. OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological means for precipitation over South America JFM

6. OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological mean Standard Dev. for precipitation over South America JFM

7. OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological means for precipitation over South America JAS

8. OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological mean Standard Dev. for precipitation over South America JAS

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

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

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

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

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

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

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

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

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

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

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

20. Preliminary conclusions (1) 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 2 nd and 3 rd (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.

21. Preliminary conclusions (2) 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.

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

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

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

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