Dominant large-scale patterns influencing the interannual variability ofprecipitation in South America as depicted by IPCC-AR4 ModelsCarolina Vera (1), Gabriel Silvestri (1), Brant Liebmann (2), and Paula Gonzalez (1)

CIMA-DCAyO/UBA-CONICET, Buenos Aires, ArgentinaNOAA/CDC, Boulder, Colorado, USA

ObjectivesTo 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.

Data and MethodologyIPCC-AR4 20c3m runs were used for the period 1970-1999Anomalies 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.

AcronymModel NameN of RunsOBSNCEP ReanalysisCMAP precipitation-CNRMMeteo France CNRM1GFDLNOAA Geophysical Fluid Dynamics Laboratory, CM2.03GISSNASA/GODDARD Institute for Space Studies, ModelE20/HYCOM5IPSLInstitute Pierre Simon Laplace CM41MIROCCSSR/NIES/FRGC, JAPAN, MIROC3.2 Medium resolution3MPIMax Planck Institute ECHAM53MRIMeteorological Research Institute Japan, CGM2.3.2a5UKMOUK Meteorological Office-HADCM32Total Number of simulations23

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

Climatological means for precipitation over South America JFMOBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRM

Climatological mean Standard Dev. for precipitation over South America JFMOBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRM

Climatological means for precipitation over South America JASOBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRM

Climatological mean Standard Dev. for precipitation over South America JASOBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRM

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

Leading Patterns of 500-hPa geop. height anomalies. Mode 1 (AAO)OBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRM

Leading Pattern 1 (AAO) & SST anomaliesOBSGFDLGISSMIROCMPIMRIUKMOCNRMIPSL

OBSGFDLLeading Patterns of 500-hPa geop. height anomalies. Mode 2 (PSA1)GISSMIROCMPIMRIUKMOIPSLCNRM

Leading Pattern 2 (PSA1) & SST anomaliesOBSGFDLGISSMIROCMPIMRIUKMOCNRMIPSL

Leading Patterns of 500-hPa geop. height anomalies. Mode 3 (PSA2)OBSGFDLGISSMIROCMPIMRIIPSLUKMOCNRM

Leading Pattern 3 (PSA2) & SST anomaliesOBSMPIGFDLGISSMIROCMRIUKMOCNRMIPSL

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

Southeastern South America (SESA)(52W-65W ; 24S-31S)

OBSGFDLGISSMIROCMPIMRIUKMOCNRMIPSLCorrelation Maps between SESA Precipitation and SST anomalies

OBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRMSESA Precipitation anomalies & 500-hPa geop. height anomalies

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 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 (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.

Climatological seasonal means of precipitation over South AmericaSESA-BOX (52W-65W ; 24S-31S)Seasonal CycleInterannual VariabilityInterannual Variability (ENSO removed)

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

Correlation between EN3.4 & SST anomaliesOBSGFDLGISSMIROCMPIMRIUKMOCNRMIPSL

EN3.4 Index & 500-hPa geopotential height anomaliesOBSMPIIPSLGISSGFDLMIROCMRIUKMOCNRM

The stars denote the objective in which we are currently concentrated on.

In this work, SESA region has been defined as the box (52W-65W ; 24S-31S)

Climatological mean precipitation is large over SESA between spring and summer. It exhibits the largest variability on ENSO-like timescales (around 3-5 years) and also on quasi-biennial timescales

Climatological mean precipitation is large over SESA between spring and summer. It exhibits the largest variability on ENSO-like timescales (around 3-5 years) and also on quasi-biennial timescales