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Home , Southern Hemisphere

Southern Hemisphere Teleconnectionsand predictability

Carolina Vera CIMA/CONICETͲUniversity ofBuenosAires,ͲUMIͲIFAECI/CNRS BuenosAires,

Motivation

• LargeͲscalecirculationvariabilityintheSouthernHemisphere(SH) exhibitlargevaluesatmiddleandhigh,particularlyinthe  Pacific sector, and fromsubseasonal to interannual and decadaltimescales.

•TheactivityoftheleadingpatternsofSHcirculationvariabilityhasa large influenceon the climate of South  America, ,  andAustraliaͲNewZealand.

•In particular, they seem to have a role explaining seasonal predictabilityatsubtropicalandextratropical ofSouth America. SeasonalpredictabilityinSouthAmerica JJA DJF

Surface Temperature Ensembleof13 modldelsof  WCRP/WGSIP/CHFP Database

Precipitation

Osman andVera(2013)

The La Plata Basin

•The Plata Basin covers about 3.6 million km2.

•The La Plata Basin is the fifth largest in the world and second only to the in in terms of geographical extent.

•The principal sub-basins are those of the Parana, and rivers.

•The La Plata Basin covers parts of five countries, AiArgentina, , , Paraguay and Uruguay. Global relevance of the la Plata Basin

•LPB is home of more than 100 million people including the capital cities of 4 of the five countries, generating 70% of the five countries GNP.

• There is more than 150 dams, and 60% of the hydroelectric potential is already used.

• It is one of the largest food producers (cereals, soybeans and livestock) of the world.

Extratropical

• The snowpack in the Andes between 30° and 37°S is the primary source of surface runoff and water supply in the Mendoza adjacent lowlands of and Argentina

• Over 10 million pppeople de pend , either directly or indirectly, on this freshwater for – domestic consumption – irrigation – industries – hydroelectric generation LeadingpatternsofyearͲtoͲyearvariabilityofthe circulationintheSH

SouthldhernAnnularMode PacificǦSouthAmerican SouthPacificWave (SAM) Pattern Pattern (27%) (PSA,PSA1) (SPW,PSA2) (13%) (10%)

(Mo,J.Climate,2000)

SOUTHERNANNULARMODE(SAM)

FirstleadingpatternofyearǦtoǦyear variabilityofthecirculationintheSH Dominantvariabilityoninterannual timescales(~5years).Largetrend.

Mainlymaintainedbytheatmospheric internalvariability

8 SouthernAnnularMode(SAM)

Surfacetemperature

Regressionof SAM index of (top) precipitation  and (bottom) surfacetemperatureanomalies.(Guptaetal.2006) CorrelationsbetweenSAM indexandprecipitation anomaliesforOND(79Ͳ 99). (Silvestri andVera,2003)

PacificSouthAmerican(PSA,PSA1)Pattern

SdSecondldileadingpatternofyearǦtoǦyear variabilityofthecirculationintheSH Dominantinterannualvariabilityy5(~5 years)

StronglyinfluencedbyElNiñoǦSouthern Oscillation(ENSO)

PSA&ENSOIndex

Regression(PSA,SST’)

(Mo,J.Climate,2000) ElNiñoͲSouthernOscillation(ENSO) OND(1979Ǧ1999)

CorrelationsbetweenElNino3.4SSTanomaliesand(left)ppprecipitationand(rig ht) 500ǦhPageopotentialheightanomalies.Significantvaluesat90,95and99%are shaded.NCEPreanalysisdata. (VeraandSilvestri,2009)

SouthͲPacificWaveorPSA2Pattern ThirdleadingpatternofyearǦtoǦyear variabilityofthecirculationintheSH

DominantquasiǦbiennialvariability(~2 years)

StronglyinfluencedbytropicalIndian variability

(Mo,J.Climate,2000) IndianͲOceanDipole(IOD)

SSTanomalypatternassociated withIODactivity Circulationanomalypattern associatedhdwithIODactivity

Rain&Wind anomaly patterns associatedwith IODactivity Chenetal.(2008)

RoleofSSTForcingonSHTeleconnections y Simulated500ǦhPageopotentialheightanomalies from10Ǧmemberensemblesperformedwiththe SPEEDYModelfrom1958Ǧ2006underdifferent forcing. y CONTROL(climatological meanSST) y GOGA(globalobservedSST) y POGA(PacificOceanobservedSST) y IOGA(IndianOceanobservedSST) y AOGA(At lant icOceanobdbservedSST)

Vera,Silvestri andBarreiro(2013) Varianceof500ͲhPageopotentialheight anomalies

NCEPObs CONTROL

GOGA

Vera,Silvestri andBarreiro(2013) 100 200 300 400 500 600 700 800 900 1000

Signal

Noise

Predictability

Vera,Silvestri andBarreiro(2013) Vera,Silvestri andBarreiro(2013)

PredictabilityAnalysisof500ͲhPageopotentialheight anomalies FromamultiǦmodelǦmultiǦmemberensembleof15modelsfrom WCRP/WGISP/CHFP DJF JJA

Signal Noise Signal Noise

200 hPa

500 hPa

850 hPa

(Osman andVera,2013) PredictabilityAnalysisof500ͲhPageopotentialheight anomalies FromamultiǦmodelǦmultiǦmemberensembleof15modelsfrom WCRP/WGISP/CHFP DJF Predictability (from all years) Predictability from ENSOyears

200hPa

500hPa

850hPa

(Osman andVera,2013)

ChangesinpredictabilityinENSOyears FromamultiǦmodelǦmultiǦmemberensembleof15modelsfrom WCRP/WGISP/CHFP DJF Pred_AY/Pred_ENSO= Senso/S N/Nenso Senso/S*N/Nenso 200 hPa

500 hPa

850 hPa

(Osman andVera,2013) DecadalvariabilitysignatureinSHcirculation anomalies

Regressionmapslinking500ǦhPaZ’to(left)ENSOand(bottom) PacificDecadalIndexes

(Dettinger etal.2001)

NonͲstationaryimpactsofSAMonSHclimate

CorrelationsoftheSAMindexwith(aͲb)inͲsituprecipitation,(cͲd)inͲsituSLP,(eͲf) reanalyzed SLP,(g Ͳh) reanalyzed Z500,and  (iͲj) inͲsitusurface  temperature.Correlations  statisticallysignificantatthe90%and95%ofaTͲStudenttestareshaded.Greydotsincasesof inͲsituobservationsindicatestationswithnosignificantcorrelation. (Silvestri&Vera2009) Concludingremarks y TeleconnectionsintheSHasforcedbyyptropicaloceanconditions contributetoenhancemeanpotentialpredictabilityatmiddleandhigh latitudes.Predictabiliy isincreasedduringENSOyearsparticularlyover theSouthPacific,andmainlyassociatedtoanincreasedsignal.

y PredictabilityinsoutheasternSouthAmericaandsouthernAndesseem tobelargelyexplainedbytheteleconnections.However,thefactthat modelsstillpresentdeficienciesintheteleconnectionrepresentation, forecastskillstherearestilllow.Strategiestoimproveitareneeded

y ThereispotentialgaininseasonalforecastskillintheSouthern Hemisphereifthemodelscancapturetheatmosphericteleconnections. However,theroleoftropicalininducingtheSHteleconnections doesnotseemstobemoreimppy(yortantthantheinternalvariability.(only windowsofopportunity?Othersourcesofpredictability?)

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