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Southern Hemisphere Teleconnections and Climate

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Southern Hemisphere Teleconnections and Climate Southern Hemisphere TeleconnectionsandClimate predictability Carolina Vera CIMA/CONICETͲUniversity ofBuenosAires,ͲUMIͲIFAECI/CNRS BuenosAires,Argentina Motivation • LargeͲscalecirculationvariabilityintheSouthernHemisphere(SH) exhibitlargevaluesatmiddleandhighlatitudes,particularlyinthe South Pacific sector, and fromsubseasonal to interannual and decadaltimescales. •TheactivityoftheleadingpatternsofSHcirculationvariabilityhasa large influenceon the climate of South America, Africa, Antarctica andAustraliaͲNewZealand. •In particular, they seem to have a role explaining seasonal predictabilityatsubtropicalandextratropical regionsofSouth 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 Amazon Basin in South America in terms of geographical extent. •The principal sub-basins are those of the Parana, Paraguay and Uruguay rivers. •The La Plata Basin covers parts of five countries, AiArgentina, Bolivia, Brazil, 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 Andes • The winter 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 Chile and Argentina Santiago • 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 Oceanvariability (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, (gͲh) 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,theroleoftropicaloceansininducingtheSHteleconnections doesnotseemstobemoreimppy(yortantthantheinternalvariability.(only windowsofopportunity?Othersourcesofpredictability?) 23.
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