Southern Hemisphere Teleconnections and Climate predictability Carolina Vera CIMA/CONICET University of Buenos Aires, UMI IFAECI/CNRS Buenos Aires, Argentina Motivation Large scale circulation variability in the Southern Hemisphere (SH) exhibit large values at middle and high latitudes, particularly in the South Pacific sector, and from subseasonal and from subseasonal to interannual and and decadal time scales. The activity of the leading patterns of SH circulation variability has a large influence on the climate of South America, Africa, Antarctica climate of Africa, Antarctica and Australia New Zealand. In particular they seem to have a role explaining seasonal In particular, they seem to have a role explaining seasonal predictability at subtropical and extratropical regions of South America.
Seasonal predictability in South America JJA DJF Surface Temperature Ensemble of 13 models of WCRP/WGSIP/CHFP Database Precipitation Osman and Vera (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, Argentina, 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 adjacent lowlands of Chile and Argentina Over 10 million people p depend, either directly or indirectly, on this freshwater for domestic consumption irrigation industries hydroelectric generation Santiago Mendoza
Leading patterns of year to year variability of the circulation in the SH Southern Annular Mode (SAM) (27%) Pacific South American Pattern (PSA, PSA1) (13%) South Pacific Wave Pattern (SPW, PSA2) (10%) (13%) (10%) (Mo, J. Climate, 2000) SOUTHERN ANNULAR MODE (SAM) First leading pattern of year to year variability of the circulation in the SH Dominant variability on interannual timescales (~5 years). Large trend. Mainly maintained by the atmospheric internal variability 8
Southern Annular Mode (SAM) Surface temperature Regression of SAM index of (top) precipitation and (bottom) of index of precipitation and (bottom) surface temperature anomalies. (Gupta et al. 2006) Correlations between SAM index and precipitation anomalies for OND (79 99). (Silvestri and Vera, 2003) Pacific South American (PSA, PSA1) Pattern Second leading pattern of year to year di variability of the circulation in the SH Dominant interannual variability (~5 years) Strongly influenced by El Niño Southern Oscillation (ENSO) PSA & ENSO Index Regression (PSA, SST ) (Mo, J. Climate, 2000)
El Niño Southern Oscillation (ENSO) OND (1979 1999) ( 999) Correlations between ElNino3.4 SST anomalies and (left) precipitation and (right) p 500 hpa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded. NCEP reanalysis data. (Vera and Silvestri, 2009) South Pacific Wave or PSA2 Pattern Third leading pattern of year year to year year variability of the circulation in the SH Dominant quasi biennial variability (~2 years) Strongly influenced by tropical Indian Ocean variability (Mo, J. Climate, 2000)
Indian Ocean Dipole (IOD) SST anomaly pattern associated with IOD activity Circulation anomaly pattern associated with IOD activity Rain & Wind anomaly patterns associated with IOD activity Chen et al. (2008) Role of SST Forcing on SH Teleconnections Simulated 500 hpa geopotential height anomalies from 10 member ensembles performed with the member ensembles the SPEEDY Model from 1958 2006 under different forcing. CONTROL (climatological mean SST) GOGA (global observed SST) POGA (Pacific Ocean observed SST) IOGA (Indian Ocean observed SST) AOGA(Atlantic Ocean observed SST) O Vera, Silvestri and Barreiro (2013)
Variance of 500 hpa geopotential height anomalies NCEP Obs CONTROL GOGA 100 200 300 400 500 600 700 800 900 1000 Vera, Silvestri and Barreiro (2013) Signal Noise Predictability Vera, Silvestri and Barreiro (2013)
Vera, Silvestri and Barreiro (2013) Predictability Analysis of 500 hpa geopotential height anomalies From a multi model multi member ensemble of 15 models from multi model multi member ensemble of models from WCRP/WGISP/CHFP DJF JJA Signal Noise Signal Noise 200 hpa 500 hpa 850 hpa (Osman and Vera,2013)
Predictability Analysis of 500 hpa geopotential height anomalies 200hPa From a multi model multi member ensemble of 15 models from multi model multi member ensemble of models from WCRP/WGISP/CHFP DJF Predictability (from all years) Predictability from ENSO years 500hPa 850hPa (Osman and Vera,2013) 200 hpa Changes in predictability in ENSO years From a multi model multi member ensemble of 15 models from WCRP/WGISP/CHFP DJF Pred_AY/Pred_ENSO= S enso /S*N/N S enso /S N/N enso enso 500 hpa 850 hpa (Osman and Vera,2013)
Decadal variability signature in SH circulation anomalies Regression maps linking 500 hpa Z to (left) ENSO and (bottom) Pacific Decadal Indexes (Dettinger et al. 2001) Non stationary impacts of SAM on SH climate Correlations of the SAMindex with (a b) in situ precipitation, (c d) in situ SLP, (e f) reanalyzed SLP, (g h) reanalyzed Z500, and (i j) in situ surface temperature. Correlations and (i in surface temperature statistically significant at the 90% and 95% of a T Student test are shaded. Grey dots in cases of in situ observations indicate stations with no significant correlation. (Silvestri & Vera 2009)
Concluding remarks Teleconnections in the SH as forced by tropical ocean conditions contribute to enhance mean potential predictability at middle and high latitudes. Predictabiliy is increased during ENSO years particularly over the South Pacific, and mainly associated to an increased signal. Predictability in southeastern South America and southern Andes seem in and southern Andes seem to be largely explained by the teleconnections. However, the fact that models still present deficiencies in the teleconnection representation, forecast skills there are still low. Strategies to improve it are needed There is potential gain in seasonal forecast skill in the Southern in seasonal skill in the Hemisphere if the models can capture the atmospheric teleconnections. However, the role of tropical oceans in inducing the SH teleconnections does not seems to be more important than the internal variability. (only y windows of opportunity? Other source s of predictability?) 23