The 2010 South-Western Hemisphere workshop series on Climate Change: CO2, the Biosphere and Climate SMR (2175) Low-Frequency Climate variability in the Southern Hemisphere Carolina Vera CIMA/Departamento de Ciencias de la Atmósfera y los Océanos Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires

Why is it important to understand climate variability in the context of climate change? 2

Motivation 3 (Grey) Annual mean precipitation anomalies (mm/year) (Red) Filtered precipitation anomalies (10-20 years) (green) Filtered precipitation anomalies (20-35 years) (blue) Filtered precipitation anomalies (> 35 years) (black) Linear trend Vera & Silvestri (2010) Low- Frequency Precipitation anomaly variability in the city of Buenos Aires

4 CLIMATE SYSTEM Atmospheric heating

5 Atmosphere cooling is mostly due to long wave radiation, that is affected by air moist and its cloudiness Most of the solar energy reaching the surface goes to evaporate water Water vapor in the atmosphere acts as a means of storing heat which can be released later Atmosphere exchanges (sensible and latent) heat with the ground and ocean surface As the air circulates, it may rise, cool and become saturated. Water vapor condensation releases large amounts of latent heat

6 DJF JJA Zonal mean heating ERA-40 Atlas

7 JJA Zonal mean meridional circulation DJF ERA-40 Atlas

8 DJF JJA Zonal mean wind ERA-40 Atlas Subtropical Jet Eddy-driven or Subpolar Jet

9 Vertically integrated mean heating DJF JJA ERA-40 Atlas

10 DJF JJA Vertically integrated mean moisture fluxes with their convergence ERA-40 Atlas

11 JJA Mean vertical wind (500 hPa) Absolute vorticity and 200-hPa divergent wind ERA-40 Atlas

12 Wind vector and isotachs (200 hPa) JJA ERA-40 Atlas DJF Subtropical Jet Eddy- driven or Subpolar Jet

13 ERA-40 Atlas DJF JJA Mean Surface Temperature

Sea-level pressure 14 ERA-40 Atlas Annual Mean Year-to- Year Variability

500-hPa Geopotential Heights 15 ERA-40 Atlas Annual Mean Year-to-Year Variability

16 The Extended Orthogonal Function Technique In the last several decades, major efforts in extracting important patterns from measurements of atmospheric variables have been made. One of the most common techniques is the Empirical Orthogonal Function (EOF) technique. EOF aims at finding a new set of variables that capture most of the observed variance from the data through a linear combination of the original variables. Kutzbach, J. E., 1967: Empirical eigenvectors of sea-level pressure, surface temperature and precipitation complexes over North America. J. Appl.Meteor., 6, von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climateresearch, Cambridge University Press, Cambridge

17 Leading patterns of year-to-year variability of the circulation in the SH (Mo, J. Climate, 2000) Southern Annular Mode (SAM) (27%) Pacific-South American Pattern (PSA, PSA1) (13%) South Pacific Wave Pattern (SPW, PSA2) (10%)

Rossby Waves 18

19 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

SAM Phases 20 SAM (+) Negative pressure anomalies at polar regions Intensified westerlies SAM (-) Positive pressure anomalies at polar regions Weakened westerlies

Southern Annular Mode (SAM) Correlations between SAM index and precipitation anomalies for OND (79- 99). (Silvestri and Vera, 2003) Regression of SAM index of (top) precipitation and (bottom) surface temperature anomalies. (Gupta et al. 2006) Surface temperature

22 Pacific South American (PSA, PSA1) Pattern (Mo, J. Climate, 2000) Second leading pattern of year-to-year variability of the circulation in the SH Dominant interannual variability (~5 years) Strongly influenced by El Niño-Southern Oscillation (ENSO) Regression (PSA, SST’) PSA & ENSO Index

El Niño-Southern Oscillation (ENSO) OND ( ) Correlations between ElNino3.4 SST anomalies and (left) precipitation and (right) 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded. NCEP reanalysis data. (Vera and Silvestri, 2009)

24 South-Pacific Wave or PSA2 Pattern (Mo, J. Climate, 2000) Third leading pattern of year-to-year variability of the circulation in the SH Dominant quasi-biennial variability (~2 years) Strongly influenced by tropical Indian Ocean variability

Indian-Ocean Dipole (IOD) 25 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)

Decadal Variability of the ENSO Teleconnection hPa geopotential height anomaly ENSO composites (El Niño minus La Niña) for: (a) SON 1980s, (b) SON 1990s Fogt and Bromwich (2006)

27 Decadal and inter-decadal oscillations Interannual ENSO variability in the tropical Pacific Decadal variability in the Pacific (Dettinger et al. 2001)

28 Decadal Variability in SST anomalies (Dettinger et al. 2001) Correlation maps between SST anomalies and ENSO (top) and Decadal (bottom) Indexes

29 Decadal variability signature in circulation anomalies Regression maps linking 500-hPa Z’ to (left) ENSO and (bottom) 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 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)

Inter-decadal variations of SAM signal on South America Climate Correlations SAM index-SLP and regressions SAM index-WIND850. Areas where correlations are statistically significant at the 90% (95%) of a T-Student test are shaded in light (dark) grey. (Silvestri and Vera 2009)

Climate Variability and Climate Change 32

C Surface temperature trends ( ) 33

Surface temperature trends (Marshall et al. 2006) Change in annual and seasonal—autumn: March–May (MAM), winter: June–August (JJA), spring: September–November (SON), and summer: December–February (DJF)—near-surface temperature coincident with the positive trend in the SAM that began in the mid-1960s. Units are °C decade1. Values are shown if the significance level of the trend is at the 1%, 5%, or 10% level. 34

Annual and seasonal SAM trends ( ). Units: 1/decade. *: significative trends (< 1%) SAM Trends (Marshall et al. 2006) SAM index computed from in situ observations (solid line, 12- month running mean). (Marshall 2003) 35

Contribution of the SAM to temperature changes in the Antarctic Peninsula (Marshall et al. 2006) Contribution of the SAM to annual and seasonal temperature changes per decade and the percentage of total near-surface temperature change (in parentheses) caused by the positive trend in the SAM [1965–2000]. Temperature increases are in °C/ decade. Negative percentage values indicate that SAM-related temperature changes are opposite to the overall observed change.. 36

C8.37 MSLP difference between the warmest and coolest third of summers at Esperanza based on detrended data from 1979 to Units are hPa. (Marshall et al. 2006) 37

38 Coupled model experiments for IPCC-AR4: WCRP CMIP3 Multi-Model Dataset The Intergovernmental Panel on Climate Change (IPCC) was established by the World Meteorological Organization and the United Nations Environmental Program to assess scientific information on climate change. The IPCC publishes reports that summarize the state of the science (and currently working in the Fourth Assessment Report, AR4)Intergovernmental Panel on Climate Change (IPCC) In response to a proposed activity of the World Climate Research Programme's (WCRP's), (~20)leading modeling centers of the world performed simulations of the past, present and future climate, that were collected by PCMDI mostly during the years 2005 and 2006,World Climate Research Programme's This archived data was also made available to any scientist outside the major modeling centers to perform research of relevance to climate scientists preparing the AR4 of the IPCC. This unprecedented collection of recent model output is officially known as the "WCRP CMIP3 multi-model dataset." It is meant to serve IPCC's Working Group 1, which focuses on the physical climate system -- atmosphere, land surface, ocean and sea ice. As of February 2007, over 32 terabytes of data were in the archive and over 171 terabytes of data had been downloaded among the more than 1000 registered users. Over 200 journal articles, based in part on the dataset, have been published.

C8.39 SAM representations in the WCRP/CMIP3 simulations for IPCC- AR4 (Miller et al. 2006) 39

C8.40 SAM evolution during XX century from obs and WCRP/CMIP3 models (Miller et al. 2006) 40

Ensemble mean sea level pressure trends (hPa 30 yr1) for the period of 1958–99 of the (a) volcanic, (b) solar, (c) GHGs, (d) sulfate aerosols, (e) ozone, and (f) all-forcings simulations from the PCM. (Arblaster and Meehl 2006) Contributions of External Forcings to Southern Annular Mode Trends 41

1980 Now ~ 2100 Ozone-depleting chlorine and bromine in the stratosphere Global ozone change Ultraviolet radiation change Ozone recovery and climate change 2006 Scientific Assessment of Ozone Depletion Stratospheric Cl and Br O3O3  UV 42

Ozone depletion Ozone recovery ∆O 3 ∆T ∆u Ozone recovery will induce a positive trend in the Southern Annular Mode Perlwitz et al. (2008 GRL)

OND ( ) Correlations between ENSO index and 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded. ENSO signal in SH Circulation anomalies from WCRP/CMIP3 models (Vera and Silvestri 2009) OBS

OND ( ) Correlations between ENSO index and precipitation anomalies. Significant values at 90, 95 and 99% are shaded. ENSO signal in South America precipitation anomalies from WCRP/CMIP3 models (Vera and Silvestri 2009) OBS

Conclusions Signals associated with natural climate variability on interannual, decadal and interdecadal timescales are large in the climate of the Southern Hemisphere. At regional scales they can even be larger than the long-term trends. Therefore, such signals produce a strong modulation of the climate change signal that needs to be taken in consideration. Current climate models are able to qualitatively represent many of the fundamental elements of the climate mean and variability in the Southern Hemisphere However, models formulations are still limited to represent all the physical mechanisms related to the natural modes of variability. Therefore, uncertainties associated to climate change projections are still considerable large. Progress can be expected in the near future from the use of decadal climate predictions that are currently being made for IPCC AR5. 46