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?

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Motivation3

(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

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CLIMATE SYSTEM

Atmospheric heating

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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

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DJF

JJA

Zonal mean heating

ERA-40 Atlas

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JJA

Zonal mean meridional circulation xkv,

DJF

ERA-40 Atlas

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DJF

JJA

Zonal mean wind

ERA-40 Atlas

Subtropical Jet

Eddy-driven or

Subpolar

Jet

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Vertically integrated mean heating

DJF

JJA

ERA-40 Atlas

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DJF

JJA

Vertically integrated mean moisture fluxes with their convergence

ERA-40 Atlas

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JJA

Mean vertical wind (500 hPa)

Absolute vorticity and 200-hPa divergent wind

ERA-40 Atlas

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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 pressure14

ERA-40 Atlas

Annual Mean

Year-to-Year

Variability

500-hPa Geopotential Heights15

ERA-40 Atlas

Annual Mean Year-to-Year Variability

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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, 791-802.

von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climateresearch, Cambridge University Press, Cambridge

K

kkk tPCyxEOFtyxQ

1

)(),(),,´(

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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 Waves18

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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 Phases20

SAM (+)Negative pressure

anomalies at polar regionsIntensified westerlies

SAM (-)Positive pressure anomalies

at polar regionsWeakened 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

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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 (1979-1999)

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)

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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

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500-hPa geopotential height anomaly ENSO composites (El Niño minus La Niña) for: (a) SON 1980s, (b) SON 1990s

Fogt and Bromwich (2006)

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Decadal and inter-decadal oscillations

Interannual ENSO variability in the tropical Pacific

Decadal variability in the Pacific

(Dettinger et al. 2001)

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Decadal Variability in SST anomalies

(Dettinger et al. 2001)

Correlation maps between SST anomalies and ENSO (top) and Decadal (bottom) Indexes

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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

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C8.33http://www.antarctica.ac.uk/met/gjma/temps.html

Surface temperature trends (1951-2006) 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.

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Annual and seasonal SAM trends (1965-2000). 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)

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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..

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C8.37

MSLP difference between the warmest and coolest third of summers at Esperanzabased on detrended data from 1979 to 2000. Units are hPa.

(Marshall et al. 2006)

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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)

• 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,

• 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.

http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php 

C8.39

SAM representations in the WCRP/CMIP3 simulations for IPCC-AR4

(Miller et al. 2006)

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C8.40

SAM evolution during XX century from obs and WCRP/CMIP3 models

(Miller et al. 2006)

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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 Trends41

1980 Now ~ 2100

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Ozone recovery and climate change

2006 Scientific Assessment of Ozone Depletion

Stratospheric Cl and Br

O3

UV

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Ozone depletion 1969-1999

Ozone recovery 2006-2094

∆O3

∆T

∆u

Ozone recovery will induce a positive trend in the Southern

Annular Mode

Perlwitz et al. (2008 GRL)

OND (1970-1999)

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 (1970-1999)

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.

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