Transcript Carolina Vera - Universidad de Buenos Aires

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
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Why is it important to
understand climate
variability in the context of
climate change?
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Motivation
LowFrequency
Precipitation
anomaly
variability in
the city of
Buenos Aires
(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)
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Atmospheric heating
CLIMATE SYSTEM
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Atmosphere cooling is mostly due to long wave radiation, that is
affected by air moist and its cloudiness
As the air circulates, it may rise, cool and become saturated. Water
vapor condensation releases large amounts of latent heat
Water vapor in the atmosphere acts as a means of storing heat
which can be released later
Most of the solar energy reaching the surface goes to evaporate
water
Atmosphere exchanges (sensible and latent) heat with the
ground and ocean surface
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Zonal mean heating
DJF
JJA
ERA-40 Atlas
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Zonal mean meridional circulation
v,    k x
DJF
JJA
ERA-40 Atlas
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Zonal mean wind
DJF
Eddy-driven
or
Subpolar
Jet
Subtropical
Jet
JJA
ERA-40 Atlas
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Vertically integrated mean heating
DJF
JJA
ERA-40 Atlas
Vertically integrated mean moisture fluxes
with their convergence
DJF
JJA
ERA-40 Atlas
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Mean vertical wind (500 hPa)
Absolute vorticity and 200-hPa divergent wind
JJA
ERA-40 Atlas
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Wind vector and isotachs (200 hPa)
DJF
Subtropical
Jet
Eddydriven or
Subpolar
Jet
JJA
ERA-40 Atlas
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Mean Surface Temperature
DJF
JJA
ERA-40 Atlas
Sea-level pressure
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Annual
Mean
Year-toYear
Variability
ERA-40 Atlas
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500-hPa Geopotential Heights
Annual Mean
Year-to-Year Variability
ERA-40 Atlas
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.
K
Q´( x, y, t )   EOFk ( x, y )  PCk (t )
k 1
•
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
16
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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%)
(Mo, J. Climate, 2000)
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Rossby Waves
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
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SAM Phases
SAM (+)
Negative pressure
anomalies at polar regions
Intensified westerlies
SAM (-)
Positive pressure anomalies
at polar regions
Weakened westerlies
Southern Annular Mode (SAM)
Surface temperature
Regression of SAM index of (top) precipitation and (bottom)
surface temperature anomalies.
(Gupta et al. 2006)
Correlations between SAM
index and precipitation
anomalies for OND (7999).
(Silvestri and Vera, 2003)
Pacific South American (PSA, PSA1) Pattern
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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)
PSA & ENSO Index
Regression (PSA, SST’)
(Mo, J. Climate, 2000)
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
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
(Mo, J. Climate, 2000)
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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)
Decadal Variability of the ENSO
Teleconnection
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)
Decadal Variability in SST anomalies
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Correlation maps
between SST
anomalies and
ENSO (top) and
Decadal
(bottom) Indexes
(Dettinger et al. 2001)
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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)
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Climate Variability and Climate
Change
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Surface temperature trends (1951-2006)
C8.33
http://www.antarctica.ac.uk/met/gjma/temps.html
Surface temperature trends
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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 .
(Marshall et al. 2006)
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SAM Trends
SAM index
computed from
in situ
observations
(solid line, 12month running
mean).
(Marshall 2003)
Annual and seasonal SAM trends (1965-2000). Units:
1/decade. *: significative trends (< 1%)
(Marshall et al. 2006)
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Contribution of the SAM to temperature
changes in the Antarctic Peninsula
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..
(Marshall et al. 2006)
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MSLP difference between the
warmest and coolest third of
summers at Esperanza
based on detrended data from
1979 to 2000. Units are hPa.
C8.37
(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
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SAM representations in the WCRP/CMIP3 simulations for IPCCAR4
(Miller et al. 2006)
C8.39
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SAM evolution during XX century from obs and WCRP/CMIP3 models
(Miller et al. 2006)
C8.40
Contributions of External Forcings to Southern Annular Mode Trends
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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)
Ozone-depleting
chlorine and bromine
in the stratosphere
Ozone recovery and climate change
Stratospheric
Cl and Br
2006 Scientific
Assessment of
Ozone Depletion
Global ozone
change
O3
Ultraviolet radiation
change
UV
1980
Now
~ 2100
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Ozone depletion
1969-1999
Ozone recovery
2006-2094
∆O3
∆T
Ozone recovery will induce a
positive trend in the Southern
Annular Mode
∆u
Perlwitz et al. (2008 GRL)
ENSO signal in SH Circulation anomalies from WCRP/CMIP3 models
OBS
OND (1970-1999)
Correlations between ENSO index and 500-hPa geopotential height
anomalies. Significant values at 90, 95 and 99% are shaded.
(Vera and Silvestri 2009)
ENSO signal in South America precipitation anomalies from WCRP/CMIP3 models
OBS
OND (1970-1999)
Correlations between ENSO index and precipitation anomalies. Significant
values at 90, 95 and 99% are shaded.
(Vera and Silvestri 2009)
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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.