Transcript Southeastern South America

Dominant large-scale patterns
influencing the interannual
variability of
precipitation in South America
as depicted by IPCC-AR4
Models
Carolina Vera (1), Gabriel Silvestri (1),
Brant Liebmann (2), and Paula Gonzalez (1)
(1)
CIMA-DCAyO/UBA-CONICET, Buenos Aires, Argentina
(2) NOAA/CDC, Boulder, Colorado, USA
Objectives
1.
To describe the relative contributions of the leading modes of
variability of the atmospheric circulation in the SH to the
precipitation variance over southeastern South America (SESA) in
present climate (from reanalyses).
•
2.
3.
4.
5.
Main conclusions presented in 2004: AAO influences SESA precipitation
during winter and spring, PSA1 does it during spring and summer, while
PSA2 does it during summer and fall.
To assess the ability of the IPCC-AR4 models in reproducing the
precipitation variability in South America in present climate.
To investigate the ability of IPCC-AR4 models in reproducing the
main features of SH leading modes and their impact on South
America precipitation.
To diagnose variations of the activity of the leading modes of
atmospheric circulation on climate change scenarios.
to assess climate change scenarios of precipitation over South
America based on such variations.
Data and Methodology
Model Name
N° of
Runs
NCEP Reanalysis
CMAP precipitation
-
Meteo France CNRM
1
NOAA Geophysical Fluid Dynamics
Laboratory, CM2.0
3
GFDL
NASA/GODDARD Institute for Space
Studies, ModelE20/HYCOM
5
GISS
Institute Pierre Simon Laplace CM4
1
CSSR/NIES/FRGC, JAPAN,
MIROC3.2 Medium resolution
3
Max Planck Institute –ECHAM5
3
Meteorological Research Institute
Japan, CGM2.3.2a
5
UK Meteorological Office-HADCM3
2
Total Number of simulations
23
Acronym
OBS
CNRM
IPSL
MIROC
MPI
MRI
UKMO
•IPCC-AR4 20c3m runs were used for the period 1970-1999
•Anomalies were defined removing the seasonal cycle and the long-term trend.
• EOFs, correlation and regression maps were based on monthly mean anomalies
and calculatedd over the whole year.
•They were computed per individual run and then the results were averaged over all
the runs available for each model.
How well do IPCC-AR4
models represent basic
precipitation features in South
America?
Climatological means for precipitation over South America
JFM
MPI
GFDL
GISS
IPSL
MRI
CNRM
OBS
UKMO
MIROC
Climatological mean Standard Dev. for precipitation over South America
JFM
MPI
GFDL
GISS
IPSL
MRI
CNRM
OBS
UKMO
MIROC
Climatological means for precipitation over South America
JAS
MPI
GFDL
GISS
IPSL
MRI
CNRM
OBS
UKMO
MIROC
Climatological mean Standard Dev. for precipitation over South America
JAS
MPI
GFDL
GISS
IPSL
MRI
CNRM
OBS
UKMO
MIROC
How well do IPCC-AR4
models represent the leading
patterns on interannual
variability of the circulation in
the SH?
Leading Patterns of 500-hPa geop. height anomalies. Mode 1 (AAO)
OBS
MPI
GFDL
GISS
IPSL
UKMO
MIROC
MRI
CNRM
Leading Pattern 1 (AAO) & SST anomalies
OBS
MPI
GFDL
GISS
IPSL
UKMO
MRI
CNRM
MIROC
Leading Patterns of 500-hPa geop. height anomalies. Mode 2 (PSA1)
OBS
MPI
GISS
IPSL
MIROC
MRI
GFDL
UKMO
CNRM
Leading Pattern 2 (PSA1) & SST anomalies
OBS
MPI
GFDL
GISS
IPSL
UKMO
MIROC
MRI
CNRM
Leading Patterns of 500-hPa geop. height anomalies. Mode 3 (PSA2)
OBS
MPI
GFDL
GISS
IPSL
UKMO
MIROC
MRI
CNRM
Leading Pattern 3 (PSA2) & SST anomalies
OBS
GISS
MIROC
MPI
IPSL
MRI
GFDL
UKMO
CNRM
How well do IPCC-AR4
models represent precipitation
variability in Southeastern
South America?
Southeastern South
America (SESA)
(52ºW-65ºW ; 24ºS-31ºS)
Correlation Maps between SESA Precipitation and SST
anomalies
OBS
MPI
GFDL
GISS
IPSL
UKMO
MRI
CNRM
MIROC
SESA Precipitation anomalies & 500-hPa geop. height anomalies
OBS
MPI
GFDL
GISS
IPSL
UKMO
MIROC
MRI
CNRM
Preliminary conclusions (1)
• Model are able to reproduce some of the features of the leading
modes of SH circulation interannual variability (particularly those
associated with the AAO). Although the simulated anomalies exhibit
different amplitude and are somewhat misplaced than those
observed.
• The ability of the models in representing the 2nd and 3rd (PSA) SH
leading modes is related with their ability in reproducing ENSO
features and the circulation along the subpolar regions of the SH
influence.
• Although some improvements are observed, models still have some
deficiencies in representing the right amounts of precipitation and its
interannual variability over the Amazon basin, SACZ, and la Plata
Basin.
Preliminary conclusions (2)
• Most of the models are able to reproduce in someway the cycloneanticyclone circulation anomalies observed over South America in
association with interannual precipitation variability in SESA.
Nevertheless, just a few of them are able to represent the main
features of the associated circulation anomalies in the SH (annular
mode and wave-3 like patterns).
• UKMO, GFDL and MPI are the models that better depict the
climatological mean and standard deviations of precipitation
anomalies in South America, as well as the main features of the SH
circulation anomalies associated with precipitation variability in
SESA.
Climatological seasonal means of precipitation over
South America
ND
JF
MA
10
10
10
-10
-10
-10
-30
-30
-30
-50
-50
-50
-80
-60
-40
-20
0
-80
20
-60
-40
-20
0
20
-80
16
12
Spectral Density
-40
-20
0
20
16
43
14
SESA-BOX
(52ºW-65ºW ; 24ºS-31ºS)
-60
Interannual
Variability
36
63
10
8
14
12
10
8
8
6
6
8
4
8
4
72
7
2
6
Seasonal
Cycle
0
0,00
6
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
0,45
Frequency
Spectral Density
7
5
4
3
2
24
7
8
6
0
0,50
Interannual Variability
(ENSO removed)
5
4
5
4
3
3
2
2
1
1
2
0
0,00
1
0,05
0,10
0,15
0,20
0,25
Frequency
0
J
A
S
O
N
D
J
F
M
A
M
J
0,30
0,35
0,40
0,45
0
0,50
How do IPCC models
represent the ENSO signal in
the Southern Hemisphere?
Correlation between EN3.4 & SST anomalies
OBS
MPI
GFDL
GISS
IPSL
UKMO
MRI
CNRM
MIROC
EN3.4 Index & 500-hPa geopotential height anomalies
OBS
MPI
GFDL
GISS
IPSL
UKMO
MIROC
MRI
CNRM