Dominant large-scale patterns influencing the interannual variability of precipitation in South...
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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
Dominant large-scale patterns influencing the interannual variability of precipitation in South America as depicted by IPCC-AR4 Models Carolina Vera (1),
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
Slide 2
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). 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. 2.To assess the ability of the IPCC-AR4 models in
reproducing the precipitation variability in South America in
present climate. 3.To investigate the ability of IPCC-AR4 models in
reproducing the main features of SH leading modes and their impact
on South America precipitation. 4.To diagnose variations of the
activity of the leading modes of atmospheric circulation on climate
change scenarios. 5.to assess climate change scenarios of
precipitation over South America based on such variations.
Objectives
Slide 3
Data and Methodology 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. Acronym
Model NameN of Runs OBS NCEP Reanalysis CMAP precipitation - CNRM
Meteo France CNRM1 GFDL NOAA Geophysical Fluid Dynamics Laboratory,
CM2.0 3 GISS NASA/GODDARD Institute for Space Studies,
ModelE20/HYCOM 5 IPSL Institute Pierre Simon Laplace CM41 MIROC
CSSR/NIES/FRGC, JAPAN, MIROC3.2 Medium resolution 3 MPI Max Planck
Institute ECHAM53 MRI Meteorological Research Institute Japan,
CGM2.3.2a 5 UKMO UK Meteorological Office-HADCM32 Total Number of
simulations 23
Slide 4
How well do IPCC-AR4 models represent basic precipitation
features in South America?
Slide 5
OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological means for
precipitation over South America JFM
Slide 6
OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological mean
Standard Dev. for precipitation over South America JFM
Slide 7
OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological means for
precipitation over South America JAS
Slide 8
OBS MPIIPSLGISSGFDL MIROCMRIUKMOCNRM Climatological mean
Standard Dev. for precipitation over South America JAS
Slide 9
How well do IPCC-AR4 models represent the leading patterns on
interannual variability of the circulation in the SH?
Slide 10
Leading Patterns of 500-hPa geop. height anomalies. Mode 1
(AAO) OBS MPI IPSL GISS GFDL MIROCMRI UKMO 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 2 nd and 3 rd (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.
Slide 21
Preliminary conclusions (2) Most of the models are able to
reproduce in someway the cyclone- anticyclone 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.
Slide 22
Slide 23
Climatological seasonal means of precipitation over South
America SESA-BOX (52W-65W ; 24S-31S) Seasonal Cycle Interannual
Variability Interannual Variability (ENSO removed)
Slide 24
How do IPCC models represent the ENSO signal in the Southern
Hemisphere?