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Transcript of Impacts of modes of climate variability, monsoons, ENSO ... · Impacts of modes of climate...
Impacts of modes of climate variability, monsoons, ENSO,
annular modes
Iracema Fonseca de Albuquerque Cavalcanti
National Institute for Space Research
INPE
Modes of variability- preferred patterns of variability .Can be associated with Sea Surface Temperature anomalies, large scale atmospheric circulation anomalies , convection anomalies in distant places
SST anomalies
Upper level circulation
Convection anomalies
How to identify?Correlation analyses
Y: Position of point P
X: all the other points
P (0, 120W)
covariance
Standard deviation
EOF analysis
• Anomalies in a domain during a period
• Apply the method
• Get: % of variance related to the total variance
• The dominant modes will be represented by the modes with large % of variance
• The timeseries of the amplitude of each mode.
Modes of variability can be related toteleconnections
• Relation between variability between distantplaces tele: distant connection: relation
Example: Southern Oscillation
Low pressureIndonesia
High pressureEastern Pacific
El Nino- Southern Oscillation (ENSO)
El NinoAnomalous convection over central and eastern Pacific
Normal or La Nina
El Nino years
Ropelewski and Halpert 1986
La Nina years
Global influences
El Nino Canonical and El Nino Modoki+A
Tedeschi et al. 2012
Canonical Modoki+A Impact over South AmericaCanonical Modoki+A
Polade et al.2013CMIP5 JFM
EN and LN intensity
Collins et al. 2010
AR5
Changes in Pacific SST
EL Nino
Projection changes CMIP3
CMIP5
Collins et al 2010
Standard deviation of SLP (SOI)
Standard deviation of Nino 3 SST
Variability of ENSO in models
SST Changes
Shading: historicalContour: future projection rcp8.5
220 c
260 c
SON280 c
Modes of variability in the Northern Hemisphere
• North Atlantic Oscilation NAO
• Pacific North America PNA
• Northern Annular mode NAM
• Multidecadal Oscillations: Pacific (PDO or PMO)
• Atlantic (AMO)
NAO
NAO influences
North Caroline State University
Teleconnections in the N.H.
Wallace e Gutzler 1981Horel and Wallace 1981
PNA influences
North Caroline State University
Simulated PNA BESM
Nobre et al. 2013
Annular modes
Geo at 850 hPaGeo at 1000 hPa hPa
zonal wind
Thompson and Wallace, 2000
Simulation of North Annular Mode
Cattioux and Cassou 2013
Main Modes of variability in the Southern Hemisphere
Pacific South America (PSA) pattern
Southern Annular Mode (SAM)
How anomalies in tropical regions can be connected to anomalies in a distant place?
A
A
A
equator
Rossbywavetrains
SST anomalies
divergence Anticycloniccirculation
Low pressureAscent
Convection
What is the influence of this patternin Brazil and South America?
Correlation aolr SACZ X v
South Atlantic Convergence ZoneSACZ
Simulation PSA BESM
Nobre et al.2013
Representation of the 2 modes in modelsimulation
HadGEM2-ES historical
SAM
PSA
Changes in the Pacific South America pattern due to changes in Pacific SST : influences over
southeastern SA
Cavalcanti and Shimizu 2013
HADGEM2-ES
Simulation Projection
Zonal anomaly: PSA EOF1
Junquas et al 2013
2001-2049
2050-2098
8 models CMIP3
Changes in the South Atlantic Convergence ZoneIntensification of the southern center
HadGEM2-ES
Cavalcanti and Shimizu 2013
historical Rcp 8.5
Also in Junquas et al 2013
Southern Annular Mode
Opposite anomalies between polar region and middle latitudes
JAN JUL
Positive phase: Strong polar jet and weak subtropical jet
Negative phase: Weak polar jet and strong subtropical jet
Vasconcellos and Cavalcanti (2009)
DJF Precipitation
INFLUENCES OVER SOUTH AMERICA
200 hPa
200 hPa
PSA type pattern
Ageo 500 hPaWET
DRY
Increase of the positive phase of SAM
Indicates a southward displacement of the stormtracks
Sea Level Pressure (2081-2100)- (1986-2005)
Changes in storm tracks
Stippling marks locations where at least 90% of the
models agree on the sign of the change.
N.H. DJF
S.H. JJA
Storm track densitty
Rcp 4.5 Rcp 8.5
AR5
Projected Changes in the jet streams
1980 to 1999 to 2081–2100 in zonal mean CMIP5
AR5
37 CMIP5 models’ merged historical and RCP 4.5 simulations (black) for each seasonColored lines show observational annular mode indices. Simulated anomalies are shown relative to an 1861–1900 climatology.
Gillet&Fyfe 2013.
Projected changes in NAO, NAM, SAM
changes between 1861–1910 and 2050–2099 for each season in the individual CMIP5models and in the multi-model ensemble mean (bottom bars).
Gillet&Fyfe 2013.
Projected changes for NAM and SAM
Observed trends in PNA and PSA
AR5
MONSOONS
AR5, Kitoh et al 2013
NH:May to September SH: November to March
Precipitation changes (2080-2099)- (1986-2005)
Precipitation changes (2080-2099)- (1986-2005)
Between (1986–2005) and the future (2080–2099) in the RCP8.5 scenario.
Changes in vertically integrated water vapor flux and convergence
Kitoh et al. 2013
American Monsoon regions Precipitation Difference (DJF-JAS) or
(JJA-DJF)>= 2.0 mm/dayGPCP
HADGEM2
Carvalho and Cavalcanti, 2015
Changes in the American Monsoon
Carvalho and Cavalcanti, 2015
Changes in the humidity fluxChanges in the area of monsoon
AR5
Global features
Summary
• Main modes of variability- ENSO, NAO, PNA, PSA, NAM, SAM
• The models can represent the patterns• Projections: Large uncertainty in NAO but slight
trends to positive phase• Trend to positive phase in NAM and SAM• Poleward displacement of storm tracks• Observed positive trend in PNA and negative
trend in PSA• Changes in the position and intensity of PSA
centers