Transcript Analysis of extreme events variability and quantification of model

Matilde Rusticucci, Olga Penalba
Assistant Researchers: Mariana Barrucand, María Laura Bettolli
Post-Doc: Bárbara Tencer, Madeleine Renom,
PhD Students: Federico Robledo, Natalia Zazulie, Juan Rivera, Vanesa
Pántano, Gustavo Almeira
Laboratorio de Extremos Climáticos de Sudamérica
Departamento de Ciencias de la Atmósfera y los Océanos- FCENUniversidad de Buenos Aires / CONICET
OCTUBRE
TX10
NOVIEMBRE
October - March
TX10
DICIEMBRE
TX10
ENERO
TX10
FEBRERO
TX10
Extreme Temperatures
ETCCDI
Linear trend 1959-2003
MARZO
TX10
Cold Days
MAX TEMP 10th perc.
Warm Days
MAX TEMP 90th perc.
TX90
ABRIL
TX90
MAYO
TX90
TX90
April-September
JUNIO
JULIO
TX90
AGOSTO
TX90
SETIEMBRE
Cold Days
MAX TEMP 10th perc.
TX10
TX10
TX10
TX10
TX10
TX10
Warm Days
MAX TEMP 90th perc.
TX90
TX90
TX90
TX90
TX90
TX90
Barrucand, PhD thesis
TEMPORAL VARIABILITY
Tucumán
November
WET CONDITION
December
Monthly accumulated extreme rainfall
greater than 75th daily percentile .
Salta
DRY CONDITION
Annual Amount of Dry days Index
Penalba, Bettolli; Robledo; Rivera; . Pántano
Observed Changes in Return Values of Annual Temperature Extremes over
Argentina Matilde Rusticucci And Bárbara Tencer Journal Of Climate Volume 21
HTn 25ºC
HTx 40ºC
1 to 5
5 to 10
10 to 50
50 to 100
>
100toyears
100
4000000
1 to 5
5 to 10
10 to 50
50 to 100
>
100toyears
100
4000000
LTn -5ºC
LTx 6ºC
1 to 5
5 to 10
10 to 50
50 to 100
>
100toyears
100
3000000
1 to 5
5 to 10
10 to 50
50 to 100
>
100toyears
100
200000
Spatial distribution of return periods - 1956-2003
GEV
observed (black - - -)
ERA-40 (solid black ) and
GCMs.
GEV
1981-1999( - - -)
2010-2040(solid )
Covariability between daily intensity of extreme rainfall
(DIER) and Sea Surface Temperature
Second mode 17%
(Singular Value Descomposition)
Austral Spring
SON
DIER correlation of the second mode
-30
-40
-50
SVD2 17%
-70
-60
0.35 to 1
0.26 to 0.35
0.2 to 0.26
0.01 to 0.2
-0.01 to 0.01
-0.2 to -0.01
-0.26 to -0.2
-0.35 to -0.26
-1 to -0.35
De-trended annual time-series (blue) and
smoothed with a 10-year running mean
(red) of indices
Interdecadal changes in the relationship between extreme temperature events in Uruguay
and the general atmospheric circulation.Madeleine Renom , Matilde Rusticucci , Marcelo Barreiro
accepted in Climate Dynamics, 2011)
Summer
1946-1975
1976-2005
Cold nights
Warm nights
1946-1975
1976-2005
Cold days
Warm days
Regressions maps of TN10 onto, for summer.
SLPa
the negative phase of the SAM is
associated with more frequent cold
nights
No relationship at all with the SAM.
1949-1975
1976-2003
vector wind at 925 hPa.
vector wind at 200 hPa
Models overestimate
Frost Days
1961-2000
mean
Figure 2: The same as Figure 1 except for FD: number of days where the minimum temperature was below 0ºC
R10
1961-2000
mean
Models overestimate
Models underestimate
Figure 3: The same as Figure 1 except for R10: number of days where the precipitation was over 10 mm/day.
SE Regional Mean f or tn90
25
20
TN90
%
15
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
10
5
0
1960
1965
1970
1975
1980
1985
1990
1995
2000
SE Regional Mean f or r10
60
55
50
45
Days
40
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
R10
35
30
25
20
15
10
1960
1965
1970
1975
1980
1985
1990
1995
SE Regional Mean f or cdd
CDD
70
60
50
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
Days
40
2000
30
20
10
0
1960
1965
1970
1975
1980
1985
1990
1995
2000
Caixa2 Regional Mean f or tn90
22
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
20
18
16
TN90
%
%
14
12
10
8
6
4
1960
1960
1965
1965
1970
1975
1970
1975
1980
1980
1985
1985
1990
1995
1990
1995
2000
2000
Caixa2 Regional Mean f or r10
60
55
50
Days
Days
45
40
35
30
25
20
15
10
1960
1960
1965
1965
1970
1970
1975
1975
1980
1980
1985
1985
1990
1990
1995
1995
2000
2000
Caixa2 Regional Mean f or cdd
140
120
100
Days
Days
R10
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
80
CDD
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
60
40
20
0
1960
1965
1970
1975
1980
1985
1990
1995
2000
Caixa3 Regional Mean f or tn90
22
20
18
16
%
%
14
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
TN90
12
10
8
6
4
2
1960
1960
1965
1970
1965
1975
1970
1980
1975
1985
1980
1990
1985
1995
2000
1990
1995
2000
Caixa3 Regional Mean f or r10
80
70
Days
Days
60
50
R10
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
40
30
20
10
1960
1960
1965
1965
1970
1975
1970
1980
1975
1985
1980
1990
1985
1995
1990
2000
1995
Caixa3 Regional Mean f or cdd
90
80
70
Days
Days
60
Stations Kriged
Stations
MIROC
GFDL0
GFDL
CCSM
CNRM
INM
MIRMED
PCM
CDD
50
40
30
20
10
0
1960
1960
1965
1965
1970
1970
1975
1975
1980
1980
1985
1985
1990
1995
1990
2000
1995
2000
Daily circulation patterns in Southern South America
Observed Circulation Types (CT) and percentage of days
corresponding to each group during austral summer. DJF
-15
-15
-20
-20
-25
-25
-30
-30
-35
-35
Latitude
Latitude
CT2
-40
-40
-45
-45
-50
-50
-55
-60
-90
CT4
-55
26.8%
-85
-80
-75
-60
-90
-70
-65
-60
-55
-50
19%
-85
-80
-75
-45
-70
-65
-60
-55
-50
-45
Longitude
Longitude
highest contribution to heavy rainy
days in the Pampas (blue square)
highest contribution to dry days
in the Pampas (blue square)
Daily mean sea level pressure (SLP) fields. 1979-1999.
Evaluation of the capacity of a set of GCMs to
reproduce these atmospheric structures
GCMs
Letter
CT 2
0
-15
1200
0.1 0.2
0.3
C
0.4
o
0.5
-20
r
r
0.6
CT2
e
l
n
Standard Deviation
0.8
0.9
5 00
S
D
400
-40
0.95
J
L
G
K
F
C
D
0.99
1
Ref
26.8%
A
B
C
D
E
F
G
H
I
J
K
L
CT 4
-65
-60
-15
-50
-55
CT4
-45
Longitude
0
-20
1200
0.1 0.2
0.3
C
0.4
o
0.5
-25
r
r
e
0.6
l
0.8
n
0.9
400
-50
-55
-60
-90
5 00
I
-80
B
E
HK
A
J
0.95
L
G
C
D
0.99
F
19%
-85
o
-45
800
i
-40
t
-35
0.7
0
1 00
a
-30
D
-70
S
-75
M
-80
R
-85
Standard Deviation
-60
-90
B
HAE
0
Latitude
-55
I
M
R
-45
-50
o
Latitude
800
i
-35
t
-30
0.7
0
1 00
a
-25
BCCR-BCM2.0
CNRM-CM3
CSIRO-Mk3.0
ECHAM5MPI-OM
EGMAM
GFDL-CM2.0
GFDL-CM2.1
GISS-EH
GISS-ER
INGV-SXG
IPSL-CM4
UKMO-HadCM3
0
Ref
-75
-70
-65
Longitude
-60
-55
-50
-45
1
Projected changes at different time horizons of 21th century
Frequency (%) of CTs for summer for NCEP (red diamond), GCMs (circles) and
ensemble of GCMs (blue diamond).
50
50
45
45
8
40
40
6
35
35
4
30
25
20
30
25
20
15
15
10
10
5
5
0
0
2046-2065
10
Frequency (%)
Frequency (%)
Frequency (%)
20th Century
2
0
-2
CT1s
CT2s
CT3s
CT4s
CT5s
-4
-6
-8
CT1s
CT2s
CT3s
CT4s
CT5s
-10
10
CT1w
8
CT2w
CT3w
CT4w
2081-2099
CT6w
CT7w
CT5w
6
Frequency (%)
4
2
0
-2
-4
Anomalies of the frequencies of the CTs
with respect to 20th Century in two horizons.
-6
-8
-10
CT1s
CT2s
CT3s
CT4s
CT5s
Future plans:
Evaluate the relevance of the decadal
Analyze the physical
Assess the ability
extreme events
variability in the occurrence of extreme events
processes involved in the occurrence of extreme events
of global models to reproduce the observed decadal variability of
Contribute to greater understanding
and prediction of future climate extremes.
Estimate the frequency of extreme events in the coming years

Matilde Rusticucci, Olga Penalba
Assistant Researchers: Mariana Barrucand, María Laura Bettolli

Post-Doc: Bárbara Tencer, Madeleine Renom,
PhD Students: Federico Robledo, Natalia Zazulie, Juan Rivera, Vanesa Pántano, Gustavo Almeira





Laboratorio de Extremos Climáticos de Sudamérica
Departamento de Ciencias de la Atmósfera y los Océanos- FCENUniversidad de Buenos Aires / CONICET