symposium on the global climate system, ankara, turkey, 24
Post on 13-Feb-2022
0 Views
Preview:
TRANSCRIPT
Climate variability and
predictability on S2S in southern
South America
Carolina Vera and Mariano Alvarez
Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires
Centro de Investigaciones del Mar y la Atmósfera (CIMA), UMI
IFAECI/CNRS, CONICET/UBA
Buenos Aires, Argentina
Symposium on the Global Climate System,
Ankara, Turkey, 24 – 26 April, 2019
(real-time)
Subseasonal
predictions of
weekly mean
precipitation
anomalies
(16 members
CFSv2 Model)
Week 1
Week 3
Week 2
Week 4
Initial Condition 10 April 2019
http://climar.cima.fcen.uba.ar
(real-time)
Subseasonal
predictions of
weekly mean
precipitation
anomalies
(16 members
CFSv2 Model)
Week 1
Week 3
Week 2
Week 4
Initial Condition 27 March 2019
http://climar.cima.fcen.uba.ar
Multi-scale interaction in the tropics and monsoon
regions
Diurnal
CycleIntraseasonal
Variability
Seasonal
Cycle
Land-Atmosphere-Ocean Interactions
Orographic forcings
Variability on
interannual and longer
time scales
Solar forcing
(WCRP/IMS 2008)
Synoptic
waves
Global monsoons
5Monsoon systems not only affect tropical circulation but also
the extratropical circulation
The variability of
the Monsoon
convection from
subseasonal to
interanual
timescales can
generate Rossby
wave trains
influencing
remote regions
Liu and Wang (2013)
7
Challenges associated with climate in South America
– Biggest continental portion
over tropical regions
– South America Monsoon
System
– Andes
– Several regions largely
influenced by the tropical
oceans
Monsoon Mature phase
Climatological seasonal mean precipitation
(shaded, NCEP reanalysis), & vertically
integrated moisture fluxes (arrows, CMAP)
(Vera et al., 2006, J. Climate)
SAMS NAMS
8
DJF climatological mean precipitation
SACZ
South
Atlantic
Convergence
zone
Monsoon
Core
ITCZ
Intraseasonal Variability (IS) in South America
Mean OLR (contours, 240 and 220 Wm-2), and standard deviation of 10-90-day filtered OLR anomalies (shaded).
DJF MAM JJA SON
IS variability of OLR activity
Alvarez 201627
Rainy season: October to April IS variability in 30-90 days
SIS Pattern: Leading pattern of IS variability in eastern South America
OLR’
regre
ssio
nat
First EOF of FOLR 30-90
(21.5% of explained
variance)
2 Vera et al. (2017)
Phases of the SIS Pattern
H
L
H
+ T. anom
- T. anom
L
H
L
- T. anom
+ T. anom
Higher frequency of extreme daily
rainfall events at the subtropics
(Liebmann, et al., 2004)
(Gonzalez, et al. 2008)
Higher frequency of heat waves
and extreme daily temperature
events at the subtropics
(Cerne and Vera, 2011)
Weakened SACZ
Intensified SALLJ poleward progression
Intensified SACZ
Inhibited SALLJ poleward progression
12
Positive
PhaseNegative
Phase
Rainy season: October to April IS variability in 30-90 days
Lagged regression maps: SIS index and OLR anomalies
4 Vera et al. (2017)
Rainy season: October to April IS variability in 30-90 days
Lagged regression maps: SIS index and upper-level streamfunction anomalies
6 Vera et al. (2017)
Rainy season: October to April IS variability in 30-90 days
Is the activity of the SIS pattern
related to the Madden-Julian
Oscillation?
7
Rainy season: October to April IS variability in 30-90 days
DJF
DJF
MJO impacts in South America
8 Alvarez et al. (2017)
17
MJO influence on climate in South America
MJO influences both divergent and rotational
component of the atmospheric circulation
18
MJO influence on SH circulation in DJF
Anomalies of 200 hPa Geopotential Heights (contours; significant values are shaded) and χ (thick
contours) as a function of MJO phase during DJF according to the linear regression model.
Alvarez et al. (2017)
Rainy season: October to April IS variability in 30-90 days
3090-SIS index (PC1)
MJO amplitude ( 𝑃𝐶12 + 𝑃𝐶22)
MJO phase
MJO event
Positive SIS event:
convection enhanced
in SESA and
suppressed in SACZ
Relationship between SIS pattern activity and MJO
11
Rainy season: October to April IS variability in 30-90 days
MJO index values for positive and negative SIS events
Positive SIS events Negative SIS events
MJO phase diagram with the daily MJO index values during positive SIS events within an MJO event.
The yellow diamond indicates the day in which the SIS index is maximum
12
Rainy season: October to April IS variability in 30-90 days
SIS index values for each MJO phase
Box plot of the SIS index values for each MJO phase achieved within an MJO event
16
IS variability in South America
Alvarez et al. 2014
May-Sep (extended Winter)
EOF1 of 10-90 FOLR (negative geen)
The leading pattern of variability during Winter
is a monopole. The main periods of variability
of the PC1 are around 17 and 30-40 days.
The region of maximum variability may be
associated to the position where cold fronts
become stationary during Winter.
25
IS variability in South America
Alvarez et al. 2014
May-Sep (extended Winter)
Linear lagged regressions between PC1 and OLR and 250 hPa geop. height26
SH circulation patterns
Southern Annular Mode/Antarctic Oscillation
Austral winter season.
• EOF1 as correlations between PC1
and geopotential height anomalies.
• Annular mode with barotropic
structure
• Leading mode across timescales (also
found on interannual time scales)
Di Gregorio Master Thesis 2015
Unfiltered 10-90 days
70 hPa
250 hPa
700 hPa
6
May-October November-April
SH circulation patterns
Southern Annular Mode/Antarctic Oscillation
Flateau & Kim 2013
The relationship between the SAM (or AAO) index and MJO changes according to SH season
There is a significant contribution of the MJO to the SAM tendency (change over 1 day) on the
intraseasonal scale, especially for strong MJO episodes
Distribution of MJO phases
for the positive and negative
states of the intraseasonal
component of AAO (SAM).
8
CONCLUSIONS
Knowledge about the leading patterns of IS variability over a
certain region is important for:
better understanding the sources of IS variability in the region
More profound monitoring of the regional IS variability
Qualitative assessment of large-scale climate pattern (e.g. MJO) over
the region.
More targeted model verification
Prediction of regional climate indexes
http://climar.cima.fcen.uba.ar/
29
IC: 12/04
SIS experimental
Forecast based on
CFSv2
http://climar.cima.fcen.uba.ar/
RELEVANT REFERENCES
• Alvarez, M., C. Vera, G. Kiladis, and B. Liebmann, 2014: Intraseasonal Variability in South
America during the Cold Season. Climate Dynamics, 42, 3253-3269.
• Alvarez, M., C. Vera, G. Kiladis, and B. Liebmann, 2016: Influence of the Madden Julian
Oscillation on Precipitation and Surface Air Temperature in South America. Climate
Dynamics, 46, 245-262.
• Alvarez, M.S.; C. S. Vera, and G. N. Kiladis, 2017: MJO Modulating the Activity of the
Leading Mode of Intraseasonal Variability in South America. Atmosphere, 8 (12), 232.
doi:10.3390/atmos8120232.
• Cerne, B., C. Vera, 2011: Influence of the intraseasonal variability on heat waves in
subtropical South America. Climate Dynamics, 36, 2265–2277.
• Flatau, M. and Y.-L. Kim, 2013: Interaction between the MJO and Polar Circulations. J.
Climate, 26, 3562-3574
• Liu F., and B. Wang (2013):Mechanisms of Global Teleconnections Associated with the Asian
Summer Monsoon: An Intermediate Model Analysis. J. Climate, 26, 1791-1806.
• Vera, C.S., M. S. Alvarez, M.S., P. L. M. Gonzalez, G. N. Kiladis, and B. Liebmann, 2018:
Seasonal cycle of precipitation variability in South America on intraseasonal timescales.
Climate Dynamics, 51, 5–6, 1991–2001. https://doi.org/10.1007/s00382-017-3994-1.
top related