symposium on the global climate system, ankara, turkey, 24

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

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


Lagged regression maps: SIS index and OLR anomalies



Lagged regression maps: SIS index and upper-level streamfunction anomalies



Is the activity of the SIS pattern

related to the Madden-Julian

Oscillation?

7


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)


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


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


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


29

IC: 12/04

SIS experimental

Forecast based on

CFSv2

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.

symposium on the global climate system, ankara, turkey, 24

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