Challenges in the study of the American Monsoon

Systems

Carolina Vera

CIMA (UBA-CONICET)

DCAO/Facultad de Ciencias Exactas y Naturales

Buenos Aires, Argentina

Monsoon societal relevance

• A large percentage of the world population live under monsoon climate, and receive benefits as well as damages from monsoon precipitation and hydrological processes.

• Most of the countries under monsoon climates are developing countries or semi-developed countries, where coordination for monsoon seasonal prediction is highly required.

• Large uncertainties exist in predicting (or projecting) all the regional monsoon climates associated with natural & anthropogenic (GHGs increase, aerosol increase, and LCLU changes) forcings due to lack of understanding of the hydro-climate processes of monsoons.

(WCRP/IMS 2008)

Challenges in monsoon system study

• The monsoon systems are manifested as land-atmosphere-ocean coupled systems, exhibiting a variety of time and space scales that are governed by complex physical processes and their interactions.

(WCRP/IMS 2008)

Multi-scale interaction in the tropics and monsoon regions

DiurnalCycle

SubseasonalVariability

SeasonalCycle

Land-Atmosphere-Ocean InteractionsOrographic forcings

InteranualVariability

Solar forcing

(WCRP/IMS 2008)

• Due to our lack of understanding of these processes and interactions, large uncertainties still exist in prediction of the monsoons on local, regional, and continental-scales.

• Monsoon predictions require better models, and better models require improved physical parameterizations, which in turn require more comprehensive observations. 

(WCRP/IMS 2008)

Challenges in monsoon system study

• Since the monsoon systems possess a large range of variability from diurnal to decadal time scales, prediction is a challenging test for the modeling community. 

• Given the importance of the monsoons in driving the energy and water cycle, improving model physics in monsoon regions will result in better models for other applications such as global change, and water resource assessments. (WCRP/IMS 2008)

Challenges in monsoon system study

• Description of some features of the:– Seasonal Cycle

– Diurnal Cycle

– Intraseasonal Variability

– Interannual Variability

• Focusing on: – Current ability of global models in describing those

features

Outline

Monsoon Mature phase

Climatological seasonal mean precipitation (shaded, NCEP reanalysis), & vertically

integrated moisture fluxes (arrows, CMAP)

(Vera et al., 2006, J. Climate)

SAMS NAMS

Seasonal Cycle of precipitation from WCRP/CMIP3 models

(1970-1999 period) (Vera et al., 2006, GRL)

OBS

(Vera and Gonzalez, 2008)

Surface temperature

Bias

DJF

(1979-1999)

(20 Models)

(Vera and Gonzalez, 2008)

Observations (CRU) Multi-model Ensemble Mean – Observed Mean

Summer surface temperature bias in WCRP/CMIP3 models

20 Models

Precipitation Bias

DJF

(1979-1999)

(Vera and Gonzalez, 2008)

(Vera and Gonzalez, 2008)

Observations (CMAP)

(mm/day)

Multi-model Ensemble Mean – Observed Mean (mm/day)

Summer precipitation bias in WCRP/CMIP3 models

20 Models

Diurnal Cycle and Mesoscale Variability

The Diurnal Cycle in South AmericaTemporal frequency of cold clouds (infrared brightness temperature Tb 235 K)

Falvey and Garreaud (2008)

MCS activity in South America

Subtropical South America has the largest fractional contribution of PFs with MCSs to rainfall of anywhere on earth between 36 N and 36 S

(Zipser et al. BAMS, 2006)

MCS activity in South America

MCS mature stage time occurrence frequency. Bars in green represent the period November 15 to December 31, in black January 1 to February 15 (Zipser et al. 2004)

05Z ~ 02 LST

Night (LPB)

Afternoon (SACZ)

MCS event on 17 January 2003

Diurnal Cycle – MCS - Synoptic Waves

Frequency of Convection (2000-2003)

SALLJ Days

NO SALLJ

Days

During SALLJ Days:

•Higher frequency of MCS occurrence (41%)

•Synoptic waves associated with SALLJ events provide the favorable enviroment for MCS development

•MCS are bigger and last longer

MCS tend to be nocturnal in both SALLJ and NO SALLJ dates over northern Argentina and Paraguay and diurnal over southern Brazil

00 UTC

00 UTC

06 UTC

06 UTC

12 UTC

12 UTC

18 UTC

18 UTC

(Salio et al. 2007)

Intraseasonal Variability

Intraseasonal variability in South America

H

LH

+ T. anom

- T. anom

L

H

L- T. anom+ T.

anom

SOUTH AMERICAN SEE-SAW PATTERN

Nogues-Paegle and Mo (1997)

Diaz and Aceituno (2003)

Higher frequency of extreme daily rainfall

events at the subtropics

(Liebmann, Kiladis, Saulo, Vera, and Carvalho, 2004)

(Gonzalez, Vera, Liebmann, Kiladis, 2007)

Higher frequency of heat waves

and extreme daily temperature events at the

subtropics

(Cerne , Vera, and Liebmann, 2007)

1st EOF leading pattern of 10-90-day filtered OLR

variability

Weakened SACZ

Intensified SALLJ poleward progression

Intensified SACZ

Inhibited SALLJ poleward progression

Intraseasonal variability from WCRP/CMIP3 ModelsEOF-1 (10-

90 days) Filtered OLR

OBS

GFDL

MPI

0.00E+00

5.00E-03

1.00E-02

1.50E-02

2.00E-02

2.50E-02

3.00E-02

0.00

00.

0030.

0070.

0100.

0130.

0170.

0200.

0230.

0260.

0300.

0330.

0360.

0400.

0430.

0460.

0500.

0530.

0560.

0600.

0630.

0660.

0700.

0730.

0760.

0790.

0830.

0860.

0890.

0930.

0960.

0990.

1030.

1060.

1090.

1130.

1160.

119

~ 56.6 days

~ 22.7 days

~ 46.5 days

~ 35 days

0.00E+00

5.00E-03

1.00E-02

1.50E-02

2.00E-02

2.50E-02

3.00E-02

0.00

00.

0030.

0070.

0100.

0130.

0170.

0200.

0230.

0260.

0300.

0330.

0360.

0400.

0430.

0460.

0500.

0530.

0560.

0600.

0630.

0660.

0700.

0730.

0760.

0790.

0830.

0860.

0890.

0930.

0960.

0990.

1030.

1060.

1090.

1130.

1160.

119

~ 42.4 days

~ 33.6 days

~ 29.2 days

~ 25.9 days

~ 22.4 days

0.00E+00

5.00E-03

1.00E-02

1.50E-02

2.00E-02

2.50E-02

3.00E-02

0.00

00.

0030.

0070.

0100.

0130.

0170.

0200.

0230.

0260.

0300.

0330.

0360.

0400.

0430.

0460.

0500.

0530.

0560.

0600.

0630.

0660.

0700.

0730.

0760.

0790.

0830.

0860.

0890.

0930.

0960.

0990.

1030.

1060.

1090.

1130.

1160.

119

~ 28 days

~ 24.2 days

~ 22.7 days

EOF-1 Spectral Density Regressions EOF-1 & 200-hPa v´

(González, and Vera, 2008)

40-60 days 20-40 days

OBS MPIGFDLLAG -15 Days

LAG -10 Days

LAG -5 Days

LAG 0 Days

Regressions between

EOF-1 (10-90 days Band)

&

OLR´

(González and Vera, 2008)

NCEP/NCAR

MPIGFDL

Day -10

Day -5

Day 0

Regressions EOF-1 & 850-

hPa v´

(Divergence shaded)

González, and Vera, 2008)

Interannual Variability

ENSO

Percentage of (c) El Niño and (d) La Niña peaks showing a maximum

amplitude located in the western, central and eastern Pacific

(Leloup et al. 2008)

Precipitation Interannual Variability in South America

OND (1970-1999)

OBS

Vera and Silvestri (2008)

ENSO

OND (1979-1999)

Correlations between ElNino3.4 SST anomalies and (left) precipitation and (right) 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded. NCEP reanalysis data.

(Vera and Silvestri, 2007)

OND (1970-1999)

Correlations between ENSO index and 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded.

ENSO signal in SH Circulation anomalies from WCRP/CMIP3 models

OBS

Vera and Silvestri (2008)

OND (1970-1999)

Correlations between ENSO index and precipitation anomalies. Significant values at 90, 95 and 99% are shaded.

ENSO signal in precipitation anomalies from WCRP/CMIP3 models

OBS

Vera and Silvestri (2008)

Southern Annular Mode (SAM)

OND (1979-1999)

Correlations between SAM index and (left) precipitation and (right) 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded. NCEP reanalysis data.

Vera and Silvestri (2008)

OND (1970-1999)

Correlations between SAM index and 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded.

SAM signal in SH circulation anomalies from WCRP/CMIP3 models

OBS

Vera and Silvestri (2008)

OND (1970-1999) Correlations between SAM index and 500-hPa geopotential height

anomalies. Significant values at 90, 95 and 99% are shaded.

SAM signal in precipitation anomalies from WCRP/CMIP3 models

OBS

Vera and Silvestri (2008)

Some of the relevant processes to American Monsoon climate

that need to be better understood and simulated

•Dynamics over complex terrain like the Andes and the Brazilian plateau

•Land-Atmosphere interaction (Land use changes)

•Air-Sea interaction in the surrounding oceans

•Diurnal cycle of precipitation

Diurnal evolution of the PBL

Diurnal cycle of the LLJ

•Feedbacks within the physical climate system (climate & biogeochemical cycles)

•Cloud related processes and associated phenomena (including aerosol-cloud interactions)