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4 th International CLIVAR Climate of the 20 th Century Workshop, 13-15 th March 2007, Exeter, UK Sensitivity of the Indo-Pacific climate variability to different forcings in XX th century simulations Annalisa Cherchi and Antonio Navarra

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slide1

4th International CLIVAR Climate of the 20th Century Workshop, 13-15th March 2007, Exeter, UK

Sensitivity of the Indo-Pacific climate variability to different forcings in XXth century simulations

Annalisa Cherchi and Antonio Navarra

Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy

Centro EuroMediterraneo per i Cambiamenti Climatici, Bologna, Italy

slide2

The Indo-Pacific climate variability is identified with the ENSO-monsoon connection

Outline of the talk

Model used & Experiments performed in the C20C framework

Simulation of the ENSO-monsoon connection in the experiments performed

Analysis of the decadal variability of the connection (1976 climate shift)

The role of the Indian Ocean

Conclusions

slide3

Ice: LIM

(Louven laneuve sea-Ice systeM)

called by OPA routines

(Fichefet et al., 1999)

Ocean: OPA 8.2

Developed at LODYC in Paris

Finite difference model

Vertical resolution: 31 levels

(10 in the first 100m)

Horizontal resolution: 2x1.5deg

(around the Eq 0.5deg)

(Madec et al., 1998)

Atmosphere: Echam4.6

Developed at MPI in Hamburg

Spectral model

Horizontal resolution: T30, T106

Vertical resolution: 19 sigma layers

MPI parallel version

(Roeckner et al., 1996)

Sea-Ice cover

Winds & fluxes

SST

Coupler: OASIS 2.4

Developed at CERFACS in Toulouse

Message passing based on MPI2

(Valcke et al., 2000)

The SINTEXG CGCM

it is a atmosphere-ocean-ice coupled model developed at INGV following the background of the SINTEX model developed among the SINTEX EU-project

For a description of the mean climate simulated by the model see the web page: https://www.cmcc.it/web/public/ANS/models/ingv-sxg

slide4

AMIP-type experiments

AMIP-GHG-type experiments

Echam4.6 at T42 resolution forced with observed SST & sea ice (HadISST)

1871-2002

(6 members)

Echam4.6 at T42 resolution forced with observed SST & sea ice (HadISST) and with GHGs (XXth simulations)

1871-2002

(6 members)

Coupled experiment

SINTEXG at T42 resolution forced with GHGs (XXth century simulation)

Setup of the experiments performed in the C20C framework

XXth simulations:

the atmospheric model is integrated with prescribed radiative forcings (GHGs, ozone and sulfate aerosols) from 1870 to 2002 following the IPCC directives (20C3M experiments). The GHGs prescribed are: CO2, CH4,N2O, CFC11 and CFC12.

slide5

About the ENSO-monsoon connection:

The Asian summer monsoon is strongly influenced by the thermal contrast between the Indian Ocean and the South Asian land mass and by the Tibetan Plateau (e.g. Webster, 1987; Li and Yanai, 1992)

On interannual timescale the ASM is influenced by ENSO (e.g. Rasmusson and Carpenter, 1983; Webster and Yang, 1992).

ENSO and the Asian summer monsoon are interactively linked (Webster and Yang, 1992)

An important component in the connection is the Walker circulation with the strongest updraft over Indonesia and the western Pacific Ocean in correspondence of the warm pool.

Other factors influencing the ENSO-monsoon connection:

the Eurasian snow cover

the Indian Ocean SSTs

slide6

A mechanism to explain the link between ENSO SST forcing and the Asian summer monsoon (Kawamura, 1998)

Warm episode in winter-spring

attenuation of the Walker circulation (convection suppressed over the Northern Tropical Indian Ocean and Maritime continents)

anomalous cyclonic circulation to the west of the Tibetan Plateau (Rossby-type response to convective heating)

decreased land surface temperature over central Asia to the north-west of the Indian subcontinent

reduced land-ocean thermal contrast

weakening of the Asian summer monsoon

The reverse is supposed to occur after a cold episode in winter-spring

slide7

How to measure and identify El Nino:

NINO3 index (monthly SST anomalies averaged

in the area 5S-5N and 150W-90W)

DMI

DMI(Dynamical Monsoon Index)

Mean JJA zonal wind shear (u850-u200) averaged over 40-110E, Eq-20N (Webster and Yang, 1992)

MTG (Meridional thermal gradient)

(H200-500)(20-40N)-(H200-500)(Eq-20N) averaged in JJA

(Kawamura, 1998)

The analysis of the ENSO-monsoon connection has been performed by means of correlation and composites analysis based on a selected numbers of indices

slide8

A precursory signal for the monsoon:

AMJ SST vs MTG index (linear correlation)

Amip-SST

Amip-GHG

Coupled model

NCEP & HadISST

slide9

The summer season:

JJA SST vs DMI index (linear correlation)

Amip-SST

Amip-GHG

Coupled model

NCEP & HadISST

slide10

Amip-GHG

Amip-SST

NCEP

DMI

r(Amip-SST – CRU)=0.61

r(Amip-GHG – CRU)=0.63

Decadal variability of the monsoon index:

11yr running mean of DMI and IMR

r(Amip-SST – NCEP)=0.40

r(Amip-GHG – NCEP)=0.61

IMR

slide11

NINO3 vs DMI (19 yr sliding window)

NINO3 vs IMR (19 yr sliding window)

Amip-SST

Amip-GHG

Obs

The ENSO-monsoon connection has a remarkable decadal variability and this relationship weakened in recent decades (Kumar et al., 1999)

Possible causes for those changes:

1) seasonality of the ENSO cycle (Kawamura et al., 2003)

2) the Indian Ocean Dipole Mode (Ashok, et al., 2001)

3) the global warming: an ISM normal despite El Nino conditions for a south-eastward shift of the Walker circulation (Kumar et al., 1999 Science), increase of the ground temperature over the Eurasian continent and consequent increase of the land-sea thermal contrast (Ashrit et al., 2001) or for increase of moisture supply from the Indian Ocean due to increased surface temperatures (Kitoh et al., 1997)

4) natural decadal variability

slide12

pre-1976

post-1976

ENSO-monsoon connection: 1976 climate shift

Correlation JJA SST vs DMI (from AMIP-type exp results)

T30

T42

T106

HadISST &

ERA40

Cherchi and Navarra, 2006

slide13

About the 1976 climate shift: MTG vs AMJ SST (linear correlation)

1948-1975

1976-2002

Amip-SST

Amip-GHG

Obs

slide14

Composite of JJA TPREP (strong minus weak monsoon)

pre76 & post76 in Amip-GHG experiments

1948-1975

1976-2002

slide15

About the role of the Indian Ocean:

The role of the TIO SST as active or passive element for the ISM has been a controversial issue:

Tropical Indian Ocean SST may be considered as a passive element of the ISM system at interannual time scale (Webster et al., 1998)

Modelling studies have shown that the Indian Ocean does significantly affect ISM rainfall (e.g. Yamazaki, 1988; Meehl and Arblaster, 2002) and that the annual cycle of SST in the Indian Ocean is crucial for a realistic simulation of the Indian summer monsoon (Shukla and Fennessy, 1994)

Positive SST anomalies over the Arabian Sea during the spring preceding the monsoon season are precursors for above normal precipitation over India (e.g. Rao and Goswami, 1988; Clark et al., 2000)

The discovery of the Indian Ocean Dipole Mode (IODM, Saji et al., 1999; Webster et al., 1999), as an important mode of variability of the Indian Ocean itself, suggested the possibility of interactions between this mode of variability and the ISM.

The issue is still controversial:

Positive IODM events enhance ISM rainfall (Ashok et al., 2001; Li et al., 2003)

Positive IODM events are linked to dry conditions over the Indian subcontinent (Webster et al., 2002; Meehl et al., 2003)

Model experiments results have confirmed that positive (negative) Indian Ocean dipole events may reduce the influence of an El Nino (La Nina) event on the Indian monsoon (Ashok et al., 2004)

slide16

Observations

Coupled model

Composites (JJA SST & Indian summer monsoon rainfall)

Cherchi et al., 2006

slide17

The problem is: Z=AS

where A becomes:

with

Coupled Manifold technique

(Navarra and Tribbia, 2005)

A new statistical method to detect the portion of co-variability between 2 climatic fields

  • The coupled manifold may be used to:
  • compute the % of variance of an atmospheric field linked to another atmospheric field, and the reverse
  • separate Z in Zfor (subspace where variation of one field are connected to variations of the other field) and Zfree (a subspace where variations are indipendent)
  • identify one-way (“forced manifold”) and two-way (“coupled manifold”) relations between the fields considered
slide18

% of variance of India TPREP linked to TrIndOc SST

Observations

Coupled model

Coupled manifolds technique used to distinguish the percentage of variance of Indian precipitation due to the Indian Ocean SSTA and other forcing

% of variance of TrIndOc SST linked to TrPacOc SST

Observations

Coupled model

Cherchi et al., 2006

slide19

Understanding of the mechanisms involved in the ENSO-monsoon-Indian Ocean Dipole mode interactions

Correlation of Indian Monsoon Rainfall vs Indian Ocean SST

Obs

Total SST

Model

Forced SST

Free SST

Cherchi et al., 2006

the shaded pattern is significant at 95%

slide20

Obs

Model

EOF1 Forced

EOF2 Forced

EOF1 Free

EOFS of Forced & Free SSTA

Cherchi et al., 2006

slide21

Conclusions

Atmospheric and coupled model are able to capture in a realistic way the direct impact of anomalous SST forcing associated with ENSO on the South Asian summer monsoon

The connection is better simulated when the GHG forcings are included

The decadal variability of the monsoon indices considered is realistically simulated by the atmospheric model, when the GHGs are included the linear correlation with the observed field is larger

The decadal variability of the ENSO-monsoon connection is captured by the model, as well as its weakening observed in recent decades

The changes observed after 1976 in the ENSO-monsoon connection are realistic in the atmospheric model experiments, especially in the Amip-GHG experiments

After 1976 the relationship between the Indian Ocean SST and the Asian monsoon index is stronger

TIO SSTA influence precipitation over India

Local effects & remote effects (influence from the Tropical Pacific Ocean) of the TIO SSTA on the ISM have been separated by means of the coupled manifold technique

The EOF analysis of the “forced” and “free” SSTA in the TIO is used to analyze the variability of the TIO and its link with the TPO (the link between the TIO & the TPO is weak in the model)