Transcript Slide 1

Science Issues and
Research Needs for
AMY and IMS
Bin Wang
AMY08 International Workshop
Beijing 4-23 to 4--25 2007
Acknowledgements: CLIVAR/AAMP
LASG/IAP
A Message from Shukla 4-21-2007
Nearly half of the world population is affected
by monsoon. I can not
think of a bigger challenge than to
understand, model, and ultimately
predict monsoon variations at ALL space and
time scales.
GEWEX: Diurnal-Intarseasonal, Land
CLIVAR: Intraseasonal-Interdecdal, Ocean
Monsoon study provides a cross cutting theme
(for GEWEX , CLIVAR, and CliC) and a focus
for CP and ACC.
LASG/IAP
Science Issues
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•
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Monsoon Modeling
Monsoon Prediction/Predictability
Monsoon Intraseasonal oscillation (MISO)
IDV and ACC
1. Monsoon Modeling
Issues
 What determines the structure and
dynamics of the annual cycle (AC) and
diurnal cycle (DC) of the coupled
atmosphere-ocean-land system?
 What are the major weaknesses of the
climate models in simulation of the AC
and DC?
 Do models getting DC and AC right will
improve the modeling of low-frequency
variability (intraseasonal
to interannual)?
LASG/IAP
AGCMs simulate climatology poorly over the WNP heat source region
Climatological Pentad Mean Precipitation
mm/day
(a) Indian Monsoon Region
mm/day
(b) Western North Pacific Region
Month
Month
Kang et al. 2004, Cli Dyn
Wang et al. 2004, Cli Dyn
Modeling/prediction of Global Monsoon Domain
Number of Model
The monsoon precipitation index (shaded) and monsoon domain (contoured) captured by (a) CMAP and (b) the
one-month lead MME prediction. (c) The number of model which simulates MPI over than 0.5 at each grid point.
2. IAV & Predictability/Prediction
Questions
 What is the current accuracy of and how
to improve the dynamic monsoon seasonal
predictions?
 How predictable is the monsoon
interannual variability (IAV)?
Understanding of the roles of the
tropical-extratropical teleconnection,
atmosphere-land interaction, monsoonwarm pool ocean interaction, and Tibetan
Plateau changes.
LASG/IAP
Performance of MMEs
in Hindcast Global Precipitation
Temporal Correlation Skill of Precipitation
Precipitation
Wet
Dry
Dry
Wet
Dry
Dry
Dry
Wet
Dry
Wet
Dry
Wet
Wet
Dry
Dry
Wet
* Impact of El-Nino on Global Climate from NOAA (based on Ropelewski and Halpert
(1987), Halpert and Ropelewski (1992), and Rasmusson and Carpenter (1982)
Hot places of land surface feedback
Koster et al. 2004
Two-tier 5-AGCM MME hindcast of JJA rainfall (21 yrs)
Pattern Correlation Coefficient
5-AGCM EM hindcast skill (21Yr)
• Two-tier system was
unable to predict
ASM rainfall.
•TTS tends to yield positive SST-rainfall
correlations in SM region that are at
odds with observation (negative).
•Treating monsoon as a slave to
prescribed SST results in the failure.
OBS SST-rainfall correlation
Model SST-rainfall correlation
(Wang et al. 2005)
Wang
et al. 2005
Asian-Australian Monsoon Predictability
S-EOF of Seasonal Mean Precipitation Anomalies
The First Mode: 30%
The Second Mode: 13%
Forecast Skills of the Leading Modes of AA-M
CliPAS
3. ISV and Predictability
What are critical processes causing
Monsoon ISV, in particular, the roles of
multi-scale interaction?
 To what extent the MISV is predictable?
 What are major challenges to modeling
and predict MJO and MISV?

LASG/IAP
Satellite Observed Boreal Summer ISO (1998-2005)
Numbers: four phases, phase interval: 8 days
4
Wang et al. 2006
1
3
1
4
1
2
3
•Northward propagation in Bay of Bengal (Yasunari 1979, 1980, Sikka
and Gadgel 1980) and northwestward propagation in WNP (Nitta 1987)
•Formation of NW-SE tilted anomaly rain band (Maloney and Hartmann
1998, Annamalai and Slingo 2001, Kemball-Cook and Wang 2001, Lawrence and
Webster 2002, Waliser et al. 2003)
•Initiation in the western EIO (60-70E) (Wang, Webster and Teng ‘05)
•Seesaw between BOB and ENP and between EEIO and WNP.
Need to understand Multi-Scale Interrelation
In Monsoon ISO
LASG/IAPWorkshop report
Slingo 2006: THORPEX/WCRP
4. IDV and Future Change
Issues
 What are the major modes of interdecadal
variation of the monsoon system?
 How will monsoon system change in a
global warming environment?
 What are sub-seasonal to interannual
factors that influence extreme events?
 What is the sensitivity of the monsoon to
external and anthropogenic climate forcing?
LASG/IAP
Global Monsoon accounts for 80% of
the Annual Variation
Global Monsoon Domain
Rainfall
MPI=Annual range/ annual mean
Annual range= Abs(MJJAS-NDJFM)
Thresholds for monsoon domain:
a) Annual range >300mm
b) MPI>0.5
Global Monsoon Changes (1948-2004)
Wang and
Ding 2006,
GRL
Annual Mean
Precipitation
In the last 56 years global land monsoon shows a weakening trend. However,
in the last 25 years, Oceanic monsoon rainfall increases while land monsoon
LASG/IAP
unchanged.
Future Scenarios for
Summer Monsoon
Rainfall and Annual
Temperature over
South Asia under A2
Scenario
The
general
conclusion that
emerges of the
diagnostics of
the IPCC AR4
simulations:
Asian summer
monsoon rainfall
is likely to be
enhanced.
From Kumar et al.
LASG/IAP
Key Monsoon Issues
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What determines the structure and dynamics of the
annual cycle (AC) and diurnal cycle (DC) of the coupled
atmosphere-ocean-land system? How to remedy the
major weaknesses of climate models in simulation of the
AC and DC?
How predictable is the monsoon interannual variability
(IAV)? How to improve the dynamic monsoon seasonal
predictions?
What cause monsoon Intraseasonal Variability (ISV)?
How to overcome the major challenges to modeling and
predict monsoon ISV?
What are the major modes of interdecadal variation of
the monsoon system? How and why will monsoon
system change in a global warming environment?
What is priority for future field and modeling studies and
for improving observing and modeling strategy of the
monsoon system?
Monsoon Research Needs
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Observation
Modeling
Prediction
Future changes
observation
Phenomena
Validate models
Calibrate Satellite
Initial conditions
Understanding
Modeling
Prediction
Observation
• Field campaign for observing specific phenomena:
e.g., organization of convection, multi-scale structure
of ISV. (Monsoon trough and Maritime Continent)
• Supper station for validate and improve models
• Provide ground truth for calibrating Satellite
measurements. Promote integrated usage of satellite
observations to study , e.g., 3-D structure and multiscale interaction in ISV.
• Improve long-term monitoring network in tropical IOWP and maritime Asia.
• Improve and develop new reanalysis datasets that
use new satellite observations, e.g., land data
assimilation, ocean data assimilation.
Modeling
• Design monsoon metrics for assessing model
performance and identify key modeling issues.
Provide one-stop data source for cross-panel use.
• Develop effective strategy for improving model
Physics.
• Determine directions for developing next generation
climate models. High resolution modeling
• Encouraging use of forecast type experiments to
evaluate models and study climate sensitivities.
• Use large-domain CR or CSR simulation to provide
surrogate data for studying convective organization,
and mulit-scale interaction.
Prediction
• Better understand physical basis for seasonal
prediction and ways to predict uncertainties of the
prediction.
• Improve representation of slow coupled physics.
• Improve initialization scheme and initial conditions
in ocean and land surface.
• Develop new strategy and methodology for subseasonal monsoon prediction.
• Design metrics for objective, quantitative assessing
predictability and prediction skill. Improve MME
prediction system.
Assess Future Changes
• Coordinate IPCC AR4 monsoon assessment to address how
and why AA-M system will change in a global climate change
environment.
• Role of the monsoon-aerosol interaction and land use in
future monsoon change.
• Use MME approach to study the sensitivity of the monsoon
to external and anthropogenic climate forcing.
• Coordinate MME experiments to investigate sub-seasonal to
interannual factors that influence extreme events, such as
TC.
• Determine coherent structure and dynamics of the global
monsoon system on Dec/Cen time scales and their linkage
to ocean.
Regional focus:
Field campaign/regional processes
• Focusing on Maritime continent-SEA and
adjacent warm pool oceans
• Understanding Atmosphere-land-ocean
interaction:
• Address processes over the MC, western
boundar currents, ITF
• Observations to validate model
parameterization: Surface fluxes, PBL and
cloud
• Diurnal cycle and MJO
How important is land-sea contrast and orography in
Controlling monsoon AC?
Chang et al. 2006
A Proposed APCC and CLIVAR Project to Conduct
High Resolution Climate Model Simulations of Recent Hurricane
and Typhoon Activity: The Impact of SSTs and the Madden Julian
Oscillation
Sieg Schubert
Project Overview
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Description: A coordinated international project to carry out and analyze highresolution simulations of tropical storm activity with a number of state-of-the-art
global climate models
Issues to be addressed: the mechanisms by which SSTs control tropical storm activity
on inter-annual and longer time scales, the modulation of that activity by the
Madden Julian Oscillation on sub-seasonal time scales, and the sensitivity to model
physics and resolution.
Approach: case studies of selected years with highly unusual tropical storm activity,
including model runs with specified SST, an anomaly mixed layer ocean, and fully
coupled models.
Resource/sponsorship requests: sponsorship from APCC and U.S. and international
CLIVAR; funding for a workshop in the fall of 2007 ($50K), and for maintaining a
central data repository (estimated at about $100K).
Expected Outcomes
• Improved understanding of the physical
mechanisms controlling major changes in tropical
storm activity on subseasonal, seasonal and
longer time scales (role of SST, role of MJO/ISO)
• An assessment of the ability of current climate
models, when run at high resolution, to simulate
hurricanes and changes in hurricane activity,
including an assessment of sensitivity to model
resolution and physics
• An assessment of the predictable of major
changes in hurricane activity (linked to prediction
of MJO and S-I ocean variability)
Atmospheric Model
•
•
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High resolution runs- minimum 1/2 degree or better (should be
sufficient to produce realistic hurricane structures).
Low resolution AMIP runs - standard climate resolution (~ 2-3
degrees).
Focus is on global models (uncoupled and coupled) but also
invite participants interested in carrying out runs with high
resolution regional models.
MJO/ISO Experiments
•
Table 2: Summary of proposed forecast experiments to assess impact of
MJO. Model resolution should be at the equivalent of ½ degree or higher.
The SSTs are either specified or predicted. The atmosphere, and for coupled
runs, the ocean, is initialized at different phases of the MJO. Multiple
ensemble members (say 5) are encouraged, including those to assess the
impact of model formulation on the simulation of the MJO.
Event
Initia l
Conditions
P otential Im pacts o f
M JO/ISO
1999 Š Two m ajo r (c at 4 an d 5)
cyc lones in B ay of B enga l
Oc t 5 , 15 , 20 ,
25
Pred il ec tio n for m ajor
cy clo n es in Ba y o f Be n ga l
2004 Š c luste r of typhoons an d
hu rr icanes (late Ju ly th rough
ea rl y Septe m be r)
Ju ly 11, 2 2,
A ug 1, 1 1, 2 1,
31,
Pred il ec tio n for ty p h oo n s
an d h urr ica n es espec ially
in eas t Pac ifi c
Regional climate modeling
and predictability study
Takehiko Satomura
Kyoto University
Japan
Difficulties in regional climate
modeling in tropics
• MJO
• Tropical cyclones
• Diurnal cycle
Efforts to coordinate regional
climate modeling activity
• Coordination of less-than-seasonaltime-scale intercomparison of RCMs
• Japan-China-Korea climate model
improvement project (A3 foresight
program (proposed))
Possible collaboration between
CLIVAR & MAHASRI through
RCM intercomparison study
• Targets
– Multi-time-scale: from
diurnal cycle via ISO to
ENSO
– Find processes reducing
prediction scores
• Methods
– Process study
– MME (GCMs and RCMs)
• Area
– E & SE Asia (including
Maritime Continent)
• Data
– IOP of GAME
– IOP of MAHASRI: AMY
• Tasks remained:
– How are models
compared?
– How are ensemble
perturbations specified?
• Are MJO or Kelvin
waves unstable modes?
Possible participating models
in Japan
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•
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MRI spectral coupling RCM (NHM)
RAMS-Utsukuba
Tohoku Univ. Non-Hydro. Model
MM5 (original version)
and others…?
A3 foresight program
•
PIs
– Japan (S. Miyahara, Kyushu Univ.)
– China (R. Lu, IAP)
– Korea (Y. Noh, Yonsei Univ.)
•
Objects
A) analysis of variability and feedbacks of the
atmosphere-ocean-land-vegetation system
B) model intercomparison and improvement by
using the global and regional climate models
owned by the three countries
C) predictability of climate variability and climate
change study induced by anthropogenic factors,
especially focusing on East Asian climate
Modeling/Prediction (AAMP input)
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Coordinate CGCM/RCM Process study on MJO/
MISO (MC-SEA): AAMP/MAHASRI, CIMS,
MAIRS
Develop Multi-model ensemble Regional Climate
prediction experiment with CGCM, RCM, GLACE
in collaboration with MAHASRI, APCC, and
MAIRS to determine impacts of the land surface data
assimilation, land surface processes, and landatmosphere interaction on monsoon seasonal
prediction
Coordinated experiment on high resolution climate
model simulation of hurricane/Typhoon activity.
(NASA/GMAO: Sieg Schubert)
LASG/IAP
Coordination of monsoon modeling
with MAHASRI/ CEOPII
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1st pan--WCRP Monsoon Modelling Workshop for
key studies of the diurnal cycle over both land and
ocean.
Coordinated GCM/RCM Process study on MJO/
MISO and monsoon onset of SEASM.
Develop Multi-model ensemble Regional Climate
prediction (Downscaling system) experiment in
collaboration with MAHASRI and APCC.
Develop land surface data base for GCM MME
hindcast Experimenst (CEOPII)
LASG/IAP
Thanks
LASG/IAP
AAMP-MAHASRI :
Coordinated GCM/RCM Process study on
Monsoon ISO and onset (SEA+MC)
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Integration of observation and modelling,
Meteorology and Hydrology
Domain: MC+SEA (70-150, 15S-40N)—a critical
region for monsoon ISO influence
Phenomenon and Issues: ISO, and its interaction
with diurnal cycle, meso-scale and synoptic scale
regulation. Onset of monsoon (summer and winter);
impacts of Tibetan Plateau land surface processes
Design: Driving field, Output, validation strategy and
Data,…
Participating model groups: both AGCM and RCM,
each 4-5
LASG/IAP
MME Downscaling Seasonal
Prediction Experiment
Develop effective strategy and
methodology for RCM downscaling
Assess the added values of RCM MME
downscaling
Determine the predictability of
monsoon precipitation
Large scale driving: 10 CGCM from
DEMETER and APCC/CliPAS models
LASG/IAP
The increasing trend in Seoul JJAS precipitation and extreme venets reflect a trend in
large scale East Asian monsoon rain belt, which appears to be related to strong trends
in northern Indian ocean SST.
Year of coordinated Observing,
modeling and Forecasting:
Addressing the Challenge of
Organized Tropical Convection
This proposed activity arose out of a
recommendation by the
THORPEX/WCRP/ICTP Workshop on
Organisation and Maintenance of Tropical
Convection and the MJO, held in Trieste in
March 2006. It was presented at the
WCRP/CLIVAR SSG Meeting in Buenos Aires
in April 2006.
Based on positive feedback from the WCRP
Director and the SSG, the SSG asked that the
proposal be developed in cooperation with
THORPEX, GEWEX, CEOP, AAMP, WOAP,
WMP, etc.
If implemented in 2008, this initiative could be
a WCRP contribution to the UN Year of Planet
Earth* and compliment IPY.
ISO Potential Predictability
Air-Sea Coupling Extends the Predictability
of Monsoon Intraseasonal Oscillation
ATM Forecast Error
Signal
CPL Forecast Error
ATM: 17 days, CPL: 24 days
Fu et al. 2006
CliPAS
CLIVAR/A-AMP
Co-Chair:
Bin Wang and Harry Hendon
Cobin Fu,
In-Sik
Kang,
Jay McCreary,
Holger Meinke,
Rajeevan,
Takehiko
Satomura, Andrews Schiller,
Julia Slingo,
Ken Sperber,
Peter Webster
LASG/IAP
Performance on Annual Cycle
and its Linkage with Seasonal Prediction
Performanceskill
on Annual mean & Annual Cycle
Linkage to Seasonal prediction skill
Annual Mean Precipitation
Pattern Correlation Skill over the Global Tropics
(0-360E, 30S-30N) Precipitation
The models’ performance in simulating and
forecasting seasonal mean states is closely
related to the models’ capability in predicting
seasonal anomalies.
LASG/IAP
Factors determining the IAV
• Remote forcing from El Nino/La Nina
• Monsoon-warm pool ocean Interaction
--Equatorial Bjeckness positive feedback (IOD/IOZM)
(Webster et al. 1999, Saji et al. 1999)
--Off-equatorial Rossby Wave-SST feedback either
positive or negative, depending on background annual
cycle (Wang et al. 2000)
--Negative feedback by monsoon-induced anomalies
(Webster et al. 2002, Loschnigg et al. 2003, Lau and Nath 2000).
--Memories of ocean mixed layer/land (Meehl 1994, 1997)
• Regulation of the annual cycle (indirect role of continent)
--Regulation of the monsoon-ocean interaction (Nicholls
1983)
--Modify monsoon response to remote ENSO (Wang et al.
2003)
Performance of MMEs
in Hindcast Global Temperature
Temporal Correlation Skill of 2m Air Temperature
JJA
DJF
 MME seasonal prediction with 1-month lead time using 17 climate models which participate
in CliPAS and DEMETER
Model Descriptions of CliPAS System
APCC/CliPAS Tier-1 Models
Institute
AGCM
Resolution
OGCM
Resolution
Ensemble
Member
Reference
FRCGC
ECHAM4
T106 L19
OPA 8.2
2o cos(lat)x2o lon L31
9
Luo et al. (2005)
GFDL
R30
R30L14
R30
R30 L18
10
Delworth et al. (2002)
NASA
NSIPP1
2o lat x 2.5o
lon L34
Poseidon
V4
1/3o lat x 5/8o lon L27
3
Vintzileos et al. (2005)
NCEP
GFS
T62 L64
MOM3
1/3o lat x 1o lon L40
15
Saha et al. (2005)
SNU
SNU
T42 L21
MOM2.2
1/3o lat x 1o lon L32
6
Kug et al. (2005)
UH
ECHAM4
T31 L19
UH Ocean
1o lat x 2o lon L2
10
Fu and Wang (2001)
APCC/CliPAS Tier-2 Models
Institute
AGCM
Resolution
Ensemble
Member
SST BC
Reference
FSU
FSUGCM
T63 L27
10
SNU SST forecast
Cocke, S. and T.E.
LaRow (2000)
GFDL
AM2
2o lat x 2.5o lon L24
10
SNU SST forecast
Anderson et al. (2004)
IAP
LASG
2.8o lat x 2.8o lon L26
6
SNU SST forecast
Wang et al. (2004)
NCEP
GFS
T62 L64
15
CFS SST forecast
Kanamitsu et al. (2002)
SNU/KMA
GCPS
T63 L21
6
SNU SST forecast
Kang et al. (2004)
CAM2/UH
CAM2
T42 L26
10
SNU SST forecast
Liu et al. (2005)
ECHAM4/UH
ECHAM4
T31 L19
10
SNU SST forecast
Roeckner et al. (1996)
CliPAS