multi-model approach for projecting future climate change … · 2014-04-01 · thanh ngo-duc, van...
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Thanh NGO-DUC,
Van Tan PHAN, Trung NGUYEN QUANG
Department of Meteorology
Hanoi University of Science,
Vietnam National University
Multi-model approach for
projecting future climate change conditions
in Central Vietnam
2011/03/03 at the ICSS-Asia 2011 conference
Outline
1• Motivations of the study
2• Models and numerical experiments
3
• Preliminary comparison with observations for the baseline period (1980-1999)
4 • Future projections (until 2050)
Outline
2
3
The National Target Program to response to climate change: Decision158/2008/QĐ-TTg
• 2007: IPCC Fourth Assessment Report (AR4)
Phase I Kick-off
Phase II Implementation
Phase III Development
2009-2010
2011-2015
post-2015Objectives: to assess
climate change’s impacts &
develop feasible action
plan to effective respond to
CC, take over
opportunities to develop
towards a low-carbon
economy, and joint
international community’s
effort to CC impacts and
protect global climatic system
I. Motivations
Climate Change, Sea level rise
scenarios for Vietnam
4
(MONRE, 2009)
MAGICC/SCENGEN
5.3 software and
Statistical Downscaling
Method
The Scenarios will be
updated by using
PRECIS & MRI
Question about the range of uncertainty?
5
• RegCM
• REMO
• CCAM
• MM5CL
HMO faculty
Hanoi University of
Science
• PRECIS
Institute of Meteorology
Hydrology and Environment (IMHEN)
I. Motivations
Tools for dynamical downscaling:Statistical downscaling: MAGICC/SCENGEN
MRI-20km
U
P
S
Computing Network
192.168.1.0/24
Data & Man. & Pub. Net10.8.52.0/24
Computing system(Faculty of Hydrology, Meteorology and Oceanography,
Hanoi University of Science)
Internet
Sun
HTTP
FTP
PCsWebmeteo
Login
Node
FC
5
Q9450
Head
Nodes
Computing Nodes
NAS
~120 CPUs
~ 80TB storage
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I. Motivations
Computing
system
- LINUX
Cluster
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Region of study: Central Vietnam
Vulnerability to natural disaster & climate
change
Heavy rainfall and flood event occurred in 9
provinces in Central Vietnam in Nov 1999: 592
deaths, 421 injured, 30 people were missing.
Damage ~220 million USD. 1841mm/2days in
Nov 2nd & 3rd, 1999.
I. Motivations
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• 60% of the country population
• poor living-standard compared to other
regions
14 observation stations: daily data
Period: 1980-1999 (baseline), 2000-2050
(projection)
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II. Models and experimentsII. Models and experiments
This study can be expanded for the whole
Vietnam
Domain for experiments: Vietnam, Thailand, Laos, Cambodia,
Bangladesh, Myanmar, Malaysia,
Singapore, part of Indonesia
Intercomparison?
Scenario choices:
A1B (average emission
scenario)
A2 (high emission scenario)
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(source IPCC, 2007)
Currently, numerical experiments is set only to 2050 due to limited
computational resources (computing speed and storage limitations).
II. Models and experiments
CCAM: Conformal Cubic Atmospheric Model, CSIRO, Australia
CCSM: Community Climate System Model, US
ECHAM: European Centre Hamburg Model, Germany
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Models and experiments II. Models and experiments
CCAM CCSM3.0 ECHAM
GCM boundary
CCAM
(26km)
RegCM
(36 km)
MM5
(36 km)
REMO
(36 km)
MRI
output?PRECIS outputs (IMHEN)?
RegCM MM5 REMO CCAM
2m-Temperature, 1980-1999 average
III. Preliminaray comparison
•Similar spatial patterns among the models
• MM5 lowest temperature12
Average 2m-temperature (oC) of the 14 stations
13
• RegCM & MM5: underestimation, similar behavior due to using the same
CCSM3 boundary condition
• Overestimation of REMO
• CCAM is good in term of amplitude
III. Preliminaray comparison
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• Seasonal variations of
Temperature can be well
represented
• RegCM & MM5:
behave similarly, cold bias
larger in Winter,
smaller in Summer
• REMO: over estimate,
largest in Winter and
Spring
• CCAM can well match
the amplitude of
observation
Average 2m-temperature (oC) of the 14 stations
MAM
JJA
SON
DJF
RegCM
MM5
REMO
CCAM
III. Preliminaray comparison
III. Preliminaray comparison
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2m-temperature (oC) of the 14 stations,
1980-1999 average for DJF, MAM, JJA, SON
• Cold bias for most stations
• RegCM overestimates
temperature for only 3
stations
• Largest cold bias in winter
in the northern part.
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2m-temperature (oC) of the 14 stations,
1980-1999 average for DJF, MAM, JJA, SON
MM5 • Cold bias for most stations,
except 2 stations
• Largest cold bias in winter in
the northern part.
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2m-temperature (oC) of the 14 stations,
1980-1999 average for DJF, MAM, JJA, SON
REMO •Warm bias for most stations,
except BaTo
• Systematic bias characteristics
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2m-temperature (oC) of the 14 stations,
1980-1999 average for DJF, MAM, JJA, SON
CCAM• well represents the obs.
RegCM3 REMO CCAM OBS
Annual Precipitation (mm/month) - 1980-1999 average
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Precipitation Validation
• Precipitation patterns are very different among models
• OBS: APHRODITE data (Yatagai et al., 2007)
III. Preliminaray comparison
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• SON is the rainfall
season in Central
Vietnam.
• RegCM3
overestimates rainfall
in winter.
• REMO largely
underestimates
rainfall
• CCAM
underestimates
rainfall during SON
14 station Average Precip: 1980-1999
MAM
JJA
SON
DJF
RegCM
REMO
CCAM
III. Preliminaray comparison
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RegCM: 1980-1999 Average Precipfor MAM, JJA, SON, DJF
• Average rainfall for the
baseline period is well
simulated at each stations,
particularly in the rainy
season.
III. Preliminaray comparison
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• REMO largely
underestimates
Precipitation
III. Preliminaray comparison REMO: 1980-1999 Average Precip
for MAM, JJA, SON, DJF
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• CCAM underestimates
Precip in the rainy season
III. Preliminaray comparison CCAM: 1980-1999 Average Precip
for MAM, JJA, SON, DJF
Future Temperature -RegCM
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A1B
JJA
A1B
DJF
A2
DJF
• rising temperature for
both A1B & A2
• for 2041-2050, JJA
increase > DJF increase
A2
JJA
IV. Future Projections
Future Precipitation - RegCM
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A1B
DJF
A2
DJF
A1B-MAM A1B-JJA A1B-SON A1B-DJF
A2-DJFA2-SONA2-JJAA2-MAM
Difference (%) between 2041-2050 & baseline period •Rainfall varies
spatially &
temporally
IV. Future Projections
Temperature
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IV. Future Projections
• Increasing
• CCAM-A2 >> CCAM-A1B
• Linear trends seem to be similar
• Increasing
• clear trend in JJA
• large variability in DJF
• Increasing
• clear trend in JJA
• CCAM A2 increase remarkably in DJF
Precipitation
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IV. Future Projections
• non-clear trend for A2
• increasing trend for A1B
• RegCM has more rainfall than
the baseline time for the whole
future 50-yr period
• CCAM-A2: less precipitation
than the baseline period
• Increasing trends (particularly CCAM) in SON: rainy season
• Large variability in DJF
• RegCM shows big increase of rainfall in MAM
• no clear trend
• large difference among models
Summary
4 models were used: RegCM, MM5, REMO, CCAM
Baseline period: 1980-1999
Temperature shows consistency among models and with obs.
Large differences for simulated precipitation
Future projection: 2000-2050
Increasing temperature
No clear trend for precipitation
Large variability among models
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Future Challenges
Thank you for your
attention!33
Intercomparison?
How to obtain the final projected scenarios (weighted average,
arithmetic average, etc.?)
• Expand the simulations to 2100
• Add more models, more scenarios, more GCM boundary inputs?
• (MRI-20km proposal accepted)
• Improve the computing system
Possible Collaboration?