Thanh NGO-DUC,

Van Tan PHAN, Trung NGUYEN QUANG

Department of Meteorology

Hanoi University of Science,

Vietnam National University

ngoducthanh@vnu.edu.vn

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

6

I. Motivations

Computing

system

- LINUX

Cluster

7

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

8

• 60% of the country population

• poor living-standard compared to other

regions

14 observation stations: daily data

Period: 1980-1999 (baseline), 2000-2050

(projection)

9

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)

10

(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

11

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

14

• 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

15

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.

16

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.

17

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

18

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

19

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

21

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

22

• REMO largely

underestimates

Precipitation

III. Preliminaray comparison REMO: 1980-1999 Average Precip

for MAM, JJA, SON, DJF

23

• CCAM underestimates

Precip in the rainy season

III. Preliminaray comparison CCAM: 1980-1999 Average Precip

for MAM, JJA, SON, DJF

Future Temperature -RegCM

24

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

25

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

32

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?