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

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

• Preliminary comparison with observations for the baseline period (1980-1999)

4 • Future projections (until 2050)

Outline

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

(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?

• 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

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

PCsWebmeteo

Computing Nodes

~120 CPUs

~ 80TB storage

I. Motivations

Computing

system

- LINUX

Cluster

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

• 60% of the country population

• poor living-standard compared to other

regions

14 observation stations: daily data

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

(projection)

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)

(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

Models and experiments II. Models and experiments

CCAM CCSM3.0 ECHAM

GCM boundary

(26km)

(36 km)

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

• RegCM & MM5: underestimation, similar behavior due to using the same

CCSM3 boundary condition

• Overestimation of REMO

• CCAM is good in term of amplitude

• 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

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.

MM5 • Cold bias for most stations,

except 2 stations

• Largest cold bias in winter in

the northern part.

REMO •Warm bias for most stations,

except BaTo

• Systematic bias characteristics

CCAM• well represents the obs.

RegCM3 REMO CCAM OBS

Annual Precipitation (mm/month) - 1980-1999 average

Precipitation Validation

• Precipitation patterns are very different among models

• OBS: APHRODITE data (Yatagai et al., 2007)

• 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

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.

• REMO largely

underestimates

Precipitation

III. Preliminaray comparison REMO: 1980-1999 Average Precip

for MAM, JJA, SON, DJF

• CCAM underestimates

Precip in the rainy season

III. Preliminaray comparison CCAM: 1980-1999 Average Precip

for MAM, JJA, SON, DJF

Future Temperature -RegCM

• rising temperature for

both A1B & A2

• for 2041-2050, JJA

increase > DJF increase

IV. Future Projections

Future Precipitation - RegCM

A1B-MAM A1B-JJA A1B-SON A1B-DJF

A2-DJFA2-SONA2-JJAA2-MAM

Difference (%) between 2041-2050 & baseline period •Rainfall varies

spatially &

temporally

Temperature

• 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

• 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

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?

Multi-model approach for projecting future climate change … · 2014-04-01 · Thanh NGO-DUC, Van...

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