11

Climate Ensemble Simulations and Projections for Vietnam using PRECIS Model

Presented by Hiep Van Nguyen

Main contributors: Mai Van Khiem, Tran Thuc, Nguyen Van Thang, Hoang Duc Cuong

IMHEN, Vietnam

Grace Redmond, David Hein, Met Office Hadley Centre, UK

Kevin HodgesThe University of Reading, UK

Outlines Outlines Experiment design for VN downscaling Experiment design for VN downscaling

DataData

Model verificationModel verification

Future projections of TCsFuture projections of TCs

SummarySummary

25x 25 km resolution

19 vertical levels

5 ensemble members

The Hadley Centre (UK) regional modelling system PRECIS

+ Providing REgional Climates for Impacts Studies

+ Can be run on Linux desktop – useful in countries with limited computing capacity.

+ Run over Vietnam (1950-2100) with scenario A1B forcing by 5 different HadCM3 runs

Experiment design for VN downscaling Experiment design for VN downscaling

HadCM3HadCM3Q0Q0– The standard model run– The standard model run HadCM3HadCM3Q3Q3– A model run with smaller temperature changes– A model run with smaller temperature changes HadCM3HadCM3Q13Q13 – A model run show larger temperature changes– A model run show larger temperature changes HadCM3HadCM3Q10Q10 – A model run that gives the driest projections – A model run that gives the driest projections HadCM3HadCM3Q11Q11 – A model run that gives the wettest projections– A model run that gives the wettest projections

Member name Driving GCM Simulation period

Validation period

Q0 HadCM3Q0 1950-2100 1971-2000

Q3 HadCM3Q10 1950-2100 1971-2000

Q10 HadCM3Q11 1950-2100 1971-2000

Q11 HadCM3Q13 1950-2100 1971-2000

Q13 HadCM3Q3 1950-2100 1971-2000

ERA-INT ERA-INTERIM 1989-2008 1990-2008

Experiment design for VN downscaling Experiment design for VN downscaling

Gridded dataGridded data

61 Meteorological stations over seven climatic zones

Station dataStation dataObserved dataObserved data

Spatial patterns of circulation, rainfall and temperature

Annual cycles of rainfall and temperature

Variability of rainfall

Extremes analysis

Validation methodValidation method

X

x

+ Temp simulation at stations: nearest grid point (elevation correction with lapse rate -0.65oC/100m )

PRECIS simulations with reanalysis (ERA-interim ) forcingPRECIS simulations with reanalysis (ERA-interim ) forcing

TemperatureTemperature

Model reproduces the geographic patterns of surface air temp reasonably well, low temp to the north and higher to the south.

Winter: DJF Summer: JJA

Obs Simulation Obs Simulation

Annual cycle of temperatureAnnual cycle of temperature

PRECIS simulations with reanalysis (ERA-interim ) forcingPRECIS simulations with reanalysis (ERA-interim ) forcing

Summer (JJA) precipitationSummer (JJA) precipitation

mm/day

Captured minimum rainfall in central Vietnam

CRU Obs SimulationAPH Obs

Winter (DJF) precipitationWinter (DJF) precipitation

Captured maximum rainfall in central Vietnam

mm/day

CRU Obs SimulationAPH Obs

Precipitation bias (model-APH)Precipitation bias (model-APH)

%

R1

0

4

8

12

16

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

Prec

ipit

atio

n (m

m/d

ay)

R4

0

4

8

12

16

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

Pre

cip

itat

ion

(m

m/d

ay)

R7

0

4

8

12

16

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

Prec

ipit

atio

n (m

m/d

ay)

Annual cycle of precipitation

PRECIS Ensemble Simulations PRECIS Ensemble Simulations forcing by 5 forcing by 5 HadCM3HadCM3 runs runs

Temperature: summer (JJA)Temperature: summer (JJA)

OBS Q0 Q3

Q10 Q11 Q13

34oC14oC

The model reproduces the geographical patterns of temperature realistically

Have a stronger east-west temperature gradient in comparison with OBS

Temperature: Winter (DJF)Temperature: Winter (DJF)

Winter mean temperature is also well captured by the models

Cold bias in the north and central provinces

OBS Q0 Q3

Q10 Q11 Q13

30oC12oC

Temperature bias (model-CRU)Temperature bias (model-CRU)

-3 -2 -1 0 1 2 3 oC -3 -2 -1 0 1 2 3 oC

Q0 Q3

Q10 Q11 Q13

Q0 Q3

Q10 Q11 Q13

DJF JJA

Annual cycle of precipitationR1

0

4

8

12

16

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

Prec

ipit

atio

n (m

m/d

ay)

R4

0

4

8

12

16

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

Prec

ipit

atio

n (m

m/d

ay)

R7

0

4

8

12

16

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

Prec

ipit

atio

n (m

m/d

ay)

Summer (JJA) 850hpa- WindSummer (JJA) 850hpa- Wind

+ Summer monsoon flow pattern is well produced, + Stronger than OBS

Obs

Winter (DJF) 850hpa- WindWinter (DJF) 850hpa- Wind

Winter monsoon is well produced both pattern and strength

Simulate ensemble members: track densitySimulate ensemble members: track densitySummer (JJAS)

Reasonable summer/winter spatial distributionReasonable summer/winter spatial distribution

Winter (OND)

Validation: annual cycle of TC numberValidation: annual cycle of TC number

Q3 appears to have reasonably comparable annual cycle

All other members underestimate, July cyclones particularly low.

OBS

Q0 Q3 Q10

Q11 Q13

Track densityTrack density((Number of TCs per Number of TCs per year per ~10year per ~1066 km km22))

Consistent decrease in most areas, except Q0, increase in SE of domain.

Future changes: 2020-2049 minus 1961-1990Future changes: 2020-2049 minus 1961-1990

Mean strengthMean strengthOverall increases, except Q10.

Overall: Number of TCs tends to decrease while intensity tends to increase

+ The domain may not capture well TC genesis?

+ Over estimate summer wind speed increase TC intensity??

Future changes: 2020-2049 minus 1961-1990Future changes: 2020-2049 minus 1961-1990

Summary Summary The PRECIS model

+ Capture the present climate reasonably well

+ Systematically underestimate temperature.

+ Overestimates precipitation about 94% and 30% for DJF and JJA.

+ Show good simulations on monsoon flow patterns, however, summer wind speed is overestimated.

+ Future projections by PRECIS show number of TCs tends to decrease while intensity tends to increase

Main reference:Main reference:1. Khiem et. al., 1. Khiem et. al., 2012: VALUATION OF DYNAMICALLY 2012: VALUATION OF DYNAMICALLY

DOWNSCALED ENSEMBLE CLIMATE SIMULATIONS FOR VIETNAMDOWNSCALED ENSEMBLE CLIMATE SIMULATIONS FOR VIETNAM, , International journal of climatology (Accepted) International journal of climatology (Accepted)

AcknowledgementsAcknowledgementsWe would like to thank IMHEN, UNDP, UK Met Office, We would like to thank IMHEN, UNDP, UK Met Office, CSIRO for supporting this workCSIRO for supporting this work

This work is supported under projectsThis work is supported under projects1.1.““Technical support in development of climate change Technical support in development of climate change scenarios in Vietnamscenarios in Vietnam”” funded by the UNDP funded by the UNDP

2.2.““High-resolution Climate Projections for Vietnam” funded High-resolution Climate Projections for Vietnam” funded by CSIRO, Australiaby CSIRO, Australia

Thanks for your attention!Thanks for your attention!

Experiment design for VN downscaling Experiment design for VN downscaling

Criteria for GCM selection • Validation

• Selected models should represent Asian summer monsoon (position, timing, magnitude), and associated rainfall and temperature

• Future • Magnitude of response: greatest/least regional/local

warming, greatest/least magnitude of change in precipitation

• Characteristics of response• Tendency in changing wet-season precipitation

(increases and decreases)• Spatial patterns of precipitation response over south-

east Asia• Response of the monsoon circulation

© Crown copyright Met Office

Relative vorticity – units of s-1

• Describes the rotation of a fluid and may be considered as the ‘circulation per unit area at a point.’

• In NH (SH) cyclones are positive (negative) vorticity anomalies.

• Relative vorticity at a point = z-component of the horizontal wind velocity field (in relation to earth's surface)

y

u

x

vcurlz

V

No vorticity Some vorticity

© Crown copyright Met Office

The TRACK program

• Written and maintained by Kevin Hodges, University of Reading, UK (can be applied to meteorological and oceanographic data)

• A General Method for Tracking Analysis and its Application to Meterological Data, 1994, K. I. Hodges, Monthly Weather Review., V122, 2573-2586

• TRACK identifies suitable features through a time sequence, based on thresholds set by the user.

• These features are then tracked through the time sequence to produce feature trajectories

• These trajectories are then analysed to produce statistical diagnostic fields: - track density, mean intensity, genesis density, lysis density, mean lifetime.