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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
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Outlines Outlines Experiment design for VN downscaling Experiment design for VN downscaling
DataData
Model verificationModel verification
Future projections of TCsFuture projections of TCs
SummarySummary
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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
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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
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Gridded dataGridded data
61 Meteorological stations over seven climatic zones
Station dataStation dataObserved dataObserved data
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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 )
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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
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Annual cycle of temperatureAnnual cycle of temperature
PRECIS simulations with reanalysis (ERA-interim ) forcingPRECIS simulations with reanalysis (ERA-interim ) forcing
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Summer (JJA) precipitationSummer (JJA) precipitation
mm/day
Captured minimum rainfall in central Vietnam
CRU Obs SimulationAPH Obs
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Winter (DJF) precipitationWinter (DJF) precipitation
Captured maximum rainfall in central Vietnam
mm/day
CRU Obs SimulationAPH Obs
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Precipitation bias (model-APH)Precipitation bias (model-APH)
%
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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
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PRECIS Ensemble Simulations PRECIS Ensemble Simulations forcing by 5 forcing by 5 HadCM3HadCM3 runs runs
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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
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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
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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
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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)
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Summer (JJA) 850hpa- WindSummer (JJA) 850hpa- Wind
+ Summer monsoon flow pattern is well produced, + Stronger than OBS
Obs
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Winter (DJF) 850hpa- WindWinter (DJF) 850hpa- Wind
Winter monsoon is well produced both pattern and strength
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Simulate ensemble members: track densitySimulate ensemble members: track densitySummer (JJAS)
Reasonable summer/winter spatial distributionReasonable summer/winter spatial distribution
Winter (OND)
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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
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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
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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
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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
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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
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Thanks for your attention!Thanks for your attention!
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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
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© 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
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© 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.