International Workshop on Climate Downscaling Studies,October 4, 2017, Tsukuba, Japan

Regional Climate Downscaling over Vietnam:
Time of Emergencein Temperature and Precipita<on Changes
Thanh NGO-DUC1, Huong NGUYEN, Long TRINH-TUAN, Tan PHAN-VAN,
Fredolin T. TANGANG, Liew JUNENG, Faye CRUZ, Gemma NARISMA, Jerasorn
SANTISIRISOMBOON, Patama SINGHRUCK, Dodo GUNAWAN, Edvin ALDRIAN

ngo-duc.thanh@usth.edu.vn
1Department of Space and Aeronautics,

University of Science and Technology of Hanoi, Vietnam

Monthly climatological distribuQon of rainfall in Vn

à Regional/local characterisQcs of rainfall can not be caught with GCM(Nguyen-Thi et al., SOLA, 2012)

Nguyen-Le, Matsumoto, & Ngo-Duc (IJOC,2013)• climatological (1979-2003)
summer monsoon onset date,criteria by Matsumoto (1997),
based on rainfall only
• crosses denote staQons have
non-typical monsoon rainfallpa]ern
à Regional/local characterisQcsof rainfall can not be caught withGCM

Spa<al distribu<on of the surface meteorological sta<ons
Vietnam surface meteorological staQons (189)Country ElevaQon
StaQon ElevaQon

0-400m:à

50% of the country area
80% of staQons

Lack of surface observa<ons in mountainous area !

Vietnam Gridded PrecipitaQon (VnGP)
Dataset• 481 staQons• Daily observaQons• ResoluQon: 0.25º, 0.1º
• Period: 1980-2010

ü The VnGP dataset has been registered to the Data Integrated and Analysis System (DIAS),Tokyo Univ. and can be accessible from h]p://dias-dmg.tkl.iis.u-tokyo.ac.jp/dmm/doc/VnGP_025-DIAS-en.html
Nguyen et al., SOLA, 2016

Dynamical downscaling over the CORDEX-SEA
region using RegCM4• SensiQvity experiments§ ICBC: ERA-Interim (1989-2008)§ 18 sensiQvity experiments (CPS, ocean ﬂux)

Juneng et al., 2016
Ngo-Duc et al., 2017
Cruz et al., 2017

Similarity index Ω(Ngo-Duc et al., IJOC, 2017)• Be]er similarity for
Temperature than for
PrecipitaQon• T2m and Pre over The MariQme
ConQnent are more sensible tophysical parameterizaQons

à Downscaling performances
depend essenQally on the choiceof physical parameterizaQon
schemeSimilarity index Ω for the 1989-2007 monthly values of the 18 experiments –Temperature & Rainfall

7Selec<on of the BEST schemes

data from 123 stations, only 52 stations were selected for theanalysis of extremes: daily rainfall, T2m, Tmax, Tmin

AOen<on: Diﬀerent DATA sources can give diﬀerent results

Annual mean total precipitaQon
climatology over Southeast Asia
Root mean square diﬀerence between
the 4 products(Juneng et al., Clim.Res. 2016)

Ranking methods: for the 14 extreme indices
1)
2)
3)
4)

minimum absolute bias
minimum rmse
maximum correlaQon
the raQo of the model standard deviaQon to observed one is the closest to 1

The ranking scores of the 18 experiments

10100Precipitation Frequency

604020Probability density
funcQon of thefrequency
distribuQon of theprecipitaQon overthe 52 staQons inthe SEA region

0Frequency (%)

80

ERA Interim
Observation
Mean 18 Exp
Exp 04
Exp 05
Exp 0601030

507090Range (mm/day)à The MIT-Emanuel convec<ve scheme has been chosen for11
downscaling CMIP5 GCMs

Downscaling CMIP5 GCMs
RegCM4.3§ MIT-Emanuel convecQve scheme§ BATS1e ocean scheme§ 25 km§ RCP4.5 & RCP8.5
Exp Name

ICBC

1

CNRM-CM523456

MPI-ESM-MR
EC-Earth
CSIRO-MK3.6.0
GFDL-ESM2M
HadGEM2

1986-2005 observed data forvalida6on§ 608 staQons: daily rainfall§ 70 staQons: daily temperature1986-2005 Rainfall
OBS
RCM

GCM
mm/d
Wet bias of RCM

1986-2005 Temperature
OBS
RCM

GCM
ºC
Cold bias of RCM (e.g. R.MPI)

Added values of downscaling: TEMPERATURE(Di Luca et al., 2013)

Withoutbias
correcQonWith bias
correcQon

Added values of downscaling: RAINFALL(Di Luca et al., 2013)

Withoutbias
correcQonWith bias
correcQon

Annual average of temperature• Ensemble mean of the RCMs & GCMs
• Shading shows the model +/- 1 standard deviaQon

Annual average of Rainfall• Ensemble mean of the RCMs & GCMs
• Shading shows the model +/- 1 standard deviaQon

7 climatological sub-regions of Vietnam (Temperature)

24N

22N

R2
R1R320N

R418N

16N

14N

12N

10N

Seasonal cycles
m are not clearly
2400
R62100 RCM
improved with1800
R78N
102E 104E 106E 108E 110E

1500
1200
9006003000

7 climatological sub-regions of Vietnam (Rainfall)

24N

22N

R2R1
R320N

R418N

16Nm

14N

12N

10N

2400
2100
1800
1500
1200
900
600
300
0

Seasonal cycles are not clearly
improved with RCM
R6R78N
102E 104E 106E 108E 110E

Time of EmergencecalculaEon method• Time of Emergence (ToE) is deﬁned when a climate change
signal starts to exceed the noise range of projecQon
uncertainty

where X is the average of the MX year baseline period(1986-2005), Y is the average of a given MY years in future. Y-X
represents for climate change signal (S)TOE of annual temperature• SQppling region indicates morethan 50% model with ToEs within
ToE (Ensemble) ±5 years• Earlier emergence of RCM
compared to GCM

TOE of annual rainfall• SQppling region indicates morethan 50% model with ToEs within
ToE (Ensemble) ±5 years• Deacreasing Rainfall in RCM
compared to increasing rainfall inGCM

Conclusions• Downscaling results produce added values for annual
average aqer a simple bias correcQon• Seasonal cycles are not clearly improved with RCMs
• Strong driq in RCP85 precipitaQon needs to be explored• Earlier of temperature TOE in RCMs compared to that inGCMs• Opposite change direcQon in future precipitaQon for RCMs &GCMs

Thank you!
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