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Downscaling activities to support the Impact of rice production - - PowerPoint PPT Presentation

Downscaling activities to support the Impact of rice production under climate change Ramkhamhaeng University www.ru.ac.th Background Information Project title : Downscaling of GCMs for the impacts study of climate change on rice p r o d u c


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Downscaling activities to support the Impact of rice production under climate change

Ramkhamhaeng University

www.ru.ac.th

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Project title : Downscaling of GCMs for the impacts study of climate change on rice p r o d u c t i o n i n T h a i l a n d Funded by : Thailand Research Fund (TRF) D u r a t i

  • n

: 2 y e a r s Collaboration : Ramkhamhaeng University D e p a r t m e n t o f R i c e D e p a r t m e n t o f I r r i g a t i o n

Background Information

www.ru.ac.th

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  • General Circulation Model, GCM: Mathematical

model use to simulate present and project future c l i m a t e .

  • G C M

: c o a r s e r e s o l u t i o n : l o c a l d e t a i l s a r e l o s t

  • Downscaling activities are required
  • There are two general approaches : dynamical and

statistical downscaling

Downscaling GCMs

www.ru.ac.th

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Statistical downscaling comprises 3 broadly t e c h n i q u e s :

  • W e a t h e r

g e n e r a t o r s

  • W e a t h e r

t y p i n g a n d

  • T

r a n s f e r f u n c t i

  • n

Statistical Downscaling

www.ru.ac.th

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  • Transfer function achieved from linear and non-linear

r e g r e s s i o n a n a l y s e s .

  • Transfer function technique is to develop quantitative

r e l a t i o n s h i p s b e t w e e n

– large-scale atmospheric variables (predictors) and – l o c a l s u r f a c e v a r i a b l e s ( p r e d i c t a n d ) .

Transfer function

www.ru.ac.th

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  • Downscale the CMIP5-GCMs by using

– S t a t i s t i c a l a p p r o a c h – Transfer function technique: Artificial Neural N e t w

  • r

k ( A N N )

  • To generate historical and future climate data with

f i n e r g r i d r e s o l u t i o n .

Activities

www.ru.ac.th

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Meteorological Stations and Data

  • Data from 123 Meteorological

s t a t i

  • n

s

  • D a t a c o n s i d e r e d :

– Mean, Max, Min Temp. – R H – S u n s h i n e d u r a t i o n – A t m o s p h e r i c p r e s s u r e – W i n d s p e e d – P r e c i p i t a t i o n

www.ru.ac.th

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Station number by meteorological data

www.ru.ac.th

Data 1951-1980 1981-1990 1991-2000 2001-2011

  • Max. Temperature

85 103 116 120 Mean Temperature 75 90 101 120

  • Min. Temperature

85 103 116 119 Precipitation 85 103 116 119 Relative Humidity 84 103 118 120 Sunshine duration 8 15 17 60

Atmospheric pressure

N/A 69 75 120 Wind speed N/A N/A 84 85

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Solar Radiation Data

  • Data from 32 stations
  • With compliment from

Assoc.Prof.Serm Janjai, Silpakorn University

www.ru.ac.th

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GFDL-ESM2M MPI-ESM-LR HadGEM2-ES Organization Geophysical Fluid Dynamic Laboratory Max Planck Institute for Meteorology Met Office Hadley Centre Base year 1961 – 2005 Future climate projection 2006 – 2100 Scenario RCP4.5 RCP6.0 RCP8.5 RCP4.5

  • RCP8.5

RCP4.5 RCP6.0 RCP8.5 G r i d r e s o l u t i o n L a t i t u d e L o n g i t u d e 2.02247o 2.50000o 1.86500o 1.87500o 1.25500o 1.87500o

  • No. of Predictor

7 7 7 www.ru.ac.th

Selected GCMs

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www.ru.ac.th Predictors for GCMs Unit D a i l y - M e a n N e a r S u r f a c e W i n d S p e e d m/s S e a L e v e l P r e s s u r e Pa P r e c i p i t a t i

  • n

kg/m2/s N e a r - S u r f a c e S p e c i f i c H u m i d i t y N e a r - S u r f a c e A i r T e m p e r a t u r e K Daily Maximum Near-Surface Air Temperature K Daily Minimum Near-Surface Air Temperature K

Predictors

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www.ru.ac.th รายละเอียด B a s e y e a r 1961 - 2005 F u t u r e y e a r 2006 - 2100 S p a t i a l s c a l e 10 km × 10 km T e m p o r a l s c a l e Daily A r e a latitude 5 – 22oN longitude 95 – 105oE O u t p u t ( P r e d i c t a n d s ) Mean, Max., Min. Temperature Precipitation Relative humidity Sunshine duration Solar radiation Atmospheric pressure Wind speed

Downscaling Output

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Statistical downscaling technique applied in this study has 4 m a i n s t e p s : 1) Develop quantitative functions between predictors (base year data from reanalysis) and predictands (statistical data from meteorological stations) by using ANN. 2) Apply the functions to project future point station data with future data from GCMs as predictors. 3) Generate grid data from future point station data 4 ) C a l i b r a t i o n o f g r i d d a t a .

Downscaling Procedures

www.ru.ac.th

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Future Mean temperature : RCP8.5

www.ru.ac.th GFDL MPI HadG Historical data 2006 2006 2025 2050 2075 2100

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Future Max. Temperature : RCP8.5

www.ru.ac.th Historical data 2006 2006 2025 2050 2075 2100 GFDL MPI HadG

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Future Precipitation : RCP8.5

www.ru.ac.th Historical data 2006 2006 2025 2050 2075 2100 GFDL MPI HadG

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  • Downscale CMIP4-GCM2 to 25 km × 25 km

– S E A C L I D / C O R D E X S E A o u t p u t

  • F u r t h e r

d o w n s c a l i n g

– A p p l y t h e s t a t i s t i c a l d o w n s c a l i n g – 1 k m

× 1 k m – Need high quality measurement data from the whole region

Ideas for future work

www.ru.ac.th

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Researchers

www.ru.ac.th

  • Asst. Prof. Dr. Jerasorn Santisirisomboon : Ramkhamhaeng University
  • Dr. Somkiat Apipattanavis : Royal Irrigation Department

D r . C h i t n u c h a B u d d h a b o o n : R i c e D e p a r t m e n t

  • Asst. Prof. Dr. Jaruthat Santisirisomboon : Ramkhamhaeng University
  • Dr. Waranyu Wongseree : King Mongkut’s University of Technology North

B a n g k

  • k

D r . Y o d S u k h a m o n g k o l : R a m k h a m h a e n g U n i v e r s i t y M s . B e n j a m a s R o s o c h a : R i c e D e p a r t m e n t

  • Ms. Pawanrat Agsornsingchai : Ramkhamhaeng University
  • Mr. Songsak Chuaibumroong : Ramkhamhaeng University
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Thank You

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www.ru.ac.th