Dynamical Downscaling ofClimate Projection Data
Akihiko Murata

Meteorological Research Institute / Japan Meteorological Agency
Jan 10, 2024

1Webinar Series on Climate Change Projection for Disaster Risk Reduction in Asia-Pacific Region

Contents
Overview of dynamical downscalingPractical examples of dynamical downscalingAnalyses for present climate
Analyses for future climate

Research program: SENTAN

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1. Overview ofdynamical downscaling3

What is downscaling?
PurposeTo project future climate on regional and local scales
20-km resolution

Two types

2-km resolution

Statistical downscaling: Based on statistical knowledge
Dynamical downscaling: Based on numerical simulations 4

Regional Climate Model (RCM)
Similar to numerical prediction model for weather
forecast
But for regional- and local-scale climate (notweather)
Present-day climate

Future climate (e.g., the end of 21C)

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Experimental design
Numerical Model:

NonHydrostatic Regional Climate Model (NHRCM; Sasaki et al. 2008),

based on Japan Meteorological Agency NonHydrostatic Model (JMA-NHM; Saito et al. 2006)

Present climate

Integration period： 20 years
Sep 1980 – Aug 2000
(1-year time slice:Sep – next Aug)

Future climate

Integration period: 20 years
Sep 2076 – Aug 2096
20-km mesh
(1-year time slice:(AGCM20)
Sep – next Aug)
Scenario: RCP8.5
SST: Change + Trend + Variability
Ensemble of boundary conditions (4 members)

5-km mesh
2-km mesh(convectionpermitting)6

Downscaling methods
Scenario in terms of greenhouse gases

 Shared Socioeconomic Pathways (SSP) Representative Concentration Pathways (RCP)

Time sliced e.g., Around the end of this century: 2080-2100

Using high-speed supercomputer e.g., Earth Simulator

Earth Simulator

RCP8.5RCP2.6
IPCC (2013)

Time sliced

http://www.jamstec.go.jp/es/jp/output/gallery/images/es3/org/002.jpg

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2. Practical examplesof dynamicaldownscaling
2-1 Analyses for present climate
2-2 Analyses for future climate8

2-1 Analyses for present climate
Remarks:

Climate simulation is different from weather forecastIn general, date in climate simulations does notrepresent real date, month, and year
Not possible to compare model and observed results
for specific date, month, and yearCalculation of long-term mean

20 years (30 years) or soCompare model climate with observed one
 Both 20-years mean

Therefore, calculate long-term mean first9

Calculation of monthly, seasonal, andannual mean
(20-years mean of) Monthly mean
1st year

2nd year…..

20th year

Similarly …
(20-years mean) of Seasonal mean
 DJF, MAM, JJA, SON, and so on(20-years mean of) Annual mean

20-years mean

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Validate simulated mean values
Using observational data
For example: Station dataModeled values for comparisonAt the grid point nearest to an observational point

Bias, RMSE, and Correlation11

Difference between model andobservational results
Surface air temperature
Model[degC]

Model - Obs[K]

Precipitation

Model[mm]100 x (Model/Obs-1)

[%]

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Murata et al. (2015)

2-2 Analyses for future climate
Remarks:

Climate simulation is different from weather forecastIn general, date in climate simulations does not
represent real date, month, and year
Not possible to compare model results (present andfuture) for specific date, month, and yearCalculation of long-term mean

20 years (30 years) or soCompare model future climate with present one
 Both 20-years mean

Therefore, calculate long-term mean first13

Difference between future andpresent surface air temperature
PresentFuture

Future - Present[degC][K]

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Difference between future andpresent precipitation
PresentFuture

Future - Present

[mm]

[mm]
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3. Research program:
SENTAN
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Special scientific programs
Sponsored by Ministry of Education, Culture, Sports,
Science and Technology (MEXT), Japan
KYOUSEI project (FY2002-2006)
KAKUSHIN program (FY2007-2011)
SOUSEI program (FY2012-2016)
TOUGOU program (FY2017-2021)
SENTAN program (FY2022-2026)

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SENTAN program web

https://www.jamstec.go.jp/sentan/eng/ Four research themes

 Theme No. 3: Increasing thesophistication of climatechange projections aroundJapan18

Area theme No. 3 of SENTAN program
Three sub-themes
Sub-theme No. 3:
Creation of high-accuracy
climate projection
datasets for vulnerable
regions in the world
International collaboration
through inviting
researchers to MRI
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International collaboration
 High-resolution climate projection datasets for their countries have been
made by using a regional climate model, called NHRCM.
 5-km and 2-km horizontal resolutions

 The datasets can be used for climate change impact assessment andadaptation strategies

An invited researcher

Model domains(By courtesy of Prof Mishra)(By courtesy of Dr Fukui)

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Studies for each country Arpornrat, T., S. Ratjiranukool, P. Ratjiranukool, and H. Sasaki, 2018: Evaluation of southwest monsoon change overThailand by high-resolution regional climate model under high RCP emission scenario, J. Phys.: Conf. Ser., 1144,012112.
 Cruz, F. T., H. Sasaki, and G. T. Narisma, 2016: Assessing the sensitivity of the Non‐Hydrostatic Regional Climate Modelto boundary conditions and convective schemes over the Philippines. J. Meteor. Soc. Japan, 94, 165–179.
 Cruz, F. T, and H. Sasaki, 2017: Simulation of present climate over Southeast Asia using the Non-Hydrostatic Regional
Climate Model. SOLA, 13, 13–18.
 Jamaluddin, A. F., F. Tangang, J. X. Chung, L. Juneng, H. Sasaki, and I. Takayabu, 2018: Investigating the mechanisms ofdiurnal rainfall variability over Peninsular Malaysia using the non-hydrostatic regional climate model. Meteor. Atmos.
Phys., 130, 6, 611–633.
 Kieu-Thi, X., H. V. U.-Thanh, T. Nguyen-Minh, D. Le, L. Nguyen-Minh, I. Takayabu, H.Sasaki, and A. Kitoh, 2016: Rainfall
and tropical cyclone activity over Vietnam simulated and projected by the Non-Hydrostatic Regional Climate Model –NHRCM. J. Meteor. Soc. Japan, 94A, 135–150.
 Ngai, S. T., H. Sasaki, A. Murata, M. Nosaka, J. X. Chung, L. Juneng, Supari, E. Salimun, and F. Tangang, 2020: Extreme
rainfall projections for Malaysia at the end of 21st century using the high resolution non-hydrostatic regional climatemodel (NHRCM), SOLA, 16, 132–139.
 Mau, N. D., N. M. Truong, H. Sasaki, and I. Takayabu, 2017: Rainfall projection for seasonal rainfall over Vietnam by theend of 21st century under RCP8.5 scenario by the NHRCM model. Vietnam Journal of Hydrometeorology, pp 7–13.
 Mau, N. D., H. Sasaki, and I. Takayabu, 2018: A study of seasonal rainfall in Vietnam at the end of 21st century
according to the Non-Hydrostatic Regional Climate Model, Vietnam Journal of Science, Technology and Engineering,60, 3, 89–96.
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Summary
Overview of dynamical downscalingPractical examples of dynamical downscalingAnalyses for present climate
Analyses for future climate

Research program: SENTAN

Dynamical downscaling for their countries using a
non-hydrostatic RCM, called NHRCM, has been
conducted
High-speed computer system, called the EarthSimulator, can be used22