gemma teresa t. narisma, phd faye t. cruz, phd€¦ · faye t. cruz, phd regional climate systems...

Regional Climate Modeling

and Needs in the Philippines

Gemma Teresa T. Narisma, PhD

Faye T. Cruz, PhD

Regional Climate Systems Program

Manila Observatory and

Ateneo de Manila University

The Jesuits' Manila observatory was founded in 1865 for the purpose of weather prediction. It later expanded its scientific pursuits to include seismology and astronomy.

Fr. Federico Faura, SJ (1840–

1897), founded the Observatorio Meteorologico de Manila to study

the typhoons that

plagued the Far East.

Analyze past and

future climate

changes that

endangers local

ecosystems, and

communities

http://www.aboutrice.com/facts/fact08.html

NYTimes

REGIONAL CLIMATE SYSTEMS PROGRAM

J.B. Dado

Regional Climate Systems GOALS Regional climate

change and variability

Determine the historical and

future changes in climate in the Philippines and Southeast Asia

High resolution local climate

impacts

Do high resolution

climate science that can

interface w/ other sectors, inc energy, health, and

local planning

Forecasting for disaster risk

management

Develop a Manila

Observatory atmospheric database for immediate forecast for

disaster response

Assessing model skill of NHRCM in capturing the seasonal variability

in the Philippine climate

Faye Cruz 1, Hidetaka Sasaki 2, Gemma Narisma 1,3

Manila Observatory, Quezon City, Philippines Meteorological Research Institute, Tsukuba, Japan

Ateneo de Manila University, Quezon City, Philippines

October 2013

NHRCM model tends to have a warm and dry bias over the Philippines.

Model simulation:

•  Domain: Philippines, 50 km resolution

•  Boundary forcing data: 2° ECMWF ERA-Interim

•  Analysis period: June 1998 – May 2007

•  9 x 14-month runs (first 2 months discarded)

NHRCM KF CRU

°C

TRMM NHRCM KF

mm/day m/s

CRU NHRCM KF

TRMM NHRCM KF

JJA Temp

JJA Precip

DJF Temp

DJF Precip

Model is warmer and drier but captures observed seasonal cycle.

18#

21#

24#

27#

30#

0#

5#

10#

15#

20#

Jan# Feb# Mar# Apr# May# Jun# Jul# Aug# Sep# Oct# Nov# Dec#

Tempe

rature)(C

))

Rainfall)(m

m/day))

Month)

West)Luzon)

APHRODITE# NHRCM#KF# NHRCM#Grell# APHRODITE# NHRCM#KF# NHRCM#Grell#

14#

18#

22#

26#

30#

0#

5#

10#

15#

20#


Tempe

rature)(C

))

Rainfall)(m

m/day))

Month)

Central)Visayas)


15#

20#

25#

30#

0#

5#

10#

15#


Tempe

rature)(C

))

Rainfall)(m

m/day))

Month)

West)Mindanao)


15#

20#

25#

30#

0#

10#

20#

30#


Tempe

rature)(C

))

Rainfall)(m

m/day))

Month)

East)Mindanao)


15#

20#

25#

30#

0#

10#

20#

30#


Tempe

rature)(C

))

Rainfall)(m

m/day))

Month)

East)Luzon)


West Luzon

West Mindanao East Mindanao

Central Visayas

East Luzon

Analysis of the performance of

RegCM3 downscaling over multiple

locations in the Philippines

Gemma NARISMA1,2, Julie DADO1, Faye CRUZ1

1Regional Climate Systems Program, Manila Observatory, Philippines

2Atmospheric Science Progra, Ateneo de Manila University,

Philippines

How good are regional

climate models in

downscaling climate for local

planning applications?

•  Model: RegCM3

•  ECHAM5 GCM, A1B Scenario

•  40km resolution

•  Kuo Cumulus Convective Parameterization

•  Zeng Ocean Flux Parameterization

•  Model run: 1961-1990

10km

12km

20km

12km Metro Manila

Albay

Leyte

Cagayan de Oro

Modified Coronas Atlas (Kintanar, 1984)

Type I climate (two pronounced season with dry period from November to April and wet period from May to October) Type II climate (no dry season with a very pronounced maximum rainfall during the months of November–December); Type III climate (seasons not very pronounced with a relatively dry period from November to April, as in Type I); Type IV climate with rainfall more or less evenly distributed along the year

http://kidlat.pagasa.dost.gov.ph/cab/climate_change/images/ClimateMap.JPG

1 2

4

0 50 100 150 200 250 300 350 400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rai

n (m

m)

1

0 50 100 150 200 250 300 350 400


Rai

n (m

m)

2

0 50

100 150 200 250 300 350 400


Rai

n (m

m)

4 2

RAINFALL RESULTS

1 2

4

2

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 50

100

150

200

250

300

350

400

450

METRO MANILA

PAGASA RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

CAGAYAN DE ORO

PAGASA RegCM3 RAINFALL HISTOGRAMS

Occurrence of high rainfall amounts that are not in observed

data

1 2

4

0 250 500 750

1000 1250 1500

Jan

Feb

Mar

Apr

May

Jun

Jul Aug

Sep

Oct

Nov Dec

Mo

nthl

y To

tal (

mm

)

METRO MANILA

PAGASA RegCM3

0

100

200

300

Jan

Feb

Mar

Apr M

ay Ju

n Ju

l Aug

Sep

Oct

Nov Dec

Mo

nthl

y To

tal (

mm

)

CAGAYAN DE ORO

PAGASA CdO RegCM3 CdO

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 50

100

150

200

250

300

350

400

450

METRO MANILA

PAGASA RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

CAGAYAN DE ORO

PAGASA RegCM3

Modelled high precipitation that are not in observed explains overestimation during rainy

season

1 2

4

2

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 50

100

150

200

250

300

350

400

450

METRO MANILA

PAGASA RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

CAGAYAN DE ORO

PAGASA RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 30

60

90

120

150

180

210

240

TIWI-MALINAO, ALBAY

Aphrodite RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 20

40

60

80

100

120

140

160

SILAGO, LEYTE

Aphrodite RegCM3

RAINFALL HISTOGRAMS

1 2

4

2

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 30

60

90

120

150

180

210

240

TIWI-MALINAO, ALBAY

Aphrodite RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 20

40

60

80

100

120

140

160

SILAGO, LEYTE

Aphrodite RegCM3

RAINFALL HISTOGRAMS

0 100 200 300 400 500

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug Se

p O

ct Nov

Dec Mo

nthl

y To

tal (

mm

) TIWI-MALINAO, ALBAY

Aphrodite V1101R2 RegCM3

0

100

200

300

400

Jan

Feb

Mar

Apr

May

Jun

Jul Aug

Sep

Oct

Nov Dec

Mo

nthl

y To

tal (

mm

)

SILAGO, LEYTE

Aphrodite V1101R2 RegCM3

Model fails to capture high precip, leads to underestimation in rainy season for Climate Type 2

1 2

4

2

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 50

100

150

200

250

300

350

400

450

METRO MANILA

PAGASA RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

CAGAYAN DE ORO

PAGASA RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 30

60

90

120

150

180

210

240

TIWI-MALINAO, ALBAY

Aphrodite RegCM3

0.00% 0.01% 0.10% 1.00%

10.00% 100.00%

0 20

40

60

80

100

120

140

160

SILAGO, LEYTE

Aphrodite RegCM3

RAINFALL HISTOGRAMS

1 2

4

2

0"50"100"150"200"250"300"350"400"

Jan"Feb"Mar"Apr"May"Jun" Jul" Aug"Sep"Oct"Nov"Dec"

Rai

n (m

m)

1"

0"50"100"150"200"250"300"350"400"

Jan"Feb"Mar"Apr"May"Jun" Jul"Aug"Sep"Oct"Nov"Dec"

Rai

n (m

m)

2"

0"50"

100"150"200"250"300"350"400"

Jan"Feb"Mar"Apr"May"Jun" Jul"Aug"Sep"Oct"Nov"Dec"

Rai

n (m

m)

4"

SIMILARITY IN CHARACTERISTICS POSSIBLY ACCORDING TO CLIMATE TYPES

Urban Expansion Impacts on Southwest Monsoon Rainfall

Julie Mae Dado1,2, Gemma Narisma1,2, and Faye Abigail Cruz2

1Ateneo de Manila University, Quezon City, Philippines 2Regional Climate Systems, Manila Observatory, Quezon City,

Philippines

Courtesy of MO Center for Environmental Geomatics

1972 2001

•  Model: MM5 with NOAH LSM

•  Area of study: Metro Manila 4 nested domains

•  Boundary Condition CDAS – NNRP

•  Schemes Cumulus Parametrization

Kain Fritsch (D01) Grell (D02, D03, D04)

PBL MRF

•  July-August- September

METHODOLOGY

D01

45km

D02

15km

D03

5km D04 1.67k

m

EXPANDED Urban DEFAULT Urban (Control)

10 ensemble experiments: 5 dry years, 5 wet years

RESULTS

WET YEARS 1970, 1972, 1986, 1990, 1995

DRY Years 1964, 1982, 1988, 2001, 2003

ALL Years

JAS AVERAGE PRECIPITATION % DIFFERENCE (EXPANDED URBAN – CONTROL)

Increase in precipitation by as much as 20% in expanded

urban areas

Potential mechanism for urban impact on SWM rain

Moist winds

SUMMARY AND CHALLENGES

Hardware, software

- parallel system, better database organization, uniform computer set up

Model Validation and Improvement

•  Availability and quality of observation data for model validation

•  Tailoring of the model not just through model physics and configuration but also through appropriate land use, given the significance of the impact of land cover on climate

•  Analysis of model performance and output in perspective of governing synoptic and local climate characteristics (e.g. climate types)

•  Appropriate implementation of bias correction for sectoral applications (e.g. agriculture (IRRI))

gemma teresa t. narisma, phd faye t. cruz, phd€¦ · faye t. cruz, phd regional climate systems...

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