SEACLID / CORDEX Southeast Asia
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Transcript of SEACLID / CORDEX Southeast Asia
SEACLID / CORDEX Southeast AsiaGemma T. Narisma, Manila Observatory
On behalf of the SEA Regional Climate Initiative (SEARCI)
Coordinator: Fredolin Tangang (The National University of Malaysia)Indonesia: Edvin Aldrian, Dodo Gunawan (BMKG)Malaysia: Fredolin Tangang, Liew Juneng (UKM)Philippines: Gemma Narisma, Faye Cruz (Manila Observatory)Thailand: Jerasorn Santisirisomboon (Ramkhamhaeng University)
Patama Singhruck (Chulalongkorn University)Vietnam: Phan Van Tan, Thanh Ngo-Duc (NVU Hanoi University of Science)
YMC Workshop, 28-30 Jan 2015, CCRS Singapore
Southeast Asia Regional Climate Initiative (SEARCI), August 2012
• Scientists from Vietnam, Malaysia, the Philippines, Indonesia and Thailand in workshop hosted by the VNU Hanoi University of Science
• Motivation: To have a platform for regional collaboration on climate related research for SEA and build the capacity of the region in regional climate science
South Asia
East Asia
CORDEX domainsCORDEX domains
CORDEX Southeast Asia
Nov 2012
Jun 14,2013
Initial correspondence with WCRP (Michel Rixen) and CORDEX (Colin Jones)
Formal invitation by Ghassem Asrar (WCRP) for SEACLID to join the CORDEX network
• SEACLID activities streamlined to CORDEX• Possible contribution of additional simulations by CORDEX
affiliated centers over SEACLID domain (e.g. India, Australia, UK)• Capacity development and training for SEACLID in coordination
with CORDEX-Asia
May 2013 APN proposal approved for funding under the ARCP Programme for 3 years beginning October 2013
Pledged Commitments in SEACLID / CORDEX SEA
Country GCM Institution & Country developed the GCM
RCP RCM
Vietnam CNRM-CM5 Centre national de Recherches Meteorologiques, France RCP8.5, 4.5 RegCM4
Philippines HadGEM2 Hadley Centre, UK RCP8.5, 4.5 RegCM4
Thailand MPI-ESM-MR Max Planck Institute for Meteorology, Germany RCP8.5, 4.5 RegCM4
Thailand EC-Earth EC-Earth consortium RCP8.5, 4.5 RegCM4
Indonesia CSIRO MK3.6 CSIRO, Australia RCP8.5, 4.5 RegCM4
Malaysia CanESM2 Canadian Centre for Climate Modeling and Analysis, Canada RCP8.5, 4.5 RegCM4
Malaysia IPSL-CM5A-LR Institute Pierre-Simon Laplace, France RCP8.5, 4.5 RegCM4
Malaysia GFDL-ESM2M GFDL, USA RCP8.5, 4.5 RegCM4
Australia CNRM-CM5 Centre national de Recherches Meteorologiques, France RCP8.5 CCAM
Australia CCSM4 NCAR, USA RCP8.5 CCAM
Australia ACCESS1.3 CSIRO, Australia CCAM
Hong Kong SAR CCSM or CESM NCAR, USA WRF
United Kingdom HadGEM2-ES Hadley Centre, UKMO PRECIS
South Korea HadGEM2-AO Hadley Centre, UKMO WRF
RegCM4 Experiments Setup• Domain: 36 km, 81.14°E-143.86°E; 15.04°S ~ 39.84°N• PBL: Holtslag (1990)• Radiation: CCSM • Large scale moisture: SUBEX (Pal et al. 20)• Land-surface treatment: BATSe• Cumulus parameterization:
- Grell / Arakawa-Schubert (closure)- MIT Emanual- MIT (O) / Grell (L)- Grell (O) / MIT (L)- Grell / Fritch-Chappell (closure)- Kuo
• Ocean flux treatment: - BATSe- Zeng (iocnrough=1)- Zeng (iocnrough=2)
• Lateral boundary conditions: ERA Interim• Run length: 1989 – 2008 (20 years)
18 Simulations (100% completed)
DJF Grell AS MIT Eman Eman(l) Grell (o) Grell FC Grell (l) Eman (o) Kuo
OCEAN FLUX
Model enhances the cold bias
DJF Grell AS MIT Eman Eman(l) Grell (o) Grell FC Grell (l) Eman (o) Kuo
High correlation for north half of domainPoor correlation in south half of domain
APHRODITE data: – daily rainfall (V1003R1), 1951-2007– 0.25º– Monsoon Asia (60E-150E, 15S-55N)
http://www.chikyu.ac.jp/precip/products/index.html
Slide courtesy of Thanh Ngo-Duc, Vietnam
Annual Precipitation Biases (vs CRU)
Too wet !!!
Still too wet over the equatorial regions!
Slide courtesy of Juneng Liew, Malaysia
…cont.
Slide courtesy of Juneng Liew, Malaysia
Validation Issue
• Variations among observational products can be large.• Compare with all the available datasets.
Rainfall (Highest – Lowest)
Observation datasets - APHRODITE, CRU, GPCC, TRMM (1999 onward)
Slide courtesy of Juneng Liew, Malaysia
Seasonal Precipitation Spatial Comparison
• Correlation – ~0.5-0.7 • Inter-model variations higher during the winter season.
DJF JJA
Slide courtesy of Juneng Liew, Malaysia
Key questions:
• What are the key processes and mechanisms in the MC that are not captured by the regional climate model, resulting to poor model performance and inability to capture seasonal dynamics?
• What are the appropriate adjustments and modifications to cumulus parameterization schemes for the model to properly simulate rainfall and rainfall dynamics?
• How do variations in SSTs affect the simulated model climatology in the MC?
• Does the model adequately capture the diurnal cycle, Madden-Julian Oscillation (MJO), and the extreme rainfall events associated with MJO and ENSO events and are the associated physical dynamics simulated well?
Coordinator: Fredolin Tangang (The National University of Malaysia)Indonesia: Edvin Aldrian, Dodo Gunawan (BMKG)Malaysia: Fredolin Tangang, Liew Juneng (UKM)Philippines: Gemma Narisma, Faye Cruz (Manila Observatory)Thailand: Jerasorn Santisirisomboon (Ramkhamhaeng University)
Patama Singhruck (Chulalongkorn University)Vietnam: Phan Van Tan, Thanh Ngo-Duc (NVU Hanoi University of Science)
(http://www.ukm.edu.my/seaclid-cordex)
ERA Interim APHRODITE ERAIN-APHRO
D. P. Dee et al (2011)Yatagai et al. (2012) and Yasutomi et al. (2011)
Bias from the Boundary Condition
Biases outside the variability of APHRODITE
MIT
MIT(O)/Grell(L)
R1R1R2R2R3R3R4R4
R5R5 R6R6 R7R7R8R8
R9R9R10R10R11R11R12R12
R13R13R14R14
R15R15R16R16R17R17
R18R18R19R19R20R20
MIT(L)/Grell(O)
Grell/FC Kuo
PRECIPITATION Annual Cycles (vs 4 Obs.): Correlation Coefficient
Grell/FC
South
North
Slide courtesy of Juneng Liew, Malaysia
TEMPERATURE Correlation per region (aphrodite)
R1R1R2R2R3R3R4R4
R5R5 R6R6 R7R7R8R8
R9R9R10R10R11R11R12R12
R13R13R14R14
R15R15R16R16R17R17
R18R18R19R19R20R20
South
North
• WCRP sponsored exercise – a framework to downscale CMIP5 output.
• Providing a global coordination of Regional Climate Downscaling for improved regional climate change adaptation and impact assessment.
• Within AR5 timeline and beyond.
Model improves the cold bias of ERAIN
Cold bias esp during
cold months
Model warm bias
Cold bias: Jan-May
Warm bias: June-Dec
Warm biasCold bias
Summary Analysis: Seasonal Cycle, PDFs
CORRELATION
BIASMODEL SKILL
Where does MIT Emanuel perform best?