research activity in japan on seasonal forecasts by t.ose (mri/jma) for 12 th wgsip at rsmas
DESCRIPTION
Research Activity in Japan on Seasonal Forecasts by T.Ose (MRI/JMA) for 12 th WGSIP at RSMAS. CHFP with JMA/MRI-CGCM03 from Yasuda, T. at MRI ENSO and IOD Prediction with SINTEX-F CGCM from Luo J.-J. at Frontier/JAMSTEC - PowerPoint PPT PresentationTRANSCRIPT
Research Activity in Japan on Seasonal Forecasts by T.Ose (MRI/JMA) for 12th WGSIP at RSMAS
• CHFP with JMA/MRI-CGCM03
from Yasuda, T. at MRI• ENSO and IOD Prediction with SINTEX-F CGCM
from Luo J.-J. at Frontier/JAMSTEC • Near-Future Prediction in KAKUSHIN project
from Prof. Kimoto at CCSR/Tokyo • Solar cycle effect on climate
from Kuroda, Y. at MRI• River discharge predictability
from Nakaegawa, T. at MRI
Seasonal Prediction Experimentin the new JMA/MRI Coupled Model
The new system for forecasting SST in the equatorial Pacific using a coupled atmosphere-ocean model has been developed at JMA/MRI. This system is being used for the new JMA operational system for ENSO forecast since spring 2008.
We have conducted the retrospective seasonal prediction experiments using this system based on the CHSP strategy.
Yasuda, T. (MRI), Y. Takaya (JMA), Y. Naruse (JMA) and T.Ose (MRI)
Seasonal Forecast System and ExperimentsCGCM (JMA/MRI-CGCM03)
System Components AGCM: JMA atmospheric model TL95L40 OGCM: MRI Community Ocean Model (MRI.COM) 1.0x(0.3-1.0)L50 Coupling time: 1 hour Flux adjustment: Momentum and heat fluxes adjustmentExperiments 7-month 10-member ensemble prediction initiated at the end of January, April, July and October from 1979 to 2006.Initial Conditions Atmosphere: JRA-25 reanalysis Ocean: Ocean Data Assimilation System “Multivariate Ocean Variational Estimation System (MOVE-G/MRI.COM)”
Asian Monsoon Precipitation is much improved by CGCM.
CGCMMSSS
CGCMCOR
AGCMMSSS
AGCMCOR
Asian Summer Monsoon Index (WYI)(4-month lead: JJA from JAN)
AGCMCGCM
WYI Definition : (0-20N,40-110E)
Mean of U850–U200
Blue: ForecastRed: Analysis
ACC: 0.59
Blue: ForecastRed: Analysis
ACC: 0.35
Seasonal-to-interannual climate prediction using SINTEX-F CGCM
– ENSO and IOD prediction–
Jing-Jia Luo (羅 京佳 , [email protected])
Climate Variations Research ProgramFrontier Research Center for Global Change
JAMSTEC, Japan
Collaborators: Sebastien Masson, Swadhin Behera, Yukio Masumoto, Hirofumi Sakuma, and Toshio Yamagata
1. Model components: AGCM (MPI, Germany): ECHAM4 (T106L19) OGCM (LODYC, France): OPA8 (2 x 0.52, L31) Coupler (CERFACS, France): OASIS2
* No flux correction, no sea ice model
2. International collaborators: LODYC: OPA model group INGV (Italy): Antonio Navarra’s group MPI-Met: ECHAM model group CERFACE: OASIS coupler group PRISM project group
The SINTEX-F Coupled GCM(Luo et al. GRL 2003, J. Clim. 2005a; Masson et al. GRL 2005)
Running on the Earth Simulator
ENSO prediction skill of 10 coupled GCMs
Nino3.4 index(1982-2001)
Adapted from Jin et al. 2008, APCC CliPAS
Nino3.4 SSTA prediction
Luo et al., J. Climate, 2008, 84-93.
Extended ENSO prediction:
Ensemble mean
Persistence
ACC
ACC
RMSE
0.5
Each member
(120º-170ºW, 5ºS-5ºN)
Rainfall Anomalies Sep-Nov 2006 Corresponding SST Anomalies
More than 1 million people in Kenya, Somalia and neighboring countries were affected by the flooding.
Severe drought devastated farmers in eastern Australia, estimated loss of 8 billion AUD.
IOD Impacts in 2006 boreal fall
fires in Borneo and Sumatra
Both winter and spring barrier exist
(90º-110ºE, 10ºS-0º)
0.5
Luo et al., J. Climate, 2007, 2178-2190.
Predictable up to ~2 seasons ahead.
Indian Ocean Dipole
9-member ensemblehindcasts
(1982-2004)
• ENSO can be predicted out to 1-year lead and even up to 2-years ahead in some cases.
• ISOs may limit ENSO predictability in certain cases.
• The results suggest a potential predictability for decadal ENSO-like process.
Summary:
Real time forecasts at one month intervals: http://www.jamstec.go.jp/frcgc/research/d1/iod/index.html
• IOD can be basically predicted up to ~2 seasons ahead.• Extreme IOD events (and their climate impacts) can be predicted up to 1-year lead.
Ensemble hindcast/forecast
Assimilation/Initialization
• A near-term prediction up to 2030 with a high-resolution coupled AOGCM
– 60km Atmos + 20x30km Ocean– w/ updated cloud PDF scheme, PBL, etc– advanced aerosol/chemistry
• Estimate of uncertainty due to initial conditions– 10(?)-member ensemble– For impact applications
• water risk assessment system• impacts on marine ecosystems• etc.
• Test run w/ 20km AOGCM (in 2011)
110km mesh model
60km mesh model
5-min topography
Japanese CLIMATE 2030 ProjectFrom Prof.Kimoto (CCSR)
Motizuki et al. (2009)
Decadal Predictability?Assimilation vs. Hindcasts w/ & w/o initialization
SPAMSPAMSPAMSPAMSPAMSPAM
SPAM: System for
Prediction and
Assimilation by
MIROC
Global SAT PDO
Solar cycle effect on climate
Yuhji Kuroda(Meteorological Research Institute, JAPAN)
-Review and recent works related on the modulation of the Annular Mode-
~0.1% variation of solar irradiance is observed for the 11-year Solar Cycle (SC)
Observation (ERA40)
Zonal wind Contour greater than 0.5
Shading greater than 0.4
Correlation with S-SAM (Nov)
0.6Correlation with surface
0.4
larger
S-SAM
Experiment with varying UV
Ultra Solar (US)
High Solar (HS)
Low Solar (LS)
UV:strong
UV:weak
Stratospheric SAM (S-SAM): EOF1-Z30 in late winter (Dec)
Compares correlation with S-SAM
Zonal wind Contour greater than 0.5
Shading greater than 0.44 (95%)
Correlation with S-SAM (Dec)
0.8Correlation with surface
0.6 0.3
Stratosphere-troposphere coupling tends to be stronger with increasing UV!!
Chemistry-Climate Model
larger
1. Solar irradiance change is too small to change climate energetically.
2. UV change is one promising process.
3. Ozone anomaly changes temperature in the lower stratosphere to upper troposphere in summer.
4. Such temperature anomaly creates anomalous zonal wind.
5. Anomalous zonal wind modifies wave propagation.
Possible Physical mechanism of the solar-cycle modulation of the SAM
Equator
UV
wave
O3
WF
interaction
MC
Strato
Tropo
North Pole
Bibliography
1, Solar-cycle modulation of winter-NAO
Kodera, K., GRL 2002, doi:10.1029/2001GL014557
Ogi et al., GRL 2003, doi:10.1029/2003GL018545
Kuroda et al., JGR 2008, doi:10.1029/2007jd009336 in press
Kuroda, Y., J. Meteorol. Soc. Japan 2007,Vol 85, 889-898
2, Solar-cycle modulation of late-winter/spring SAM
Kuroda and Kodera, GRL 2005, doi:10.1029/2005GL022516
Kuroda et al., GRL 2007, doi:10.1029/2007GL030983
3, Simulation of solar-cycle modulation of AO or SAM by CCM
Tourpali et al., GRL 2005, doi:10.1029/2005GL023509
Kuroda and Shibata, GRL 2006, doi:10.1029/2005GL025095
Potential predictability of seasonal mean river
discharge in dynamical ensemble prediction using
MRI/JMA GCM
Tosiyuki NakaegawaMRI, Japan
Physical characteristics of river discharge
• River discharge is a collection of total runoffs in an upper river basin, which is similar to the area average process.
The collection is likely to reduce the unpredictable variability and, as a result, to enhance the predictability.
P-E: each grid
River discharge: accumulation
C20C Experiment setup• AGCM: MJ98 , T42 with 30 vertical layers
• River Routing Model: GRiveT, 0.5o river channel network of TRIP, velocity: 0.4m/s
• Member: 6• SST & Sea Ice : HadISST (Rayner et al. 2003)
• CO2 : annualy varying
• Integration period: 1872-2005
• Analysis period : 1951-2000
Potential Predictability
• Definition: The maximum value that an ensemble approach can reach, assuming that perfectly predicted SSTs are available and that the model perfectly reproduces atmospheric and hydrological processes.
• Variance ratio : measure of
PP based on the ANOVA
(Rowell 1998).222
222
22
/
/
INTSSTTOT
INTEMSST
TOTSST
n
R
Collection Effect
• How much influence does the collection effect over a river basin have on the potential predictability of river discharge?
Variance Ratio: (Discharge)-(P-E)
ImprovementBasin areas >106km2Does not work effectively
Cause deterioration