Research Activity in Japan on Seasonal Forecasts by T'Ose MRIJMA for 12th WGSIP at RSMAS

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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

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Seasonal Prediction Experiment in the new JMA/MRI
Coupled Model
Yasuda, T. (MRI), Y. Takaya (JMA), Y. Naruse
(JMA) and T.Ose (MRI)
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.
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Seasonal Forecast System and Experiments
CGCM (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 adjustment Experiments
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)
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Asian Monsoon Precipitation is much improved by
CGCM.
AGCM MSSS
CGCM MSSS
CGCM COR
AGCM COR
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Asian Summer Monsoon Index (WYI)(4-month lead
JJA from JAN)
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Seasonal-to-interannual climate prediction using
SINTEX-F CGCM ENSO and IOD prediction Jing-Jia
Luo (? ??, luo_at_jamstec.go.jp) Climate
Variations Research Program Frontier Research
Center for Global Change JAMSTEC, Japan

Collaborators Sebastien Masson, Swadhin
Behera, Yukio Masumoto, Hirofumi Sakuma,
and Toshio Yamagata
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The SINTEX-F Coupled GCM (Luo et al. GRL 2003, J.
Clim. 2005a Masson et al. GRL 2005)
1. Model components AGCM (MPI, Germany)
ECHAM4 (T106L19) OGCM (LODYC,
France) OPA8 (2? x 0.5??2?, L31)
Coupler (CERFACS, France) OASIS2
No flux correction, no sea ice model 2.
International collaborators LODYC
OPA model group INGV (Italy) Antonio
Navarras group MPI-Met ECHAM model
group CERFACE OASIS coupler group
PRISM project group

Running on the Earth Simulator
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ENSO prediction skill of 10 coupled GCMs
Nino3.4 index (1982-2001)
Adapted from Jin et al. 2008, APCC CliPAS
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Extended ENSO prediction
(120º-170ºW, 5ºS-5ºN)
Nino3.4 SSTA prediction
Luo et al., J. Climate, 2008, 84-93.
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IOD Impacts in 2006 boreal fall
Rainfall Anomalies Sep-Nov 2006
Corresponding SST Anomalies
fires in Borneo and Sumatra
Severe drought devastated farmers in eastern
Australia, estimated loss of 8 billion AUD.
More than 1 million people in Kenya, Somalia and
neighboring countries were affected by the
flooding.
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Both winter and spring barrier exist
(90º-110ºE, 10ºS-0º)
Indian Ocean Dipole 9-member ensemble hindcasts
(1982-2004)
0.5
Predictable up to 2 seasons ahead.
Luo et al., J. Climate, 2007, 2178-2190.
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Summary

• 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.

• 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.

Real time forecasts at one month intervals
http//www.jamstec.go.jp/frcgc/research/d1/iod/in
dex.html
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Japanese CLIMATE 2030 Project
From Prof.Kimoto (CCSR)
110km mesh model

• 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)

60km mesh model
5-min topography
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Decadal Predictability?Assimilation vs.
Hindcasts w/ w/o initialization
Global SAT
PDO
SPAM System for Prediction and Assimilation by
MIROC
Motizuki et al. (2009)
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Solar cycle effect on climate
-Review and recent works related on the
modulation of the Annular Mode-
Yuhji Kuroda (Meteorological Research Institute,
JAPAN)
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0.1 variation of solar irradiance is observed
for the 11-year Solar Cycle (SC)
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Observation (ERA40)
Correlation with S-SAM (Nov)
Contour greater than 0.5 Shading greater than 0.4
Zonal wind
S-SAM
0.6
Correlation with surface
0.4
larger
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Experiment with varying UV
Ultra Solar (US) High Solar (HS) Low Solar (LS)
UVstrong
UVweak
Stratospheric SAM (S-SAM) EOF1-Z30 in late
winter (Dec) Compares correlation with S-SAM
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Chemistry-Climate Model
Correlation with S-SAM (Dec)
Contour greater than 0.5 Shading greater than
0.44 (95)
Zonal wind
0.8
Correlation with surface
0.6
0.3
larger
Stratosphere-troposphere coupling tends to be
stronger with increasing UV!!
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Possible Physical mechanism of the solar-cycle
modulation of the SAM

• Solar irradiance change is too small to change
  climate energetically.
• UV change is one promising process.
• Ozone anomaly changes temperature in the lower
  stratosphere to upper troposphere in summer.
• Such temperature anomaly creates anomalous zonal
  wind.
• Anomalous zonal wind modifies wave propagation.

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Bibliography 1, Solar-cycle modulation of
winter-NAO Kodera, K., GRL 2002,
doi10.1029/2001GL014557 Ogi et al., GRL 2003,
doi10.1029/2003GL018545 Kuroda et al., JGR 2008,
doi10.1029/2007jd009336 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,
doi10.1029/2005GL022516 Kuroda et al., GRL 2007,
doi10.1029/2007GL030983 3, Simulation of
solar-cycle modulation of AO or SAM by
CCM Tourpali et al., GRL 2005, doi10.1029/2005GL0
23509 Kuroda and Shibata, GRL 2006,
doi10.1029/2005GL025095
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Potential predictability of seasonal mean river
discharge in dynamical ensemble prediction using
MRI/JMA GCM

• Tosiyuki Nakaegawa
• MRI, Japan

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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.
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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 period1951-2000

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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).

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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)
Improvement Basin areas gt106km2
Does not work effectively Cause deterioration