CDEP Consortium

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CDEP Consortium Ocean Data Assimilation
Consortium for Seasonal-to-Interannual Prediction
(ODASI)
COLA, GFDL, IRI, LDEO, NCEP, GMAO(NSIPP)
Ed Schneider (COLA) and Chaojiao Sun
(GMAO) Michele Rienecker, Steve Zebiak, Tony
Rosati Jim Kinter, Alexey Kaplan, Dave Behringer
http//nsipp.gsfc.nasa.gov/ODASI
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CDEP Consortium Ocean Data Assimilation
Consortium for Seasonal-to-Interannual Prediction
(ODASI)
COLA, GFDL, IRI, LDEO, NCEP, GMAO
GMAO Michele Rienecker Chaojiao Sun Jossy
Jacob Nicole Kurkowski Robin Kovach Anna Borovikov
GFDL Tony Rosati Matt Harrison Andrew Wittenberg
COLA Jim Kinter Ed Schneider Ben Kirtman Bohua
Huang
NCEP Dave Behringer
IRI Steve Zebiak Eli Galanti Michael Tippett
LDEO Alexey Kaplan Dake Chen
http//nsipp.gsfc.nasa.gov/ODASI
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• ODASI Themes
• ODA product intercomparisons (models,
  assimilation methodologies, assimilation
  parameters) using a common forcing data set and
  common QCd in situ data streams
• Models MOM4, MOM3, Poseidon, Cane-Patton,
  LDEO4
• Methodologies 3DVAR, OI, EnKF, Reduced state KF
  and optimal smoother, bias correction strategies
• Coupled Forecast Sytems CGCMs, Hybrid models,
  Intermediate models
• Development of observational data streams
• Validation of assimilation products in forecast
  experiments
• Observing system impacts - focused on TAO
• TAO array was established for S-I forecasting.
• Is it effective in its present configuration?
• Could it be modified to provide better support
  for S-I forecasts?
• what is its role c.f. other elements of the
  ocean observing system?

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• Coupled Data Assimilation Workshop, Portland,
  April 2003
• Assimilation of subsurface temperature improves
  Niño-3 forecast skill (usually), but we arent
  sure why (initialization of state, anomalies)
• Forecast errors are dominated by coupled model
  shocks and drifts
• It is not yet clear as to the best method for
  forecast initialization
• consistent with observed state
• consistent with CGCM climatological biases
• initialize the models coupled modes
• Can we use seasonal forecast skill to comment on
  observing system issues?

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• The Experiments
• initial conditions for 1 January and 1 July,
  1993 to 2002
• Forecast duration 12 months
• 6-member ensembles for each system
• The observations assembled and QC'd by Dave
  Behringer at NCEP
• historical XBTs from NODC, MEDS
• TAO from PMEL web site
• Argo profiles from GODAE/Monterey server
• Surface forcing assembled by GFDL
• NCEP GDAS daily forcing momentum, heat,
  freshwater
• surface wind climatology replaced by Atlass
  SSMI surface wind analysis
• include a restoration to observed SST and SSS

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• The Experiments (ctd)
• Initial conditions for forecast experiments
  prepared using
• 1. All in situ temperature profiles, including
  the full TAO array
• 2. Western Pacific (west of 170?W) TAO moorings
• 3. Eastern Pacific TAO moorings
• Hypothesis the Eastern Pacific data important
  for shorter lead forecasts and the Western
  Pacific data important for longer lead forecasts.
• Address uncertainty in the results by use of
• ensembles
• different assimilation systems
• different CGCMs
• different classes of models (CGCMs, hybrid,
  intermediate)

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• Outline
• Niño 3 SST anomaly Forecast skill
• from different models, assimilation systems,
  observational constraints
• January consensus forecast from CGCMs
• Reynolds SST is verification
• Ensemble spread
• Skill in the equatorial band (analysis is
  verification)
• Impacts on the Analysis
• Conclusions

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Metrics GODAE Equatorial Pacific Intercomparison
Project Climatologies Annual mean statistics An
Equatorial zonal section from 143?E to
95?W Meridional sections between 30?S-30?N at
165?E, 155?W, 140?W, and 110?W Annual mean
volume transport of the EUC, NECC, SEC, NGCUC,
and MC Seasonal variability monthly mean
structure of zonal current, temperature and
salinity in the upper 400m, heat content in the
upper 300m and sea surface height and/or dynamic
height An Equatorial zonal section from 143?E to
95?W Meridional sections between 30?S-30?N at
165?E, 155?W, 140?W, and 110?W Annual cycle at
165?E, 170?W, 140?W, and 110?W on the equator
Annual cycle of volume transport in the EUC,
NECC, and SEC. Annual cycle of currents at 15m,
20?S and 20?N Temporal Variability Pentads on an
equatorial zonal section from 143?E to 95? and on
meridional sections at 165?E, 155?W, 140?W, and
110?W between 8?S and 8?N Daily averages of
temperature, currents, and surface height at
TAO/Triton and PIRATA moorings Daily average of
surface height and/or dynamic height (relative to
100 db) at selected tropical tide
gauges Historical interannual variability Warm
water volume (spatial integral of D20, 5?S-5?N,
120?E to 80?W Meinen and McPhaden)
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Niño-3 SST anomalies
January Starts
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Niño-3 SST anomalies
CGCM2a
CGCM2b
July Starts
Intermed1
hybrid2b
hybrid2a
Intermed
hybrid1
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CGCM Forecast skill - January starts - multimodel
ensemble
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January starts
July starts
Niño3
Niño4
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CGCM2a - forecast anomaly correlations
SST - July start
HC - Jan start
HC - July start
3mo
6mo
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Jan
Jul
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Analysis Average Temperature in upper 300m
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Analysis SST
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(No Transcript)
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Seasonal drift of NSIPP CGCMv1 as a function of
forecast lead time
June for each initialization month. January for
each initialization month Niño 3 anomaly
correlation of 0.9.
Vintzileos et al. (GSFC)
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• Coupled Data Assimilation Workshop, Portland,
  April 2003
• Assimilation of subsurface temperature improves
  Niño-3 forecast skill (usually), but we arent
  sure why (initialization of state, anomalies)
• Forecast errors are dominated by coupled model
  shocks and drifts
• It is not yet clear as to the best method for
  forecast initialization
• consistent with observed state
• consistent with CGCM climatological biases
• initialize the models coupled modes
• Can we use seasonal forecast skill to comment on
  observing system issues?

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• Conclusions
• Early stage of the analysis - we have to study
  the results in more detail
• Statistical significance of results - need more
  ensemble members and more cases of both warm and
  cold events for robust conclusions
• Eastern array definitely improves forecast skill
• Western array improves skill in central Pacific
• Entire array
• best results
• probably associated with atmospheric response
  across the entire Pacific
• some indication that get a tighter spread
• results are subtle - complicated by coupled
  model shocks and drifts