CDEP ConsortiumOcean 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 RosatiJim Kinter, Alexey Kaplan, Dave Behringer

http://nsipp.gsfc.nasa.gov/ODASI

CDEP ConsortiumOcean Data Assimilation Consortium for Seasonal-to-Interannual Prediction

(ODASI)

COLA, GFDL, IRI, LDEO, NCEP, GMA

http://nsipp.gsfc.nasa.gov/ODASIhttp://nsipp.gsfc.nasa.gov/ODASI

COLAJim KinterEd SchneiderBen KirtmanBohua Huang

GMAOMichele RieneckerChaojiao SunJossy JacobRobin KovachAnna Borovikov

GFDLTony RosatiMatt HarrisonAndrew Wittenberg

IRISteve ZebiakEli GalantiMichael Tippett

LDEOAlexey KaplanDake Chen

NCEPDave Behringer

ODASI Themes:1. ODA product intercomparisons (models, assimilation

methodologies, assimilation parameters) using a common forcing data set and common QC’d in situ data streams

Models: MOM4, MOM3, Poseidon, Cane-Patton, LDEO4Methodologies: 3DVAR, OI, EnKF, Reduced state KF and optimal

smoother, bias correction strategiesCoupled Forecast Sytems: CGCMs, Hybrid models, Intermediate

models

2. Development of observational data streams3. Validation of assimilation products in forecast experiments

4. 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?

Coupled Data Assimilation Workshop, Portland, April 2003:• Assimilation of subsurface temperature improves Niño-3 forecast skill (usually), but we aren’t 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 model’s coupled modes

Can we use seasonal forecast skill to comment on observing system issues?

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 Atlas’s SSMI surface wind analysis— include a restoration to observed SST and SSS

The Experiments (ctd):

Initial conditions for forecast experiments prepared using1. All in situ temperature profiles, including the full TAO array2. Western Pacific (west of 170W) TAO moorings3. 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)

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

CGCM1

hybrid1 hybrid2a hybrid2b Intermed1

CGCM2a CGCM2b

January StartsJanuary Starts

All TAO moorings

West TAO moorings

East TAO moorings

Obs (Reynolds)

Niño-3 SST anomaliesNiño-3 SST anomalies

hybrid2b

Intermed1

CGCM2a CGCM2b

July StartsJuly Starts

hybrid2a Intermedhybrid1

All TAO moorings

West TAO moorings

East TAO moorings

Obs (Reynolds)

Niño-3 SST anomaliesNiño-3 SST anomalies

CGCM Forecast skill - January starts - multimodel ensemble

All TAO moorings

West TAO moorings

East TAO moorings

Obs (Reynolds)

Niño3Niño3

Niño4Niño4

January startsJanuary starts July startsJuly starts

CGCM2a - forecast anomaly correlationsCGCM2a - forecast anomaly correlations

SST - July start HC - July start HC - Jan start

3mo3mo

6mo6mo

Jan

Jul

Analysis: Average Temperature in upper 300mAnalysis: Average Temperature in upper 300m

XBT profiles available per month

Dec 1996: 1440 Jun 1997: 2021

Vintzileos et al. (GSFC)

Seasonal drift of NSIPP CGCMv1 as a function of forecast lead time

June for each initialization month. January for each initialization monthNiño 3 anomaly correlation of 0.9.

Coupled Data Assimilation Workshop, Portland, April 2003:• Assimilation of subsurface temperature improves Niño-3 forecast skill (usually), but we aren’t 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 model’s coupled modes

Can we use seasonal forecast skill to comment on observing system issues?

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