# Presentation: 858106

## 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**

## CDEP Consortium

- Ocean Data Assimilation Consortium for Seasonal-to-Interannual Prediction
- (ODASI)

- COLA, GFDL, IRI, LDEO, NCEP, GMA

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

- _COLA_
- Jim Kinter
- Ed Schneider
- Ben Kirtman
- Bohua Huang

- _GMAO_
- Michele Rienecker
- Chaojiao Sun
- Jossy Jacob
- Robin Kovach
- Anna Borovikov

- _GFDL_
- Tony Rosati
- Matt Harrison
- Andrew Wittenberg

- _IRI_
- Steve Zebiak
- Eli Galanti
- Michael Tippett

- _LDEO_
- Alexey Kaplan
- Dake Chen

- _NCEP_
- Dave Behringer

## ODASI Themes:

- 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, 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?

## 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 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)

## 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

## January Starts

**January Starts**

**Niño-3 SST anomalies**

## July Starts

***hybrid2b***

***Intermed1***

***CGCM2a***

***CGCM2b***

**July Starts**

***hybrid2a***

***Intermed***

***hybrid1***

**Niño-3 SST anomalies**

## CGCM Forecast skill - January starts - multimodel ensemble

**CGCM Forecast skill - January starts - multimodel ensemble**

## Niño3

- Niño4

- January starts

- July starts

## CGCM2a - forecast anomaly correlations

***CGCM2a - forecast anomaly correlations***

***SST - July start ***

***HC  - July start***

***HC  - Jan start***

***3mo***

***6mo***

## Jan

- Jul

## Analysis: Average Temperature in upper 300m

## XBT profiles available per month

- Dec 1996:  1440

- Jun 1997:  2021

## 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.

## 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 **