# Presentation: 674580

## CDEP Consortium

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

- COLA, GFDL, IRI, LDEO, NCEP, GMAO(NSIPP)

**Report: 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?

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

## Findings from 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**
**Question: Can we use seasonal forecast skill to investigate observing system issues?**

## Design of 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

## Design of 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 of Results:

- 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

##  An Equatorial zonal section from 143E to 95W

**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)

## January Starts

**January Starts**

**Niño-3 SST anomalies**

**Notes:**
CGCM1 (GFDL) is ensemble mean where ensemble comes from atmospheric perturbations in the initial conditions.  Only 7 cases (years) included here.
CGCM2a is one of the ensembles in NSIPP where the ensemble members come from differences in the ocean analysis (different forcing, different error covariance structures, etc. Only 2 are shown here, but we now have 4 ensemble members).  9 cases (years), with the difficult 2000 etc cases - impacts skill statistics!
We don’t want to emphasize the differences between all, east, west experiments too much - the question is one of statistical significance.  However, as you will see with the multi-model ensemble for January - there is an indication that the western array doesn’t provide as much as the eastern array  - at least for nino3.  We have decided that we need to look at heat content since there is some indication that the western array helps with the central Pacific.  Perhaps this is not too surprising.  Bottom line:  this is just the first cut and we need to do much more analysis as well as more experiments.

## July Starts

***hybrid2b***

***Intermed1***

***CGCM2a***

***CGCM2b***

**July Starts**

***hybrid2a***

***Intermed***

***hybrid1***

**Niño-3 SST anomalies**

**Notes:**
July starts have more skill.
The difference between hybrid2a and 2b is that 2a weights more heavily to the observations in the ocean analysis.   The hybrid model seems very sensitive to balanced states.

## CGCM Forecast skill - January starts - multimodel ensemble

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

**Notes:**
Left panel is CGCM1 ensemble.
Centre panel is CGCM2a, with statistics calculated for the same cases as for CGCM1.
Even for the difficult january case, multimodel ensemble skill is increased vs individual skill!
Troubling is the reduced skill for west case, but the statistic is for the eastern Pacific.  Still too few cases included in the statistics.  For the large signal case (1997 El Nino), there is not really any difference between west and east.  But the “all” mooring case does better.
Forecast busts are fairly consistent between models!

## Niño3

- Niño4

- January starts

- July starts

**Notes:**
Ensemble members from NSIPP coupled forecasts.  Tendency for “all” ensemble to have tighter spread than the “east” or “west” ensembles - although only 3 members.  However, GFDL had similar result.

## CGCM2a - forecast anomaly correlations

***CGCM2a - forecast anomaly correlations***

***SST - July start ***

***HC  - July start***

***HC  - Jan start***

***3mo***

***6mo***

**Notes:**
This is result from one NSIPP ensemble membr (only because we didn’t have the GFDL ensembles for January at the time). Heat content may provide more of a discriminator on mooring influence than SST.  With only the eastern moorings, the forecast skill tends to diminish in the center of the basin where the skill seems to be highest at 6 months from the western mooring case.  Question, of course, is of statistical significance!

## Jan

- Jul

**Notes:**
NSIPP ocean analyses.  Corrections to model errors (seen in too cold ocean below the thermocline even while the thermocline is slightly too diffuse) achieved across the Pacific in the TAO-east case, but not in TAO-west case (still too diffuse in the east) because the XBTs are effective in removing the bias.  (Not many XBT obs in the east, as seen in a following slide).

## Analysis: Average Temperature in upper 300m

**Notes:**
Even though difference inforecast skill, there is little difference in heat content between analyses.  The differences from TAO are under investigation - most likely just a difference in calculation, with coarser resolution in the TAO data than in the models.

## Analysis: SST

**Notes:**
NSIPP SST analysis - even though relaxing to observed SST, the subsurface ocean state is important to the SST balance.  Hence SST in the west is not recovered with TAO-east case.  Similarly, SST in the east is not recovered in the TAO-west case.  Note that this result is probably model dependent.  However, with the NSIPP model, the entire TAO array is essential to a good SST field.

## Slide 15

**Notes:**
Just to show that our results are complicated by the fact that we include the XBT data base.  However, there aren’t a lot of observations in the eastern equatorial pacific.

## 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.

**Notes:**
Just a reminder that (again), forecast skill is seasonal and impacted significantly by the seasonal coupled model shocks and drifts.  January starts are a challenge.  

## 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**
**Preliminary results:**
**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**