## An improved ocean state product

The main goal of this project is to develop a state-of-the-art data assimilation system that incorporates

near-realtime data with which we can provide the community a high quality ocean state product. This

assimilation system consists of an Ensemble Filter applied to GFDL’s second generation fully

coupled climate model (CM2.1,

Delworth et al. [2006]).

The ocean component of the coupled data assimilation (CDA) is the

fourth version of the Modular Ocean Model (MOM4, Griffies et al. [2004]) configured with 50

vertical levels (22 levels of 10-m thickness each in the top 220 m) and 1°

horizontal B-grid resolution, telescoping to 1/3° meridional spacing

by 1° near the equator.

To the right is a cartoon from Zhang et al. [2007]. It illustrates how a two-step data assimilation procedure works for updating the estimate

of the probability distribution of a single state variable x given a single observation y in

the ensemble adjustment filter under the least squares framework. The right-hand

column represents step 1: updating the probability density function (PDF) at the observation

location as a new observation comes in (denoted by the thick-dotted arrow labeled STEP 1).

The solid arrow 1 denotes that the prior PDF at the observation

location is squashed by a new observation (denoted by the bottom-right

dashed curve) and the solid arrow 2 represents the shift of the prior

ensemble mean at the observation location due to the new observation.

The thick-dotted arrow extending from the right-hand column to the

left-hand column denotes step 2: using the correlation distribution

(shaded region) to distribute the observation increments to

impacted grid points. The solid arrow 3 represents the process

of updating the PDF of a grid point.

The figure to the above shows

anomalies of the Niño-3.4 ocean temperature for the control (denoted

CTL), the ODA (denoted ASSIM), and the truth. Curves in the bottom

panel are the vertical averages over the top 250 m for the control

(blue), the ODA (red), and the truth (black). The upper (lower)

bounds of the control-ODA spread are plotted by the green-dashed

(pink dashed) lines in the bottom panel. The control (model

climatological) spread is estimated by six 25-yr nonoverlapping time

series and the ODA spread is computed by six ensemble members in the filter. All anomalies are

computed using the truth’s climatology, and the contour interval for

the first three panels is 0.5°C.

More details about CDA system can be found in

Zhang et al. [2007] and Zhang et al. [2008a ,b and c].

Griffies et al. [2004];

A marked improvement in the data assimilation’s skill is seen when

the Argo observational data is included (Chang et al. [2008]).

Argo is a global array of 3,000 free-drifting profiling floats that

measures the temperature and salinity of the upper 2000 m of the ocean.

This allows, for the first time, continuous monitoring of the temperature,

salinity, and velocity of the upper ocean, with all data being relayed

and made publicly available within hours after collection (http://www-argo.ucsd.edu). Below is shown the positions of the floats that have delivered data within the last 30 days

#### ODA home

#### ODA output

#### GSOP

#### Bibliography

**Recent Publications**

- Chang, Y.-S., S. Zhang, A. Rosati, T. Delworth, and W. F. Stern, March 2012: An assessment of oceanic variability for 1960-2010 from the GFDL ensemble coupled data assimilation, Climate Dynamic, Accepted.

Zhang, Shaoqing, M. Winton, A. Rosati, T. Delworth and B. Huang, 2012: Impact of Enthalpy-Based Ensemble Filtering Sea-Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model. Journal of Climate. in press.

Chang, Y-S, Anthony Rosati, and Shaoqing Zhang, February 2011: A construction of pseudo salinity profiles for the global ocean: Method and evaluation. *Journal of Geophysical Research*, 116, C02002, DOI:10.1029/2010JC006386.

Chang, Y-S, Shaoqing Zhang, and Anthony Rosati, July 2011: Improvement of salinity representation in an ensemble coupled data assimilation system using pseudo salinity profiles. *Geophysical Research Letters*, 38, L13609, DOI:10.1029/2011GL048064.

Zhang, Shaoqing, January 2011: Impact of observation-optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model. *Geophysical Research Letters*, 38, L02702, DOI:10.1029/2010GL046133.

Zhang, Shaoqing, Z Liu, Anthony Rosati, and Thomas L Delworth, January 2012: A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model. *Tellus A*, 64, 10963, DOI:10.3402/tellusa.v64i0.10963.

- Zhang, Shaoqing, January 2011: Impact of observation-optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model.

*Geophysical Research Letters*, 38, L02702, DOI:10.1029/2010GL046133.

Zhang, Shaoqing, December 2011:A study of impacts of coupled model initial shocks and state-parameter optimization on climate predictions using a simple pycnocline prediction model. *Journal of Climate*, 24(23), DOI:10.1175/JCLI-D-10-05003.1.