NOAA/GFDL OCEAN DATA ASSIMILATION ACTIVITIES

A. ROSATI

M. HARRISON

A. WITTENBERG

S. ZHANG

Motivation for Ocean Data Assimilation

• ODA produces consistent ocean states serving as initial conditions for model forecasts (S/I, Dec/Cen)

• The reconstructed time series of ocean states with a 3D structure aids further understanding of the dynamical and physical mechanisms of ocean evolution

• Ocean analysis for model simulation or forecast verification• Restoring SST may only change the top layer structure,

instead of building up the vertical thermal structure• Forcing errors (wind stress, heat flux, water flux)• Model errors

ODA COMPONENTS

• Data and “quality control procedures”

• The dynamical model

• The analysis and assimilation techniques

Ocean Data Stream Requirements for ODA

• GODAE server (www.usgodae.org) provides a near real-time repository for ocean data assimilation needs.

• The server is maintained by the Office of Naval Research and has been in "operational" use for nearly 5 years (?)

• Forcing: “Diurnal to Decadal Global Forcing For Ocean & Sea-Ice Models” W. Large, S. Yeager

( GFDL will keep the data set current)

GFDL perspectives

• The process of bringing new datasets into our ocean analyses has been greatly simplified by the GODAE server.

• Having a unified data structure and metadata would facilitate sharing of ODA tools between the involved parties. This would also ease the transition to an operational setting.

ARGO

• In order to use ARGO in ODA we must analyze how a large scale signal can be mapped from sparse measurements with low signal to noise ratio (mainly due to mesoscale variability).

• How much data is required to initialize?

• OSSE-Simulate with 1/10 deg ocean model

OCEAN MODEL

• OM3 Model Basics1. Resolution: horiz.-10 with enhanced 1/30 in tropics.

Vertical 50 levels (uniform 10m down to 210m)

2. Grid: Tripolar grid, with bipolar Arctic starting north of 650

3. Barotropic Mode: Explicit free surface with fresh water flux affecting surface height.

4. Time Stepping: Staggered scheme:no time splitting mode, conservative of volume and tracer.

OCEAN MODEL

• Parameterizations1. Tracer Advection: third order Sweby scheme2. Neutral Physics: GM skewsion and neutral

diffusion3. Horiz. Friction: Anisotropic friction4. Penetrative SW Radiation: with prescribed

Chlorophyll based on SeaWIFS climatology5. Vertical Friction & Diffusion: KPP mixed

layer, Bryan-Lewis background

ODA RESEARCH• 3D-variational method – used in operational S/I prediction

for over a decade. A minimum variance estimate using a constant prior covariance matrix,unchanged in time.Stationary filter.

• Two new classes of methods– 4D-variational-A minimum variance estimate by minimizing a

distance between model trajectory and obs using adjoint to derive the gradient under model’s constraint. Linear filter.

– Ensemble filtering - accounts for the nonlinear time evolution of covariance matrix

• To evaluate these methods, it is essential that each be developed and tested in the same model framework using the same observations

3D-VARIATIONAL ODA

• Retrospective 45 year (’59-’04) analysis– Bi-weekly ocean I.C.s for

GFDL coupled model S/I predictions

– ODASI Consortium– ODA product intercomparisons and observing system impacts

– On web through interactive browsing software (LAS/DODS) data1.gfdl.noaa.gov (current within 1 month)

– Dec/Cen Climate trends (eg. ocean heat content)

4D-VARIATIONAL ODA

• Development– Continue development of adjoint of MOM4/OM3

using automatic differentiation tools (TAF, Giering) in collaboration with MIT, JPL, Harvard.

– Current Status 1. Tangent Linear Model of OM3 nearly complete ( GFDL )

2. Adjoint of prototype model ( Harvard )

3. Communications for parallel computers ( JPL)

– Build 4D-var. assimilation system in MOM4/OM3

Test Driverin adjoint and 4D-Var development

Motivations Easy to maintain a shared trunk which continuously incorporates the

new/modified subroutines/functions to ensure the convergence of efforts from all parties

Easy to test potential issues in 4D-Var/sensitivity study experiments (e.g. the adjoint tactics in Massively Parallel Processing)

Easy to locate the problem once experiment results are showing flaws

4 test sessions Based on the MOM4 syntax and structure, a test driver is deliverable

for: Tangent Linear test Adjoint test Gradient test Minimization test

What does an ensemble filter do for Ocean Data Assimilation?

Given:ENSO: a product of air-sea interaction that contains many

uncertaintiesEnsemble filter: using nonlinearly varying error

covariance directly derived from model dynamics to emphasize the probabilistic nature of non-stationary stochastic processes in system (Zhang and Anderson 2003)

Question:Can an ensemble filter do a good job for tropical Pacific data assimilation?

Model configuration and spin-up Hybrid coupled model

– Ocean model: MOM4 (180x96x25)• Uniform 2o zonally; dense near equator (0.5o), telescoping toward poles• 15m above 150m, telescoping toward the bottom

– Statistical atmospheric model (Andrew Wittenberg) • Deterministic linear regression based on NCEP2 reanalysis wind stress, heat flux

and SST during 1979-2002.• Stochastic forcing from the residual (subtracting the deterministic part from

wind stress and heat flux). • Each ensemble member (6 in this case) sees a different year of the stochastic

forcing

Why hybrid coupling?– Coupled model prototype– Initial test bed representing the forcing uncertainties in coupled model

Spin-up– Forced with NCEP2 climatological fluxes & restoring for 70 years– Compute climatological flux adjustment– 10-year stochastically-forced ensemble spin-up

EAKF Summary A parallelized ensemble filter has been implemented in a

GFDL coupled ocean model with statistical atmospheric responses for Ocean Data Assimilation

The ensemble filter with 6 sample members produces comparable assimilation results to the existing 3D-Var(OI) ODA, i.e. both are able to establish the subsurface temperature and current structure using subsurface temperature observations

Due to using the temporally and spatially varying err cov derived from model dynamics, the ensemble filter appears to produce a more physically consistent ocean state estimate than OI, in terms of T, u, anomalies and climatology with a smoother solution

An ensemble filter provides an estimate for uncertainty of analysis

OVERVIEW

Ocean Data Assimilation

OBSCommon metadata

3D-variational

Ensemble filter

4D-variational

Common infrastructureOM3

ENSO forecasts

GODAE-global change

NCEP Operations – when mature

Routine Products

•Heat & salt storage•Sea level rise•Carbon storage•Initializations dec-cen forecasts

GOALS

• To develop and improve assimilation methodologies to integrate diverse data streams for initialization of seasonal-to-decadal climate forecasts.

• High-resolution,decadal time scale global ocean analyses of ocean temp, salinity and flow fields, to

support scientific research.• Infrastructure to facilitate access to obs and assim

products.• Climate time scale sensitivity analysis of ocean

circulation