NOAA/GFDL OCEAN DATA ASSIMILATION ACTIVITIES

1. NOAA/GFDL OCEAN DATA ASSIMILATION ACTIVITIES ROSATI M. HARRISON A. WITTENBERG S. ZHANG

2. 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

3. ODA COMPONENTS • Data and “quality control procedures” • The dynamical model • The analysis and assimilation techniques

4. 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)

5. 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.

6. 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

7. OCEAN MODEL • OM3 Model Basics • Resolution: horiz.-10 with enhanced 1/30 in tropics. Vertical 50 levels (uniform 10m down to 210m) • Grid: Tripolar grid, with bipolar Arctic starting north of 650 • Barotropic Mode: Explicit free surface with fresh water flux affecting surface height. • Time Stepping: Staggered scheme:no time splitting mode, conservative of volume and tracer.

8. OCEAN MODEL • Parameterizations • Tracer Advection: third order Sweby scheme • Neutral Physics: GM skewsion and neutral diffusion • Horiz. Friction: Anisotropic friction • Penetrative SW Radiation: with prescribed Chlorophyll based on SeaWIFS climatology • Vertical Friction & Diffusion: KPP mixed layer, Bryan-Lewis background

9. 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

10. 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)

11. 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 • Tangent Linear Model of OM3 nearly complete ( GFDL ) • Adjoint of prototype model ( Harvard ) • Communications for parallel computers ( JPL) • Build 4D-var. assimilation system in MOM4/OM3

12. Test Driver in 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

13. What does an ensemble filter do for Ocean Data Assimilation? Given: • ENSO: a product of air-sea interaction that contains many uncertainties • Ensemble 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?

14. 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

19. 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

20. 3D-variational Common infrastructure Ensemble filter Common metadata OM3 OBS 4D-variational Routine Products ENSO forecasts GODAE-global change • Heat & salt storage • Sea level rise • Carbon storage • Initializations dec-cen forecasts NCEP Operations – when mature OVERVIEW Ocean Data Assimilation

21. 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