Ocean Data Assimilation Activities at NOAA/GFDL Current status and future directions Matthew Harrison, Ants Leetmaa, Anthony Rosati, Andrew Wittenberg, Shaoqing Zhang

Ocean Modeling Needs Data Assimilation Uncertainty in air-sea fluxes Uncertainty in model physics. ODA produces consistent ocean states serving as initial conditions for model forecasts The reconstructed time series of ocean states with a 3D structure aids further understanding of the dynamical and physical mechanisms of ocean evolution Ocean analyses for model simulation or hindcast verification

… however Ocean data assimilation products do not all agree reflecting uncertainty in models and data assimilation methods ODA does not necessarily lead to model improvement nor to increased understanding

ODA Components Quality Control Model Assimilation algorithm

Ocean Observations GODAE Server near real-time data stream ( –VOS XBT –CTD –Argo –TAO/PIRATA –Altimetry –maintain data center QC flags In-house quality control

Ocean Model MOM4 OM3 –Re-engineered for multi-processor platforms –Utilize FMS infrastructure for communications, i/o and coupling –Global 1 degree x 50 level z-coordinate with 1/3 degree tropical resolution –Tripolar grid (no polar filter required, full Artic Ocean) –KPP Vertical mixing –SWEBY advection scheme (courtesy of A. Adcroft ) –Rotated isopycnal diffusion with G-M thickness flux Coupled with Ice Model

Ocean Data Assimilation 3DVar –Error covariance is stationary in time. Kalman Filter –Error covariance evolves based on model linearization Ensemble Adjustment Kalman Filter –Use full model equations to propagate error 4DVar –“strong constraint” to model equations –No source/sink terms

Towards understanding the ocean’s role in climate GFDL will be expanding its ocean data assimilation activities starting this year through partnership with the ECCO group Estimating the Circulation and Climate of the Ocean ( MIT (Carl Wunsch, Patrick Heimbach, Alistair Adcroft) /JPL (Ichiro Fukimori, Tony Lee) / Harvard (Eli Tziperman, Jake Gebbe)

OVERVIEW- ECCO Collaboration Ocean Data Assimilation for Climate Testbed Indicates common/additions from ECCO OBS Common metadata 3D-variational Ensemble filter Start 4d-var Common infrastructure MOM4+ ENSO forecasts GODAE-global change NCEP Operations – when mature Kalman filter Routine Products Heat & salt storage Sea level rise Carbon storage Initializations dec-cen forecasts

3DVar LAS/DODS 1980-present analyses Routinely used for SI forecast initialization Tropical Pacific is well constrained in upper 300m Mid-to-high latitudes are more problematic –Sparse data outside of trade routes –Few salinity measurements –Use T/S covariance? –Multivariate using ARGO Salinity?

Ensemble Adjustment Kalman Filter (EAKF) Initial tests in the tropical Pacific basin for ENSO prediction Linearized atmospheric model with added noise Each model realization is integrated between analysis steps which determines the PDF of the model guess. Followed by a least square adjustment based on the model and observational PDF of the respective ensemble members.

Simulation OI EAKF TAO Seasonal Cycle of Temperature at 140W, Equator

Time Series of Temperature at 140W, Equator Simulation OI EAKF TAO

Simulation OI EAKF TAO Temperature at 140W, Equator

CDEP Consortium Ocean Data Assimilation Consortium for Seasonal-to-Interannual Prediction (ODASI) COLA, GFDL, IRI, LDEO, NCEP, GMAO COLA Jim Kinter Ed Schneider Ben Kirtman Bohua Huang GMAO Michele Rienecker Chaojiao Sun Jossy Jacob Nicole Kurkowski 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

CGCM Forecast skill - January starts - multimodel ensemble All TAO moorings West TAO moorings East TAO moorings Obs (Reynolds)

Longer term change Globally averaged heat content Blue line is the GFDL R30 Climate Model Red Line is R30 Model with added aerosol forcings including volcanic Black line is the Levitus analysis Dashed line is GFDL 3DVar analysis

zonal average temperature trend from 3DVar Analysis

SSH Trend JPL Kalman Filter smoother (top) GFDL OI (middle) ECCO 1deg Iter69 (bottom) We need to understand the differences between these analyses

Overview of Early Activities: GFDL, NCEP, Goddard, MIT(AER), JPL, Harvard Year 1 Begin global state estimation at GFDL with ECCO Produce and utilize routine data streams including ARGO T&S Tangent linear/adjoint model. Ocean model and model parameterization development Derivation of Kalman filter and smoother for MOM4 and Poseiden Year 2 Global estimates with ECCO continue Optimization of ECCO and GFDL “ECCO” like models for Dec/Cen applications Experiments for S/I forecasts utilizing different assimilation schemes/models Experiments with new data types (GRACE) & comparison to independent data types like length of day and earth polar motion Adjoint sensitivity analyses to various controls and observing systems Year 3 Year 4