1. Towards the utilization of GHRSST data for improving estimates of the global ocean circulation Dimitris Menemenlis 1, Hong Zhang 1, Gael Forget 2, Patrick Heimbach 2, and Chris Hill 2 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA 2 Massachusetts Institute of Technology, Cambridge, USA Thanks:Jorge Vazquez, Ed Armstrong, Chelle Gentemann, and Chris Henze NASA Physical Oceanography; NASA Modeling, Analysis, and Prediction (MAP) NASA Advanced Supercomputing (NAS); JPL Supercomputing and Visualization Facility (SVF) Global ocean and sea ice state estimation - Green’s function optimization - Adjoint-method optimization Adding AMSR-E L2P data constraints - Bias and diurnal corrections - Data errors and cost function weights

2. http://ecco2.org/ ECCO2: Estimating Circulation & Climate of Ocean, Phase II ECCO2: ocean state estimation in presence of eddies and ice animation: C. Henze, NAS

3. 1992–2002, 0–750-m time-mean temperature difference of ECCO2 baseline and optimized solutions relative to World Ocean Atlas 2005. Baseline simulation exhibits a global warm bias of up to 3° C, which is not present in optimized solution. A first ECCO2 solution was obtained using a Green’s function approach to adjust 80 model parameters.

4. A second ECCO2 solution is being obtained using the adjoint method to adjust order 10 9 model parameters. Adjoint sensitivity of model-data quadratic misfit to initial temperature field at 15 m depth. Red and blue patches indicate that model- data misfit increases or decreases, respectively, with increasing initial temperature. Adjoint sensitivities are used to constrain this global ocean and sea- ice model with satellite and in situ data. (°C) -1

5. ECCO2 AMSR-E L2P bias diurnal Atlantic Indian Arctic Pacific Antarctic °C Comparison of simulated and observed SST on Jan 1, 2004

6. °C Standard deviation of model-data difference for 2004-2005 σ (model-data) σ explained by bias σ explained by diurnal σ explained bias + diurnal Atlantic Indian Arctic Pacific Antarctic

7. °C Data error specification for cost function σ (model-data) AMSRE standard error on 1 Jan 04 AMSRE standard error on 1 Jul 04 AMSRE max standard error Atlantic Indian Arctic Pacific Antarctic

8. Summary Aim to add GHRSST data constraints to a global, eddying, ocean and sea ice simulation Use adjoint method to permit adjustment of large number of parameters while remaining physically consistent with model physics AMSR-E L2P bias and diurnal corrections reduce model-data misfit AMSR-E L2P error estimate is generally larger than model-data misfit except in regions of strong eddy variability (ACC and western boundaries)

9. Surface wind stress and SST anomaly relative to mean seasonal cycle from ECCO2 solution. animation: C. Henze, NAS