1. G. Panteleev, P.Stabeno, V.Luchin, D.Nechaev,N.Nezlin, M.Ikeda. Estimates of the summer transport of the Kamchatka Current a variational inverse of hydrographic and surface drifter data. *** Goals: 1.Reconstruction and analysis of the summer circulation in the Bering Sea through the variational assimilation of the hydrophysical and surface drifters data. 2Estimate the Kamchatka Strait transport and compare it with previous estimates. (12 Sv Stabeno et al., 1999?). 3. Reconstruction of the reliable absolute climatological SSH that can be used for the hind-cast and forecast of the local circulation.

2. Overview. 1. 4dvar data assimilation approach in quasi-stationary mode (Tziperman and Thacker, 1989). 2. Reconstruction of the climate summer circulation in the Bering Sea. a) Data. b) Analysis of the results of two experiments. c) The absolute SSH. 3. Hindcast and nowcast of the summer circulation in the local regions in the Bering Sea through the assimilation of the SSH anomaly. 4. Concluding remarks.

3. Quasi-stationary 4Dvar data assimilation variational approach. GOAL: Find model solution, which minimize the next cost function. Where, y –model variables, W – inverse covariance of the corresponding physical values. Model (forward): The model is a modification of the C-grid, z-coordinate, primitive equation OGCM designed at the Laboratiore d'Oceanographie Dynamique et de Climatologie (Madec et al., 1999). The numerical scheme of the model is based on the implicit treatment of both barotropic and baroclinic modes (Nechaev et al., 2005, Panteleev et al., 2006) and implicit description of the Coriolis terms in the momentum equation (Nechaev and Yaremchuk, 2004). Control vector: Initial conditions (SSH, T/S, U/V), boundary conditions (T/S, U/V, heat/salt flux, wind stress) Adjoint Model: analytical transposition of the operator of the tangent linear model Efficient assimilation of any kind of data.

4. Data: Distribution of historical salinity data in August. Historical T/S data. Model domain 1.Historical T/S data 120,000 stations since 1920 (Luchin et al., 2002). Data base consist from Russian, American, Japanese and classified Russian sources.

5. The comparison with Ermold and Steel, 2002, hydrophysical Atlas. August, 300-350m September, 17.5m September, 300-350m Comparison between temperature fields from a) Ermold and Steel, 2002, hydrophysical atlas; b) Results of optimal interpolation; d) Results of 4Dvar data assimilation approach; shows: 1. T fields from Ermold and Steel, 2002, atlas (a), is essentially smother than the T distribution obtained by optimal interpolation or 4Dvar approach. 2. 4Dvar technique allows us to reconstruct the realistic T distribution (b) and other components (c) of the climate state.

6. Data: 3. NCEP/NCAR summer wind stress and surface heat/salt flux climatology. 4. Summer Bering Strait transport data (Woodgate at al., 2005) ~1.1 +-0.2 Sv 5. Summer Kamchatka Strait transport (Stabeno et at al., 1999) ~12 Sv. Traditional estimates through the dynamical method: Verkhunov and Tkachenko, 1992, 5 Sv and 9 Sv Ohtani, 1970 15 Sv. Numerical models: Overland et al., 8.5 Sv (summer), 13 Sv (winter). 2. ~500 satellite-tracked drifters (NOAA, P. Stabeno) Reliable estimates of summer surface velocities. ?

7. Experiment A. Reconstruct the Bering Sea circulation through the assimilation of: 1) Surface drifter data. 2) Hydrophysical data. 3) Bering Strait transport. 4) Atmospheric climatology. Experiment B. Reconstruct the Bering Sea circulation through the assimilation of: 1)Kamchatka Strait Transport ~ 12 Sv. 2) Hydrophysical data. 3) Bering Strait transport. 4) Atmospheric climatology. Assimilate different data and compare results with velocities of ARGO drifters at 1000 m Analysis of ARGO drifters: 1.Mean speed 4.6 cm/s at 1000 m. 2.”Branching” of the Bering slope current. 3.”Branching” of the Kamchatka current. Local topography.

8. Results Experiment AExperiment B 1. Mean speed at 1000 m – 3.9cm/s. 2. Kamchatka Current transport 24 Sv. 3. “Branching” of the Bering Slope Current. 4. “Branching” of the Kamchatka Current. 1. Mean speed at 1000 m – 1.7 cm/s 2. Kamchatka Current transport 12 Sv. Analysis of ARGO drifters: 1.Mean speed 4.6 cm/s. 2.”Branching” of the Bering slope current. 3.”Branching” of the Kamchatka current.

9. Hughes et al., 1974, Circulation, transport and water exchange in the western Bering Sea. in Oceanography of the Bering Sea Proposed approximate water balance: Summer Winter Kamchatka Strait: -20 Sv -35 Sv Near Strait. +25 Sv +25 Sv

10. 1)Mean summer SSH can be used as a reference level for recalculating the weekly SSH Anomaly distributed by the Archiving Validation and Interpretation of Satellite Data in Oceanography (AVISO) project (www.aviso.cls.fr).www.aviso.cls.fr 2) The obtained SSH distribution can be used for monitoring and prediction in any region of the Bering Sea. + Real SSH= Mean SSH AVISO SSH anomaly

11. Preliminary results of the hindcast and nowcast in the Bering Sea through the assimilation of the SSH data. Drawbacks and ways to overcame: 1) Assimilation of noisy SSH data causes the recovering of noisy initial conditions. a) Estimates of error covariance matrix of the SSH data, i.e. “quality of SSH data”. b) Additional geostrophic adjustment of the model initial conditions. 2) Assimilation of SSH data is not sufficient for the reconstruction of reliable temperature/salinity fields. a) Assimilation of the Sea Surface Temperature (SST). b) Preliminary analysis of the SST, SSH and historical measurements of the T/S: cross-covariance between SST, SSH, temperature and salinity. Blue arrows – model results. Black arrows – surface drifters. Green lines - surface drifters trajectories.

12. Hindcast and forecast of the Bering Sea circulation derived through the assimilation of the anomaly SSH data. Red arrows- the real surface drifter velocities. The mean relative model- drifter velocity data error - 0.71 Surface velocities derived from the AVISO (Topex-Poseidon) absolute SSH data. The mean relative AVISO- drifter velocity data error - 1.68 2002 Preliminary results of hindcast and forecast on the surface circulation in Bering Sea.

13. Conclusions: 1. The reconstruction of the Bering Sea circulation through the variational assimilation of hydrophysical and surface drifter data allows to define the Kamchatka Current transport of 24 Sv, i.e. two times higher previous estimates. Recent measurements of the Stabeno et al., 2005, revel 4 Sv transport through the Amutka Pass that is ~4 times higher previous estimates. Therefore: a) The barotropic circulation is very significant in the Bering Sea. b) Velocity observations in the passages and straits are extremely important. c) Water balance in the Bering Sea should be revised. 2. The Kamchatka and Bering Slope currents reveal branching or modes. The “branching” can be caused by the local topography. 3. The mean climalogical SSH can be useful for recalculation of the SSH anomaly into the absolute SSH. These new SSH data can significantly improve the hindcast and forecast of the surface currents in the Bering Sea. Plans: 1. The reconstruction of the climate Bering Sea circulation in the whole domain through the assimilation of hydrophysical, surface drifter and moorings data: Water balance analysis, reference SSH level and ext. 2. Development of the operational data assimilation system based on the recently developed 4Dvar data assimilation model and/or incremental approach based on 4Dvar model and ROMS: Operational hindcast and forecast of circulation in the Western part of the Bering Sea.

14. Gleb Panteleev, IARC, (907)-474-26-80. gleb@iarc.uaf.edu

15. An example of AVISO SSH data product: merged (Topex/Poseidon, Jason) anomaly SSH SHH anomaly. Formal STD of SSH anomaly.