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D.I. Lysaker, O.C.D. Omang , D. Solheim ESA Living Planet Symposium, Bergen 30. June 2010

D.I. Lysaker, O.C.D. Omang , D. Solheim ESA Living Planet Symposium, Bergen 30. June 2010. Geostrophic Surface Currents in the Northern North Atlantic revealed by GOCE, Gravity Anomalies and MSSH. This presentation shows an example of how to use geodetic data in climate studies.

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D.I. Lysaker, O.C.D. Omang , D. Solheim ESA Living Planet Symposium, Bergen 30. June 2010

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  1. D.I. Lysaker, O.C.D. Omang, D. Solheim ESA Living Planet Symposium, Bergen 30. June 2010 Geostrophic Surface Currents in the Northern North Atlantic revealed by GOCE, Gravity Anomalies and MSSH

  2. This presentation shows an example of how to use geodetic data in climate studies Gravity + MSSH Direct Synthetic Geostrophic ocean surface currents

  3. Ocean circulation may be estimated if height above geoid is known www.science.nasa.gov Ocean Circulation and Transport between the North Atlantic and the Arctic Sea

  4. OCTAS -Ocean Circulation and Transport between the North Atlantic and the Arctic Sea Courtesy Kourosh Ghazavi

  5. OCCAM Oceanographic models • Salinity • Temperature

  6. Synthetic MDT derived by subtracting a regional gravimetric geoid from a MSSH model Two methods for estimating geostrophic surface currents • Direct • MDT obtained using LSC • Combine MSSH and gravity data

  7. Direct method uses LSC Cross-covariance between observations y and signal x MDT Covariance matrix for the measurement noise Auto-covariance of observations Error covariances a-priori signal variance matrix

  8. Synthetic method MDT = MSSH – N + e

  9. Data used in the direct and synthetic method MSSH (OCTAS06v3/DNSC08) Gravity data

  10. Gravity coverage is not complete in our test area … fill with gravity data from altimetry

  11. Both models give a lot of features Combined model (MSSH and Dg) Direct model (MSSH and N)

  12. Geodetic model has smaller features than oceanographic model Geodetic model (MSSH-N) Oceanographic model (OCCAM)

  13. Synthetic model fits drifter data better Average: 10.5 cm/s Standard deviation: 9.3 cm/s

  14. Combination model and drifter data Average: 11.2 cm/s Standard deviation: 9.8 cm/s

  15. OCCAM fits better with drifter data

  16. Mooring data

  17. Drifter data fits as good/bad with mooring data as combination model

  18. Conclusions • Using geodetic data reveals smaller features than oceanographic models • Redistribution of water is due to change in climate • Gravity observations may contribute to monitor this change

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