1 / 21

Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis, S. Somot, E. Álvarez-Fanjul, I. Ferrer

ESTIMATION OF SEA LEVEL VARIABILITY FROM OCEAN MODELS. Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis, S. Somot, E. Álvarez-Fanjul, I. Ferrer. Introduction - Sea Level variability. December 2001. December 2008. Introduction – Factors affecting SL variability. Tsunamis

ronnie
Télécharger la présentation

Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis, S. Somot, E. Álvarez-Fanjul, I. Ferrer

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ESTIMATION OF SEA LEVEL VARIABILITY FROM OCEAN MODELS Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis, S. Somot, E. Álvarez-Fanjul, I. Ferrer

  2. Introduction - Sea Level variability December 2001 December 2008

  3. Introduction – Factors affecting SL variability Tsunamis Astronomical forcing – tides Waves Atmospheric mechanical forcing – A. pressure, wind Steric Contribution Mass content variations

  4. Introduction – Modelling SL variability High accuracy This talk WHY ? To understand To predict KINDS OF MODELS Wave models Tidal models 2D barotropic models Mechanical forcing 3D baroclinic modelsSteric and sometimes mass content

  5. 2D models – Overview GOAL To model the atmospheric component of the Sea Level variability Relevant at regional scale (not at global scale) Based on Shallow-water equations Barotropic models Forced by wind, atmospheric pressure and/or tides Finite difference (i.e. HAMSOM) or finite Elements models (i.e. MOG2D) Usually provide high quality results “short-memory” systems

  6. 2D models – Example: HIPOCAS project • - HAMSOM model • Spatial resolution 1/6x1/8º • - Forcing: downscaling of atmospheric pressure and wind fields generated by the model REMO (from a NCEP re-analysis) • Period 1958-2001 • Hourly sea level output

  7. 2D models – Example: HIPOCAS project Gijón Málaga Measured (red); Model simulation (blue) Validation Improvement respect to IB: Difference between the variance of TG data corrected by the IB response and the variance of TG data corrected by the atmospheric models [VAR(TG-IB) – VAR(TG – model)]. Ratsimandresy et al., 2008

  8. 2D models – Example: HIPOCAS project 1958-1993 -1.25 mm/year -0.25 mm/year 1993-2000 Results - Trends Sea level trends (mm/yr) induced by atmospheric pressure and wind The model helped to understand the low sea level increase between 1960-1993 3 mm/year Gomis et al., 2006 -2 mm/year

  9. 2D models – Example: HIPOCAS project Results - Extremes Values for 50-yr return period Extreme values for the periods measured by tide gauges (few yr to decades) Tidal component removed Results from a 2D model Even if the model underpredicts the results it provides a good estimation over the whole basin Marcos et al., 2009

  10. 3D models – Overview GOAL To model the steric (and mass changes)component of the Sea Level variability Primitive equations models Baroclinic terms Air-sea interaction River runoff Rigid lid (z0=0) / Free surface (z0=h) Usually provide lower quality results “long-memory” systems

  11. 3D models – Overview Global Models No lateral boundary conditions problems Can directly account for global mass increase Low resolution (some processes not solved) Gibraltar Strait not solved Usually they are rigid lid Regional Models Higher resolution – local processes can be solved Gibraltrar Strait could be explicitely solved (but not always) At present they are switching to free surface Lateral boundary conditions problems Link to global processes is not straightforward

  12. 3D models – Example Regional model High resolution (1/8˚ x 1/8˚, 43 non-uniform vertical Z-levels) model for the Mediterranean Sea (OPA model). Somot et al., 2006 Period 1961-2000 Atmospheric forcing was based on ERA-40 high resolution Rigid lid configuration The Mediterranean Sea simulation is then driven by air-sea fluxes which (1) have a high resolution (50 km), (2) are homogeneous over a long period of time (no change in the model configuration), (3) follow the real synoptic chronology and (4) have a realistic interannual variability. Global model ORCA025 global configuration of the ocean/sea-ice general circulation model NEMO horizontal resolution of 1/4° and 46 vertical levels Period 1958-2004 Barnier et al.,2006

  13. 3D models – Example: Hindcast mode • Comparison of different models with in situ data: Yearly time series of steric sea level (ref. level at 300 m) and averaged over two sub-basins ORCA025 (global) model OPAMED8 (regional) model MEDAR data base Results - Trends • Model results give positive trends, but are submitted to eventual drifts… • MEDAR data give negative trends, but the coverage might be partial…

  14. 3D models – Example: Hindcast mode Results - Trends Comparison of altimetric (blue) and modelled (red) averaged sea level for selected areas. Dashed lines are 12 month running averages. At regional scale results improve but there can be relevant extreme events (climate transitions very difficult to predict)

  15. 3D models – Example: Forecast mode T and S projections of an ensemble of ten global models and one regional model Temperature Salinity Committed CC SRES A1B T changes 0-2.5 ºC S changes 0-2 psu 0.3 psu SRES A2 1.2ºC Marcos et al., 2008 Results – XXI century trends

  16. 3D models – Example: Forecast mode Total steric component Halosteric component Thermosteric component Committed CC SRES A1B 35 cm SRES A2 -25 cm Results – XXI century trends Components of the steric part of sea level trends Halosteric sea level: -70 to 20 cmThermosteric sea level: 5 to 55 cm Range of total variation -42 to 52 cm Marcos et al., 2008

  17. 3D models – Example: Forecast mode Results – XXI century trends Spatial patterns of steric sea level + circulation changes

  18. 3D models – Are they doing a proper job in the Med Sea? ATLANTIC Tatl=ct Satl=ct E-P-RHeat Fluxes MED Tmed Smed GIB Conceptual model - Effects of exchanges across Gibraltar Other open issues: Mixing, lack of DW formation, LBC Jordà et al., in prep.

  19. 3D models – Are they doing a proper job in the Med Sea? Conceptual model - Effects of exchanges across Gibraltar Simple model Rigid lid model

  20. 3D models – Are they doing a proper job in the Med Sea? Conceptual model - Effects of exchanges across Gibraltar Rigid lid model Simple model >>>>> Initial state Input flux =0.80 Sv Tmed=12.44º Smed=38.86psu >>>>> Atlantic Tatl=16.00 Satl=34.00 Assuming invariant >>>>> Final State Input flux =0.79 Sv Tmed=13.83º Smed=38.19psu >>>>> Final State Input fluxe =0.79 Sv Tmed=13.83º Smed=39.86psu

  21. Summary FUTURE WORK • 2D Models – good results. Give reliable information about atmospheric influence on Sea Level • Quality rely on bathymetry and Atmospheric fields • 3D Models – More complex models - Not as good as 2D model results. • Ocean climate models present large discrepancies at regional scale • Models cannot predict climate transients. • High resolution is needed to solve particular processes (Gibraltar exchanges, DW formation, internal mixing , …) • At present there are some essential questions that must be solved: Gibraltar Strait parametrization, LBC ( link to global processes), role of DW formation in the climate simulations

More Related