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

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Presentation on theme: "Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis, S. Somot, E. Álvarez-Fanjul, I. Ferrer ESTIMATION OF SEA LEVEL VARIABILITY FROM OCEAN MODELS."— Presentation transcript:

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

2 December 2001 Introduction - Sea Level variability December 2008

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

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

5 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 2D models – OverviewGOAL To model the atmospheric component of the Sea Level variability

6 - 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 Hourly sea level output 2D models – Example: HIPOCAS project

7 Gijón Málaga Measured (red); Model simulation (blue) 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)]. 2D models – Example: HIPOCAS project Validation Ratsimandresy et al., 2008

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

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

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

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 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 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 Barnier et al.,2006 3D models – Example

13  Model results give positive trends, but are submitted to eventual drifts…  MEDAR data give negative trends, but the coverage might be partial… 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 3D models – Example: Hindcast mode Results - Trends

14 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) 3D models – Example: Hindcast mode Results - Trends

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

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

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

18 Other open issues: Mixing, lack of DW formation, LBC 3D models – Are they doing a proper job in the Med Sea? Conceptual model - Effects of exchanges across Gibraltar MED Tmed Smed ATLANTIC Tatl=ct Satl=ct GIB E-P-R Heat Fluxes 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 >>>>> Initial state Input flux =0.80 Sv Tmed=12.44º Smed=38.86psu >>>>> Final State Input flux =0.79 Sv Tmed=13.83º Smed=38.19psu >>>>> Atlantic Tatl=16.00 Satl=34.00 Assuming invariant >>>>> Final State Input fluxe =0.79 Sv Tmed=13.83º Smed=39.86psu 3D models – Are they doing a proper job in the Med Sea? Conceptual model - Effects of exchanges across Gibraltar Simple model Rigid lid model

21 2D Models – good results. Give reliable information about atmospheric influence on Sea Level2D Models – good results. Give reliable information about atmospheric influence on Sea Level Quality rely on bathymetry and Atmospheric fieldsQuality rely on bathymetry and Atmospheric fields 3D Models – More complex models - Not as good as 2D model results.3D Models – More complex models - Not as good as 2D model results. Ocean climate models present large discrepancies at regional scaleOcean climate models present large discrepancies at regional scale Models cannot predict climate transients.Models cannot predict climate transients. High resolution is needed to solve particular processes (Gibraltar exchanges, DW formation, internal mixing, …)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 simulationsAt 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 Summary FUTURE WORK


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