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

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

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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 - PowerPoint PPT Presentation

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Page 1: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

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

ESTIMATION OF SEA LEVEL VARIABILITY ESTIMATION OF SEA LEVEL VARIABILITY FROM OCEAN MODELSFROM OCEAN MODELS

Page 2: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

December 2001December 2001

Introduction - Sea Level variability

December 2008December 2008

Page 3: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

TsunamisTsunamis

Astronomical forcing – Astronomical forcing – tidestides

Waves Waves

Atmospheric mechanical forcing Atmospheric mechanical forcing – – A. pressure, windA. pressure, wind

Steric ContributionSteric Contribution

Mass content variationsMass content variations

Introduction – Factors affecting SL variability

Page 4: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

WHY ?WHY ?

To understand

To predict

KINDS OF MODELSKINDS OF MODELS

Wave models

Tidal models

2D barotropic models Mechanical forcing

3D baroclinic modelsSteric and sometimes mass content

Introduction – Modelling SL variability

High accuracy

This talk

Page 5: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

Relevant at regional scale (not at global Relevant at regional scale (not at global scale)scale)

Based on Shallow-water equationsBased on Shallow-water equations

Barotropic modelsBarotropic models

Forced by wind, atmospheric pressure Forced by wind, atmospheric pressure and/or tidesand/or tides

Finite difference (i.e. HAMSOM) or finite Finite difference (i.e. HAMSOM) or finite Elements models (i.e. MOG2D)Elements models (i.e. MOG2D)

Usually provide high quality results Usually provide high quality results “short-memory” systems“short-memory” systems

2D models – Overview

GOAL GOAL

To model the atmospheric component of To model the atmospheric component of the Sea Level variabilitythe Sea Level variability

Page 6: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

- HAMSOM model - HAMSOM model

- Spatial resolution 1/6x1/8ºSpatial resolution 1/6x1/8º

- Forcing: downscaling of atmospheric pressure and wind fields generated - Forcing: downscaling of atmospheric pressure and wind fields generated by the model REMO (from a NCEP re-analysis) by the model REMO (from a NCEP re-analysis)

- Period 1958-2001 Period 1958-2001

- Hourly sea level outputHourly sea level output

2D models – Example: HIPOCAS project

Page 7: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

Gijón

Málaga

Measured (red); Model simulation (blue)

Improvement respect to IB: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

Page 8: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

Sea level trends (mm/yr) induced by atmospheric pressure and wind

1993-20001993-2000

2D models – Example: HIPOCAS project

Results - Trends

-2 mm/year3 mm/year

-1.25 mm/year -0.25 mm/year

1958-19931958-1993

The model helped to understand the low sea level increase between 1960-1993

Gomis et al., 2006

Page 9: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

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

Page 10: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

01z

H

SSL dpg

Primitive equations modelsPrimitive equations models

Baroclinic termsBaroclinic terms

Air-sea interactionAir-sea interaction

River runoffRiver runoff

Rigid lid Rigid lid (z(z00=0)=0) / Free surface / Free surface (z(z00

Usually provide lower quality results Usually provide lower quality results “long-memory” systems“long-memory” systems

3D models – Overview

GOAL GOAL

To model the steric (To model the steric (and mass changes)and mass changes) component of the Sea Level variabilitycomponent of the Sea Level variability

Page 11: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

3D models – Overview

Global ModelsGlobal Models

No lateral boundary conditions problemsNo lateral boundary conditions problems

Can directly account for global mass increaseCan directly account for global mass increase

Low resolution (some processes not solved)Low resolution (some processes not solved)

Gibraltar Strait not solvedGibraltar Strait not solved

Usually they are rigid lidUsually they are rigid lid

Regional ModelsRegional Models

Higher resolution – local processes can be solvedHigher resolution – local processes can be solved

Gibraltrar Strait could be explicitely solved (but not always)Gibraltrar Strait could be explicitely solved (but not always)

At present they are switching to free surfaceAt present they are switching to free surface

Lateral boundary conditions problemsLateral boundary conditions problems

Link to global processes is not straightforwardLink to global processes is not straightforward

Page 12: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

Regional modelRegional model

High resolution (1/8˚ x 1/8˚, 43 non-High resolution (1/8˚ x 1/8˚, 43 non-uniform vertical Z-levels) model for the uniform vertical Z-levels) model for the Mediterranean Sea (OPA model). Somot Mediterranean Sea (OPA model). Somot et al., 2006et al., 2006

Period 1961-2000Period 1961-2000

Atmospheric forcing was based on ERA-Atmospheric forcing was based on ERA-40 high resolution 40 high resolution

Rigid lid configurationRigid lid configuration

The Mediterranean Sea simulation is then The Mediterranean Sea simulation is then driven by air-sea fluxes which (1) have a driven by air-sea fluxes which (1) have a high resolution (50 km), (2) are high resolution (50 km), (2) are homogeneous over a long period of time homogeneous over a long period of time (no change in the model configuration), (no change in the model configuration), (3) follow the real synoptic chronology and (3) follow the real synoptic chronology and (4) have a realistic interannual variability. (4) have a realistic interannual variability.

Global Global modelmodel

ORCA025 global configuration of the ORCA025 global configuration of the ocean/sea-ice general circulation model ocean/sea-ice general circulation model NEMO horizontal resolution of 1/4° and 46 NEMO horizontal resolution of 1/4° and 46 vertical levelsvertical levels

Period 1958-2004Period 1958-2004

Barnier et al.,2006Barnier et al.,2006

01

H

dpg

SSL

3D models – Example

Page 13: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

Model results give positive trends, but are submitted to eventual drifts…Model results give positive trends, but are submitted to eventual drifts…

MEDAR data give negative trends, but the coverage might be partial…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

Page 14: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

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

Page 15: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

3D models – Example: Forecast mode

Results – XXI century trends

T changes 0-2.5 ºC

S changes 0-2 psu

Temperature Salinity

SR

ES

A1B

SR

ES

A2

Com

mitt

ed C

C

1.2ºC

0.3 psu

T and S projections of an ensemble of ten global models and one regional model

Marcos et al., 2008

Page 16: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

3D models – Example: Forecast mode

Results – XXI century trends

Halosteric component

Thermosteric component

SR

ES

A1B

SR

ES

A2

Com

mitt

ed C

C

35 cm

-25 cm

Total steric component

Components of the steric part of sea

level trends

Halosteric sea level:Halosteric sea level: -70 to 20 cm-70 to 20 cmThermosteric sea level: Thermosteric sea level: 5 to 55 cm5 to 55 cmRange of total variationRange of total variation-42 to 52 cm -42 to 52 cm

Marcos et al., 2008

Page 17: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

Spatial patterns of steric sea level

+ circulation changes

3D models – Example: Forecast mode

Results – XXI century trends

Page 18: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

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-RHeat Fluxes

IN

OUT

Jordà et al., in prep.

Page 19: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

2 1

1

2 2 1 1

0.7

( )

( , ), ( , )

IN OUT

OUT

dilatation

dilatation Med

ref ref

Sv

AE P R

t

z

T S T S

netMED ATL

e IN

Q AT T

C

INMED ATL

OUT

S S

3D models – Are they doing a proper job in the Med Sea?

Conceptual model - Effects of exchanges across Gibraltar

Simple modelSimple model

netMED ATL

e IN

Q AT T

C

IN

MED ATLIN

t

VS St V

V V V

0

( )

IN

OUT IN

dilatation

AE P R

t

Rigid lid modelRigid lid model

Page 20: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

>>>>> 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

>>>>> AtlanticTatl=16.00Satl=34.00Assuming 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 modelSimple model Rigid lid modelRigid lid model

Page 21: Gabriel Jordà, F. M. Calafat, M. Marcos, D. Gomis,  S. Somot, E. Álvarez-Fanjul, I. Ferrer

•2D Models – good results. 2D Models – good results. Give reliable information about atmospheric influence on Sea LevelGive reliable information about atmospheric influence on Sea Level

•Quality rely on bathymetry and Quality rely on bathymetry and Atmospheric fieldsAtmospheric fields

•3D Models – More complex models - 3D Models – More complex models - Not as good as 2D model resultsNot 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 High resolution is needed to solve particular processes (Gibraltar exchanges, DW formation, internal mixing , …)exchanges, DW formation, internal mixing , …)

•At present there are some essential questions that must be solved: At present there are some essential questions that must be solved: Gibraltar Strait parametrization, LBC (Gibraltar Strait parametrization, LBC ( link to global processes link to global processes), role of DW ), role of DW formation in the climate simulationsformation in the climate simulations

Summary

FUTURE WORKFUTURE WORK