The MERCATOR Quarterly newsletter #10 - July 2003 - article 3, page 1 CNES CNRS/INSU IFREMER IRD METEO-FRANCE SHOM Composite sea level prediction in the Mediterranean Sea - comparisons with observations By Florent Lyard and Laurent Roblou 1. Abstract In this presentation, we focus on the sea level recorded and modelled in the Mediterranean Sea during the year 2002. Two dynamical models are made available to us, the first one designed to solve the ocean circulation (Mercator Psy2-v1 (Newsletter Mercator N°8)) and the second one to solve the tide and storm surge processes (Mog2D). We challenge the assumption that a combined use of those two models (i.e. through a full or partial summation) should provide an optimal sea level predicting tool. By comparing with tide gauge measurements, the predicting skills of models, alone and/or combined together, are estimated for different frequency ranges. The two major conclusions that can be drawn from this study is that first a combination of low-pass filtered Mercator plus Mog2D closely fits the recorded data, and second the Mog2D low frequency sea level signal is surprisingly needed in this combination to obtain the best prediction (instead of the low-pass filtered Inverted Barometer (IB)). Further investigations will be necessary to understand precisely the reasons of the latter finding. 2. Introduction The sea level variations are one of the most measured parameter of the physical oceanography. Beside the classical tide gauge networks, the recent and present satellite altimetry missions provide a constant flow of data which continuity extends now for more than ten years. Most of the sea level variability is due to the tides and the meteorologically driven dynamic, but ocean surface circulation or steric effects are clearly depicted in sea surface measurements. Therefore the sea level is an highly valuable observation, either for ocean dynamic models validation or assimilation, however its full and precise exploitation requests some additional treatment, like horizontal and spatial scale separation and de-aliasing. Among other modelling efforts (Mathers [2000], Ponte [1997], Ponte et al. [1991] and Ponte [1991]), the global simulation of the ocean response to the atmospheric forcing has been completed at the LEGOS for the 1992-2003 period from the barotropic finite element (FE) model Mog2D-G, on a medium resolution mesh. The main objective of this simulation is to provide the scientific community with improved high frequency sea level corrections (compared to the classical inverted barometer parameterisation; see Wunsch et al., [1997], Woodworth et al., [1995]) in the altimetric GDR in order to de-aliase the ocean circulation signal (see e.g. Stammer et al., [2000], Ponte and Gaspar, [1999], Gaspar and Ponte, [1997]). In addition, a Mediterranean regional model has been developed on a high resolution mesh for the Albicocca project where altimetric and tide gauge measurements are compared together for calibration and coastal circulation observation purposes. Due to the quasi-barotropic nature of the ocean dynamical response to atmospheric forcing at periods lower than about 30 days, Mog2D has most of the skills needed to model the ocean high frequency dynamic. For the larger time scales, the true ocean response to the various forcing includes a significant baroclinic contribution. Moreover, the ocean dilation due to the heat content variation (steric effects) is a major contributor to the sea level variability at the annual and seasonal time scales. The OGCM models shows a very complementary pattern, as most of the high frequency dynamic (mostly barotropic) is filtered out, while thermo-haline and low frequency wind-driven dynamic is properly represented. Thus a simplistic idea would be that the total sea level variation signal can be predicted by a summation of a Mog2D prediction with an OGCM sea surface. Nevertheless, two problems need to be addressed: first, the barotropic model and the OGCM have a common forcing term, namely the low-frequency wind, and therefore may content some redundant dynamic contributions. Secondly, the OGCM assimilates sea level data where inverted barometer is applied, thus a residual, dynamical aliased gravity wave type signal is introduced in the simulation, but possibly filtered later on by the model. To that point, it is nearly impossible to quantify exactly what is the OGCM high frequency content, and what is the Mog2D low frequency content. Therefore the usually recommended approach would be to perform the model sea level combination by filtering the low-frequency signal in the barotropic prediction, and reversely the high frequency signal in the OGCM simulation before adding them together. Doing so, the low frequency inverted barometer needs to be added to the combination as the atmospheric pressure loading contribution is not insignificant in the low frequency band and it is not present in the OGCM sea level. For similar reasons, the combination of high-pass (20 days) filtered Mog2D simulation with low-pass filtered inverted barometer is usually seen as the best guess for altimetric data correction before their assimilation in an OGCM. Although considered as very reasonable, these assumptions need to be verified more closely. The Mediterranean Mog2D and the Mercator PSY2 simulations, plus the existence of a large tide gauge network, provide us the opportunity to scrutinise the various hypotheses.
Transcript
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 1
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations By Florent Lyard and Laurent Roblou
1. Abstract In this presentation, we focus on the sea level recorded and modelled in the Mediterranean Sea during the year 2002. Two dynamical models are made available to us, the first one designed to solve the ocean circulation (Mercator Psy2-v1 (Newsletter Mercator N°8)) and the second one to solve the tide and storm surge processes (Mog2D). We challenge the assumption that a combined use of those two models (i.e. through a full or partial summation) should provide an optimal sea level predicting tool. By comparing with tide gauge measurements, the predicting skills of models, alone and/or combined together, are estimated for different frequency ranges. The two major conclusions that can be drawn from this study is that first a combination of low-pass filtered Mercator plus Mog2D closely fits the recorded data, and second the Mog2D low frequency sea level signal is surprisingly needed in this combination to obtain the best prediction (instead of the low-pass filtered Inverted Barometer (IB)). Further investigations will be necessary to understand precisely the reasons of the latter finding.
2. Introduction The sea level variations are one of the most measured parameter of the physical oceanography. Beside the classical tide gauge networks, the recent and present satellite altimetry missions provide a constant flow of data which continuity extends now for more than ten years. Most of the sea level variability is due to the tides and the meteorologically driven dynamic, but ocean surface circulation or steric effects are clearly depicted in sea surface measurements. Therefore the sea level is an highly valuable observation, either for ocean dynamic models validation or assimilation, however its full and precise exploitation requests some additional treatment, like horizontal and spatial scale separation and de-aliasing.
Among other modelling efforts (Mathers [2000], Ponte [1997], Ponte et al. [1991] and Ponte [1991]), the global simulation of the ocean response to the atmospheric forcing has been completed at the LEGOS for the 1992-2003 period from the barotropic finite element (FE) model Mog2D-G, on a medium resolution mesh. The main objective of this simulation is to provide the scientific community with improved high frequency sea level corrections (compared to the classical inverted barometer parameterisation; see Wunsch et al., [1997], Woodworth et al., [1995]) in the altimetric GDR in order to de-aliase the ocean circulation signal (see e.g. Stammer et al., [2000], Ponte and Gaspar, [1999], Gaspar and Ponte, [1997]). In addition, a Mediterranean regional model has been developed on a high resolution mesh for the Albicocca project where altimetric and tide gauge measurements are compared together for calibration and coastal circulation observation purposes.
Due to the quasi-barotropic nature of the ocean dynamical response to atmospheric forcing at periods lower than about 30 days, Mog2D has most of the skills needed to model the ocean high frequency dynamic. For the larger time scales, the true ocean response to the various forcing includes a significant baroclinic contribution. Moreover, the ocean dilation due to the
heat content variation (steric effects) is a major contributor to the sea level variability at the annual and seasonal time scales. The OGCM models shows a very complementary pattern, as most of the high frequency dynamic (mostly barotropic) is filtered out, while thermo-haline and low frequency wind-driven dynamic is properly represented. Thus a simplistic idea would be that the total sea level variation signal can be predicted by a summation of a Mog2D prediction with an OGCM sea surface. Nevertheless, two problems need to be addressed: first, the barotropic model and the OGCM have a common forcing term, namely the low-frequency wind, and therefore may content some redundant dynamic contributions. Secondly, the OGCM assimilates sea level data where inverted barometer is applied, thus a residual, dynamical aliased gravity wave type signal is introduced in the simulation, but possibly filtered later on by the model. To that point, it is nearly impossible to quantify exactly what is the OGCM high frequency content, and what is the Mog2D low frequency content.
Therefore the usually recommended approach would be to perform the model sea level combination by filtering the low-frequency signal in the barotropic prediction, and reversely the high frequency signal in the OGCM simulation before adding them together. Doing so, the low frequency inverted barometer needs to be added to the combination as the atmospheric pressure loading contribution is not insignificant in the low frequency band and it is not present in the OGCM sea level. For similar reasons, the combination of high-pass (20 days) filtered Mog2D simulation with low-pass filtered inverted barometer is usually seen as the best guess for altimetric data correction before their assimilation in an OGCM. Although considered as very reasonable, these assumptions need to be verified more closely. The Mediterranean Mog2D and the Mercator PSY2 simulations, plus the existence of a large tide gauge network, provide us the opportunity to scrutinise the various hypotheses.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 2
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
3. The Mog2D model
Figure 1: Mid-resolution finite element mesh used.
Mog2D (2D Gravity Wave model) is a barotropic, nonlinear and time stepping model, derived originally fromLynch and Gray [1979] and developed since for coastal to global tidal and atmospheric driven applications(Greenberg and Lyard, personal communication).Model governing equations are based on the classicalshallow water continuity and momentum equations.These are solved through a non linear shallow-water wave equation with a quasi-elliptic formulation which improves numerical stability. The currents are derivedthrough the non conservative momentum equation. The
model capabilities include tidal and meteorological forcing (atmospheric surface pressure and wind). Its main originality is a finite element space discretisation (FE), which allows to raise resolution in regions of interest like high topographic gradient areas (showing strong current variability and internal waves generation) and shallow waters, where most of bottom friction dissipation occurs. To improve the computational efficiency, a reduced time-stepping scheme is dedicated to care on unstable model nodes.
The bottom friction is taken from a Chezy-type quadratic parameterisation. A novel parameterisation of the barotropic to baroclinic energy transfer (through the internal waves generation on topographic slopes) is included in the model. The horizontal viscosity is prescribed following the Smagorinsky viscosity scheme (Smagorinsky [1963]) which allows to take into account the varying FE mesh cells size. By essence, the barotropic model does not include any vertical energy dissipation (like ocean de-stratification processes or vertical shear drag), which appears to be a problem when the annual mean wind stress is kept in the simulation forcing. In this case, some unrealistically strong deep ocean circulations can appear. An additional rough Raleigh-type dissipation term is thus introduced in order to parameterise the internal dissipation (Egbert and Ray [ 2000], Morozov [2000]).
Boundary conditions have been extracted from the Mog2D-G global simulation through a radiative condition (characteristics method). For the present study, only atmospheric forcing is applied to the model (no tidal forcing, neither potential nor boundary conditions). Surface pressure and 10 metres wind are taken from the ARPEGE (Météo france) and ECMWF fields (ECMWF [1991]) with a temporal resolution of 6 hours (which implies that frequencies lower than 12 hours are widely misrepresented). The wind stress is derived from the classical formula of Rosati and Miyakoda [1988] (with ocean surface and atmospheric temperatures taken equal to zero in the present application). At each time step, the atmospheric pressure is corrected from its instantaneous global mean, to guaranty the oceanic mass conservation and thus be able to compare our simulations with the IB approximation.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 3
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
4. Wind and pressure driven sea level overview
Computed on year 2002, the Mediterranean seasurface shows a 10 centimetre gradient from East toWest (figure 2). This gradient is mainly due to theatmospheric pressure mean gradient. Some regionsshow local coastal gradient, linked generally with acoastal mean transport (Adriatic Sea, Gulf of Gabes,Lybian-egyptian coast) or local wind regime. On average, the sea level variability due to the wind andpressure forcing ranges from 5 to 10 centimetres(figure 3), with maximum values observed on shallowwater regions. In most part of the Mediterranean Sea,it is comparable to the tidal variability. Theatmospheric pressure driven sea level variations in theMediterranean Sea are well known to poorly fit the
inverted barometer parameterisation (Candela et Lozano [1994], Candela [1991]). Adding the wind effect, the departure is even greater, especially in the Eastern basin (Gulf of Gabes and Adriatic Sea), with a strong seasonal modulation. As shown on Figure 4, the mean IB departure is of the order of 1 to 2 cm, nearly uniform in the eastern and western basin, but showing the signature of the coastal circulation mentioned above. The model departure from IB standard deviation (Figure 5) ranges from 6 to 9 centimetres, which compares to 50% up to 100% of the sea level variability itself. In the western basin, the Gulf of Lyon shows the maximum IB departure, due to the presence of the continental shelf and the intense wind regime.
Figure 2: Mean sea level computed from Mog2D over the year 2002. Units are centimetres.
Figure 3: Sea level standard deviation computed from Mog2D over the year 2002. Units are centimetres.
Figure 4: Mean inverted barometer departure over the year 2002. Units are centimetres.
Figure 5: Inverted barometer departure standard deviation over the year 2002. Units are centimetres.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 4
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
5. Comparisons with tide gauge observations
As an illustration, we show the instantaneous sea level(13th of February 2002 at noon) computed fromMog2D (figure 6), deduced from Mercator surfacepressure (figure 7) and the summation of the twomaps (figure 8). Apart from the tides and the geoïd, the latter represents probably the best approximationof the true sea level at this date, if our assumption thatthe two models are complementary is correct.
Figure 6: Mog2D instantaneous sea level at noon 13/02/2002. Units in centimetres.
Figure 7: Mercator instantaneous surface pressure at noon 13/02/2002. Units in centimetres.
Figure 8: Composite (Mercator plus Mog2D) instantaneous sea level at noon 13/02/2002. Units in centimetres.
For the present study, focusing on year 2002, most ofavailable observations come from the SONELdistribution and the Italian network (SIMN) (see Figure9 and table 1). Only a subset of typical stations hasbeen chosen to avoid a tedious presentation. Theselected sites are marked in grey in table 1. The hourlydelivered time series are detided in a preliminary step[Ponchaut et al., 2001]. The comparisons covers the full year period to insure a reasonable statisticalsignificance. We compare three original predictions(Mog2D, IB, and Mercator) and three compositepredictions (CP-1, Mog2D plus Mercator; CP-2, Mog2D plus filtered Mercator; CP-3, IB plus Mercator) with the observations. All models and observations arecorrected of an yearly average sea level to narrow thecurves (relative levels). Comparisons are detailed for aset of different period range to investigate the modelprediction skill in the whole frequency spectrum range.
Figure 9: Available tide gauge observation sites for the 2002 period. The tide gauge presented in this paper have been named. The map background indicates the bathymetry (in metres), with colour interval's chosen to highlight the shallower regions.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 5
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
On plate 1, we display the variance histograms for theobserved signal and the residuals signal (after modelcorrection). The first bar’s group shows the total variance, the following are computed for specificfrequency bands. The total variance of the sea levelcorrected by the composite model CP-2 ranges from 10 to 20 square centimetres over the tide gauge network,which means roughly that the sea level CP-2 has a typical 4 cm mean accuracy. On figures 10 to 18, wehave plotted the observed sea level versus theMercator and composite prediction CP-2. The composite prediction has been built as the summationof Mog2D seal level plus the low-pass filtered Mercator. Cut-off frequencies (given in Table 2) has been chosen so that the composite prediction shows the minimumresidual variance in each frequency band aftercorrecting the observed sea level. It is graphically clearthat for all locations the composite model fits muchbetter the observations than Mercator sea level alone.Apart from some very high frequency oscillations andsome abnormal peaks in the observations, most of theobserved signal is well simulated by the compositemodel. The very high frequency left in the observationsafter detiding can be true signal (local resonance likeseiches, for instance in the Adriatic Sea, diurnal windexcitation, intermittent internal waves) mis-represented by the models or observation errors (inparticular tides can not be properly removed in case ofa problem with the tide gauge clock, thus leaving alarge high frequency signal in the ill detidedobservations). The agreement with the observationsare quite spectacular at Nice and Genova tide gauge,apart during September (CNES day 19236 up to19266). During this month, both tide gauge record aquasi-linear sea level rise of about 20 centimetres. Infact, this event can be seen in all north western tidegauge records. Neither Mercator nor Mog2D show asimilar pattern. The most likely explanation of such asea level rise is that the Liguro-provencal current has intensified and/or got closer to the coast during thisperiod. Because of the proximity with the shorelinesand the narrowness of the current main jet, it might beplausible that the hydrodynamic modelling could notcapture this event nor the assimilation correct theforecast. Additional investigations (possibly withinMFSTEP and Albicocca projects) are needed tounderstand exactly the nature of the September 2002event and the reasons of its absence in the models.
Table 1: Tide gauges locations.
Table 2: Cut-off period for CP-2 construction.
Code Station marégraphique Longtitude Latitude1 Ajaccio 8,767 41,917
2 Ancona-1 13,502 43,625
3 Bari 16,867 41,137
4 Cagliari 9,108 39,207
5 Carloforte 8,305 39,14
6 Catania-1 15,09 37,492
7 Civitavecchia 11,783 42,087
8 Crotone 17,135 39,073
9 Genova 8,922 44,405
10 Imperia-1 8,018 43,873
11 Lampedusa 12,617 35,483
12 Livorno 10,293 43,54
13 Marseille 5,35 43,283
14 Monaco 7,417 43,733
15 Messina-1 15,558 38,187
16 Nice 7,267 43,267
17 Napoli-1 14,268 40,837
18 Ortona 14,41 42,353
19 Otranto 18,492 40,142
20 Palermo 13,368 38,285
21 Palinuro 15,272 40,025
22 Porto-Empedocle-1 13,522 37,287
23 Porto-Torres 8,403 40,838
24 Ravenna 12,275 44,492
25 Reggio-Calabria 15,643 38,118
26 Salerno 14,742 40,675
27 Toulon 5,917 43,117
28 Taranto 17,222 40,472
29 Trieste 13,753 45,653
30 Venezia 12,422 45,42
31 Vieste 16,172 41,888
32 Senetosa-MX 8,813 41,55
33 San-Antonio 1,3 38,967
Station marégraphique Période de coupureBari 30 jours
Cagliari 60 jours
Crotone 30 jours
Genova 10 jours
Lampedusa 30 jours
Nice 60 jours
Salerno 10 jours
Trieste 60 jours
San Antonio 60 jours
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 6
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
Plate 1 : Sea level variance for observed sea level corrected from models and models combinations. Units are cm². The first bar's group shows the total variance. The following are computed for specific frequency bands. The period intervals (T) are given in days. As mentioned in the text, PSY2v1 + IB corrected is the composite prediction CP3, PSY2v1 +MOG2D corrected is CP1 and PSY2v1/MOD2D composite corrected is CP2.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 7
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
Figure 10: Bari tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002
Figure 11: Cagliari tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
Figure 12: Crotone tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 8
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
Figure 13: Genova tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
Figure 14: Lampedusa tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
Figure 15: Nice tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 9
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
Figure 16: Salerno tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
Figure 17: San Antonio tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
Figure 18: Trieste tide gauge: observed sea level versus Mercator (left), versus composite (CP-2; Mog2D plus low-pass filtered Mercator) sea level (right). Series start February the first 2002.
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 10
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
6. Discussion
7. Conclusion
This preliminary comparisons of Mercator simulations to the sea level observed along the Mediterranean coast is an additional contribution to the validation of this model. From this study, it is clear that the Mog2D simulations is needed to reconstruct a synthetic sea level that can be more easily compared with tide gauge observations. In fact, the combination of Mercator and Mog2D simulation can be seen as a valuable valorisation product for sea level analysis or prediction, even in coastal regions.
Nevertheless, the pertinence of Mog2D low frequency signal rises many theorical and practical questions, like the role of non-linearities due to high frequency dynamic in the low frequency sea level changes, and the definition of an optimal approach to perform either Mercator’s sea level validation from tide gauge and satellite altimetry data or data correction for assimilation in Mercator’s model.
As expected, the Mog2D prediction does explains muchof the high frequency sea level signal, and a muchsmaller part of the signal for periods larger than 30 or60 days. Similar conclusions can be drawn from the IBprediction, except that it does not perform as well asthe numerical model does. On the reverse, theMercator prediction does explain a large amount of lowfrequency signal, and shows poor performances forperiods less than 30 days.
The composite signals, i.e. Mercator plus IB andMercator plus Mog2D, show expected numbers in thehigh frequency band, which is that adding Mercatordeteriorate the sea level prediction compared to Mog2Dor IB prediction alone. The reason being that Mercatorsignal is not relevant and adds noise in this frequencyband. More surprisingly, the examination of the lowfrequency band does not fit our preliminary view. Wewould have expected that the optimal combination forthe low frequencies would have been Mercator plus theinverted barometer, because first the barotropicnumerical model is not appropriate for ocean lowfrequency dynamic, and second, part of the windforced signal may be redundant. But what we see isthat the best fitting to observations composite issystematically Mercator plus Mog2D composite, whichmight mean that Mog2D simulation contains a relevant
low frequency signal that is not present or partially present in the Mercator simulation.
Of course, Mercator PSY2-v1 can not be considered as a proper coastal model, mainly because the filtering of the gravity waves, the lack of tidal and atmospheric pressure forcing and its spatial resolution. Comparing Mercator sea surface with coastal tide gauges observations is somehow unfair to the model, especially if the tide gauges are locating on a large continental shelf. Nevertheless, there is no systematic trend in the model low frequency misfit’s magnitude if one considers a shelf tide gauge (like Trieste or Lampedusa), and more pelagic-like one (like San Antonio or Nice). So, except perhaps in the case of a coastal jet, the Mercator sea surface low frequency component extrapolates quite well to the shorelines. To complete the picture, it is necessary to extend the sea level comparisons with altimetric data, and to consider in addition a coastal circulation model (possibly forced at its boundary by Mercator’s simulations; note that the POC, Pôle d’Océanographie Côtière de Toulouse, is already investigating these topics for coastal circulation modelling applications and for coastal altimetry products development, in collaboration with the Groupe Mission Mercator).
The MERCATOR Quarterly newsletter
#10 - July 2003 - article 3, page 11
CNESCNRS/INSU
IFREMERIRD
METEO-FRANCESHOM
Composite sea level prediction in the Mediterranean Sea - comparisons with observations (continued)
8. References
Candela, J. and Lozano, C.J., barotropic response of the western Mediterranean to observed atmospheric pressure forcing, Seasonal and Interannual variability of the western Mediterranean Sea, Coastal and Estuarine Studies, 46, 3215-359, 1994.
Candela J., The Gibraltar Strait and its role in the dynamics of the Mediterranean Sea, Dyn. Atmos. Oceans, 15, 267-299, 1991.
ECMWF, European Center for Medium-Range Forecasts Model, ECMWF research manual, 1991.
Egbert, G.D., Ray, R.D., Significant dissipation of tidal energy in the deep ocean inferred from satellite altimeter data, Nature, 405, 2000.
Gaspar, P., Ponte, R.M., Relation between sea level and barometric pressure determined from altimeter data and model simulations, JGR, 102 (C1), 961-971, 1997.
Lynch, D.R., Gray, W.G., A wave equation model for finite element tidal computations, Computers and fluids, 7, 207-228,1979.
Mathers, E.L., Sea level response to atmospheric pressure and wind forcing in the global deep ocean, PhD Thesis, University of Liverpool, 2000.
Ponchaut, F., Lyard, F., Le Provost, C., An analysis of the tidal signal in the WOCE sea level dataset , J. Atmos. Oceans Tech., 18, 2001.
Ponte, R.M., Salstein, D.A., Rosen, R.D., Sea level response to pressure forcing in a barotropic numerical model, J. Phys. Oceanogr. , 1043- 1057, 1991.
Ponte, R.M., The sea level response of a stratified ocean to barometric pressure forcing, JPO, 109-113, 1991.
Ponte, R.M., Non equilibrium response of the global ocean to the 5-day Rossby-Haurwitz wave in atmospheric surface pressure, 1997.
Ponte, R.M., Gaspar, P., Regional analysis of the inverted barometer effect over the global ocean using TOPEX/POSEIDON data and model results, JGR, 104 (C7), 15587-15601, 1999.
Smagorinsky, J., General circulation experiments with the primitive equations, Monthly Weather Review, 1963.
Stammer, D., Wunsch, C., Ponte, R.M., De-aliasing of global high frequency barotropic motions in altimeter observations, Geophys. Res. Letters, 27 (8), pp 1175, 2000.
Woodworth, P.L., Windle, S.A., Vassie, J.M., Departures from the local inverse barometer model at periods of 5 days in the central South Atlantic, JGR, 100 (C9), 18281-18290, 1995.
Wunsch, C., Stammer, D., Atmospheric loading and the oceanic inverted barometer effect, Reviews of Geophysics, 35, 1997.
